diff --git a/.config/.last_opt_in_prompt.yaml b/.config/.last_opt_in_prompt.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0967ef424bce6791893e9a57bb952f80fd536e93
--- /dev/null
+++ b/.config/.last_opt_in_prompt.yaml
@@ -0,0 +1 @@
+{}
diff --git a/.config/.last_survey_prompt.yaml b/.config/.last_survey_prompt.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..094405a51fed14b8c23e7b5f2e83fed405b85f89
--- /dev/null
+++ b/.config/.last_survey_prompt.yaml
@@ -0,0 +1 @@
+last_prompt_time: 1697117106.6098588
diff --git a/.config/.last_update_check.json b/.config/.last_update_check.json
new file mode 100644
index 0000000000000000000000000000000000000000..7057045a161d526f3c64cdcbb37b02def9463094
--- /dev/null
+++ b/.config/.last_update_check.json
@@ -0,0 +1 @@
+{"last_update_check_time": 1697117121.9680088, "last_update_check_revision": 20231006153333, "notifications": [], "last_nag_times": {}}
\ No newline at end of file
diff --git a/.config/active_config b/.config/active_config
new file mode 100644
index 0000000000000000000000000000000000000000..331d858ce9b12fa6720414196a9dd6e0b6a0faaa
--- /dev/null
+++ b/.config/active_config
@@ -0,0 +1 @@
+default
\ No newline at end of file
diff --git a/.config/config_sentinel b/.config/config_sentinel
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.config/configurations/config_default b/.config/configurations/config_default
new file mode 100644
index 0000000000000000000000000000000000000000..ee06685b6841afd85a59e8ea5bc7ee8a27d6fe74
--- /dev/null
+++ b/.config/configurations/config_default
@@ -0,0 +1,6 @@
+[component_manager]
+disable_update_check = true
+
+[compute]
+gce_metadata_read_timeout_sec = 0
+
diff --git a/.config/default_configs.db b/.config/default_configs.db
new file mode 100644
index 0000000000000000000000000000000000000000..e8a2c56e9e0369b0e66531a0ddfec7c2b10a73ee
Binary files /dev/null and b/.config/default_configs.db differ
diff --git a/.config/gce b/.config/gce
new file mode 100644
index 0000000000000000000000000000000000000000..c1f22fbc23bb6ee67824843d6685826db10313d3
--- /dev/null
+++ b/.config/gce
@@ -0,0 +1 @@
+False
\ No newline at end of file
diff --git a/.config/logs/2023.10.12/13.24.23.720152.log b/.config/logs/2023.10.12/13.24.23.720152.log
new file mode 100644
index 0000000000000000000000000000000000000000..dac6415c447d2935fc02ed14ff0c2c741382a6fd
--- /dev/null
+++ b/.config/logs/2023.10.12/13.24.23.720152.log
@@ -0,0 +1,596 @@
+2023-10-12 13:24:23,726 DEBUG root Loaded Command Group: ['gcloud', 'components']
+2023-10-12 13:24:23,731 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
+2023-10-12 13:24:23,734 DEBUG root Running [gcloud.components.update] with arguments: [--allow-no-backup: "True", --compile-python: "True", --quiet: "True", COMPONENT-IDS:7: "['core', 'gcloud-deps', 'bq', 'gcloud', 'gcloud-crc32c', 'gsutil', 'anthoscli']"]
+2023-10-12 13:24:23,735 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
+
+2023-10-12 13:24:35,788 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:35,935 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 209941
+2023-10-12 13:24:35,956 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,956 INFO ___FILE_ONLY___
+Your current Google Cloud CLI version is: 450.0.0
+
+2023-10-12 13:24:35,957 INFO ___FILE_ONLY___ Installing components from version: 450.0.0
+
+2023-10-12 13:24:35,957 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,957 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
+2023-10-12 13:24:35,958 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
+2023-10-12 13:24:35,959 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
+2023-10-12 13:24:35,967 INFO ___FILE_ONLY___ ┌─────────────────────────────────────────────────────────────────────────────┐
+2023-10-12 13:24:35,967 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,967 INFO ___FILE_ONLY___ │ These components will be installed. │
+2023-10-12 13:24:35,967 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,967 INFO ___FILE_ONLY___ ├─────────────────────────────────────────────────────┬────────────┬──────────┤
+2023-10-12 13:24:35,968 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,968 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
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+
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+2023-10-12 13:24:35,968 INFO ___FILE_ONLY___
+
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+
+2023-10-12 13:24:35,969 INFO ___FILE_ONLY___ │
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+2023-10-12 13:24:35,970 INFO ___FILE_ONLY___ │
+2023-10-12 13:24:35,970 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,971 INFO ___FILE_ONLY___ │
+2023-10-12 13:24:35,971 INFO ___FILE_ONLY___ Bundled Python 3.9
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+
+2023-10-12 13:24:35,972 INFO ___FILE_ONLY___ │
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+
+2023-10-12 13:24:35,973 INFO ___FILE_ONLY___ │
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+2023-10-12 13:24:35,974 INFO ___FILE_ONLY___
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+
+2023-10-12 13:24:35,974 INFO ___FILE_ONLY___ │
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+2023-10-12 13:24:35,975 INFO ___FILE_ONLY___ 2023.09.15
+2023-10-12 13:24:35,975 INFO ___FILE_ONLY___
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+2023-10-12 13:24:35,975 INFO ___FILE_ONLY___
+2023-10-12 13:24:35,975 INFO ___FILE_ONLY___ │
+2023-10-12 13:24:35,975 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,975 INFO ___FILE_ONLY___ │
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+2023-10-12 13:24:35,976 INFO ___FILE_ONLY___
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+
+2023-10-12 13:24:35,977 INFO ___FILE_ONLY___ │
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+2023-10-12 13:24:35,978 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,978 INFO ___FILE_ONLY___ │
+2023-10-12 13:24:35,978 INFO ___FILE_ONLY___ gcloud cli dependencies
+2023-10-12 13:24:35,978 INFO ___FILE_ONLY___
+2023-10-12 13:24:35,978 INFO ___FILE_ONLY___ │
+2023-10-12 13:24:35,978 INFO ___FILE_ONLY___ 2021.04.16
+2023-10-12 13:24:35,978 INFO ___FILE_ONLY___
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+2023-10-12 13:24:35,979 INFO ___FILE_ONLY___
+2023-10-12 13:24:35,979 INFO ___FILE_ONLY___ │
+2023-10-12 13:24:35,979 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,979 INFO ___FILE_ONLY___ └─────────────────────────────────────────────────────┴────────────┴──────────┘
+2023-10-12 13:24:35,979 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,980 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:35,984 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:36,140 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/1.1" 200 1089789
+2023-10-12 13:24:36,228 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
+ https://cloud.google.com/sdk/release_notes
+
+
+2023-10-12 13:24:36,231 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
+
+2023-10-12 13:24:36,232 INFO ___FILE_ONLY___ ╠═ Creating update staging area ═╣
+
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+2023-10-12 13:24:41,328 INFO ___FILE_ONLY___ ╝
+
+2023-10-12 13:24:41,587 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
+
+2023-10-12 13:24:41,587 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
+
+2023-10-12 13:24:41,587 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:24:41,592 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:41,699 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-20230913232318.tar.gz HTTP/1.1" 200 1659622
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+2023-10-12 13:24:42,109 INFO ___FILE_ONLY___ ╝
+
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+
+2023-10-12 13:24:42,127 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
+
+2023-10-12 13:24:42,127 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:24:42,132 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:42,281 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-bq-nix-20230915145114.tar.gz HTTP/1.1" 200 1943
+2023-10-12 13:24:42,282 INFO ___FILE_ONLY___ ══════════════════════════════
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+
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+
+2023-10-12 13:24:42,293 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.9 ═╣
+
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+
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+
+2023-10-12 13:24:42,301 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.9 ═╣
+
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+
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+
+2023-10-12 13:24:47,396 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
+
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+
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+
+2023-10-12 13:24:49,976 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
+
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+2023-10-12 13:24:49,981 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:50,127 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20230915145114.tar.gz HTTP/1.1" 200 1955
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+
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+
+2023-10-12 13:24:50,140 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
+
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+
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+
+2023-10-12 13:24:50,148 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
+
+2023-10-12 13:24:50,148 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:24:50,153 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:50,299 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20230915145114.tar.gz HTTP/1.1" 200 2319
+2023-10-12 13:24:50,300 INFO ___FILE_ONLY___ ══════════════════════════════
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+
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+
+2023-10-12 13:24:50,312 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
+
+2023-10-12 13:24:50,312 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:24:50,316 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:50,422 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20230922151743.tar.gz HTTP/1.1" 200 1288666
+2023-10-12 13:24:50,469 INFO ___FILE_ONLY___ ═
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+
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+
+2023-10-12 13:24:50,526 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
+
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+
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+
+2023-10-12 13:24:50,534 INFO ___FILE_ONLY___ ╠═ Installing: anthoscli ═╣
+
+2023-10-12 13:24:50,534 INFO ___FILE_ONLY___ ╚
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+
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+
+2023-10-12 13:24:50,542 INFO ___FILE_ONLY___ ╠═ Installing: anthoscli ═╣
+
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+2023-10-12 13:24:50,547 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:50,649 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-anthoscli-linux-x86_64-20230915145114.tar.gz HTTP/1.1" 200 72006203
+2023-10-12 13:24:51,156 INFO ___FILE_ONLY___ ═
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+2023-10-12 13:24:54,078 INFO ___FILE_ONLY___ ══════════
+2023-10-12 13:24:54,086 INFO ___FILE_ONLY___ ═════════
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+2023-10-12 13:24:54,121 INFO ___FILE_ONLY___ ╝
+
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+
+2023-10-12 13:24:54,162 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies ═╣
+
+2023-10-12 13:24:54,162 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:24:54,166 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:24:54,313 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/1.1" 200 104
+2023-10-12 13:24:54,314 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:24:54,314 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:24:54,314 INFO ___FILE_ONLY___ ╝
+
+2023-10-12 13:24:54,324 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
+
+2023-10-12 13:24:54,324 INFO ___FILE_ONLY___ ╠═ Creating backup and activating new installation ═╣
+
+2023-10-12 13:24:54,325 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:24:54,325 DEBUG root Attempting to move directory [/tools/google-cloud-sdk] to [/tools/google-cloud-sdk.staging/.install/.backup]
+2023-10-12 13:24:54,325 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:24:54,325 DEBUG root Attempting to move directory [/tools/google-cloud-sdk.staging] to [/tools/google-cloud-sdk]
+2023-10-12 13:24:54,325 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:24:54,325 INFO ___FILE_ONLY___ ╝
+
+2023-10-12 13:24:54,331 DEBUG root Updating notification cache...
+2023-10-12 13:24:54,331 INFO ___FILE_ONLY___
+
+2023-10-12 13:24:54,334 INFO ___FILE_ONLY___ Performing post processing steps...
+2023-10-12 13:24:54,334 DEBUG root Executing command: ['python3', '-S', '/tools/google-cloud-sdk/lib/gcloud.py', 'components', 'post-process']
+2023-10-12 13:25:06,360 DEBUG ___FILE_ONLY___
+2023-10-12 13:25:06,360 DEBUG ___FILE_ONLY___
+2023-10-12 13:25:06,603 INFO ___FILE_ONLY___
+Update done!
+
+
+2023-10-12 13:25:06,608 DEBUG root Chosen display Format:none
+2023-10-12 13:25:06,609 INFO root Display format: "none"
diff --git a/.config/logs/2023.10.12/13.24.55.075099.log b/.config/logs/2023.10.12/13.24.55.075099.log
new file mode 100644
index 0000000000000000000000000000000000000000..2281b154a927c54aa2fed6922ee144a70ecb17bd
--- /dev/null
+++ b/.config/logs/2023.10.12/13.24.55.075099.log
@@ -0,0 +1,5 @@
+2023-10-12 13:24:55,076 DEBUG root Loaded Command Group: ['gcloud', 'components']
+2023-10-12 13:24:55,079 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
+2023-10-12 13:24:55,082 DEBUG root Running [gcloud.components.post-process] with arguments: []
+2023-10-12 13:25:06,227 DEBUG root Chosen display Format:none
+2023-10-12 13:25:06,228 INFO root Display format: "none"
diff --git a/.config/logs/2023.10.12/13.25.07.457568.log b/.config/logs/2023.10.12/13.25.07.457568.log
new file mode 100644
index 0000000000000000000000000000000000000000..e1889dc5e92a44e74aeff4bb05285bf8a1b6e5d8
--- /dev/null
+++ b/.config/logs/2023.10.12/13.25.07.457568.log
@@ -0,0 +1,169 @@
+2023-10-12 13:25:07,458 DEBUG root Loaded Command Group: ['gcloud', 'components']
+2023-10-12 13:25:07,462 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
+2023-10-12 13:25:07,465 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
+2023-10-12 13:25:07,466 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
+
+2023-10-12 13:25:07,473 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:25:07,622 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 209941
+2023-10-12 13:25:07,662 WARNING root Component [compute] no longer exists.
+2023-10-12 13:25:07,663 WARNING root Component [preview] no longer exists.
+2023-10-12 13:25:07,664 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,665 INFO ___FILE_ONLY___
+Your current Google Cloud CLI version is: 450.0.0
+
+2023-10-12 13:25:07,665 INFO ___FILE_ONLY___ Installing components from version: 450.0.0
+
+2023-10-12 13:25:07,665 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,665 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
+2023-10-12 13:25:07,666 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
+2023-10-12 13:25:07,667 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
+2023-10-12 13:25:07,669 INFO ___FILE_ONLY___ ┌──────────────────────────────────────────────┐
+2023-10-12 13:25:07,669 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,669 INFO ___FILE_ONLY___ │ These components will be installed. │
+2023-10-12 13:25:07,669 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,669 INFO ___FILE_ONLY___ ├───────────────────────┬────────────┬─────────┤
+2023-10-12 13:25:07,669 INFO ___FILE_ONLY___
+
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+
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+2023-10-12 13:25:07,670 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,670 INFO ___FILE_ONLY___ │
+2023-10-12 13:25:07,670 INFO ___FILE_ONLY___ gcloud Alpha Commands
+2023-10-12 13:25:07,670 INFO ___FILE_ONLY___
+2023-10-12 13:25:07,670 INFO ___FILE_ONLY___ │
+2023-10-12 13:25:07,671 INFO ___FILE_ONLY___ 2023.10.06
+2023-10-12 13:25:07,671 INFO ___FILE_ONLY___
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+2023-10-12 13:25:07,671 INFO ___FILE_ONLY___ < 1 MiB
+2023-10-12 13:25:07,671 INFO ___FILE_ONLY___
+2023-10-12 13:25:07,671 INFO ___FILE_ONLY___ │
+2023-10-12 13:25:07,671 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,671 INFO ___FILE_ONLY___ │
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___ gcloud Beta Commands
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___ │
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___ 2023.10.06
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___ │
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___ < 1 MiB
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___
+2023-10-12 13:25:07,672 INFO ___FILE_ONLY___ │
+2023-10-12 13:25:07,673 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,673 INFO ___FILE_ONLY___ └───────────────────────┴────────────┴─────────┘
+2023-10-12 13:25:07,673 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,673 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:07,679 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:25:07,833 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/RELEASE_NOTES HTTP/1.1" 200 1089789
+2023-10-12 13:25:07,923 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
+ https://cloud.google.com/sdk/release_notes
+
+
+2023-10-12 13:25:07,926 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
+
+2023-10-12 13:25:07,927 INFO ___FILE_ONLY___ ╠═ Creating update staging area ═╣
+
+2023-10-12 13:25:07,927 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:25:07,927 INFO ___FILE_ONLY___ ══════
+2023-10-12 13:25:09,380 INFO ___FILE_ONLY___ ══════
+2023-10-12 13:25:09,380 INFO ___FILE_ONLY___ ══════
+2023-10-12 13:25:09,917 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:10,171 INFO ___FILE_ONLY___ ═
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+2023-10-12 13:25:14,953 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,083 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,223 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,317 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,444 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,567 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,673 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,777 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:15,938 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:16,365 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:16,475 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:16,590 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:16,712 INFO ___FILE_ONLY___ ═
+2023-10-12 13:25:16,712 INFO ___FILE_ONLY___ ╝
+
+2023-10-12 13:25:21,638 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
+
+2023-10-12 13:25:21,638 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Alpha Commands ═╣
+
+2023-10-12 13:25:21,638 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:25:21,643 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:25:21,788 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-alpha-20231006153333.tar.gz HTTP/1.1" 200 800
+2023-10-12 13:25:21,789 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:25:21,791 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:25:21,791 INFO ___FILE_ONLY___ ╝
+
+2023-10-12 13:25:21,800 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
+
+2023-10-12 13:25:21,800 INFO ___FILE_ONLY___ ╠═ Installing: gcloud Beta Commands ═╣
+
+2023-10-12 13:25:21,800 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:25:21,805 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
+2023-10-12 13:25:21,950 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-beta-20231006153333.tar.gz HTTP/1.1" 200 797
+2023-10-12 13:25:21,951 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:25:21,952 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:25:21,952 INFO ___FILE_ONLY___ ╝
+
+2023-10-12 13:25:21,961 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
+
+2023-10-12 13:25:21,961 INFO ___FILE_ONLY___ ╠═ Creating backup and activating new installation ═╣
+
+2023-10-12 13:25:21,961 INFO ___FILE_ONLY___ ╚
+2023-10-12 13:25:21,961 DEBUG root Attempting to move directory [/tools/google-cloud-sdk] to [/tools/google-cloud-sdk.staging/.install/.backup]
+2023-10-12 13:25:21,962 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:25:21,962 DEBUG root Attempting to move directory [/tools/google-cloud-sdk.staging] to [/tools/google-cloud-sdk]
+2023-10-12 13:25:21,962 INFO ___FILE_ONLY___ ══════════════════════════════
+2023-10-12 13:25:21,962 INFO ___FILE_ONLY___ ╝
+
+2023-10-12 13:25:21,967 DEBUG root Updating notification cache...
+2023-10-12 13:25:21,968 INFO ___FILE_ONLY___
+
+2023-10-12 13:25:21,971 INFO ___FILE_ONLY___ Performing post processing steps...
+2023-10-12 13:25:21,971 DEBUG root Executing command: ['python3', '-S', '/tools/google-cloud-sdk/lib/gcloud.py', 'components', 'post-process']
+2023-10-12 13:25:34,763 DEBUG ___FILE_ONLY___
+2023-10-12 13:25:34,763 DEBUG ___FILE_ONLY___
+2023-10-12 13:25:34,990 INFO ___FILE_ONLY___
+Update done!
+
+
+2023-10-12 13:25:34,995 DEBUG root Chosen display Format:none
+2023-10-12 13:25:34,996 INFO root Display format: "none"
diff --git a/.config/logs/2023.10.12/13.25.22.709674.log b/.config/logs/2023.10.12/13.25.22.709674.log
new file mode 100644
index 0000000000000000000000000000000000000000..22ad3f18b527da865000e8d8c5b77c7d493b58f7
--- /dev/null
+++ b/.config/logs/2023.10.12/13.25.22.709674.log
@@ -0,0 +1,5 @@
+2023-10-12 13:25:22,710 DEBUG root Loaded Command Group: ['gcloud', 'components']
+2023-10-12 13:25:22,713 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
+2023-10-12 13:25:22,716 DEBUG root Running [gcloud.components.post-process] with arguments: []
+2023-10-12 13:25:34,632 DEBUG root Chosen display Format:none
+2023-10-12 13:25:34,633 INFO root Display format: "none"
diff --git a/.config/logs/2023.10.12/13.25.35.872929.log b/.config/logs/2023.10.12/13.25.35.872929.log
new file mode 100644
index 0000000000000000000000000000000000000000..2934ccf60e9c7758fd6de66ac24c2bae8cce94c5
--- /dev/null
+++ b/.config/logs/2023.10.12/13.25.35.872929.log
@@ -0,0 +1,8 @@
+2023-10-12 13:25:35,875 DEBUG root Loaded Command Group: ['gcloud', 'config']
+2023-10-12 13:25:35,912 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
+2023-10-12 13:25:35,915 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "component_manager/disable_update_check", VALUE: "true"]
+2023-10-12 13:25:35,916 INFO ___FILE_ONLY___ Updated property [component_manager/disable_update_check].
+
+2023-10-12 13:25:35,917 DEBUG root Chosen display Format:default
+2023-10-12 13:25:35,918 INFO root Display format: "default"
+2023-10-12 13:25:35,918 DEBUG root SDK update checks are disabled.
diff --git a/.config/logs/2023.10.12/13.25.36.773088.log b/.config/logs/2023.10.12/13.25.36.773088.log
new file mode 100644
index 0000000000000000000000000000000000000000..035910904436cbb2f7f038ed43769c66225b19c3
--- /dev/null
+++ b/.config/logs/2023.10.12/13.25.36.773088.log
@@ -0,0 +1,8 @@
+2023-10-12 13:25:36,775 DEBUG root Loaded Command Group: ['gcloud', 'config']
+2023-10-12 13:25:36,810 DEBUG root Loaded Command Group: ['gcloud', 'config', 'set']
+2023-10-12 13:25:36,813 DEBUG root Running [gcloud.config.set] with arguments: [SECTION/PROPERTY: "compute/gce_metadata_read_timeout_sec", VALUE: "0"]
+2023-10-12 13:25:36,814 INFO ___FILE_ONLY___ Updated property [compute/gce_metadata_read_timeout_sec].
+
+2023-10-12 13:25:36,815 DEBUG root Chosen display Format:default
+2023-10-12 13:25:36,816 INFO root Display format: "default"
+2023-10-12 13:25:36,817 DEBUG root SDK update checks are disabled.
diff --git a/.gitattributes b/.gitattributes
index 28df5f900b358436f0267334b3e3e9af33f917ba..4c9a4cdeb157bf60b1e468ec9c5da968592c14ae 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.jpg filter=lfs diff=lfs merge=lfs -text
*.jpeg filter=lfs diff=lfs merge=lfs -text
*.webp filter=lfs diff=lfs merge=lfs -text
+sample_data/mnist_test.csv filter=lfs diff=lfs merge=lfs -text
+sample_data/mnist_train_small.csv filter=lfs diff=lfs merge=lfs -text
diff --git a/sample_data/README.md b/sample_data/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e46cdae34844234bc75daeefda03a47aa7f19516
--- /dev/null
+++ b/sample_data/README.md
@@ -0,0 +1,19 @@
+This directory includes a few sample datasets to get you started.
+
+* `california_housing_data*.csv` is California housing data from the 1990 US
+ Census; more information is available at:
+ https://developers.google.com/machine-learning/crash-course/california-housing-data-description
+
+* `mnist_*.csv` is a small sample of the
+ [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
+ described at: http://yann.lecun.com/exdb/mnist/
+
+* `anscombe.json` contains a copy of
+ [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
+ was originally described in
+
+ Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
+ Statistician. 27 (1): 17-21. JSTOR 2682899.
+
+ and our copy was prepared by the
+ [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
diff --git a/sample_data/anscombe.json b/sample_data/anscombe.json
new file mode 100644
index 0000000000000000000000000000000000000000..d6c17c29f303f700bc66e1a8abdf1a0c9ce21660
--- /dev/null
+++ b/sample_data/anscombe.json
@@ -0,0 +1,49 @@
+[
+ {"Series":"I", "X":10.0, "Y":8.04},
+ {"Series":"I", "X":8.0, "Y":6.95},
+ {"Series":"I", "X":13.0, "Y":7.58},
+ {"Series":"I", "X":9.0, "Y":8.81},
+ {"Series":"I", "X":11.0, "Y":8.33},
+ {"Series":"I", "X":14.0, "Y":9.96},
+ {"Series":"I", "X":6.0, "Y":7.24},
+ {"Series":"I", "X":4.0, "Y":4.26},
+ {"Series":"I", "X":12.0, "Y":10.84},
+ {"Series":"I", "X":7.0, "Y":4.81},
+ {"Series":"I", "X":5.0, "Y":5.68},
+
+ {"Series":"II", "X":10.0, "Y":9.14},
+ {"Series":"II", "X":8.0, "Y":8.14},
+ {"Series":"II", "X":13.0, "Y":8.74},
+ {"Series":"II", "X":9.0, "Y":8.77},
+ {"Series":"II", "X":11.0, "Y":9.26},
+ {"Series":"II", "X":14.0, "Y":8.10},
+ {"Series":"II", "X":6.0, "Y":6.13},
+ {"Series":"II", "X":4.0, "Y":3.10},
+ {"Series":"II", "X":12.0, "Y":9.13},
+ {"Series":"II", "X":7.0, "Y":7.26},
+ {"Series":"II", "X":5.0, "Y":4.74},
+
+ {"Series":"III", "X":10.0, "Y":7.46},
+ {"Series":"III", "X":8.0, "Y":6.77},
+ {"Series":"III", "X":13.0, "Y":12.74},
+ {"Series":"III", "X":9.0, "Y":7.11},
+ {"Series":"III", "X":11.0, "Y":7.81},
+ {"Series":"III", "X":14.0, "Y":8.84},
+ {"Series":"III", "X":6.0, "Y":6.08},
+ {"Series":"III", "X":4.0, "Y":5.39},
+ {"Series":"III", "X":12.0, "Y":8.15},
+ {"Series":"III", "X":7.0, "Y":6.42},
+ {"Series":"III", "X":5.0, "Y":5.73},
+
+ {"Series":"IV", "X":8.0, "Y":6.58},
+ {"Series":"IV", "X":8.0, "Y":5.76},
+ {"Series":"IV", "X":8.0, "Y":7.71},
+ {"Series":"IV", "X":8.0, "Y":8.84},
+ {"Series":"IV", "X":8.0, "Y":8.47},
+ {"Series":"IV", "X":8.0, "Y":7.04},
+ {"Series":"IV", "X":8.0, "Y":5.25},
+ {"Series":"IV", "X":19.0, "Y":12.50},
+ {"Series":"IV", "X":8.0, "Y":5.56},
+ {"Series":"IV", "X":8.0, "Y":7.91},
+ {"Series":"IV", "X":8.0, "Y":6.89}
+]
diff --git a/sample_data/california_housing_test.csv b/sample_data/california_housing_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5210d8c5330bed9070a9b12a4f35bc01f5faed4c
--- /dev/null
+++ b/sample_data/california_housing_test.csv
@@ -0,0 +1,3001 @@
+"longitude","latitude","housing_median_age","total_rooms","total_bedrooms","population","households","median_income","median_house_value"
+-122.050000,37.370000,27.000000,3885.000000,661.000000,1537.000000,606.000000,6.608500,344700.000000
+-118.300000,34.260000,43.000000,1510.000000,310.000000,809.000000,277.000000,3.599000,176500.000000
+-117.810000,33.780000,27.000000,3589.000000,507.000000,1484.000000,495.000000,5.793400,270500.000000
+-118.360000,33.820000,28.000000,67.000000,15.000000,49.000000,11.000000,6.135900,330000.000000
+-119.670000,36.330000,19.000000,1241.000000,244.000000,850.000000,237.000000,2.937500,81700.000000
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+-120.650000,35.480000,19.000000,2310.000000,471.000000,1341.000000,441.000000,3.225000,166900.000000
+-122.840000,38.400000,15.000000,3080.000000,617.000000,1446.000000,599.000000,3.669600,194400.000000
+-118.020000,34.080000,31.000000,2402.000000,632.000000,2830.000000,603.000000,2.333300,164200.000000
+-118.240000,33.980000,45.000000,972.000000,249.000000,1288.000000,261.000000,2.205400,125000.000000
+-119.120000,35.850000,37.000000,736.000000,166.000000,564.000000,138.000000,2.416700,58300.000000
+-121.930000,37.250000,36.000000,1089.000000,182.000000,535.000000,170.000000,4.690000,252600.000000
+-117.030000,32.970000,16.000000,3936.000000,694.000000,1935.000000,659.000000,4.562500,231200.000000
+-117.970000,33.730000,27.000000,2097.000000,325.000000,1217.000000,331.000000,5.712100,222500.000000
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diff --git a/sample_data/california_housing_train.csv b/sample_data/california_housing_train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6e6023df366b8fee2e924429c25ad6fbdf10798d
--- /dev/null
+++ b/sample_data/california_housing_train.csv
@@ -0,0 +1,17001 @@
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diff --git a/sample_data/mnist_test.csv b/sample_data/mnist_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0bea413bf24149ab4bbc3e446c7984478ce371d4
--- /dev/null
+++ b/sample_data/mnist_test.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51c292478d94ec3a01461bdfa82eb0885d262eb09e615679b2d69dedb6ad09e7
+size 18289443
diff --git a/sample_data/mnist_train_small.csv b/sample_data/mnist_train_small.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7aa361d977d328126edf44b4dad536a83291408c
--- /dev/null
+++ b/sample_data/mnist_train_small.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
+size 36523880
diff --git a/stable-diffusion-webui/.eslintignore b/stable-diffusion-webui/.eslintignore
new file mode 100644
index 0000000000000000000000000000000000000000..1cfd9487674ff4db01a4285097f5eae74010b2ae
--- /dev/null
+++ b/stable-diffusion-webui/.eslintignore
@@ -0,0 +1,4 @@
+extensions
+extensions-disabled
+repositories
+venv
\ No newline at end of file
diff --git a/stable-diffusion-webui/.eslintrc.js b/stable-diffusion-webui/.eslintrc.js
new file mode 100644
index 0000000000000000000000000000000000000000..4777c276e9b13fa04ce3e9c7222df3d357fd824e
--- /dev/null
+++ b/stable-diffusion-webui/.eslintrc.js
@@ -0,0 +1,97 @@
+/* global module */
+module.exports = {
+ env: {
+ browser: true,
+ es2021: true,
+ },
+ extends: "eslint:recommended",
+ parserOptions: {
+ ecmaVersion: "latest",
+ },
+ rules: {
+ "arrow-spacing": "error",
+ "block-spacing": "error",
+ "brace-style": "error",
+ "comma-dangle": ["error", "only-multiline"],
+ "comma-spacing": "error",
+ "comma-style": ["error", "last"],
+ "curly": ["error", "multi-line", "consistent"],
+ "eol-last": "error",
+ "func-call-spacing": "error",
+ "function-call-argument-newline": ["error", "consistent"],
+ "function-paren-newline": ["error", "consistent"],
+ "indent": ["error", 4],
+ "key-spacing": "error",
+ "keyword-spacing": "error",
+ "linebreak-style": ["error", "unix"],
+ "no-extra-semi": "error",
+ "no-mixed-spaces-and-tabs": "error",
+ "no-multi-spaces": "error",
+ "no-redeclare": ["error", {builtinGlobals: false}],
+ "no-trailing-spaces": "error",
+ "no-unused-vars": "off",
+ "no-whitespace-before-property": "error",
+ "object-curly-newline": ["error", {consistent: true, multiline: true}],
+ "object-curly-spacing": ["error", "never"],
+ "operator-linebreak": ["error", "after"],
+ "quote-props": ["error", "consistent-as-needed"],
+ "semi": ["error", "always"],
+ "semi-spacing": "error",
+ "semi-style": ["error", "last"],
+ "space-before-blocks": "error",
+ "space-before-function-paren": ["error", "never"],
+ "space-in-parens": ["error", "never"],
+ "space-infix-ops": "error",
+ "space-unary-ops": "error",
+ "switch-colon-spacing": "error",
+ "template-curly-spacing": ["error", "never"],
+ "unicode-bom": "error",
+ },
+ globals: {
+ //script.js
+ gradioApp: "readonly",
+ executeCallbacks: "readonly",
+ onAfterUiUpdate: "readonly",
+ onOptionsChanged: "readonly",
+ onUiLoaded: "readonly",
+ onUiUpdate: "readonly",
+ uiCurrentTab: "writable",
+ uiElementInSight: "readonly",
+ uiElementIsVisible: "readonly",
+ //ui.js
+ opts: "writable",
+ all_gallery_buttons: "readonly",
+ selected_gallery_button: "readonly",
+ selected_gallery_index: "readonly",
+ switch_to_txt2img: "readonly",
+ switch_to_img2img_tab: "readonly",
+ switch_to_img2img: "readonly",
+ switch_to_sketch: "readonly",
+ switch_to_inpaint: "readonly",
+ switch_to_inpaint_sketch: "readonly",
+ switch_to_extras: "readonly",
+ get_tab_index: "readonly",
+ create_submit_args: "readonly",
+ restart_reload: "readonly",
+ updateInput: "readonly",
+ //extraNetworks.js
+ requestGet: "readonly",
+ popup: "readonly",
+ // from python
+ localization: "readonly",
+ // progrssbar.js
+ randomId: "readonly",
+ requestProgress: "readonly",
+ // imageviewer.js
+ modalPrevImage: "readonly",
+ modalNextImage: "readonly",
+ // token-counters.js
+ setupTokenCounters: "readonly",
+ // localStorage.js
+ localSet: "readonly",
+ localGet: "readonly",
+ localRemove: "readonly",
+ // resizeHandle.js
+ setupResizeHandle: "writable"
+ }
+};
diff --git a/stable-diffusion-webui/.git-blame-ignore-revs b/stable-diffusion-webui/.git-blame-ignore-revs
new file mode 100644
index 0000000000000000000000000000000000000000..4104da632b8fcacf3a6f52eba093e63059749725
--- /dev/null
+++ b/stable-diffusion-webui/.git-blame-ignore-revs
@@ -0,0 +1,2 @@
+# Apply ESlint
+9c54b78d9dde5601e916f308d9a9d6953ec39430
\ No newline at end of file
diff --git a/stable-diffusion-webui/.github/ISSUE_TEMPLATE/bug_report.yml b/stable-diffusion-webui/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 0000000000000000000000000000000000000000..cf6a2be86fa691b6f34f0aa3c160850742326ff2
--- /dev/null
+++ b/stable-diffusion-webui/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,74 @@
+name: Bug Report
+description: You think somethings is broken in the UI
+title: "[Bug]: "
+labels: ["bug-report"]
+
+body:
+ - type: checkboxes
+ attributes:
+ label: Is there an existing issue for this?
+ description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
+ options:
+ - label: I have searched the existing issues and checked the recent builds/commits
+ required: true
+ - type: markdown
+ attributes:
+ value: |
+ *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
+ - type: textarea
+ id: what-did
+ attributes:
+ label: What happened?
+ description: Tell us what happened in a very clear and simple way
+ validations:
+ required: true
+ - type: textarea
+ id: steps
+ attributes:
+ label: Steps to reproduce the problem
+ description: Please provide us with precise step by step instructions on how to reproduce the bug
+ value: |
+ 1. Go to ....
+ 2. Press ....
+ 3. ...
+ validations:
+ required: true
+ - type: textarea
+ id: what-should
+ attributes:
+ label: What should have happened?
+ description: Tell us what you think the normal behavior should be
+ validations:
+ required: true
+ - type: textarea
+ id: sysinfo
+ attributes:
+ label: Sysinfo
+ description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
+ validations:
+ required: true
+ - type: dropdown
+ id: browsers
+ attributes:
+ label: What browsers do you use to access the UI ?
+ multiple: true
+ options:
+ - Mozilla Firefox
+ - Google Chrome
+ - Brave
+ - Apple Safari
+ - Microsoft Edge
+ - Other
+ - type: textarea
+ id: logs
+ attributes:
+ label: Console logs
+ description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
+ render: Shell
+ validations:
+ required: true
+ - type: textarea
+ id: misc
+ attributes:
+ label: Additional information
+ description: Please provide us with any relevant additional info or context.
diff --git a/stable-diffusion-webui/.github/ISSUE_TEMPLATE/config.yml b/stable-diffusion-webui/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000000000000000000000000000000000000..f58c94a9be6847193a971ac67aa83e9a6d75c0ae
--- /dev/null
+++ b/stable-diffusion-webui/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,5 @@
+blank_issues_enabled: false
+contact_links:
+ - name: WebUI Community Support
+ url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
+ about: Please ask and answer questions here.
diff --git a/stable-diffusion-webui/.github/ISSUE_TEMPLATE/feature_request.yml b/stable-diffusion-webui/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 0000000000000000000000000000000000000000..35a887408c1a0cb7d5bbf0a8444d0903a708be75
--- /dev/null
+++ b/stable-diffusion-webui/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,40 @@
+name: Feature request
+description: Suggest an idea for this project
+title: "[Feature Request]: "
+labels: ["enhancement"]
+
+body:
+ - type: checkboxes
+ attributes:
+ label: Is there an existing issue for this?
+ description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit.
+ options:
+ - label: I have searched the existing issues and checked the recent builds/commits
+ required: true
+ - type: markdown
+ attributes:
+ value: |
+ *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
+ - type: textarea
+ id: feature
+ attributes:
+ label: What would your feature do ?
+ description: Tell us about your feature in a very clear and simple way, and what problem it would solve
+ validations:
+ required: true
+ - type: textarea
+ id: workflow
+ attributes:
+ label: Proposed workflow
+ description: Please provide us with step by step information on how you'd like the feature to be accessed and used
+ value: |
+ 1. Go to ....
+ 2. Press ....
+ 3. ...
+ validations:
+ required: true
+ - type: textarea
+ id: misc
+ attributes:
+ label: Additional information
+ description: Add any other context or screenshots about the feature request here.
diff --git a/stable-diffusion-webui/.github/pull_request_template.md b/stable-diffusion-webui/.github/pull_request_template.md
new file mode 100644
index 0000000000000000000000000000000000000000..c9fcda2e2790861c7bf4aa4cb37e01545c48fb95
--- /dev/null
+++ b/stable-diffusion-webui/.github/pull_request_template.md
@@ -0,0 +1,15 @@
+## Description
+
+* a simple description of what you're trying to accomplish
+* a summary of changes in code
+* which issues it fixes, if any
+
+## Screenshots/videos:
+
+
+## Checklist:
+
+- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
+- [ ] I have performed a self-review of my own code
+- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style)
+- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests)
diff --git a/stable-diffusion-webui/.github/workflows/on_pull_request.yaml b/stable-diffusion-webui/.github/workflows/on_pull_request.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..78e608ee945831e36ab832636e9a7ed9e180c462
--- /dev/null
+++ b/stable-diffusion-webui/.github/workflows/on_pull_request.yaml
@@ -0,0 +1,38 @@
+name: Linter
+
+on:
+ - push
+ - pull_request
+
+jobs:
+ lint-python:
+ name: ruff
+ runs-on: ubuntu-latest
+ if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
+ steps:
+ - name: Checkout Code
+ uses: actions/checkout@v3
+ - uses: actions/setup-python@v4
+ with:
+ python-version: 3.11
+ # NB: there's no cache: pip here since we're not installing anything
+ # from the requirements.txt file(s) in the repository; it's faster
+ # not to have GHA download an (at the time of writing) 4 GB cache
+ # of PyTorch and other dependencies.
+ - name: Install Ruff
+ run: pip install ruff==0.0.272
+ - name: Run Ruff
+ run: ruff .
+ lint-js:
+ name: eslint
+ runs-on: ubuntu-latest
+ if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
+ steps:
+ - name: Checkout Code
+ uses: actions/checkout@v3
+ - name: Install Node.js
+ uses: actions/setup-node@v3
+ with:
+ node-version: 18
+ - run: npm i --ci
+ - run: npm run lint
diff --git a/stable-diffusion-webui/.github/workflows/run_tests.yaml b/stable-diffusion-webui/.github/workflows/run_tests.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3dafaf8dcfcd14fd7a7ca3385806efad5550b871
--- /dev/null
+++ b/stable-diffusion-webui/.github/workflows/run_tests.yaml
@@ -0,0 +1,73 @@
+name: Tests
+
+on:
+ - push
+ - pull_request
+
+jobs:
+ test:
+ name: tests on CPU with empty model
+ runs-on: ubuntu-latest
+ if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
+ steps:
+ - name: Checkout Code
+ uses: actions/checkout@v3
+ - name: Set up Python 3.10
+ uses: actions/setup-python@v4
+ with:
+ python-version: 3.10.6
+ cache: pip
+ cache-dependency-path: |
+ **/requirements*txt
+ launch.py
+ - name: Install test dependencies
+ run: pip install wait-for-it -r requirements-test.txt
+ env:
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
+ PIP_PROGRESS_BAR: "off"
+ - name: Setup environment
+ run: python launch.py --skip-torch-cuda-test --exit
+ env:
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
+ PIP_PROGRESS_BAR: "off"
+ TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
+ WEBUI_LAUNCH_LIVE_OUTPUT: "1"
+ PYTHONUNBUFFERED: "1"
+ - name: Start test server
+ run: >
+ python -m coverage run
+ --data-file=.coverage.server
+ launch.py
+ --skip-prepare-environment
+ --skip-torch-cuda-test
+ --test-server
+ --do-not-download-clip
+ --no-half
+ --disable-opt-split-attention
+ --use-cpu all
+ --api-server-stop
+ 2>&1 | tee output.txt &
+ - name: Run tests
+ run: |
+ wait-for-it --service 127.0.0.1:7860 -t 600
+ python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
+ - name: Kill test server
+ if: always()
+ run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
+ - name: Show coverage
+ run: |
+ python -m coverage combine .coverage*
+ python -m coverage report -i
+ python -m coverage html -i
+ - name: Upload main app output
+ uses: actions/upload-artifact@v3
+ if: always()
+ with:
+ name: output
+ path: output.txt
+ - name: Upload coverage HTML
+ uses: actions/upload-artifact@v3
+ if: always()
+ with:
+ name: htmlcov
+ path: htmlcov
diff --git a/stable-diffusion-webui/.github/workflows/warns_merge_master.yml b/stable-diffusion-webui/.github/workflows/warns_merge_master.yml
new file mode 100644
index 0000000000000000000000000000000000000000..ae2aab6ba8ce5684755b5fb4083267111bcd23cd
--- /dev/null
+++ b/stable-diffusion-webui/.github/workflows/warns_merge_master.yml
@@ -0,0 +1,19 @@
+name: Pull requests can't target master branch
+
+"on":
+ pull_request:
+ types:
+ - opened
+ - synchronize
+ - reopened
+ branches:
+ - master
+
+jobs:
+ check:
+ runs-on: ubuntu-latest
+ steps:
+ - name: Warning marge into master
+ run: |
+ echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch."
+ exit 1
diff --git a/stable-diffusion-webui/.gitignore b/stable-diffusion-webui/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..09734267ff5c4d51c2f9f1c85f6f8bf2cc225fb9
--- /dev/null
+++ b/stable-diffusion-webui/.gitignore
@@ -0,0 +1,39 @@
+__pycache__
+*.ckpt
+*.safetensors
+*.pth
+/ESRGAN/*
+/SwinIR/*
+/repositories
+/venv
+/tmp
+/model.ckpt
+/models/**/*
+/GFPGANv1.3.pth
+/gfpgan/weights/*.pth
+/ui-config.json
+/outputs
+/config.json
+/log
+/webui.settings.bat
+/embeddings
+/styles.csv
+/params.txt
+/styles.csv.bak
+/webui-user.bat
+/webui-user.sh
+/interrogate
+/user.css
+/.idea
+notification.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt
+/cache.json*
+/config_states/
+/node_modules
+/package-lock.json
+/.coverage*
diff --git a/stable-diffusion-webui/.pylintrc b/stable-diffusion-webui/.pylintrc
new file mode 100644
index 0000000000000000000000000000000000000000..53254e5dcfd871c8c0f0f4dec9dceeb1ba967eda
--- /dev/null
+++ b/stable-diffusion-webui/.pylintrc
@@ -0,0 +1,3 @@
+# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
+[MESSAGES CONTROL]
+disable=C,R,W,E,I
diff --git a/stable-diffusion-webui/CHANGELOG.md b/stable-diffusion-webui/CHANGELOG.md
new file mode 100644
index 0000000000000000000000000000000000000000..130ad44ad0d8d809f2d71a689540a978f39dc6b4
--- /dev/null
+++ b/stable-diffusion-webui/CHANGELOG.md
@@ -0,0 +1,507 @@
+## 1.6.0
+
+### Features:
+ * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371)
+ * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards
+ * add style editor dialog
+ * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181))
+ * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227))
+ * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542))
+ * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers:
+ * makes all of them work with img2img
+ * makes prompt composition posssible (AND)
+ * makes them available for SDXL
+ * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808))
+ * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599))
+ * textual inversion inference support for SDXL
+ * extra networks UI: show metadata for SD checkpoints
+ * checkpoint merger: add metadata support
+ * prompt editing and attention: add support for whitespace after the number ([ red : green : 0.5 ]) (seed breaking change) ([#12177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12177))
+ * VAE: allow selecting own VAE for each checkpoint (in user metadata editor)
+ * VAE: add selected VAE to infotext
+ * options in main UI: add own separate setting for txt2img and img2img, correctly read values from pasted infotext, add setting for column count ([#12551](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12551))
+ * add resize handle to txt2img and img2img tabs, allowing to change the amount of horizontable space given to generation parameters and resulting image gallery ([#12687](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12687), [#12723](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12723))
+ * change default behavior for batching cond/uncond -- now it's on by default, and is disabled by an UI setting (Optimizatios -> Batch cond/uncond) - if you are on lowvram/medvram and are getting OOM exceptions, you will need to enable it
+ * show current position in queue and make it so that requests are processed in the order of arrival ([#12707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12707))
+ * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models
+ * prompt editing timeline has separate range for first pass and hires-fix pass (seed breaking change) ([#12457](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12457))
+
+### Minor:
+ * img2img batch: RAM savings, VRAM savings, .tif, .tiff in img2img batch ([#12120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12120), [#12514](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12514), [#12515](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12515))
+ * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479))
+ * XYZ: in the axis labels, remove pathnames from model filenames
+ * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298))
+ * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491))
+ * add gradio version warning
+ * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297))
+ * use transparent white for mask in inpainting, along with an option to select the color ([#12326](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12326))
+ * move some settings to their own section: img2img, VAE
+ * add checkbox to show/hide dirs for extra networks
+ * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311))
+ * gradio theme cache, new gradio themes, along with explanation that the user can input his own values ([#12346](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12346), [#12355](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12355))
+ * sampler fixes/tweaks: s_tmax, s_churn, s_noise, s_tmax ([#12354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12354), [#12356](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12356), [#12357](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12357), [#12358](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12358), [#12375](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12375), [#12521](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12521))
+ * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352))
+ * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338))
+ * enable cond cache by default
+ * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230))
+ * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379))
+ * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254))
+ * put commonly used samplers on top, make DPM++ 2M Karras the default choice
+ * zoom and pan: option to auto-expand a wide image, improved integration ([#12413](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12413), [#12727](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12727))
+ * option to cache Lora networks in memory
+ * rework hires fix UI to use accordion
+ * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back
+ * change quicksettings items to have variable width
+ * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503))
+ * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console
+ * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510))
+ * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564))
+ * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584))
+ * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634))
+ * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648))
+ * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645))
+ * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639))
+ * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635))
+ * make progress bar work independently from live preview display which results in it being updated a lot more often
+ * forbid Full live preview method for medvram and add a setting to undo the forbidding
+ * make it possible to localize tooltips and placeholders
+ * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
+ * Restore faces and Tiling generation parameters have been moved to settings out of main UI
+ * if you want to put them back into main UI, use `Options in main UI` setting on the UI page.
+
+### Extensions and API:
+ * gradio 3.41.2
+ * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd
+ * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world')
+ * properly clear the total console progressbar when using txt2img and img2img from API
+ * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294))
+ * shared.py and webui.py split into many files
+ * add --loglevel commandline argument for logging
+ * add a custom UI element that combines accordion and checkbox
+ * avoid importing gradio in tests because it spams warnings
+ * put infotext label for setting into OptionInfo definition rather than in a separate list
+ * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470))
+ * option to make scripts UI without gr.Group
+ * add a way for scripts to register a callback for before/after just a single component's creation
+ * use dataclass for StableDiffusionProcessing
+ * store patches for Lora in a specialized module instead of inside torch
+ * support http/https URLs in API ([#12663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12663), [#12698](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12698))
+ * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616))
+ * dump current stack traces when exiting with SIGINT
+ * add type annotations for extra fields of shared.sd_model
+
+### Bug Fixes:
+ * Don't crash if out of local storage quota for javascriot localStorage
+ * XYZ plot do not fail if an exception occurs
+ * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269))
+ * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307))
+ * fix sdxl model invalid configuration after the hijack
+ * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304))
+ * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318))
+ * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319))
+ * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327))
+ * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387))
+ * fix options in main UI misbehaving when there's just one element
+ * make it possible to use a sampler from infotext even if it's hidden in the dropdown
+ * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images
+ * prevent bogus progress output in console when calculating hires fix dimensions
+ * fix --use-textbox-seed
+ * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466))
+ * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463))
+ * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526))
+ * add second_order to samplers that mistakenly didn't have it
+ * when refreshing cards in extra networks UI, do not discard user's custom resolution
+ * fix processing error that happens if batch_size is not a multiple of how many prompts/negative prompts there are ([#12509](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12509))
+ * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588))
+ * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586))
+ * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596))
+ * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603))
+ * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607))
+ * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638))
+ * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633))
+ * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630))
+ * implement missing undo hijack for SDXL
+ * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684))
+ * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689))
+ * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685))
+ * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667))
+ * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717))
+ * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version
+ * set devices.dtype_unet correctly
+ * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745))
+ * fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
+ * fix defaults settings page breaking when any of main UI tabs are hidden
+ * fix incorrect save/display of new values in Defaults page in settings
+ * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
+ * fix an error that prevents VAE being reloaded after an option change if a VAE near the checkpoint exists ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
+ * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
+ * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity)
+ * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
+ * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
+ * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855))
+ * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
+ * get progressbar to display correctly in extensions tab
+
+
+## 1.5.2
+
+### Bug Fixes:
+ * fix memory leak when generation fails
+ * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk
+
+
+## 1.5.1
+
+### Minor:
+ * support parsing text encoder blocks in some new LoRAs
+ * delete scale checker script due to user demand
+
+### Extensions and API:
+ * add postprocess_batch_list script callback
+
+### Bug Fixes:
+ * fix TI training for SD1
+ * fix reload altclip model error
+ * prepend the pythonpath instead of overriding it
+ * fix typo in SD_WEBUI_RESTARTING
+ * if txt2img/img2img raises an exception, finally call state.end()
+ * fix composable diffusion weight parsing
+ * restyle Startup profile for black users
+ * fix webui not launching with --nowebui
+ * catch exception for non git extensions
+ * fix some options missing from /sdapi/v1/options
+ * fix for extension update status always saying "unknown"
+ * fix display of extra network cards that have `<>` in the name
+ * update lora extension to work with python 3.8
+
+
+## 1.5.0
+
+### Features:
+ * SD XL support
+ * user metadata system for custom networks
+ * extended Lora metadata editor: set activation text, default weight, view tags, training info
+ * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
+ * show github stars for extenstions
+ * img2img batch mode can read extra stuff from png info
+ * img2img batch works with subdirectories
+ * hotkeys to move prompt elements: alt+left/right
+ * restyle time taken/VRAM display
+ * add textual inversion hashes to infotext
+ * optimization: cache git extension repo information
+ * move generate button next to the generated picture for mobile clients
+ * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface
+ * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds
+
+### Minor:
+ * checkbox to check/uncheck all extensions in the Installed tab
+ * add gradio user to infotext and to filename patterns
+ * allow gif for extra network previews
+ * add options to change colors in grid
+ * use natural sort for items in extra networks
+ * Mac: use empty_cache() from torch 2 to clear VRAM
+ * added automatic support for installing the right libraries for Navi3 (AMD)
+ * add option SWIN_torch_compile to accelerate SwinIR upscale
+ * suppress printing TI embedding info at start to console by default
+ * speedup extra networks listing
+ * added `[none]` filename token.
+ * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)
+ * add always_discard_next_to_last_sigma option to XYZ plot
+ * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.
+
+### Extensions and API:
+ * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop
+ * allow Script to have custom metaclass
+ * add model exists status check /sdapi/v1/options
+ * rename --add-stop-route to --api-server-stop
+ * add `before_hr` script callback
+ * add callback `after_extra_networks_activate`
+ * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable
+ * return http 404 when thumb file not found
+ * allow replacing extensions index with environment variable
+
+### Bug Fixes:
+ * fix for catch errors when retrieving extension index #11290
+ * fix very slow loading speed of .safetensors files when reading from network drives
+ * API cache cleanup
+ * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode
+ * fix warning of 'has_mps' deprecated from PyTorch
+ * fix problem with extra network saving images as previews losing generation info
+ * fix throwing exception when trying to resize image with I;16 mode
+ * fix for #11534: canvas zoom and pan extension hijacking shortcut keys
+ * fixed launch script to be runnable from any directory
+ * don't add "Seed Resize: -1x-1" to API image metadata
+ * correctly remove end parenthesis with ctrl+up/down
+ * fixing --subpath on newer gradio version
+ * fix: check fill size none zero when resize (fixes #11425)
+ * use submit and blur for quick settings textbox
+ * save img2img batch with images.save_image()
+ * prevent running preload.py for disabled extensions
+ * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included
+
+
+## 1.4.1
+
+### Bug Fixes:
+ * add queue lock for refresh-checkpoints
+
+## 1.4.0
+
+### Features:
+ * zoom controls for inpainting
+ * run basic torch calculation at startup in parallel to reduce the performance impact of first generation
+ * option to pad prompt/neg prompt to be same length
+ * remove taming_transformers dependency
+ * custom k-diffusion scheduler settings
+ * add an option to show selected settings in main txt2img/img2img UI
+ * sysinfo tab in settings
+ * infer styles from prompts when pasting params into the UI
+ * an option to control the behavior of the above
+
+### Minor:
+ * bump Gradio to 3.32.0
+ * bump xformers to 0.0.20
+ * Add option to disable token counters
+ * tooltip fixes & optimizations
+ * make it possible to configure filename for the zip download
+ * `[vae_filename]` pattern for filenames
+ * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE
+ * change UI reorder setting to multiselect
+ * read version info form CHANGELOG.md if git version info is not available
+ * link footer API to Wiki when API is not active
+ * persistent conds cache (opt-in optimization)
+
+### Extensions:
+ * After installing extensions, webui properly restarts the process rather than reloads the UI
+ * Added VAE listing to web API. Via: /sdapi/v1/sd-vae
+ * custom unet support
+ * Add onAfterUiUpdate callback
+ * refactor EmbeddingDatabase.register_embedding() to allow unregistering
+ * add before_process callback for scripts
+ * add ability for alwayson scripts to specify section and let user reorder those sections
+
+### Bug Fixes:
+ * Fix dragging text to prompt
+ * fix incorrect quoting for infotext values with colon in them
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
+ * Fix s_min_uncond default type int
+ * Fix for #10643 (Inpainting mask sometimes not working)
+ * fix bad styling for thumbs view in extra networks #10639
+ * fix for empty list of optimizations #10605
+ * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility
+ * fix --ui-debug-mode exit
+ * patch GitPython to not use leaky persistent processes
+ * fix duplicate Cross attention optimization after UI reload
+ * torch.cuda.is_available() check for SdOptimizationXformers
+ * fix hires fix using wrong conds in second pass if using Loras.
+ * handle exception when parsing generation parameters from png info
+ * fix upcast attention dtype error
+ * forcing Torch Version to 1.13.1 for RX 5000 series GPUs
+ * split mask blur into X and Y components, patch Outpainting MK2 accordingly
+ * don't die when a LoRA is a broken symlink
+ * allow activation of Generate Forever during generation
+
+
+## 1.3.2
+
+### Bug Fixes:
+ * fix files served out of tmp directory even if they are saved to disk
+ * fix postprocessing overwriting parameters
+
+## 1.3.1
+
+### Features:
+ * revert default cross attention optimization to Doggettx
+
+### Bug Fixes:
+ * fix bug: LoRA don't apply on dropdown list sd_lora
+ * fix png info always added even if setting is not enabled
+ * fix some fields not applying in xyz plot
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
+ * fix lora hashes not being added properly to infotex if there is only one lora
+ * fix --use-cpu failing to work properly at startup
+ * make --disable-opt-split-attention command line option work again
+
+## 1.3.0
+
+### Features:
+ * add UI to edit defaults
+ * token merging (via dbolya/tomesd)
+ * settings tab rework: add a lot of additional explanations and links
+ * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup
+ * update extensions table: show branch, show date in separate column, and show version from tags if available
+ * TAESD - another option for cheap live previews
+ * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
+ * calculate hashes for Lora
+ * add lora hashes to infotext
+ * when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
+ * select cross attention optimization from UI
+
+### Minor:
+ * bump Gradio to 3.31.0
+ * bump PyTorch to 2.0.1 for macOS and Linux AMD
+ * allow setting defaults for elements in extensions' tabs
+ * allow selecting file type for live previews
+ * show "Loading..." for extra networks when displaying for the first time
+ * suppress ENSD infotext for samplers that don't use it
+ * clientside optimizations
+ * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories
+ * allow whitespace in styles.csv
+ * add option to reorder tabs
+ * move some functionality (swap resolution and set seed to -1) to client
+ * option to specify editor height for img2img
+ * button to copy image resolution into img2img width/height sliders
+ * switch from pyngrok to ngrok-py
+ * lazy-load images in extra networks UI
+ * set "Navigate image viewer with gamepad" option to false by default, by request
+ * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
+ * allow hiding buttons in ui-config.json
+
+### Extensions:
+ * add /sdapi/v1/script-info api
+ * use Ruff to lint Python code
+ * use ESlint to lint Javascript code
+ * add/modify CFG callbacks for Self-Attention Guidance extension
+ * add command and endpoint for graceful server stopping
+ * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
+ * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously)
+ * add /sdapi/v1/refresh-loras api checkpoint post request
+ * tests overhaul
+
+### Bug Fixes:
+ * fix an issue preventing the program from starting if the user specifies a bad Gradio theme
+ * fix broken prompts from file script
+ * fix symlink scanning for extra networks
+ * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS
+ * allow web UI to be ran fully offline
+ * fix inability to run with --freeze-settings
+ * fix inability to merge checkpoint without adding metadata
+ * fix extra networks' save preview image not adding infotext for jpeg/webm
+ * remove blinking effect from text in hires fix and scale resolution preview
+ * make links to `http://<...>.git` extensions work in the extension tab
+ * fix bug with webui hanging at startup due to hanging git process
+
+
+## 1.2.1
+
+### Features:
+ * add an option to always refer to LoRA by filenames
+
+### Bug Fixes:
+ * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none
+ * fix upscalers disappearing after the user reloads UI
+ * allow bf16 in safe unpickler (resolves problems with loading some LoRAs)
+ * allow web UI to be ran fully offline
+ * fix localizations not working
+ * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'`
+
+## 1.2.0
+
+### Features:
+ * do not wait for Stable Diffusion model to load at startup
+ * add filename patterns: `[denoising]`
+ * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
+ * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metdata of the file, if present, instead of filename (both can be used to activate LoRA)
+ * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
+ * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
+ * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
+ * add version to infotext, footer and console output when starting
+ * add links to wiki for filename pattern settings
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
+
+### Minor:
+ * bump Gradio to 3.29.0
+ * bump PyTorch to 2.0.1
+ * `--subpath` option for gradio for use with reverse proxy
+ * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
+ * do not apply localizations if there are none (possible frontend optimization)
+ * add extra `None` option for VAE in XYZ plot
+ * print error to console when batch processing in img2img fails
+ * create HTML for extra network pages only on demand
+ * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc
+ * put infotext options into their own category in settings tab
+ * do not show licenses page when user selects Show all pages in settings
+
+### Extensions:
+ * tooltip localization support
+ * add API method to get LoRA models with prompt
+
+### Bug Fixes:
+ * re-add `/docs` endpoint
+ * fix gamepad navigation
+ * make the lightbox fullscreen image function properly
+ * fix squished thumbnails in extras tab
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
+ * fix webui showing the same image if you configure the generation to always save results into same file
+ * fix bug with upscalers not working properly
+ * fix MPS on PyTorch 2.0.1, Intel Macs
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
+ * fix prompts from file script failing to read contents from a drag/drop file
+
+
+## 1.1.1
+### Bug Fixes:
+ * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle
+
+## 1.1.0
+### Features:
+ * switch to PyTorch 2.0.0 (except for AMD GPUs)
+ * visual improvements to custom code scripts
+ * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducability
+ * automatically select current word when adjusting weight with ctrl+up/down
+ * add dropdowns for X/Y/Z plot
+ * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
+ * support Gradio's theme API
+ * use TCMalloc on Linux by default; possible fix for memory leaks
+ * add optimization option to remove negative conditioning at low sigma values #9177
+ * embed model merge metadata in .safetensors file
+ * extension settings backup/restore feature #9169
+ * add "resize by" and "resize to" tabs to img2img
+ * add option "keep original size" to textual inversion images preprocess
+ * image viewer scrolling via analog stick
+ * button to restore the progress from session lost / tab reload
+
+### Minor:
+ * bump Gradio to 3.28.1
+ * change "scale to" to sliders in Extras tab
+ * add labels to tool buttons to make it possible to hide them
+ * add tiled inference support for ScuNET
+ * add branch support for extension installation
+ * change Linux installation script to install into current directory rather than `/home/username`
+ * sort textual inversion embeddings by name (case-insensitive)
+ * allow styles.csv to be symlinked or mounted in docker
+ * remove the "do not add watermark to images" option
+ * make selected tab configurable with UI config
+ * make the extra networks UI fixed height and scrollable
+ * add `disable_tls_verify` arg for use with self-signed certs
+
+### Extensions:
+ * add reload callback
+ * add `is_hr_pass` field for processing
+
+### Bug Fixes:
+ * fix broken batch image processing on 'Extras/Batch Process' tab
+ * add "None" option to extra networks dropdowns
+ * fix FileExistsError for CLIP Interrogator
+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319
+ * fix disappearing live previews and progressbar during slow tasks
+ * fix fullscreen image view not working properly in some cases
+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
+ * fix prompt schedule for second order samplers
+ * fix image mask/composite for weird resolutions #9628
+ * use correct images for previews when using AND (see #9491)
+ * one broken image in img2img batch won't stop all processing
+ * fix image orientation bug in train/preprocess
+ * fix Ngrok recreating tunnels every reload
+ * fix `--realesrgan-models-path` and `--ldsr-models-path` not working
+ * fix `--skip-install` not working
+ * use SAMPLE file format in Outpainting Mk2 & Poorman
+ * do not fail all LoRAs if some have failed to load when making a picture
+
+## 1.0.0
+ * everything
diff --git a/stable-diffusion-webui/CITATION.cff b/stable-diffusion-webui/CITATION.cff
new file mode 100644
index 0000000000000000000000000000000000000000..2c781aff450c8604eb3cf876d2c3585a96a5a590
--- /dev/null
+++ b/stable-diffusion-webui/CITATION.cff
@@ -0,0 +1,7 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+ - given-names: AUTOMATIC1111
+title: "Stable Diffusion Web UI"
+date-released: 2022-08-22
+url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui"
diff --git a/stable-diffusion-webui/CODEOWNERS b/stable-diffusion-webui/CODEOWNERS
new file mode 100644
index 0000000000000000000000000000000000000000..2c937f6f1e519f864d15d5233e1fb86c6cdfac2f
--- /dev/null
+++ b/stable-diffusion-webui/CODEOWNERS
@@ -0,0 +1,12 @@
+* @AUTOMATIC1111
+
+# if you were managing a localization and were removed from this file, this is because
+# the intended way to do localizations now is via extensions. See:
+# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
+# Make a repo with your localization and since you are still listed as a collaborator
+# you can add it to the wiki page yourself. This change is because some people complained
+# the git commit log is cluttered with things unrelated to almost everyone and
+# because I believe this is the best overall for the project to handle localizations almost
+# entirely without my oversight.
+
+
diff --git a/stable-diffusion-webui/LICENSE.txt b/stable-diffusion-webui/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..211d32e752cb61bd056436e8f7a806f12a626bb7
--- /dev/null
+++ b/stable-diffusion-webui/LICENSE.txt
@@ -0,0 +1,663 @@
+ GNU AFFERO GENERAL PUBLIC LICENSE
+ Version 3, 19 November 2007
+
+ Copyright (c) 2023 AUTOMATIC1111
+
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diff --git a/stable-diffusion-webui/README.md b/stable-diffusion-webui/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..41a1e8aa743b0d424648ab48b29f153131274151
--- /dev/null
+++ b/stable-diffusion-webui/README.md
@@ -0,0 +1,177 @@
+# Stable Diffusion web UI
+A browser interface based on Gradio library for Stable Diffusion.
+
+
+
+## Features
+[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
+- Original txt2img and img2img modes
+- One click install and run script (but you still must install python and git)
+- Outpainting
+- Inpainting
+- Color Sketch
+- Prompt Matrix
+- Stable Diffusion Upscale
+- Attention, specify parts of text that the model should pay more attention to
+ - a man in a `((tuxedo))` - will pay more attention to tuxedo
+ - a man in a `(tuxedo:1.21)` - alternative syntax
+ - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
+- Loopback, run img2img processing multiple times
+- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
+- Textual Inversion
+ - have as many embeddings as you want and use any names you like for them
+ - use multiple embeddings with different numbers of vectors per token
+ - works with half precision floating point numbers
+ - train embeddings on 8GB (also reports of 6GB working)
+- Extras tab with:
+ - GFPGAN, neural network that fixes faces
+ - CodeFormer, face restoration tool as an alternative to GFPGAN
+ - RealESRGAN, neural network upscaler
+ - ESRGAN, neural network upscaler with a lot of third party models
+ - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+ - LDSR, Latent diffusion super resolution upscaling
+- Resizing aspect ratio options
+- Sampling method selection
+ - Adjust sampler eta values (noise multiplier)
+ - More advanced noise setting options
+- Interrupt processing at any time
+- 4GB video card support (also reports of 2GB working)
+- Correct seeds for batches
+- Live prompt token length validation
+- Generation parameters
+ - parameters you used to generate images are saved with that image
+ - in PNG chunks for PNG, in EXIF for JPEG
+ - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
+ - can be disabled in settings
+ - drag and drop an image/text-parameters to promptbox
+- Read Generation Parameters Button, loads parameters in promptbox to UI
+- Settings page
+- Running arbitrary python code from UI (must run with `--allow-code` to enable)
+- Mouseover hints for most UI elements
+- Possible to change defaults/mix/max/step values for UI elements via text config
+- Tiling support, a checkbox to create images that can be tiled like textures
+- Progress bar and live image generation preview
+ - Can use a separate neural network to produce previews with almost none VRAM or compute requirement
+- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
+- Styles, a way to save part of prompt and easily apply them via dropdown later
+- Variations, a way to generate same image but with tiny differences
+- Seed resizing, a way to generate same image but at slightly different resolution
+- CLIP interrogator, a button that tries to guess prompt from an image
+- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
+- Batch Processing, process a group of files using img2img
+- Img2img Alternative, reverse Euler method of cross attention control
+- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
+- Reloading checkpoints on the fly
+- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
+- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
+- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
+ - separate prompts using uppercase `AND`
+ - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
+- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
+- DeepDanbooru integration, creates danbooru style tags for anime prompts
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
+- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
+- Generate forever option
+- Training tab
+ - hypernetworks and embeddings options
+ - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
+- Clip skip
+- Hypernetworks
+- Loras (same as Hypernetworks but more pretty)
+- A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
+- Can select to load a different VAE from settings screen
+- Estimated completion time in progress bar
+- API
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
+- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
+- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
+- Now without any bad letters!
+- Load checkpoints in safetensors format
+- Eased resolution restriction: generated image's dimension must be a multiple of 8 rather than 64
+- Now with a license!
+- Reorder elements in the UI from settings screen
+
+## Installation and Running
+Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for:
+- [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended)
+- [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
+- [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page)
+
+Alternatively, use online services (like Google Colab):
+
+- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
+
+### Installation on Windows 10/11 with NVidia-GPUs using release package
+1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
+2. Run `update.bat`.
+3. Run `run.bat`.
+> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
+
+### Automatic Installation on Windows
+1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
+2. Install [git](https://git-scm.com/download/win).
+3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
+4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
+
+### Automatic Installation on Linux
+1. Install the dependencies:
+```bash
+# Debian-based:
+sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
+# Red Hat-based:
+sudo dnf install wget git python3
+# Arch-based:
+sudo pacman -S wget git python3
+```
+2. Navigate to the directory you would like the webui to be installed and execute the following command:
+```bash
+wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
+```
+3. Run `webui.sh`.
+4. Check `webui-user.sh` for options.
+### Installation on Apple Silicon
+
+Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
+
+## Contributing
+Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
+
+## Documentation
+
+The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
+
+For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).
+
+## Credits
+Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
+
+- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
+- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
+- GFPGAN - https://github.com/TencentARC/GFPGAN.git
+- CodeFormer - https://github.com/sczhou/CodeFormer
+- ESRGAN - https://github.com/xinntao/ESRGAN
+- SwinIR - https://github.com/JingyunLiang/SwinIR
+- Swin2SR - https://github.com/mv-lab/swin2sr
+- LDSR - https://github.com/Hafiidz/latent-diffusion
+- MiDaS - https://github.com/isl-org/MiDaS
+- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
+- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
+- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
+- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
+- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
+- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
+- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
+- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
+- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
+- xformers - https://github.com/facebookresearch/xformers
+- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
+- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
+- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
+- Security advice - RyotaK
+- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
+- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
+- LyCORIS - KohakuBlueleaf
+- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
+- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
+- (You)
diff --git a/stable-diffusion-webui/configs/alt-diffusion-inference.yaml b/stable-diffusion-webui/configs/alt-diffusion-inference.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cfbee72d71bfd7deed2075e423ca51bd1da0521c
--- /dev/null
+++ b/stable-diffusion-webui/configs/alt-diffusion-inference.yaml
@@ -0,0 +1,72 @@
+model:
+ base_learning_rate: 1.0e-04
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false # Note: different from the one we trained before
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+
+ scheduler_config: # 10000 warmup steps
+ target: ldm.lr_scheduler.LambdaLinearScheduler
+ params:
+ warm_up_steps: [ 10000 ]
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+ f_start: [ 1.e-6 ]
+ f_max: [ 1. ]
+ f_min: [ 1. ]
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: modules.xlmr.BertSeriesModelWithTransformation
+ params:
+ name: "XLMR-Large"
\ No newline at end of file
diff --git a/stable-diffusion-webui/configs/instruct-pix2pix.yaml b/stable-diffusion-webui/configs/instruct-pix2pix.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4e896879dd7ac5697b89cb323ec43eb41c03596c
--- /dev/null
+++ b/stable-diffusion-webui/configs/instruct-pix2pix.yaml
@@ -0,0 +1,98 @@
+# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
+# See more details in LICENSE.
+
+model:
+ base_learning_rate: 1.0e-04
+ target: modules.models.diffusion.ddpm_edit.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: edited
+ cond_stage_key: edit
+ # image_size: 64
+ # image_size: 32
+ image_size: 16
+ channels: 4
+ cond_stage_trainable: false # Note: different from the one we trained before
+ conditioning_key: hybrid
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: false
+
+ scheduler_config: # 10000 warmup steps
+ target: ldm.lr_scheduler.LambdaLinearScheduler
+ params:
+ warm_up_steps: [ 0 ]
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+ f_start: [ 1.e-6 ]
+ f_max: [ 1. ]
+ f_min: [ 1. ]
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 8
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 128
+ num_workers: 1
+ wrap: false
+ validation:
+ target: edit_dataset.EditDataset
+ params:
+ path: data/clip-filtered-dataset
+ cache_dir: data/
+ cache_name: data_10k
+ split: val
+ min_text_sim: 0.2
+ min_image_sim: 0.75
+ min_direction_sim: 0.2
+ max_samples_per_prompt: 1
+ min_resize_res: 512
+ max_resize_res: 512
+ crop_res: 512
+ output_as_edit: False
+ real_input: True
diff --git a/stable-diffusion-webui/configs/v1-inference.yaml b/stable-diffusion-webui/configs/v1-inference.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6
--- /dev/null
+++ b/stable-diffusion-webui/configs/v1-inference.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 1.0e-04
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false # Note: different from the one we trained before
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+
+ scheduler_config: # 10000 warmup steps
+ target: ldm.lr_scheduler.LambdaLinearScheduler
+ params:
+ warm_up_steps: [ 10000 ]
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+ f_start: [ 1.e-6 ]
+ f_max: [ 1. ]
+ f_min: [ 1. ]
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
diff --git a/stable-diffusion-webui/configs/v1-inpainting-inference.yaml b/stable-diffusion-webui/configs/v1-inpainting-inference.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f9eec37d24bce33ce92320a782d16ae72308190a
--- /dev/null
+++ b/stable-diffusion-webui/configs/v1-inpainting-inference.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 7.5e-05
+ target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false # Note: different from the one we trained before
+ conditioning_key: hybrid # important
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ finetune_keys: null
+
+ scheduler_config: # 10000 warmup steps
+ target: ldm.lr_scheduler.LambdaLinearScheduler
+ params:
+ warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+ f_start: [ 1.e-6 ]
+ f_max: [ 1. ]
+ f_min: [ 1. ]
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 9 # 4 data + 4 downscaled image + 1 mask
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
diff --git a/stable-diffusion-webui/embeddings/Place Textual Inversion embeddings here.txt b/stable-diffusion-webui/embeddings/Place Textual Inversion embeddings here.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/environment-wsl2.yaml b/stable-diffusion-webui/environment-wsl2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0c4ae6809997ec38e7cf62cf0f71360b8cb61a7e
--- /dev/null
+++ b/stable-diffusion-webui/environment-wsl2.yaml
@@ -0,0 +1,11 @@
+name: automatic
+channels:
+ - pytorch
+ - defaults
+dependencies:
+ - python=3.10
+ - pip=23.0
+ - cudatoolkit=11.8
+ - pytorch=2.0
+ - torchvision=0.15
+ - numpy=1.23
diff --git a/stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py b/stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..7cac36ce55ae295c6d0e444a93ea12bf8cfe893c
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py
@@ -0,0 +1,250 @@
+import os
+import gc
+import time
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+import safetensors.torch
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config, ismap
+from modules import shared, sd_hijack, devices
+
+cached_ldsr_model: torch.nn.Module = None
+
+
+# Create LDSR Class
+class LDSR:
+ def load_model_from_config(self, half_attention):
+ global cached_ldsr_model
+
+ if shared.opts.ldsr_cached and cached_ldsr_model is not None:
+ print("Loading model from cache")
+ model: torch.nn.Module = cached_ldsr_model
+ else:
+ print(f"Loading model from {self.modelPath}")
+ _, extension = os.path.splitext(self.modelPath)
+ if extension.lower() == ".safetensors":
+ pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
+ else:
+ pl_sd = torch.load(self.modelPath, map_location="cpu")
+ sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
+ config = OmegaConf.load(self.yamlPath)
+ config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
+ model: torch.nn.Module = instantiate_from_config(config.model)
+ model.load_state_dict(sd, strict=False)
+ model = model.to(shared.device)
+ if half_attention:
+ model = model.half()
+ if shared.cmd_opts.opt_channelslast:
+ model = model.to(memory_format=torch.channels_last)
+
+ sd_hijack.model_hijack.hijack(model) # apply optimization
+ model.eval()
+
+ if shared.opts.ldsr_cached:
+ cached_ldsr_model = model
+
+ return {"model": model}
+
+ def __init__(self, model_path, yaml_path):
+ self.modelPath = model_path
+ self.yamlPath = yaml_path
+
+ @staticmethod
+ def run(model, selected_path, custom_steps, eta):
+ example = get_cond(selected_path)
+
+ n_runs = 1
+ guider = None
+ ckwargs = None
+ ddim_use_x0_pred = False
+ temperature = 1.
+ eta = eta
+ custom_shape = None
+
+ height, width = example["image"].shape[1:3]
+ split_input = height >= 128 and width >= 128
+
+ if split_input:
+ ks = 128
+ stride = 64
+ vqf = 4 #
+ model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
+ "vqf": vqf,
+ "patch_distributed_vq": True,
+ "tie_braker": False,
+ "clip_max_weight": 0.5,
+ "clip_min_weight": 0.01,
+ "clip_max_tie_weight": 0.5,
+ "clip_min_tie_weight": 0.01}
+ else:
+ if hasattr(model, "split_input_params"):
+ delattr(model, "split_input_params")
+
+ x_t = None
+ logs = None
+ for _ in range(n_runs):
+ if custom_shape is not None:
+ x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
+ x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
+
+ logs = make_convolutional_sample(example, model,
+ custom_steps=custom_steps,
+ eta=eta, quantize_x0=False,
+ custom_shape=custom_shape,
+ temperature=temperature, noise_dropout=0.,
+ corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
+ ddim_use_x0_pred=ddim_use_x0_pred
+ )
+ return logs
+
+ def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
+ model = self.load_model_from_config(half_attention)
+
+ # Run settings
+ diffusion_steps = int(steps)
+ eta = 1.0
+
+
+ gc.collect()
+ devices.torch_gc()
+
+ im_og = image
+ width_og, height_og = im_og.size
+ # If we can adjust the max upscale size, then the 4 below should be our variable
+ down_sample_rate = target_scale / 4
+ wd = width_og * down_sample_rate
+ hd = height_og * down_sample_rate
+ width_downsampled_pre = int(np.ceil(wd))
+ height_downsampled_pre = int(np.ceil(hd))
+
+ if down_sample_rate != 1:
+ print(
+ f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
+ im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
+ else:
+ print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
+
+ # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+ pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+ im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+
+ logs = self.run(model["model"], im_padded, diffusion_steps, eta)
+
+ sample = logs["sample"]
+ sample = sample.detach().cpu()
+ sample = torch.clamp(sample, -1., 1.)
+ sample = (sample + 1.) / 2. * 255
+ sample = sample.numpy().astype(np.uint8)
+ sample = np.transpose(sample, (0, 2, 3, 1))
+ a = Image.fromarray(sample[0])
+
+ # remove padding
+ a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
+ del model
+ gc.collect()
+ devices.torch_gc()
+
+ return a
+
+
+def get_cond(selected_path):
+ example = {}
+ up_f = 4
+ c = selected_path.convert('RGB')
+ c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
+ c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
+ antialias=True)
+ c_up = rearrange(c_up, '1 c h w -> 1 h w c')
+ c = rearrange(c, '1 c h w -> 1 h w c')
+ c = 2. * c - 1.
+
+ c = c.to(shared.device)
+ example["LR_image"] = c
+ example["image"] = c_up
+
+ return example
+
+
+@torch.no_grad()
+def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
+ mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
+ corrector_kwargs=None, x_t=None
+ ):
+ ddim = DDIMSampler(model)
+ bs = shape[0]
+ shape = shape[1:]
+ print(f"Sampling with eta = {eta}; steps: {steps}")
+ samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
+ normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
+ mask=mask, x0=x0, temperature=temperature, verbose=False,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs, x_t=x_t)
+
+ return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
+ corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
+ log = {}
+
+ z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=not (hasattr(model, 'split_input_params')
+ and model.cond_stage_key == 'coordinates_bbox'),
+ return_original_cond=True)
+
+ if custom_shape is not None:
+ z = torch.randn(custom_shape)
+ print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
+
+ z0 = None
+
+ log["input"] = x
+ log["reconstruction"] = xrec
+
+ if ismap(xc):
+ log["original_conditioning"] = model.to_rgb(xc)
+ if hasattr(model, 'cond_stage_key'):
+ log[model.cond_stage_key] = model.to_rgb(xc)
+
+ else:
+ log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
+ if model.cond_stage_model:
+ log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
+ if model.cond_stage_key == 'class_label':
+ log[model.cond_stage_key] = xc[model.cond_stage_key]
+
+ with model.ema_scope("Plotting"):
+ t0 = time.time()
+
+ sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
+ eta=eta,
+ quantize_x0=quantize_x0, mask=None, x0=z0,
+ temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
+ x_t=x_T)
+ t1 = time.time()
+
+ if ddim_use_x0_pred:
+ sample = intermediates['pred_x0'][-1]
+
+ x_sample = model.decode_first_stage(sample)
+
+ try:
+ x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
+ log["sample_noquant"] = x_sample_noquant
+ log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
+ except Exception:
+ pass
+
+ log["sample"] = x_sample
+ log["time"] = t1 - t0
+
+ return log
diff --git a/stable-diffusion-webui/extensions-builtin/LDSR/preload.py b/stable-diffusion-webui/extensions-builtin/LDSR/preload.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfd478d545ed12ef74e73fa40b6defe0156859da
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/LDSR/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))
diff --git a/stable-diffusion-webui/extensions-builtin/LDSR/scripts/ldsr_model.py b/stable-diffusion-webui/extensions-builtin/LDSR/scripts/ldsr_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd78decea1ec9fc66d61d66ee64457458a290f72
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/LDSR/scripts/ldsr_model.py
@@ -0,0 +1,68 @@
+import os
+
+from modules.modelloader import load_file_from_url
+from modules.upscaler import Upscaler, UpscalerData
+from ldsr_model_arch import LDSR
+from modules import shared, script_callbacks, errors
+import sd_hijack_autoencoder # noqa: F401
+import sd_hijack_ddpm_v1 # noqa: F401
+
+
+class UpscalerLDSR(Upscaler):
+ def __init__(self, user_path):
+ self.name = "LDSR"
+ self.user_path = user_path
+ self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
+ self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+ super().__init__()
+ scaler_data = UpscalerData("LDSR", None, self)
+ self.scalers = [scaler_data]
+
+ def load_model(self, path: str):
+ # Remove incorrect project.yaml file if too big
+ yaml_path = os.path.join(self.model_path, "project.yaml")
+ old_model_path = os.path.join(self.model_path, "model.pth")
+ new_model_path = os.path.join(self.model_path, "model.ckpt")
+
+ local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
+ local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
+ local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
+ local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
+
+ if os.path.exists(yaml_path):
+ statinfo = os.stat(yaml_path)
+ if statinfo.st_size >= 10485760:
+ print("Removing invalid LDSR YAML file.")
+ os.remove(yaml_path)
+
+ if os.path.exists(old_model_path):
+ print("Renaming model from model.pth to model.ckpt")
+ os.rename(old_model_path, new_model_path)
+
+ if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
+ model = local_safetensors_path
+ else:
+ model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
+
+ yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
+
+ return LDSR(model, yaml)
+
+ def do_upscale(self, img, path):
+ try:
+ ldsr = self.load_model(path)
+ except Exception:
+ errors.report(f"Failed loading LDSR model {path}", exc_info=True)
+ return img
+ ddim_steps = shared.opts.ldsr_steps
+ return ldsr.super_resolution(img, ddim_steps, self.scale)
+
+
+def on_ui_settings():
+ import gradio as gr
+
+ shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
+ shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c29d274da825d2500b77a2022db3421b40b18886
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_autoencoder.py
@@ -0,0 +1,293 @@
+# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
+# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
+# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
+import numpy as np
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from torch.optim.lr_scheduler import LambdaLR
+
+from ldm.modules.ema import LitEma
+from vqvae_quantize import VectorQuantizer2 as VectorQuantizer
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.util import instantiate_from_config
+
+import ldm.models.autoencoder
+from packaging import version
+
+class VQModel(pl.LightningModule):
+ def __init__(self,
+ ddconfig,
+ lossconfig,
+ n_embed,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=None,
+ image_key="image",
+ colorize_nlabels=None,
+ monitor=None,
+ batch_resize_range=None,
+ scheduler_config=None,
+ lr_g_factor=1.0,
+ remap=None,
+ sane_index_shape=False, # tell vector quantizer to return indices as bhw
+ use_ema=False
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.n_embed = n_embed
+ self.image_key = image_key
+ self.encoder = Encoder(**ddconfig)
+ self.decoder = Decoder(**ddconfig)
+ self.loss = instantiate_from_config(lossconfig)
+ self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+ remap=remap,
+ sane_index_shape=sane_index_shape)
+ self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ if colorize_nlabels is not None:
+ assert type(colorize_nlabels)==int
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ if monitor is not None:
+ self.monitor = monitor
+ self.batch_resize_range = batch_resize_range
+ if self.batch_resize_range is not None:
+ print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
+ self.scheduler_config = scheduler_config
+ self.lr_g_factor = lr_g_factor
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.parameters())
+ self.model_ema.copy_to(self)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ def init_from_ckpt(self, path, ignore_keys=None):
+ sd = torch.load(path, map_location="cpu")["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys or []:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ missing, unexpected = self.load_state_dict(sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if missing:
+ print(f"Missing Keys: {missing}")
+ if unexpected:
+ print(f"Unexpected Keys: {unexpected}")
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self)
+
+ def encode(self, x):
+ h = self.encoder(x)
+ h = self.quant_conv(h)
+ quant, emb_loss, info = self.quantize(h)
+ return quant, emb_loss, info
+
+ def encode_to_prequant(self, x):
+ h = self.encoder(x)
+ h = self.quant_conv(h)
+ return h
+
+ def decode(self, quant):
+ quant = self.post_quant_conv(quant)
+ dec = self.decoder(quant)
+ return dec
+
+ def decode_code(self, code_b):
+ quant_b = self.quantize.embed_code(code_b)
+ dec = self.decode(quant_b)
+ return dec
+
+ def forward(self, input, return_pred_indices=False):
+ quant, diff, (_,_,ind) = self.encode(input)
+ dec = self.decode(quant)
+ if return_pred_indices:
+ return dec, diff, ind
+ return dec, diff
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ if self.batch_resize_range is not None:
+ lower_size = self.batch_resize_range[0]
+ upper_size = self.batch_resize_range[1]
+ if self.global_step <= 4:
+ # do the first few batches with max size to avoid later oom
+ new_resize = upper_size
+ else:
+ new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+ if new_resize != x.shape[2]:
+ x = F.interpolate(x, size=new_resize, mode="bicubic")
+ x = x.detach()
+ return x
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+ # https://github.com/pytorch/pytorch/issues/37142
+ # try not to fool the heuristics
+ x = self.get_input(batch, self.image_key)
+ xrec, qloss, ind = self(x, return_pred_indices=True)
+
+ if optimizer_idx == 0:
+ # autoencode
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train",
+ predicted_indices=ind)
+
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+ return aeloss
+
+ if optimizer_idx == 1:
+ # discriminator
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+ return discloss
+
+ def validation_step(self, batch, batch_idx):
+ log_dict = self._validation_step(batch, batch_idx)
+ with self.ema_scope():
+ self._validation_step(batch, batch_idx, suffix="_ema")
+ return log_dict
+
+ def _validation_step(self, batch, batch_idx, suffix=""):
+ x = self.get_input(batch, self.image_key)
+ xrec, qloss, ind = self(x, return_pred_indices=True)
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split="val"+suffix,
+ predicted_indices=ind
+ )
+
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split="val"+suffix,
+ predicted_indices=ind
+ )
+ rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+ self.log(f"val{suffix}/rec_loss", rec_loss,
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+ self.log(f"val{suffix}/aeloss", aeloss,
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+ if version.parse(pl.__version__) >= version.parse('1.4.0'):
+ del log_dict_ae[f"val{suffix}/rec_loss"]
+ self.log_dict(log_dict_ae)
+ self.log_dict(log_dict_disc)
+ return self.log_dict
+
+ def configure_optimizers(self):
+ lr_d = self.learning_rate
+ lr_g = self.lr_g_factor*self.learning_rate
+ print("lr_d", lr_d)
+ print("lr_g", lr_g)
+ opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+ list(self.decoder.parameters())+
+ list(self.quantize.parameters())+
+ list(self.quant_conv.parameters())+
+ list(self.post_quant_conv.parameters()),
+ lr=lr_g, betas=(0.5, 0.9))
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+ lr=lr_d, betas=(0.5, 0.9))
+
+ if self.scheduler_config is not None:
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ },
+ {
+ 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ },
+ ]
+ return [opt_ae, opt_disc], scheduler
+ return [opt_ae, opt_disc], []
+
+ def get_last_layer(self):
+ return self.decoder.conv_out.weight
+
+ def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+ log = {}
+ x = self.get_input(batch, self.image_key)
+ x = x.to(self.device)
+ if only_inputs:
+ log["inputs"] = x
+ return log
+ xrec, _ = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec.shape[1] > 3
+ x = self.to_rgb(x)
+ xrec = self.to_rgb(xrec)
+ log["inputs"] = x
+ log["reconstructions"] = xrec
+ if plot_ema:
+ with self.ema_scope():
+ xrec_ema, _ = self(x)
+ if x.shape[1] > 3:
+ xrec_ema = self.to_rgb(xrec_ema)
+ log["reconstructions_ema"] = xrec_ema
+ return log
+
+ def to_rgb(self, x):
+ assert self.image_key == "segmentation"
+ if not hasattr(self, "colorize"):
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ x = F.conv2d(x, weight=self.colorize)
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ return x
+
+
+class VQModelInterface(VQModel):
+ def __init__(self, embed_dim, *args, **kwargs):
+ super().__init__(*args, embed_dim=embed_dim, **kwargs)
+ self.embed_dim = embed_dim
+
+ def encode(self, x):
+ h = self.encoder(x)
+ h = self.quant_conv(h)
+ return h
+
+ def decode(self, h, force_not_quantize=False):
+ # also go through quantization layer
+ if not force_not_quantize:
+ quant, emb_loss, info = self.quantize(h)
+ else:
+ quant = h
+ quant = self.post_quant_conv(quant)
+ dec = self.decoder(quant)
+ return dec
+
+ldm.models.autoencoder.VQModel = VQModel
+ldm.models.autoencoder.VQModelInterface = VQModelInterface
diff --git a/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..04adc5eb2cfe9aa1d5f75e5653624456c5e37a47
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
@@ -0,0 +1,1443 @@
+# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
+# Original filename: ldm/models/diffusion/ddpm.py
+# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
+# Some models such as LDSR require VQ to work correctly
+# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager
+from functools import partial
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+import ldm.models.diffusion.ddpm
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPMV1(pl.LightningModule):
+ # classic DDPM with Gaussian diffusion, in image space
+ def __init__(self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule="linear",
+ loss_type="l2",
+ ckpt_path=None,
+ ignore_keys=None,
+ load_only_unet=False,
+ monitor="val/loss",
+ use_ema=True,
+ first_stage_key="image",
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.,
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.,
+ conditioning_key=None,
+ parameterization="eps", # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.,
+ ):
+ super().__init__()
+ assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+ self.parameterization = parameterization
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ self.cond_stage_model = None
+ self.clip_denoised = clip_denoised
+ self.log_every_t = log_every_t
+ self.first_stage_key = first_stage_key
+ self.image_size = image_size # try conv?
+ self.channels = channels
+ self.use_positional_encodings = use_positional_encodings
+ self.model = DiffusionWrapperV1(unet_config, conditioning_key)
+ count_params(self.model, verbose=True)
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ self.use_scheduler = scheduler_config is not None
+ if self.use_scheduler:
+ self.scheduler_config = scheduler_config
+
+ self.v_posterior = v_posterior
+ self.original_elbo_weight = original_elbo_weight
+ self.l_simple_weight = l_simple_weight
+
+ if monitor is not None:
+ self.monitor = monitor
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
+
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+ self.loss_type = loss_type
+
+ self.learn_logvar = learn_logvar
+ self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ if self.learn_logvar:
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if exists(given_betas):
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+ 1. - alphas_cumprod) + self.v_posterior * betas
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+ self.register_buffer('posterior_mean_coef1', to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+ self.register_buffer('posterior_mean_coef2', to_torch(
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+ if self.parameterization == "eps":
+ lvlb_weights = self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+ elif self.parameterization == "x0":
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ else:
+ raise NotImplementedError("mu not supported")
+ # TODO how to choose this term
+ lvlb_weights[0] = lvlb_weights[1]
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+ assert not torch.isnan(self.lvlb_weights).all()
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.model.parameters())
+ self.model_ema.copy_to(self.model)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.model.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys or []:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if missing:
+ print(f"Missing Keys: {missing}")
+ if unexpected:
+ print(f"Unexpected Keys: {unexpected}")
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def predict_start_from_noise(self, x_t, t, noise):
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ )
+
+ def q_posterior(self, x_start, x_t, t):
+ posterior_mean = (
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, x, t, clip_denoised: bool):
+ model_out = self.model(x, t)
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+ b, *_, device = *x.shape, x.device
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ noise = noise_like(x.shape, device, repeat_noise)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, shape, return_intermediates=False):
+ device = self.betas.device
+ b = shape[0]
+ img = torch.randn(shape, device=device)
+ intermediates = [img]
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised)
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+ intermediates.append(img)
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, batch_size=16, return_intermediates=False):
+ image_size = self.image_size
+ channels = self.channels
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates)
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_loss(self, pred, target, mean=True):
+ if self.loss_type == 'l1':
+ loss = (target - pred).abs()
+ if mean:
+ loss = loss.mean()
+ elif self.loss_type == 'l2':
+ if mean:
+ loss = torch.nn.functional.mse_loss(target, pred)
+ else:
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+ else:
+ raise NotImplementedError("unknown loss type '{loss_type}'")
+
+ return loss
+
+ def p_losses(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_out = self.model(x_noisy, t)
+
+ loss_dict = {}
+ if self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "x0":
+ target = x_start
+ else:
+ raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+ log_prefix = 'train' if self.training else 'val'
+
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+ loss_simple = loss.mean() * self.l_simple_weight
+
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+ loss_dict.update({f'{log_prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def forward(self, x, *args, **kwargs):
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ return self.p_losses(x, t, *args, **kwargs)
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = rearrange(x, 'b h w c -> b c h w')
+ x = x.to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def shared_step(self, batch):
+ x = self.get_input(batch, self.first_stage_key)
+ loss, loss_dict = self(x)
+ return loss, loss_dict
+
+ def training_step(self, batch, batch_idx):
+ loss, loss_dict = self.shared_step(batch)
+
+ self.log_dict(loss_dict, prog_bar=True,
+ logger=True, on_step=True, on_epoch=True)
+
+ self.log("global_step", self.global_step,
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ if self.use_scheduler:
+ lr = self.optimizers().param_groups[0]['lr']
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ return loss
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ _, loss_dict_no_ema = self.shared_step(batch)
+ with self.ema_scope():
+ _, loss_dict_ema = self.shared_step(batch)
+ loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self.model)
+
+ def _get_rows_from_list(self, samples):
+ n_imgs_per_row = len(samples)
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ log = {}
+ x = self.get_input(batch, self.first_stage_key)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ x = x.to(self.device)[:N]
+ log["inputs"] = x
+
+ # get diffusion row
+ diffusion_row = []
+ x_start = x[:n_row]
+
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(x_start)
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ diffusion_row.append(x_noisy)
+
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+ log["samples"] = samples
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.learn_logvar:
+ params = params + [self.logvar]
+ opt = torch.optim.AdamW(params, lr=lr)
+ return opt
+
+
+class LatentDiffusionV1(DDPMV1):
+ """main class"""
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ *args, **kwargs):
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ if conditioning_key is None:
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
+ if cond_stage_config == '__is_unconditional__':
+ conditioning_key = None
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ super().__init__(*args, conditioning_key=conditioning_key, **kwargs)
+ self.concat_mode = concat_mode
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except Exception:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ self.instantiate_first_stage(first_stage_config)
+ self.instantiate_cond_stage(cond_stage_config)
+ self.cond_stage_forward = cond_stage_forward
+ self.clip_denoised = False
+ self.bbox_tokenizer = None
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+ self.restarted_from_ckpt = True
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ @rank_zero_only
+ @torch.no_grad()
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+ # only for very first batch
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ # set rescale weight to 1./std of encodings
+ print("### USING STD-RESCALING ###")
+ x = super().get_input(batch, self.first_stage_key)
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ del self.scale_factor
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
+ print(f"setting self.scale_factor to {self.scale_factor}")
+ print("### USING STD-RESCALING ###")
+
+ def register_schedule(self,
+ given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
+ if self.shorten_cond_schedule:
+ self.make_cond_schedule()
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ if not self.cond_stage_trainable:
+ if config == "__is_first_stage__":
+ print("Using first stage also as cond stage.")
+ self.cond_stage_model = self.first_stage_model
+ elif config == "__is_unconditional__":
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
+ self.cond_stage_model = None
+ # self.be_unconditional = True
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ assert config != '__is_first_stage__'
+ assert config != '__is_unconditional__'
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization))
+ n_imgs_per_row = len(denoise_row)
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def meshgrid(self, h, w):
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+ arr = torch.cat([y, x], dim=-1)
+ return arr
+
+ def delta_border(self, h, w):
+ """
+ :param h: height
+ :param w: width
+ :return: normalized distance to image border,
+ wtith min distance = 0 at border and max dist = 0.5 at image center
+ """
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+ arr = self.meshgrid(h, w) / lower_right_corner
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ return edge_dist
+
+ def get_weighting(self, h, w, Ly, Lx, device):
+ weighting = self.delta_border(h, w)
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+ self.split_input_params["clip_max_weight"], )
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+ if self.split_input_params["tie_braker"]:
+ L_weighting = self.delta_border(Ly, Lx)
+ L_weighting = torch.clip(L_weighting,
+ self.split_input_params["clip_min_tie_weight"],
+ self.split_input_params["clip_max_tie_weight"])
+
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+ weighting = weighting * L_weighting
+ return weighting
+
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ """
+ :param x: img of size (bs, c, h, w)
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+ """
+ bs, nc, h, w = x.shape
+
+ # number of crops in image
+ Ly = (h - kernel_size[0]) // stride[0] + 1
+ Lx = (w - kernel_size[1]) // stride[1] + 1
+
+ if uf == 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+ elif uf > 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1, padding=0,
+ stride=(stride[0] * uf, stride[1] * uf))
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+ elif df > 1 and uf == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1, padding=0,
+ stride=(stride[0] // df, stride[1] // df))
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+ else:
+ raise NotImplementedError
+
+ return fold, unfold, normalization, weighting
+
+ @torch.no_grad()
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+ cond_key=None, return_original_cond=False, bs=None):
+ x = super().get_input(batch, k)
+ if bs is not None:
+ x = x[:bs]
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+ if self.model.conditioning_key is not None:
+ if cond_key is None:
+ cond_key = self.cond_stage_key
+ if cond_key != self.first_stage_key:
+ if cond_key in ['caption', 'coordinates_bbox']:
+ xc = batch[cond_key]
+ elif cond_key == 'class_label':
+ xc = batch
+ else:
+ xc = super().get_input(batch, cond_key).to(self.device)
+ else:
+ xc = x
+ if not self.cond_stage_trainable or force_c_encode:
+ if isinstance(xc, dict) or isinstance(xc, list):
+ # import pudb; pudb.set_trace()
+ c = self.get_learned_conditioning(xc)
+ else:
+ c = self.get_learned_conditioning(xc.to(self.device))
+ else:
+ c = xc
+ if bs is not None:
+ c = c[:bs]
+
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ ckey = __conditioning_keys__[self.model.conditioning_key]
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+ else:
+ c = None
+ xc = None
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
+ out = [z, c]
+ if return_first_stage_outputs:
+ xrec = self.decode_first_stage(z)
+ out.extend([x, xrec])
+ if return_original_cond:
+ out.append(xc)
+ return out
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+
+ if hasattr(self, "split_input_params"):
+ if self.split_input_params["patch_distributed_vq"]:
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+ uf = self.split_input_params["vqf"]
+ bs, nc, h, w = z.shape
+ if ks[0] > h or ks[1] > w:
+ ks = (min(ks[0], h), min(ks[1], w))
+ print("reducing Kernel")
+
+ if stride[0] > h or stride[1] > w:
+ stride = (min(stride[0], h), min(stride[1], w))
+ print("reducing stride")
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+ z = unfold(z) # (bn, nc * prod(**ks), L)
+ # 1. Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ # 2. apply model loop over last dim
+ if isinstance(self.first_stage_model, VQModelInterface):
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+ force_not_quantize=predict_cids or force_not_quantize)
+ for i in range(z.shape[-1])]
+ else:
+
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])]
+
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
+ o = o * weighting
+ # Reverse 1. reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ decoded = fold(o)
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
+ return decoded
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ # same as above but without decorator
+ def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+
+ if hasattr(self, "split_input_params"):
+ if self.split_input_params["patch_distributed_vq"]:
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+ uf = self.split_input_params["vqf"]
+ bs, nc, h, w = z.shape
+ if ks[0] > h or ks[1] > w:
+ ks = (min(ks[0], h), min(ks[1], w))
+ print("reducing Kernel")
+
+ if stride[0] > h or stride[1] > w:
+ stride = (min(stride[0], h), min(stride[1], w))
+ print("reducing stride")
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+ z = unfold(z) # (bn, nc * prod(**ks), L)
+ # 1. Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ # 2. apply model loop over last dim
+ if isinstance(self.first_stage_model, VQModelInterface):
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+ force_not_quantize=predict_cids or force_not_quantize)
+ for i in range(z.shape[-1])]
+ else:
+
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])]
+
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
+ o = o * weighting
+ # Reverse 1. reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ decoded = fold(o)
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
+ return decoded
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ if hasattr(self, "split_input_params"):
+ if self.split_input_params["patch_distributed_vq"]:
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+ df = self.split_input_params["vqf"]
+ self.split_input_params['original_image_size'] = x.shape[-2:]
+ bs, nc, h, w = x.shape
+ if ks[0] > h or ks[1] > w:
+ ks = (min(ks[0], h), min(ks[1], w))
+ print("reducing Kernel")
+
+ if stride[0] > h or stride[1] > w:
+ stride = (min(stride[0], h), min(stride[1], w))
+ print("reducing stride")
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+ z = unfold(x) # (bn, nc * prod(**ks), L)
+ # Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])]
+
+ o = torch.stack(output_list, axis=-1)
+ o = o * weighting
+
+ # Reverse reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ decoded = fold(o)
+ decoded = decoded / normalization
+ return decoded
+
+ else:
+ return self.first_stage_model.encode(x)
+ else:
+ return self.first_stage_model.encode(x)
+
+ def shared_step(self, batch, **kwargs):
+ x, c = self.get_input(batch, self.first_stage_key)
+ loss = self(x, c)
+ return loss
+
+ def forward(self, x, c, *args, **kwargs):
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ if self.model.conditioning_key is not None:
+ assert c is not None
+ if self.cond_stage_trainable:
+ c = self.get_learned_conditioning(c)
+ if self.shorten_cond_schedule: # TODO: drop this option
+ tc = self.cond_ids[t].to(self.device)
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ return self.p_losses(x, c, t, *args, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
+
+ if isinstance(cond, dict):
+ # hybrid case, cond is exptected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+
+ if hasattr(self, "split_input_params"):
+ assert len(cond) == 1 # todo can only deal with one conditioning atm
+ assert not return_ids
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+
+ h, w = x_noisy.shape[-2:]
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+
+ z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
+ # Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+ z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
+
+ if self.cond_stage_key in ["image", "LR_image", "segmentation",
+ 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
+ c_key = next(iter(cond.keys())) # get key
+ c = next(iter(cond.values())) # get value
+ assert (len(c) == 1) # todo extend to list with more than one elem
+ c = c[0] # get element
+
+ c = unfold(c)
+ c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+
+ elif self.cond_stage_key == 'coordinates_bbox':
+ assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+
+ # assuming padding of unfold is always 0 and its dilation is always 1
+ n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
+ full_img_h, full_img_w = self.split_input_params['original_image_size']
+ # as we are operating on latents, we need the factor from the original image size to the
+ # spatial latent size to properly rescale the crops for regenerating the bbox annotations
+ num_downs = self.first_stage_model.encoder.num_resolutions - 1
+ rescale_latent = 2 ** (num_downs)
+
+ # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+ # need to rescale the tl patch coordinates to be in between (0,1)
+ tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
+ rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
+ for patch_nr in range(z.shape[-1])]
+
+ # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
+ patch_limits = [(x_tl, y_tl,
+ rescale_latent * ks[0] / full_img_w,
+ rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+ # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
+
+ # tokenize crop coordinates for the bounding boxes of the respective patches
+ patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
+ for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
+ print(patch_limits_tknzd[0].shape)
+ # cut tknzd crop position from conditioning
+ assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+ cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
+ print(cut_cond.shape)
+
+ adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+ adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
+ print(adapted_cond.shape)
+ adapted_cond = self.get_learned_conditioning(adapted_cond)
+ print(adapted_cond.shape)
+ adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+ print(adapted_cond.shape)
+
+ cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
+
+ else:
+ cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
+
+ # apply model by loop over crops
+ output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
+ assert not isinstance(output_list[0],
+ tuple) # todo cant deal with multiple model outputs check this never happens
+
+ o = torch.stack(output_list, axis=-1)
+ o = o * weighting
+ # Reverse reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ x_recon = fold(o) / normalization
+
+ else:
+ x_recon = self.model(x_noisy, t, **cond)
+
+ if isinstance(x_recon, tuple) and not return_ids:
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+ This term can't be optimized, as it only depends on the encoder.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def p_losses(self, x_start, cond, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_output = self.apply_model(x_noisy, t, cond)
+
+ loss_dict = {}
+ prefix = 'train' if self.training else 'val'
+
+ if self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "eps":
+ target = noise
+ else:
+ raise NotImplementedError()
+
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+ logvar_t = self.logvar[t].to(self.device)
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+ if self.learn_logvar:
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+ loss_dict.update({'logvar': self.logvar.data.mean()})
+
+ loss = self.l_simple_weight * loss.mean()
+
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+ loss += (self.original_elbo_weight * loss_vlb)
+ loss_dict.update({f'{prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
+ t_in = t
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if return_codebook_ids:
+ model_out, logits = model_out
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+ if quantize_denoised:
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ if return_codebook_ids:
+ return model_mean, posterior_variance, posterior_log_variance, logits
+ elif return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if return_codebook_ids:
+ raise DeprecationWarning("Support dropped.")
+ model_mean, _, model_log_variance, logits = outputs
+ elif return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_codebook_ids:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+ log_every_t=None):
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ timesteps = self.num_timesteps
+ if batch_size is not None:
+ b = batch_size if batch_size is not None else shape[0]
+ shape = [batch_size] + list(shape)
+ else:
+ b = batch_size = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=self.device)
+ else:
+ img = x_T
+ intermediates = []
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ [x[:batch_size] for x in cond[key]] for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+ total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+ if type(temperature) == float:
+ temperature = [temperature] * timesteps
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img, x0_partial = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised, return_x0=True,
+ temperature=temperature[i], noise_dropout=noise_dropout,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(x0_partial)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(img, i)
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, start_T=None,
+ log_every_t=None):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+ verbose=True, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, shape=None,**kwargs):
+ if shape is None:
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ [x[:batch_size] for x in cond[key]] for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ return self.p_sample_loop(cond,
+ shape,
+ return_intermediates=return_intermediates, x_T=x_T,
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+ mask=mask, x0=x0)
+
+ @torch.no_grad()
+ def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+
+ if ddim:
+ ddim_sampler = DDIMSampler(self)
+ shape = (self.channels, self.image_size, self.image_size)
+ samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
+ shape,cond,verbose=False,**kwargs)
+
+ else:
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+ return_intermediates=True,**kwargs)
+
+ return samples, intermediates
+
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, **kwargs):
+
+ use_ddim = ddim_steps is not None
+
+ log = {}
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ bs=N)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
+ log["conditioning"] = xc
+ elif self.cond_stage_key == 'class_label':
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+ log['conditioning'] = xc
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = []
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+ ddim_steps=ddim_steps,eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+ self.first_stage_model, IdentityFirstStage):
+ # also display when quantizing x0 while sampling
+ with self.ema_scope("Plotting Quantized Denoised"):
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+ ddim_steps=ddim_steps,eta=ddim_eta,
+ quantize_denoised=True)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+ # quantize_denoised=True)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_x0_quantized"] = x_samples
+
+ if inpaint:
+ # make a simple center square
+ h, w = z.shape[2], z.shape[3]
+ mask = torch.ones(N, h, w).to(self.device)
+ # zeros will be filled in
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask = mask[:, None, ...]
+ with self.ema_scope("Plotting Inpaint"):
+
+ samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_inpainting"] = x_samples
+ log["mask"] = mask
+
+ # outpaint
+ with self.ema_scope("Plotting Outpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_outpainting"] = x_samples
+
+ if plot_progressive_rows:
+ with self.ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.cond_stage_trainable:
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ params = params + list(self.cond_stage_model.parameters())
+ if self.learn_logvar:
+ print('Diffusion model optimizing logvar')
+ params.append(self.logvar)
+ opt = torch.optim.AdamW(params, lr=lr)
+ if self.use_scheduler:
+ assert 'target' in self.scheduler_config
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ }]
+ return [opt], scheduler
+ return opt
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
+
+class DiffusionWrapperV1(pl.LightningModule):
+ def __init__(self, diff_model_config, conditioning_key):
+ super().__init__()
+ self.diffusion_model = instantiate_from_config(diff_model_config)
+ self.conditioning_key = conditioning_key
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
+ if self.conditioning_key is None:
+ out = self.diffusion_model(x, t)
+ elif self.conditioning_key == 'concat':
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t)
+ elif self.conditioning_key == 'crossattn':
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc)
+ elif self.conditioning_key == 'hybrid':
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'adm':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, y=cc)
+ else:
+ raise NotImplementedError()
+
+ return out
+
+
+class Layout2ImgDiffusionV1(LatentDiffusionV1):
+ # TODO: move all layout-specific hacks to this class
+ def __init__(self, cond_stage_key, *args, **kwargs):
+ assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+ super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
+
+ def log_images(self, batch, N=8, *args, **kwargs):
+ logs = super().log_images(*args, batch=batch, N=N, **kwargs)
+
+ key = 'train' if self.training else 'validation'
+ dset = self.trainer.datamodule.datasets[key]
+ mapper = dset.conditional_builders[self.cond_stage_key]
+
+ bbox_imgs = []
+ map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+ for tknzd_bbox in batch[self.cond_stage_key][:N]:
+ bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+ bbox_imgs.append(bboximg)
+
+ cond_img = torch.stack(bbox_imgs, dim=0)
+ logs['bbox_image'] = cond_img
+ return logs
+
+ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
+ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
+ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
+ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1
diff --git a/stable-diffusion-webui/extensions-builtin/LDSR/vqvae_quantize.py b/stable-diffusion-webui/extensions-builtin/LDSR/vqvae_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd14b8fda5ce25a8cea8b70eb1d387b9c46c80d8
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/LDSR/vqvae_quantize.py
@@ -0,0 +1,147 @@
+# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
+# where the license is as follows:
+#
+# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+# OR OTHER DEALINGS IN THE SOFTWARE./
+
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+
+
+class VectorQuantizer2(nn.Module):
+ """
+ Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
+ avoids costly matrix multiplications and allows for post-hoc remapping of indices.
+ """
+
+ # NOTE: due to a bug the beta term was applied to the wrong term. for
+ # backwards compatibility we use the buggy version by default, but you can
+ # specify legacy=False to fix it.
+ def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
+ sane_index_shape=False, legacy=True):
+ super().__init__()
+ self.n_e = n_e
+ self.e_dim = e_dim
+ self.beta = beta
+ self.legacy = legacy
+
+ self.embedding = nn.Embedding(self.n_e, self.e_dim)
+ self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+
+ self.remap = remap
+ if self.remap is not None:
+ self.register_buffer("used", torch.tensor(np.load(self.remap)))
+ self.re_embed = self.used.shape[0]
+ self.unknown_index = unknown_index # "random" or "extra" or integer
+ if self.unknown_index == "extra":
+ self.unknown_index = self.re_embed
+ self.re_embed = self.re_embed + 1
+ print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
+ f"Using {self.unknown_index} for unknown indices.")
+ else:
+ self.re_embed = n_e
+
+ self.sane_index_shape = sane_index_shape
+
+ def remap_to_used(self, inds):
+ ishape = inds.shape
+ assert len(ishape) > 1
+ inds = inds.reshape(ishape[0], -1)
+ used = self.used.to(inds)
+ match = (inds[:, :, None] == used[None, None, ...]).long()
+ new = match.argmax(-1)
+ unknown = match.sum(2) < 1
+ if self.unknown_index == "random":
+ new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
+ else:
+ new[unknown] = self.unknown_index
+ return new.reshape(ishape)
+
+ def unmap_to_all(self, inds):
+ ishape = inds.shape
+ assert len(ishape) > 1
+ inds = inds.reshape(ishape[0], -1)
+ used = self.used.to(inds)
+ if self.re_embed > self.used.shape[0]: # extra token
+ inds[inds >= self.used.shape[0]] = 0 # simply set to zero
+ back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
+ return back.reshape(ishape)
+
+ def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
+ assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
+ assert rescale_logits is False, "Only for interface compatible with Gumbel"
+ assert return_logits is False, "Only for interface compatible with Gumbel"
+ # reshape z -> (batch, height, width, channel) and flatten
+ z = rearrange(z, 'b c h w -> b h w c').contiguous()
+ z_flattened = z.view(-1, self.e_dim)
+ # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+ d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
+ torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
+ torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
+
+ min_encoding_indices = torch.argmin(d, dim=1)
+ z_q = self.embedding(min_encoding_indices).view(z.shape)
+ perplexity = None
+ min_encodings = None
+
+ # compute loss for embedding
+ if not self.legacy:
+ loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
+ torch.mean((z_q - z.detach()) ** 2)
+ else:
+ loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
+ torch.mean((z_q - z.detach()) ** 2)
+
+ # preserve gradients
+ z_q = z + (z_q - z).detach()
+
+ # reshape back to match original input shape
+ z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
+
+ if self.remap is not None:
+ min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
+ min_encoding_indices = self.remap_to_used(min_encoding_indices)
+ min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
+
+ if self.sane_index_shape:
+ min_encoding_indices = min_encoding_indices.reshape(
+ z_q.shape[0], z_q.shape[2], z_q.shape[3])
+
+ return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
+
+ def get_codebook_entry(self, indices, shape):
+ # shape specifying (batch, height, width, channel)
+ if self.remap is not None:
+ indices = indices.reshape(shape[0], -1) # add batch axis
+ indices = self.unmap_to_all(indices)
+ indices = indices.reshape(-1) # flatten again
+
+ # get quantized latent vectors
+ z_q = self.embedding(indices)
+
+ if shape is not None:
+ z_q = z_q.view(shape)
+ # reshape back to match original input shape
+ z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+ return z_q
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/extra_networks_lora.py b/stable-diffusion-webui/extensions-builtin/Lora/extra_networks_lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..88425009c7150f303b10bec8a42a3aa7a8c4ff93
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/extra_networks_lora.py
@@ -0,0 +1,67 @@
+from modules import extra_networks, shared
+import networks
+
+
+class ExtraNetworkLora(extra_networks.ExtraNetwork):
+ def __init__(self):
+ super().__init__('lora')
+
+ self.errors = {}
+ """mapping of network names to the number of errors the network had during operation"""
+
+ def activate(self, p, params_list):
+ additional = shared.opts.sd_lora
+
+ self.errors.clear()
+
+ if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
+ p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
+ names = []
+ te_multipliers = []
+ unet_multipliers = []
+ dyn_dims = []
+ for params in params_list:
+ assert params.items
+
+ names.append(params.positional[0])
+
+ te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0
+ te_multiplier = float(params.named.get("te", te_multiplier))
+
+ unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier
+ unet_multiplier = float(params.named.get("unet", unet_multiplier))
+
+ dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
+ dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
+
+ te_multipliers.append(te_multiplier)
+ unet_multipliers.append(unet_multiplier)
+ dyn_dims.append(dyn_dim)
+
+ networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
+
+ if shared.opts.lora_add_hashes_to_infotext:
+ network_hashes = []
+ for item in networks.loaded_networks:
+ shorthash = item.network_on_disk.shorthash
+ if not shorthash:
+ continue
+
+ alias = item.mentioned_name
+ if not alias:
+ continue
+
+ alias = alias.replace(":", "").replace(",", "")
+
+ network_hashes.append(f"{alias}: {shorthash}")
+
+ if network_hashes:
+ p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
+
+ def deactivate(self, p):
+ if self.errors:
+ p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
+
+ self.errors.clear()
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/lora.py b/stable-diffusion-webui/extensions-builtin/Lora/lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..6186538e956e39c843a2a22a77c5ab53fdfec3c7
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/lora.py
@@ -0,0 +1,9 @@
+import networks
+
+list_available_loras = networks.list_available_networks
+
+available_loras = networks.available_networks
+available_lora_aliases = networks.available_network_aliases
+available_lora_hash_lookup = networks.available_network_hash_lookup
+forbidden_lora_aliases = networks.forbidden_network_aliases
+loaded_loras = networks.loaded_networks
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/lora_patches.py b/stable-diffusion-webui/extensions-builtin/Lora/lora_patches.py
new file mode 100644
index 0000000000000000000000000000000000000000..59859e6f94f434a032c2c07f040bceb79fcd1dc2
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/lora_patches.py
@@ -0,0 +1,31 @@
+import torch
+
+import networks
+from modules import patches
+
+
+class LoraPatches:
+ def __init__(self):
+ self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
+ self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
+ self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
+ self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
+ self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
+ self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
+ self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward)
+ self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict)
+ self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward)
+ self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict)
+
+ def undo(self):
+ self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward')
+ self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict')
+ self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward')
+ self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict')
+ self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward')
+ self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict')
+ self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward')
+ self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict')
+ self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward')
+ self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict')
+
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/lyco_helpers.py b/stable-diffusion-webui/extensions-builtin/Lora/lyco_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2f42e83a0188cc8650ea79def7f95df0e9bac34
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/lyco_helpers.py
@@ -0,0 +1,21 @@
+import torch
+
+
+def make_weight_cp(t, wa, wb):
+ temp = torch.einsum('i j k l, j r -> i r k l', t, wb)
+ return torch.einsum('i j k l, i r -> r j k l', temp, wa)
+
+
+def rebuild_conventional(up, down, shape, dyn_dim=None):
+ up = up.reshape(up.size(0), -1)
+ down = down.reshape(down.size(0), -1)
+ if dyn_dim is not None:
+ up = up[:, :dyn_dim]
+ down = down[:dyn_dim, :]
+ return (up @ down).reshape(shape)
+
+
+def rebuild_cp_decomposition(up, down, mid):
+ up = up.reshape(up.size(0), -1)
+ down = down.reshape(down.size(0), -1)
+ return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/network.py b/stable-diffusion-webui/extensions-builtin/Lora/network.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5b60e6f613f4df705b0288b2b5f9b09ef3cfed1
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/network.py
@@ -0,0 +1,158 @@
+from __future__ import annotations
+import os
+from collections import namedtuple
+import enum
+
+from modules import sd_models, cache, errors, hashes, shared
+
+NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
+
+metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
+
+
+class SdVersion(enum.Enum):
+ Unknown = 1
+ SD1 = 2
+ SD2 = 3
+ SDXL = 4
+
+
+class NetworkOnDisk:
+ def __init__(self, name, filename):
+ self.name = name
+ self.filename = filename
+ self.metadata = {}
+ self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
+ def read_metadata():
+ metadata = sd_models.read_metadata_from_safetensors(filename)
+ metadata.pop('ssmd_cover_images', None) # those are cover images, and they are too big to display in UI as text
+
+ return metadata
+
+ if self.is_safetensors:
+ try:
+ self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
+ except Exception as e:
+ errors.display(e, f"reading lora {filename}")
+
+ if self.metadata:
+ m = {}
+ for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
+ m[k] = v
+
+ self.metadata = m
+
+ self.alias = self.metadata.get('ss_output_name', self.name)
+
+ self.hash = None
+ self.shorthash = None
+ self.set_hash(
+ self.metadata.get('sshs_model_hash') or
+ hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
+ ''
+ )
+
+ self.sd_version = self.detect_version()
+
+ def detect_version(self):
+ if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
+ return SdVersion.SDXL
+ elif str(self.metadata.get('ss_v2', "")) == "True":
+ return SdVersion.SD2
+ elif len(self.metadata):
+ return SdVersion.SD1
+
+ return SdVersion.Unknown
+
+ def set_hash(self, v):
+ self.hash = v
+ self.shorthash = self.hash[0:12]
+
+ if self.shorthash:
+ import networks
+ networks.available_network_hash_lookup[self.shorthash] = self
+
+ def read_hash(self):
+ if not self.hash:
+ self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
+
+ def get_alias(self):
+ import networks
+ if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
+ return self.name
+ else:
+ return self.alias
+
+
+class Network: # LoraModule
+ def __init__(self, name, network_on_disk: NetworkOnDisk):
+ self.name = name
+ self.network_on_disk = network_on_disk
+ self.te_multiplier = 1.0
+ self.unet_multiplier = 1.0
+ self.dyn_dim = None
+ self.modules = {}
+ self.mtime = None
+
+ self.mentioned_name = None
+ """the text that was used to add the network to prompt - can be either name or an alias"""
+
+
+class ModuleType:
+ def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
+ return None
+
+
+class NetworkModule:
+ def __init__(self, net: Network, weights: NetworkWeights):
+ self.network = net
+ self.network_key = weights.network_key
+ self.sd_key = weights.sd_key
+ self.sd_module = weights.sd_module
+
+ if hasattr(self.sd_module, 'weight'):
+ self.shape = self.sd_module.weight.shape
+
+ self.dim = None
+ self.bias = weights.w.get("bias")
+ self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
+ self.scale = weights.w["scale"].item() if "scale" in weights.w else None
+
+ def multiplier(self):
+ if 'transformer' in self.sd_key[:20]:
+ return self.network.te_multiplier
+ else:
+ return self.network.unet_multiplier
+
+ def calc_scale(self):
+ if self.scale is not None:
+ return self.scale
+ if self.dim is not None and self.alpha is not None:
+ return self.alpha / self.dim
+
+ return 1.0
+
+ def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
+ if self.bias is not None:
+ updown = updown.reshape(self.bias.shape)
+ updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown = updown.reshape(output_shape)
+
+ if len(output_shape) == 4:
+ updown = updown.reshape(output_shape)
+
+ if orig_weight.size().numel() == updown.size().numel():
+ updown = updown.reshape(orig_weight.shape)
+
+ if ex_bias is not None:
+ ex_bias = ex_bias * self.multiplier()
+
+ return updown * self.calc_scale() * self.multiplier(), ex_bias
+
+ def calc_updown(self, target):
+ raise NotImplementedError()
+
+ def forward(self, x, y):
+ raise NotImplementedError()
+
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/network_full.py b/stable-diffusion-webui/extensions-builtin/Lora/network_full.py
new file mode 100644
index 0000000000000000000000000000000000000000..545e254e0c675c8b81668cbd234d41edaba524e7
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/network_full.py
@@ -0,0 +1,27 @@
+import network
+
+
+class ModuleTypeFull(network.ModuleType):
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
+ if all(x in weights.w for x in ["diff"]):
+ return NetworkModuleFull(net, weights)
+
+ return None
+
+
+class NetworkModuleFull(network.NetworkModule):
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
+ super().__init__(net, weights)
+
+ self.weight = weights.w.get("diff")
+ self.ex_bias = weights.w.get("diff_b")
+
+ def calc_updown(self, orig_weight):
+ output_shape = self.weight.shape
+ updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ if self.ex_bias is not None:
+ ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
+ else:
+ ex_bias = None
+
+ return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/network_hada.py b/stable-diffusion-webui/extensions-builtin/Lora/network_hada.py
new file mode 100644
index 0000000000000000000000000000000000000000..b62e88840866f2801b5bafa657cfd9b0377054b7
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/network_hada.py
@@ -0,0 +1,55 @@
+import lyco_helpers
+import network
+
+
+class ModuleTypeHada(network.ModuleType):
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
+ if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]):
+ return NetworkModuleHada(net, weights)
+
+ return None
+
+
+class NetworkModuleHada(network.NetworkModule):
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
+ super().__init__(net, weights)
+
+ if hasattr(self.sd_module, 'weight'):
+ self.shape = self.sd_module.weight.shape
+
+ self.w1a = weights.w["hada_w1_a"]
+ self.w1b = weights.w["hada_w1_b"]
+ self.dim = self.w1b.shape[0]
+ self.w2a = weights.w["hada_w2_a"]
+ self.w2b = weights.w["hada_w2_b"]
+
+ self.t1 = weights.w.get("hada_t1")
+ self.t2 = weights.w.get("hada_t2")
+
+ def calc_updown(self, orig_weight):
+ w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+
+ output_shape = [w1a.size(0), w1b.size(1)]
+
+ if self.t1 is not None:
+ output_shape = [w1a.size(1), w1b.size(1)]
+ t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
+ output_shape += t1.shape[2:]
+ else:
+ if len(w1b.shape) == 4:
+ output_shape += w1b.shape[2:]
+ updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
+
+ if self.t2 is not None:
+ t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
+ else:
+ updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
+
+ updown = updown1 * updown2
+
+ return self.finalize_updown(updown, orig_weight, output_shape)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/network_ia3.py b/stable-diffusion-webui/extensions-builtin/Lora/network_ia3.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddf5d68983c3b8d57ad3d58b293e6bc462d52159
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/network_ia3.py
@@ -0,0 +1,30 @@
+import network
+
+
+class ModuleTypeIa3(network.ModuleType):
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
+ if all(x in weights.w for x in ["weight"]):
+ return NetworkModuleIa3(net, weights)
+
+ return None
+
+
+class NetworkModuleIa3(network.NetworkModule):
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
+ super().__init__(net, weights)
+
+ self.w = weights.w["weight"]
+ self.on_input = weights.w["on_input"].item()
+
+ def calc_updown(self, orig_weight):
+ w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
+
+ output_shape = [w.size(0), orig_weight.size(1)]
+ if self.on_input:
+ output_shape.reverse()
+ else:
+ w = w.reshape(-1, 1)
+
+ updown = orig_weight * w
+
+ return self.finalize_updown(updown, orig_weight, output_shape)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/network_lokr.py b/stable-diffusion-webui/extensions-builtin/Lora/network_lokr.py
new file mode 100644
index 0000000000000000000000000000000000000000..87fbafa1b406de73cc394a3a0c9068da4119b0d8
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/network_lokr.py
@@ -0,0 +1,64 @@
+import torch
+
+import lyco_helpers
+import network
+
+
+class ModuleTypeLokr(network.ModuleType):
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
+ has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w)
+ has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w)
+ if has_1 and has_2:
+ return NetworkModuleLokr(net, weights)
+
+ return None
+
+
+def make_kron(orig_shape, w1, w2):
+ if len(w2.shape) == 4:
+ w1 = w1.unsqueeze(2).unsqueeze(2)
+ w2 = w2.contiguous()
+ return torch.kron(w1, w2).reshape(orig_shape)
+
+
+class NetworkModuleLokr(network.NetworkModule):
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
+ super().__init__(net, weights)
+
+ self.w1 = weights.w.get("lokr_w1")
+ self.w1a = weights.w.get("lokr_w1_a")
+ self.w1b = weights.w.get("lokr_w1_b")
+ self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim
+ self.w2 = weights.w.get("lokr_w2")
+ self.w2a = weights.w.get("lokr_w2_a")
+ self.w2b = weights.w.get("lokr_w2_b")
+ self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim
+ self.t2 = weights.w.get("lokr_t2")
+
+ def calc_updown(self, orig_weight):
+ if self.w1 is not None:
+ w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
+ else:
+ w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1 = w1a @ w1b
+
+ if self.w2 is not None:
+ w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
+ elif self.t2 is None:
+ w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2 = w2a @ w2b
+ else:
+ t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
+
+ output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
+ if len(orig_weight.shape) == 4:
+ output_shape = orig_weight.shape
+
+ updown = make_kron(output_shape, w1, w2)
+
+ return self.finalize_updown(updown, orig_weight, output_shape)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/network_lora.py b/stable-diffusion-webui/extensions-builtin/Lora/network_lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb63807a09a6883fa636822ebc01753e2cb4848f
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/network_lora.py
@@ -0,0 +1,86 @@
+import torch
+
+import lyco_helpers
+import network
+from modules import devices
+
+
+class ModuleTypeLora(network.ModuleType):
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
+ if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
+ return NetworkModuleLora(net, weights)
+
+ return None
+
+
+class NetworkModuleLora(network.NetworkModule):
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
+ super().__init__(net, weights)
+
+ self.up_model = self.create_module(weights.w, "lora_up.weight")
+ self.down_model = self.create_module(weights.w, "lora_down.weight")
+ self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True)
+
+ self.dim = weights.w["lora_down.weight"].shape[0]
+
+ def create_module(self, weights, key, none_ok=False):
+ weight = weights.get(key)
+
+ if weight is None and none_ok:
+ return None
+
+ is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
+ is_conv = type(self.sd_module) in [torch.nn.Conv2d]
+
+ if is_linear:
+ weight = weight.reshape(weight.shape[0], -1)
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+ elif is_conv and key == "lora_down.weight" or key == "dyn_up":
+ if len(weight.shape) == 2:
+ weight = weight.reshape(weight.shape[0], -1, 1, 1)
+
+ if weight.shape[2] != 1 or weight.shape[3] != 1:
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
+ else:
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+ elif is_conv and key == "lora_mid.weight":
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
+ elif is_conv and key == "lora_up.weight" or key == "dyn_down":
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+ else:
+ raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
+
+ with torch.no_grad():
+ if weight.shape != module.weight.shape:
+ weight = weight.reshape(module.weight.shape)
+ module.weight.copy_(weight)
+
+ module.to(device=devices.cpu, dtype=devices.dtype)
+ module.weight.requires_grad_(False)
+
+ return module
+
+ def calc_updown(self, orig_weight):
+ up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+
+ output_shape = [up.size(0), down.size(1)]
+ if self.mid_model is not None:
+ # cp-decomposition
+ mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
+ output_shape += mid.shape[2:]
+ else:
+ if len(down.shape) == 4:
+ output_shape += down.shape[2:]
+ updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim)
+
+ return self.finalize_updown(updown, orig_weight, output_shape)
+
+ def forward(self, x, y):
+ self.up_model.to(device=devices.device)
+ self.down_model.to(device=devices.device)
+
+ return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
+
+
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/network_norm.py b/stable-diffusion-webui/extensions-builtin/Lora/network_norm.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce450158068ef85ebe11cc60756ed991465c0e54
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/network_norm.py
@@ -0,0 +1,28 @@
+import network
+
+
+class ModuleTypeNorm(network.ModuleType):
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
+ if all(x in weights.w for x in ["w_norm", "b_norm"]):
+ return NetworkModuleNorm(net, weights)
+
+ return None
+
+
+class NetworkModuleNorm(network.NetworkModule):
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
+ super().__init__(net, weights)
+
+ self.w_norm = weights.w.get("w_norm")
+ self.b_norm = weights.w.get("b_norm")
+
+ def calc_updown(self, orig_weight):
+ output_shape = self.w_norm.shape
+ updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+
+ if self.b_norm is not None:
+ ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+ else:
+ ex_bias = None
+
+ return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/networks.py b/stable-diffusion-webui/extensions-builtin/Lora/networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5e704be91feebd7ae19c340a6ef360761785a6b
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/networks.py
@@ -0,0 +1,571 @@
+import logging
+import os
+import re
+
+import lora_patches
+import network
+import network_lora
+import network_hada
+import network_ia3
+import network_lokr
+import network_full
+import network_norm
+
+import torch
+from typing import Union
+
+from modules import shared, devices, sd_models, errors, scripts, sd_hijack
+
+module_types = [
+ network_lora.ModuleTypeLora(),
+ network_hada.ModuleTypeHada(),
+ network_ia3.ModuleTypeIa3(),
+ network_lokr.ModuleTypeLokr(),
+ network_full.ModuleTypeFull(),
+ network_norm.ModuleTypeNorm(),
+]
+
+
+re_digits = re.compile(r"\d+")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+ "attentions": {},
+ "resnets": {
+ "conv1": "in_layers_2",
+ "conv2": "out_layers_3",
+ "norm1": "in_layers_0",
+ "norm2": "out_layers_0",
+ "time_emb_proj": "emb_layers_1",
+ "conv_shortcut": "skip_connection",
+ }
+}
+
+
+def convert_diffusers_name_to_compvis(key, is_sd2):
+ def match(match_list, regex_text):
+ regex = re_compiled.get(regex_text)
+ if regex is None:
+ regex = re.compile(regex_text)
+ re_compiled[regex_text] = regex
+
+ r = re.match(regex, key)
+ if not r:
+ return False
+
+ match_list.clear()
+ match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
+ return True
+
+ m = []
+
+ if match(m, r"lora_unet_conv_in(.*)"):
+ return f'diffusion_model_input_blocks_0_0{m[0]}'
+
+ if match(m, r"lora_unet_conv_out(.*)"):
+ return f'diffusion_model_out_2{m[0]}'
+
+ if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
+ return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
+
+ if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
+
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+ if is_sd2:
+ if 'mlp_fc1' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+ elif 'mlp_fc2' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+ else:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
+ return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
+
+ if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
+ if 'mlp_fc1' in m[1]:
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+ elif 'mlp_fc2' in m[1]:
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+ else:
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
+ return key
+
+
+def assign_network_names_to_compvis_modules(sd_model):
+ network_layer_mapping = {}
+
+ if shared.sd_model.is_sdxl:
+ for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
+ if not hasattr(embedder, 'wrapped'):
+ continue
+
+ for name, module in embedder.wrapped.named_modules():
+ network_name = f'{i}_{name.replace(".", "_")}'
+ network_layer_mapping[network_name] = module
+ module.network_layer_name = network_name
+ else:
+ for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
+ network_name = name.replace(".", "_")
+ network_layer_mapping[network_name] = module
+ module.network_layer_name = network_name
+
+ for name, module in shared.sd_model.model.named_modules():
+ network_name = name.replace(".", "_")
+ network_layer_mapping[network_name] = module
+ module.network_layer_name = network_name
+
+ sd_model.network_layer_mapping = network_layer_mapping
+
+
+def load_network(name, network_on_disk):
+ net = network.Network(name, network_on_disk)
+ net.mtime = os.path.getmtime(network_on_disk.filename)
+
+ sd = sd_models.read_state_dict(network_on_disk.filename)
+
+ # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
+ if not hasattr(shared.sd_model, 'network_layer_mapping'):
+ assign_network_names_to_compvis_modules(shared.sd_model)
+
+ keys_failed_to_match = {}
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
+
+ matched_networks = {}
+
+ for key_network, weight in sd.items():
+ key_network_without_network_parts, network_part = key_network.split(".", 1)
+
+ key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+ if sd_module is None:
+ m = re_x_proj.match(key)
+ if m:
+ sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)
+
+ # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
+ if sd_module is None and "lora_unet" in key_network_without_network_parts:
+ key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+ elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
+ key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+ # some SD1 Loras also have correct compvis keys
+ if sd_module is None:
+ key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+ if sd_module is None:
+ keys_failed_to_match[key_network] = key
+ continue
+
+ if key not in matched_networks:
+ matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)
+
+ matched_networks[key].w[network_part] = weight
+
+ for key, weights in matched_networks.items():
+ net_module = None
+ for nettype in module_types:
+ net_module = nettype.create_module(net, weights)
+ if net_module is not None:
+ break
+
+ if net_module is None:
+ raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")
+
+ net.modules[key] = net_module
+
+ if keys_failed_to_match:
+ logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
+
+ return net
+
+
+def purge_networks_from_memory():
+ while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
+ name = next(iter(networks_in_memory))
+ networks_in_memory.pop(name, None)
+
+ devices.torch_gc()
+
+
+def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
+ already_loaded = {}
+
+ for net in loaded_networks:
+ if net.name in names:
+ already_loaded[net.name] = net
+
+ loaded_networks.clear()
+
+ networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+ if any(x is None for x in networks_on_disk):
+ list_available_networks()
+
+ networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+
+ failed_to_load_networks = []
+
+ for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
+ net = already_loaded.get(name, None)
+
+ if network_on_disk is not None:
+ if net is None:
+ net = networks_in_memory.get(name)
+
+ if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
+ try:
+ net = load_network(name, network_on_disk)
+
+ networks_in_memory.pop(name, None)
+ networks_in_memory[name] = net
+ except Exception as e:
+ errors.display(e, f"loading network {network_on_disk.filename}")
+ continue
+
+ net.mentioned_name = name
+
+ network_on_disk.read_hash()
+
+ if net is None:
+ failed_to_load_networks.append(name)
+ logging.info(f"Couldn't find network with name {name}")
+ continue
+
+ net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
+ net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
+ net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
+ loaded_networks.append(net)
+
+ if failed_to_load_networks:
+ sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
+
+ purge_networks_from_memory()
+
+
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+ weights_backup = getattr(self, "network_weights_backup", None)
+ bias_backup = getattr(self, "network_bias_backup", None)
+
+ if weights_backup is None and bias_backup is None:
+ return
+
+ if weights_backup is not None:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ self.in_proj_weight.copy_(weights_backup[0])
+ self.out_proj.weight.copy_(weights_backup[1])
+ else:
+ self.weight.copy_(weights_backup)
+
+ if bias_backup is not None:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ self.out_proj.bias.copy_(bias_backup)
+ else:
+ self.bias.copy_(bias_backup)
+ else:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ self.out_proj.bias = None
+ else:
+ self.bias = None
+
+
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+ """
+ Applies the currently selected set of networks to the weights of torch layer self.
+ If weights already have this particular set of networks applied, does nothing.
+ If not, restores orginal weights from backup and alters weights according to networks.
+ """
+
+ network_layer_name = getattr(self, 'network_layer_name', None)
+ if network_layer_name is None:
+ return
+
+ current_names = getattr(self, "network_current_names", ())
+ wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
+
+ weights_backup = getattr(self, "network_weights_backup", None)
+ if weights_backup is None and wanted_names != ():
+ if current_names != ():
+ raise RuntimeError("no backup weights found and current weights are not unchanged")
+
+ if isinstance(self, torch.nn.MultiheadAttention):
+ weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
+ else:
+ weights_backup = self.weight.to(devices.cpu, copy=True)
+
+ self.network_weights_backup = weights_backup
+
+ bias_backup = getattr(self, "network_bias_backup", None)
+ if bias_backup is None:
+ if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
+ bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
+ elif getattr(self, 'bias', None) is not None:
+ bias_backup = self.bias.to(devices.cpu, copy=True)
+ else:
+ bias_backup = None
+ self.network_bias_backup = bias_backup
+
+ if current_names != wanted_names:
+ network_restore_weights_from_backup(self)
+
+ for net in loaded_networks:
+ module = net.modules.get(network_layer_name, None)
+ if module is not None and hasattr(self, 'weight'):
+ try:
+ with torch.no_grad():
+ updown, ex_bias = module.calc_updown(self.weight)
+
+ if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+ # inpainting model. zero pad updown to make channel[1] 4 to 9
+ updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+
+ self.weight += updown
+ if ex_bias is not None and hasattr(self, 'bias'):
+ if self.bias is None:
+ self.bias = torch.nn.Parameter(ex_bias)
+ else:
+ self.bias += ex_bias
+ except RuntimeError as e:
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+ continue
+
+ module_q = net.modules.get(network_layer_name + "_q_proj", None)
+ module_k = net.modules.get(network_layer_name + "_k_proj", None)
+ module_v = net.modules.get(network_layer_name + "_v_proj", None)
+ module_out = net.modules.get(network_layer_name + "_out_proj", None)
+
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
+ try:
+ with torch.no_grad():
+ updown_q, _ = module_q.calc_updown(self.in_proj_weight)
+ updown_k, _ = module_k.calc_updown(self.in_proj_weight)
+ updown_v, _ = module_v.calc_updown(self.in_proj_weight)
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+ updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
+
+ self.in_proj_weight += updown_qkv
+ self.out_proj.weight += updown_out
+ if ex_bias is not None:
+ if self.out_proj.bias is None:
+ self.out_proj.bias = torch.nn.Parameter(ex_bias)
+ else:
+ self.out_proj.bias += ex_bias
+
+ except RuntimeError as e:
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+ continue
+
+ if module is None:
+ continue
+
+ logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+ self.network_current_names = wanted_names
+
+
+def network_forward(module, input, original_forward):
+ """
+ Old way of applying Lora by executing operations during layer's forward.
+ Stacking many loras this way results in big performance degradation.
+ """
+
+ if len(loaded_networks) == 0:
+ return original_forward(module, input)
+
+ input = devices.cond_cast_unet(input)
+
+ network_restore_weights_from_backup(module)
+ network_reset_cached_weight(module)
+
+ y = original_forward(module, input)
+
+ network_layer_name = getattr(module, 'network_layer_name', None)
+ for lora in loaded_networks:
+ module = lora.modules.get(network_layer_name, None)
+ if module is None:
+ continue
+
+ y = module.forward(input, y)
+
+ return y
+
+
+def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+ self.network_current_names = ()
+ self.network_weights_backup = None
+
+
+def network_Linear_forward(self, input):
+ if shared.opts.lora_functional:
+ return network_forward(self, input, originals.Linear_forward)
+
+ network_apply_weights(self)
+
+ return originals.Linear_forward(self, input)
+
+
+def network_Linear_load_state_dict(self, *args, **kwargs):
+ network_reset_cached_weight(self)
+
+ return originals.Linear_load_state_dict(self, *args, **kwargs)
+
+
+def network_Conv2d_forward(self, input):
+ if shared.opts.lora_functional:
+ return network_forward(self, input, originals.Conv2d_forward)
+
+ network_apply_weights(self)
+
+ return originals.Conv2d_forward(self, input)
+
+
+def network_Conv2d_load_state_dict(self, *args, **kwargs):
+ network_reset_cached_weight(self)
+
+ return originals.Conv2d_load_state_dict(self, *args, **kwargs)
+
+
+def network_GroupNorm_forward(self, input):
+ if shared.opts.lora_functional:
+ return network_forward(self, input, originals.GroupNorm_forward)
+
+ network_apply_weights(self)
+
+ return originals.GroupNorm_forward(self, input)
+
+
+def network_GroupNorm_load_state_dict(self, *args, **kwargs):
+ network_reset_cached_weight(self)
+
+ return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
+
+
+def network_LayerNorm_forward(self, input):
+ if shared.opts.lora_functional:
+ return network_forward(self, input, originals.LayerNorm_forward)
+
+ network_apply_weights(self)
+
+ return originals.LayerNorm_forward(self, input)
+
+
+def network_LayerNorm_load_state_dict(self, *args, **kwargs):
+ network_reset_cached_weight(self)
+
+ return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
+
+
+def network_MultiheadAttention_forward(self, *args, **kwargs):
+ network_apply_weights(self)
+
+ return originals.MultiheadAttention_forward(self, *args, **kwargs)
+
+
+def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+ network_reset_cached_weight(self)
+
+ return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
+
+
+def list_available_networks():
+ available_networks.clear()
+ available_network_aliases.clear()
+ forbidden_network_aliases.clear()
+ available_network_hash_lookup.clear()
+ forbidden_network_aliases.update({"none": 1, "Addams": 1})
+
+ os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
+
+ candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
+ candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
+ for filename in candidates:
+ if os.path.isdir(filename):
+ continue
+
+ name = os.path.splitext(os.path.basename(filename))[0]
+ try:
+ entry = network.NetworkOnDisk(name, filename)
+ except OSError: # should catch FileNotFoundError and PermissionError etc.
+ errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
+ continue
+
+ available_networks[name] = entry
+
+ if entry.alias in available_network_aliases:
+ forbidden_network_aliases[entry.alias.lower()] = 1
+
+ available_network_aliases[name] = entry
+ available_network_aliases[entry.alias] = entry
+
+
+re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
+
+
+def infotext_pasted(infotext, params):
+ if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
+ return # if the other extension is active, it will handle those fields, no need to do anything
+
+ added = []
+
+ for k in params:
+ if not k.startswith("AddNet Model "):
+ continue
+
+ num = k[13:]
+
+ if params.get("AddNet Module " + num) != "LoRA":
+ continue
+
+ name = params.get("AddNet Model " + num)
+ if name is None:
+ continue
+
+ m = re_network_name.match(name)
+ if m:
+ name = m.group(1)
+
+ multiplier = params.get("AddNet Weight A " + num, "1.0")
+
+ added.append(f"<lora:{name}:{multiplier}>")
+
+ if added:
+ params["Prompt"] += "\n" + "".join(added)
+
+
+originals: lora_patches.LoraPatches = None
+
+extra_network_lora = None
+
+available_networks = {}
+available_network_aliases = {}
+loaded_networks = []
+networks_in_memory = {}
+available_network_hash_lookup = {}
+forbidden_network_aliases = {}
+
+list_available_networks()
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/preload.py b/stable-diffusion-webui/extensions-builtin/Lora/preload.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f85bc5338d77df91e60f35ebb4ce11d2573f01f
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/preload.py
@@ -0,0 +1,7 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+ parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
+ parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py b/stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py
new file mode 100644
index 0000000000000000000000000000000000000000..83b6678db128f44a7a556221e02022fe8e416beb
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py
@@ -0,0 +1,99 @@
+import re
+
+import gradio as gr
+from fastapi import FastAPI
+
+import network
+import networks
+import lora # noqa:F401
+import lora_patches
+import extra_networks_lora
+import ui_extra_networks_lora
+from modules import script_callbacks, ui_extra_networks, extra_networks, shared
+
+
+def unload():
+ networks.originals.undo()
+
+
+def before_ui():
+ ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
+
+ networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
+ extra_networks.register_extra_network(networks.extra_network_lora)
+ extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
+
+
+networks.originals = lora_patches.LoraPatches()
+
+script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
+script_callbacks.on_script_unloaded(unload)
+script_callbacks.on_before_ui(before_ui)
+script_callbacks.on_infotext_pasted(networks.infotext_pasted)
+
+
+shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
+ "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
+ "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
+ "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
+ "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
+ "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
+ "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
+}))
+
+
+shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
+ "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
+}))
+
+
+def create_lora_json(obj: network.NetworkOnDisk):
+ return {
+ "name": obj.name,
+ "alias": obj.alias,
+ "path": obj.filename,
+ "metadata": obj.metadata,
+ }
+
+
+def api_networks(_: gr.Blocks, app: FastAPI):
+ @app.get("/sdapi/v1/loras")
+ async def get_loras():
+ return [create_lora_json(obj) for obj in networks.available_networks.values()]
+
+ @app.post("/sdapi/v1/refresh-loras")
+ async def refresh_loras():
+ return networks.list_available_networks()
+
+
+script_callbacks.on_app_started(api_networks)
+
+re_lora = re.compile("<lora:([^:]+):")
+
+
+def infotext_pasted(infotext, d):
+ hashes = d.get("Lora hashes")
+ if not hashes:
+ return
+
+ hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
+ hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
+
+ def network_replacement(m):
+ alias = m.group(1)
+ shorthash = hashes.get(alias)
+ if shorthash is None:
+ return m.group(0)
+
+ network_on_disk = networks.available_network_hash_lookup.get(shorthash)
+ if network_on_disk is None:
+ return m.group(0)
+
+ return f'<lora:{network_on_disk.get_alias()}:'
+
+ d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
+
+
+script_callbacks.on_infotext_pasted(infotext_pasted)
+
+shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/ui_edit_user_metadata.py b/stable-diffusion-webui/extensions-builtin/Lora/ui_edit_user_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..97ed580b89681681b5606ff6acf0c2f4dc607f9d
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/ui_edit_user_metadata.py
@@ -0,0 +1,217 @@
+import datetime
+import html
+import random
+
+import gradio as gr
+import re
+
+from modules import ui_extra_networks_user_metadata
+
+
+def is_non_comma_tagset(tags):
+ average_tag_length = sum(len(x) for x in tags.keys()) / len(tags)
+
+ return average_tag_length >= 16
+
+
+re_word = re.compile(r"[-_\w']+")
+re_comma = re.compile(r" *, *")
+
+
+def build_tags(metadata):
+ tags = {}
+
+ for _, tags_dict in metadata.get("ss_tag_frequency", {}).items():
+ for tag, tag_count in tags_dict.items():
+ tag = tag.strip()
+ tags[tag] = tags.get(tag, 0) + int(tag_count)
+
+ if tags and is_non_comma_tagset(tags):
+ new_tags = {}
+
+ for text, text_count in tags.items():
+ for word in re.findall(re_word, text):
+ if len(word) < 3:
+ continue
+
+ new_tags[word] = new_tags.get(word, 0) + text_count
+
+ tags = new_tags
+
+ ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True)
+
+ return [(tag, tags[tag]) for tag in ordered_tags]
+
+
+class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
+ def __init__(self, ui, tabname, page):
+ super().__init__(ui, tabname, page)
+
+ self.select_sd_version = None
+
+ self.taginfo = None
+ self.edit_activation_text = None
+ self.slider_preferred_weight = None
+ self.edit_notes = None
+
+ def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
+ user_metadata = self.get_user_metadata(name)
+ user_metadata["description"] = desc
+ user_metadata["sd version"] = sd_version
+ user_metadata["activation text"] = activation_text
+ user_metadata["preferred weight"] = preferred_weight
+ user_metadata["notes"] = notes
+
+ self.write_user_metadata(name, user_metadata)
+
+ def get_metadata_table(self, name):
+ table = super().get_metadata_table(name)
+ item = self.page.items.get(name, {})
+ metadata = item.get("metadata") or {}
+
+ keys = {
+ 'ss_output_name': "Output name:",
+ 'ss_sd_model_name': "Model:",
+ 'ss_clip_skip': "Clip skip:",
+ 'ss_network_module': "Kohya module:",
+ }
+
+ for key, label in keys.items():
+ value = metadata.get(key, None)
+ if value is not None and str(value) != "None":
+ table.append((label, html.escape(value)))
+
+ ss_training_started_at = metadata.get('ss_training_started_at')
+ if ss_training_started_at:
+ table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M')))
+
+ ss_bucket_info = metadata.get("ss_bucket_info")
+ if ss_bucket_info and "buckets" in ss_bucket_info:
+ resolutions = {}
+ for _, bucket in ss_bucket_info["buckets"].items():
+ resolution = bucket["resolution"]
+ resolution = f'{resolution[1]}x{resolution[0]}'
+
+ resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"])
+
+ resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True)
+ resolutions_text = html.escape(", ".join(resolutions_list[0:4]))
+ if len(resolutions) > 4:
+ resolutions_text += ", ..."
+ resolutions_text = f"<span title='{html.escape(', '.join(resolutions_list))}'>{resolutions_text}</span>"
+
+ table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text))
+
+ image_count = 0
+ for _, params in metadata.get("ss_dataset_dirs", {}).items():
+ image_count += int(params.get("img_count", 0))
+
+ if image_count:
+ table.append(("Dataset size:", image_count))
+
+ return table
+
+ def put_values_into_components(self, name):
+ user_metadata = self.get_user_metadata(name)
+ values = super().put_values_into_components(name)
+
+ item = self.page.items.get(name, {})
+ metadata = item.get("metadata") or {}
+
+ tags = build_tags(metadata)
+ gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]]
+
+ return [
+ *values[0:5],
+ item.get("sd_version", "Unknown"),
+ gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
+ user_metadata.get('activation text', ''),
+ float(user_metadata.get('preferred weight', 0.0)),
+ gr.update(visible=True if tags else False),
+ gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
+ ]
+
+ def generate_random_prompt(self, name):
+ item = self.page.items.get(name, {})
+ metadata = item.get("metadata") or {}
+ tags = build_tags(metadata)
+
+ return self.generate_random_prompt_from_tags(tags)
+
+ def generate_random_prompt_from_tags(self, tags):
+ max_count = None
+ res = []
+ for tag, count in tags:
+ if not max_count:
+ max_count = count
+
+ v = random.random() * max_count
+ if count > v:
+ res.append(tag)
+
+ return ", ".join(sorted(res))
+
+ def create_extra_default_items_in_left_column(self):
+
+ # this would be a lot better as gr.Radio but I can't make it work
+ self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
+
+ def create_editor(self):
+ self.create_default_editor_elems()
+
+ self.taginfo = gr.HighlightedText(label="Training dataset tags")
+ self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
+ self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
+
+ with gr.Row() as row_random_prompt:
+ with gr.Column(scale=8):
+ random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
+
+ with gr.Column(scale=1, min_width=120):
+ generate_random_prompt = gr.Button('Generate', size="lg", scale=1)
+
+ self.edit_notes = gr.TextArea(label='Notes', lines=4)
+
+ generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False)
+
+ def select_tag(activation_text, evt: gr.SelectData):
+ tag = evt.value[0]
+
+ words = re.split(re_comma, activation_text)
+ if tag in words:
+ words = [x for x in words if x != tag and x.strip()]
+ return ", ".join(words)
+
+ return activation_text + ", " + tag if activation_text else tag
+
+ self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False)
+
+ self.create_default_buttons()
+
+ viewed_components = [
+ self.edit_name,
+ self.edit_description,
+ self.html_filedata,
+ self.html_preview,
+ self.edit_notes,
+ self.select_sd_version,
+ self.taginfo,
+ self.edit_activation_text,
+ self.slider_preferred_weight,
+ row_random_prompt,
+ random_prompt,
+ ]
+
+ self.button_edit\
+ .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
+ .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
+
+ edited_components = [
+ self.edit_description,
+ self.select_sd_version,
+ self.edit_activation_text,
+ self.slider_preferred_weight,
+ self.edit_notes,
+ ]
+
+ self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)
diff --git a/stable-diffusion-webui/extensions-builtin/Lora/ui_extra_networks_lora.py b/stable-diffusion-webui/extensions-builtin/Lora/ui_extra_networks_lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..e89af57d1b4283c969df4fd007d02c107ff8264e
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -0,0 +1,79 @@
+import os
+
+import network
+import networks
+
+from modules import shared, ui_extra_networks
+from modules.ui_extra_networks import quote_js
+from ui_edit_user_metadata import LoraUserMetadataEditor
+
+
+class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Lora')
+
+ def refresh(self):
+ networks.list_available_networks()
+
+ def create_item(self, name, index=None, enable_filter=True):
+ lora_on_disk = networks.available_networks.get(name)
+
+ path, ext = os.path.splitext(lora_on_disk.filename)
+
+ alias = lora_on_disk.get_alias()
+
+ item = {
+ "name": name,
+ "filename": lora_on_disk.filename,
+ "shorthash": lora_on_disk.shorthash,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
+ "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
+ "local_preview": f"{path}.{shared.opts.samples_format}",
+ "metadata": lora_on_disk.metadata,
+ "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
+ "sd_version": lora_on_disk.sd_version.name,
+ }
+
+ self.read_user_metadata(item)
+ activation_text = item["user_metadata"].get("activation text")
+ preferred_weight = item["user_metadata"].get("preferred weight", 0.0)
+ item["prompt"] = quote_js(f"<lora:{alias}:") + " + " + (str(preferred_weight) if preferred_weight else "opts.extra_networks_default_multiplier") + " + " + quote_js(">")
+
+ if activation_text:
+ item["prompt"] += " + " + quote_js(" " + activation_text)
+
+ sd_version = item["user_metadata"].get("sd version")
+ if sd_version in network.SdVersion.__members__:
+ item["sd_version"] = sd_version
+ sd_version = network.SdVersion[sd_version]
+ else:
+ sd_version = lora_on_disk.sd_version
+
+ if shared.opts.lora_show_all or not enable_filter:
+ pass
+ elif sd_version == network.SdVersion.Unknown:
+ model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
+ if model_version.name in shared.opts.lora_hide_unknown_for_versions:
+ return None
+ elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL:
+ return None
+ elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2:
+ return None
+ elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1:
+ return None
+
+ return item
+
+ def list_items(self):
+ for index, name in enumerate(networks.available_networks):
+ item = self.create_item(name, index)
+
+ if item is not None:
+ yield item
+
+ def allowed_directories_for_previews(self):
+ return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat]
+
+ def create_user_metadata_editor(self, ui, tabname):
+ return LoraUserMetadataEditor(ui, tabname, self)
diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py b/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ce82b1d4349b24192b1915d022ed4fda9f31e5c
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/ScuNET/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+ parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))
diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/scunet_model.py b/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/scunet_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..167d2f64b8e8ef1c506d89026e5d2ac8687d8098
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -0,0 +1,144 @@
+import sys
+
+import PIL.Image
+import numpy as np
+import torch
+from tqdm import tqdm
+
+import modules.upscaler
+from modules import devices, modelloader, script_callbacks, errors
+from scunet_model_arch import SCUNet
+
+from modules.modelloader import load_file_from_url
+from modules.shared import opts
+
+
+class UpscalerScuNET(modules.upscaler.Upscaler):
+ def __init__(self, dirname):
+ self.name = "ScuNET"
+ self.model_name = "ScuNET GAN"
+ self.model_name2 = "ScuNET PSNR"
+ self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
+ self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
+ self.user_path = dirname
+ super().__init__()
+ model_paths = self.find_models(ext_filter=[".pth"])
+ scalers = []
+ add_model2 = True
+ for file in model_paths:
+ if file.startswith("http"):
+ name = self.model_name
+ else:
+ name = modelloader.friendly_name(file)
+ if name == self.model_name2 or file == self.model_url2:
+ add_model2 = False
+ try:
+ scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
+ scalers.append(scaler_data)
+ except Exception:
+ errors.report(f"Error loading ScuNET model: {file}", exc_info=True)
+ if add_model2:
+ scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
+ scalers.append(scaler_data2)
+ self.scalers = scalers
+
+ @staticmethod
+ @torch.no_grad()
+ def tiled_inference(img, model):
+ # test the image tile by tile
+ h, w = img.shape[2:]
+ tile = opts.SCUNET_tile
+ tile_overlap = opts.SCUNET_tile_overlap
+ if tile == 0:
+ return model(img)
+
+ device = devices.get_device_for('scunet')
+ assert tile % 8 == 0, "tile size should be a multiple of window_size"
+ sf = 1
+
+ stride = tile - tile_overlap
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+ E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+ for h_idx in h_idx_list:
+
+ for w_idx in w_idx_list:
+
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+
+ out_patch = model(in_patch)
+ out_patch_mask = torch.ones_like(out_patch)
+
+ E[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch)
+ W[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch_mask)
+ pbar.update(1)
+ output = E.div_(W)
+
+ return output
+
+ def do_upscale(self, img: PIL.Image.Image, selected_file):
+
+ devices.torch_gc()
+
+ try:
+ model = self.load_model(selected_file)
+ except Exception as e:
+ print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
+ return img
+
+ device = devices.get_device_for('scunet')
+ tile = opts.SCUNET_tile
+ h, w = img.height, img.width
+ np_img = np.array(img)
+ np_img = np_img[:, :, ::-1] # RGB to BGR
+ np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
+ torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
+
+ if tile > h or tile > w:
+ _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
+ _img[:, :, :h, :w] = torch_img # pad image
+ torch_img = _img
+
+ torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+ torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+ np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+ del torch_img, torch_output
+ devices.torch_gc()
+
+ output = np_output.transpose((1, 2, 0)) # CHW to HWC
+ output = output[:, :, ::-1] # BGR to RGB
+ return PIL.Image.fromarray((output * 255).astype(np.uint8))
+
+ def load_model(self, path: str):
+ device = devices.get_device_for('scunet')
+ if path.startswith("http"):
+ # TODO: this doesn't use `path` at all?
+ filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
+ else:
+ filename = path
+ model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+ model.load_state_dict(torch.load(filename), strict=True)
+ model.eval()
+ for _, v in model.named_parameters():
+ v.requires_grad = False
+ model = model.to(device)
+
+ return model
+
+
+def on_ui_settings():
+ import gradio as gr
+ from modules import shared
+
+ shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
+ shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/stable-diffusion-webui/extensions-builtin/ScuNET/scunet_model_arch.py b/stable-diffusion-webui/extensions-builtin/ScuNET/scunet_model_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..b51a880629baa492ffcbebe682bcf101f06699a6
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/ScuNET/scunet_model_arch.py
@@ -0,0 +1,268 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+from timm.models.layers import trunc_normal_, DropPath
+
+
+class WMSA(nn.Module):
+ """ Self-attention module in Swin Transformer
+ """
+
+ def __init__(self, input_dim, output_dim, head_dim, window_size, type):
+ super(WMSA, self).__init__()
+ self.input_dim = input_dim
+ self.output_dim = output_dim
+ self.head_dim = head_dim
+ self.scale = self.head_dim ** -0.5
+ self.n_heads = input_dim // head_dim
+ self.window_size = window_size
+ self.type = type
+ self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
+
+ self.relative_position_params = nn.Parameter(
+ torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
+
+ self.linear = nn.Linear(self.input_dim, self.output_dim)
+
+ trunc_normal_(self.relative_position_params, std=.02)
+ self.relative_position_params = torch.nn.Parameter(
+ self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
+ 2).transpose(
+ 0, 1))
+
+ def generate_mask(self, h, w, p, shift):
+ """ generating the mask of SW-MSA
+ Args:
+ shift: shift parameters in CyclicShift.
+ Returns:
+ attn_mask: should be (1 1 w p p),
+ """
+ # supporting square.
+ attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
+ if self.type == 'W':
+ return attn_mask
+
+ s = p - shift
+ attn_mask[-1, :, :s, :, s:, :] = True
+ attn_mask[-1, :, s:, :, :s, :] = True
+ attn_mask[:, -1, :, :s, :, s:] = True
+ attn_mask[:, -1, :, s:, :, :s] = True
+ attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
+ return attn_mask
+
+ def forward(self, x):
+ """ Forward pass of Window Multi-head Self-attention module.
+ Args:
+ x: input tensor with shape of [b h w c];
+ attn_mask: attention mask, fill -inf where the value is True;
+ Returns:
+ output: tensor shape [b h w c]
+ """
+ if self.type != 'W':
+ x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+
+ x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
+ h_windows = x.size(1)
+ w_windows = x.size(2)
+ # square validation
+ # assert h_windows == w_windows
+
+ x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
+ qkv = self.embedding_layer(x)
+ q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
+ sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
+ # Adding learnable relative embedding
+ sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
+ # Using Attn Mask to distinguish different subwindows.
+ if self.type != 'W':
+ attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
+ sim = sim.masked_fill_(attn_mask, float("-inf"))
+
+ probs = nn.functional.softmax(sim, dim=-1)
+ output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
+ output = rearrange(output, 'h b w p c -> b w p (h c)')
+ output = self.linear(output)
+ output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
+
+ if self.type != 'W':
+ output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
+
+ return output
+
+ def relative_embedding(self):
+ cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
+ relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
+ # negative is allowed
+ return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
+
+
+class Block(nn.Module):
+ def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+ """ SwinTransformer Block
+ """
+ super(Block, self).__init__()
+ self.input_dim = input_dim
+ self.output_dim = output_dim
+ assert type in ['W', 'SW']
+ self.type = type
+ if input_resolution <= window_size:
+ self.type = 'W'
+
+ self.ln1 = nn.LayerNorm(input_dim)
+ self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.ln2 = nn.LayerNorm(input_dim)
+ self.mlp = nn.Sequential(
+ nn.Linear(input_dim, 4 * input_dim),
+ nn.GELU(),
+ nn.Linear(4 * input_dim, output_dim),
+ )
+
+ def forward(self, x):
+ x = x + self.drop_path(self.msa(self.ln1(x)))
+ x = x + self.drop_path(self.mlp(self.ln2(x)))
+ return x
+
+
+class ConvTransBlock(nn.Module):
+ def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
+ """ SwinTransformer and Conv Block
+ """
+ super(ConvTransBlock, self).__init__()
+ self.conv_dim = conv_dim
+ self.trans_dim = trans_dim
+ self.head_dim = head_dim
+ self.window_size = window_size
+ self.drop_path = drop_path
+ self.type = type
+ self.input_resolution = input_resolution
+
+ assert self.type in ['W', 'SW']
+ if self.input_resolution <= self.window_size:
+ self.type = 'W'
+
+ self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
+ self.type, self.input_resolution)
+ self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+ self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
+
+ self.conv_block = nn.Sequential(
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
+ nn.ReLU(True),
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
+ )
+
+ def forward(self, x):
+ conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
+ conv_x = self.conv_block(conv_x) + conv_x
+ trans_x = Rearrange('b c h w -> b h w c')(trans_x)
+ trans_x = self.trans_block(trans_x)
+ trans_x = Rearrange('b h w c -> b c h w')(trans_x)
+ res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
+ x = x + res
+
+ return x
+
+
+class SCUNet(nn.Module):
+ # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
+ def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
+ super(SCUNet, self).__init__()
+ if config is None:
+ config = [2, 2, 2, 2, 2, 2, 2]
+ self.config = config
+ self.dim = dim
+ self.head_dim = 32
+ self.window_size = 8
+
+ # drop path rate for each layer
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
+
+ self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
+
+ begin = 0
+ self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution)
+ for i in range(config[0])] + \
+ [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
+
+ begin += config[0]
+ self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
+ for i in range(config[1])] + \
+ [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
+
+ begin += config[1]
+ self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
+ for i in range(config[2])] + \
+ [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
+
+ begin += config[2]
+ self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 8)
+ for i in range(config[3])]
+
+ begin += config[3]
+ self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
+ [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
+ for i in range(config[4])]
+
+ begin += config[4]
+ self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
+ [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
+ for i in range(config[5])]
+
+ begin += config[5]
+ self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
+ [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
+ 'W' if not i % 2 else 'SW', input_resolution)
+ for i in range(config[6])]
+
+ self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
+
+ self.m_head = nn.Sequential(*self.m_head)
+ self.m_down1 = nn.Sequential(*self.m_down1)
+ self.m_down2 = nn.Sequential(*self.m_down2)
+ self.m_down3 = nn.Sequential(*self.m_down3)
+ self.m_body = nn.Sequential(*self.m_body)
+ self.m_up3 = nn.Sequential(*self.m_up3)
+ self.m_up2 = nn.Sequential(*self.m_up2)
+ self.m_up1 = nn.Sequential(*self.m_up1)
+ self.m_tail = nn.Sequential(*self.m_tail)
+ # self.apply(self._init_weights)
+
+ def forward(self, x0):
+
+ h, w = x0.size()[-2:]
+ paddingBottom = int(np.ceil(h / 64) * 64 - h)
+ paddingRight = int(np.ceil(w / 64) * 64 - w)
+ x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
+
+ x1 = self.m_head(x0)
+ x2 = self.m_down1(x1)
+ x3 = self.m_down2(x2)
+ x4 = self.m_down3(x3)
+ x = self.m_body(x4)
+ x = self.m_up3(x + x4)
+ x = self.m_up2(x + x3)
+ x = self.m_up1(x + x2)
+ x = self.m_tail(x + x1)
+
+ x = x[..., :h, :w]
+
+ return x
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py b/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py
new file mode 100644
index 0000000000000000000000000000000000000000..e912c6402bc80faa797cf2e95183101fb9a10286
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/preload.py
@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+ parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/swinir_model.py b/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/swinir_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae0d0e6a8ea04f3054c1e8e5baefd2f76b57f246
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -0,0 +1,192 @@
+import sys
+import platform
+
+import numpy as np
+import torch
+from PIL import Image
+from tqdm import tqdm
+
+from modules import modelloader, devices, script_callbacks, shared
+from modules.shared import opts, state
+from swinir_model_arch import SwinIR
+from swinir_model_arch_v2 import Swin2SR
+from modules.upscaler import Upscaler, UpscalerData
+
+SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
+
+device_swinir = devices.get_device_for('swinir')
+
+
+class UpscalerSwinIR(Upscaler):
+ def __init__(self, dirname):
+ self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs
+ self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings
+ self.name = "SwinIR"
+ self.model_url = SWINIR_MODEL_URL
+ self.model_name = "SwinIR 4x"
+ self.user_path = dirname
+ super().__init__()
+ scalers = []
+ model_files = self.find_models(ext_filter=[".pt", ".pth"])
+ for model in model_files:
+ if model.startswith("http"):
+ name = self.model_name
+ else:
+ name = modelloader.friendly_name(model)
+ model_data = UpscalerData(name, model, self)
+ scalers.append(model_data)
+ self.scalers = scalers
+
+ def do_upscale(self, img, model_file):
+ use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
+ and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
+ current_config = (model_file, opts.SWIN_tile)
+
+ if use_compile and self._cached_model_config == current_config:
+ model = self._cached_model
+ else:
+ self._cached_model = None
+ try:
+ model = self.load_model(model_file)
+ except Exception as e:
+ print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
+ return img
+ model = model.to(device_swinir, dtype=devices.dtype)
+ if use_compile:
+ model = torch.compile(model)
+ self._cached_model = model
+ self._cached_model_config = current_config
+ img = upscale(img, model)
+ devices.torch_gc()
+ return img
+
+ def load_model(self, path, scale=4):
+ if path.startswith("http"):
+ filename = modelloader.load_file_from_url(
+ url=path,
+ model_dir=self.model_download_path,
+ file_name=f"{self.model_name.replace(' ', '_')}.pth",
+ )
+ else:
+ filename = path
+ if filename.endswith(".v2.pth"):
+ model = Swin2SR(
+ upscale=scale,
+ in_chans=3,
+ img_size=64,
+ window_size=8,
+ img_range=1.0,
+ depths=[6, 6, 6, 6, 6, 6],
+ embed_dim=180,
+ num_heads=[6, 6, 6, 6, 6, 6],
+ mlp_ratio=2,
+ upsampler="nearest+conv",
+ resi_connection="1conv",
+ )
+ params = None
+ else:
+ model = SwinIR(
+ upscale=scale,
+ in_chans=3,
+ img_size=64,
+ window_size=8,
+ img_range=1.0,
+ depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
+ embed_dim=240,
+ num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
+ mlp_ratio=2,
+ upsampler="nearest+conv",
+ resi_connection="3conv",
+ )
+ params = "params_ema"
+
+ pretrained_model = torch.load(filename)
+ if params is not None:
+ model.load_state_dict(pretrained_model[params], strict=True)
+ else:
+ model.load_state_dict(pretrained_model, strict=True)
+ return model
+
+
+def upscale(
+ img,
+ model,
+ tile=None,
+ tile_overlap=None,
+ window_size=8,
+ scale=4,
+):
+ tile = tile or opts.SWIN_tile
+ tile_overlap = tile_overlap or opts.SWIN_tile_overlap
+
+
+ img = np.array(img)
+ img = img[:, :, ::-1]
+ img = np.moveaxis(img, 2, 0) / 255
+ img = torch.from_numpy(img).float()
+ img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
+ with torch.no_grad(), devices.autocast():
+ _, _, h_old, w_old = img.size()
+ h_pad = (h_old // window_size + 1) * window_size - h_old
+ w_pad = (w_old // window_size + 1) * window_size - w_old
+ img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
+ img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
+ output = inference(img, model, tile, tile_overlap, window_size, scale)
+ output = output[..., : h_old * scale, : w_old * scale]
+ output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+ if output.ndim == 3:
+ output = np.transpose(
+ output[[2, 1, 0], :, :], (1, 2, 0)
+ ) # CHW-RGB to HCW-BGR
+ output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
+ return Image.fromarray(output, "RGB")
+
+
+def inference(img, model, tile, tile_overlap, window_size, scale):
+ # test the image tile by tile
+ b, c, h, w = img.size()
+ tile = min(tile, h, w)
+ assert tile % window_size == 0, "tile size should be a multiple of window_size"
+ sf = scale
+
+ stride = tile - tile_overlap
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+ E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
+
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+ for h_idx in h_idx_list:
+ if state.interrupted or state.skipped:
+ break
+
+ for w_idx in w_idx_list:
+ if state.interrupted or state.skipped:
+ break
+
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+ out_patch = model(in_patch)
+ out_patch_mask = torch.ones_like(out_patch)
+
+ E[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch)
+ W[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch_mask)
+ pbar.update(1)
+ output = E.div_(W)
+
+ return output
+
+
+def on_ui_settings():
+ import gradio as gr
+
+ shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
+ shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
+ if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows
+ shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..93b9327473a6e77c3a3dc6a7743e932c9083a996
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch.py
@@ -0,0 +1,867 @@
+# -----------------------------------------------------------------------------------
+# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
+# Originally Written by Ze Liu, Modified by Jingyun Liang.
+# -----------------------------------------------------------------------------------
+
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+
+class WindowAttention(nn.Module):
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim ** -0.5
+
+ # define a parameter table of relative position bias
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ trunc_normal_(self.relative_position_bias_table, std=.02)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ q = q * self.scale
+ attn = (q @ k.transpose(-2, -1))
+
+ relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
+
+ def flops(self, N):
+ # calculate flops for 1 window with token length of N
+ flops = 0
+ # qkv = self.qkv(x)
+ flops += N * self.dim * 3 * self.dim
+ # attn = (q @ k.transpose(-2, -1))
+ flops += self.num_heads * N * (self.dim // self.num_heads) * N
+ # x = (attn @ v)
+ flops += self.num_heads * N * N * (self.dim // self.num_heads)
+ # x = self.proj(x)
+ flops += N * self.dim * self.dim
+ return flops
+
+
+class SwinTransformerBlock(nn.Module):
+ r""" Swin Transformer Block.
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ if min(self.input_resolution) <= self.window_size:
+ # if window size is larger than input resolution, we don't partition windows
+ self.shift_size = 0
+ self.window_size = min(self.input_resolution)
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+ qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ if self.shift_size > 0:
+ attn_mask = self.calculate_mask(self.input_resolution)
+ else:
+ attn_mask = None
+
+ self.register_buffer("attn_mask", attn_mask)
+
+ def calculate_mask(self, x_size):
+ # calculate attention mask for SW-MSA
+ H, W = x_size
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
+ h_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ w_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+ return attn_mask
+
+ def forward(self, x, x_size):
+ H, W = x_size
+ B, L, C = x.shape
+ # assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = self.norm1(x)
+ x = x.view(B, H, W, C)
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ else:
+ shifted_x = x
+
+ # partition windows
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+ if self.input_resolution == x_size:
+ attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
+ else:
+ attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+ x = x.view(B, H * W, C)
+
+ # FFN
+ x = shortcut + self.drop_path(x)
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+ def flops(self):
+ flops = 0
+ H, W = self.input_resolution
+ # norm1
+ flops += self.dim * H * W
+ # W-MSA/SW-MSA
+ nW = H * W / self.window_size / self.window_size
+ flops += nW * self.attn.flops(self.window_size * self.window_size)
+ # mlp
+ flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+ # norm2
+ flops += self.dim * H * W
+ return flops
+
+
+class PatchMerging(nn.Module):
+ r""" Patch Merging Layer.
+
+ Args:
+ input_resolution (tuple[int]): Resolution of input feature.
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.input_resolution = input_resolution
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(4 * dim)
+
+ def forward(self, x):
+ """
+ x: B, H*W, C
+ """
+ H, W = self.input_resolution
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+ assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+ x = x.view(B, H, W, C)
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.norm(x)
+ x = self.reduction(x)
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = H * W * self.dim
+ flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+ return flops
+
+
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+ num_heads=num_heads, window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer)
+ for i in range(depth)])
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, x_size):
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, x_size)
+ else:
+ x = blk(x, x_size)
+ if self.downsample is not None:
+ x = self.downsample(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+ def flops(self):
+ flops = 0
+ for blk in self.blocks:
+ flops += blk.flops()
+ if self.downsample is not None:
+ flops += self.downsample.flops()
+ return flops
+
+
+class RSTB(nn.Module):
+ """Residual Swin Transformer Block (RSTB).
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ img_size: Input image size.
+ patch_size: Patch size.
+ resi_connection: The convolutional block before residual connection.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ img_size=224, patch_size=4, resi_connection='1conv'):
+ super(RSTB, self).__init__()
+
+ self.dim = dim
+ self.input_resolution = input_resolution
+
+ self.residual_group = BasicLayer(dim=dim,
+ input_resolution=input_resolution,
+ depth=depth,
+ num_heads=num_heads,
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path,
+ norm_layer=norm_layer,
+ downsample=downsample,
+ use_checkpoint=use_checkpoint)
+
+ if resi_connection == '1conv':
+ self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+ norm_layer=None)
+
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
+ norm_layer=None)
+
+ def forward(self, x, x_size):
+ return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+ def flops(self):
+ flops = 0
+ flops += self.residual_group.flops()
+ H, W = self.input_resolution
+ flops += H * W * self.dim * self.dim * 9
+ flops += self.patch_embed.flops()
+ flops += self.patch_unembed.flops()
+
+ return flops
+
+
+class PatchEmbed(nn.Module):
+ r""" Image to Patch Embedding
+
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
+ if self.norm is not None:
+ x = self.norm(x)
+ return x
+
+ def flops(self):
+ flops = 0
+ H, W = self.img_size
+ if self.norm is not None:
+ flops += H * W * self.embed_dim
+ return flops
+
+
+class PatchUnEmbed(nn.Module):
+ r""" Image to Patch Unembedding
+
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ def forward(self, x, x_size):
+ B, HW, C = x.shape
+ x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
+ return x
+
+ def flops(self):
+ flops = 0
+ return flops
+
+
+class Upsample(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+ super(Upsample, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+ """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+ Used in lightweight SR to save parameters.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+
+ """
+
+ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+ self.num_feat = num_feat
+ self.input_resolution = input_resolution
+ m = []
+ m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+ m.append(nn.PixelShuffle(scale))
+ super(UpsampleOneStep, self).__init__(*m)
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = H * W * self.num_feat * 3 * 9
+ return flops
+
+
+class SwinIR(nn.Module):
+ r""" SwinIR
+ A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
+
+ Args:
+ img_size (int | tuple(int)): Input image size. Default 64
+ patch_size (int | tuple(int)): Patch size. Default: 1
+ in_chans (int): Number of input image channels. Default: 3
+ embed_dim (int): Patch embedding dimension. Default: 96
+ depths (tuple(int)): Depth of each Swin Transformer layer.
+ num_heads (tuple(int)): Number of attention heads in different layers.
+ window_size (int): Window size. Default: 7
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
+ drop_rate (float): Dropout rate. Default: 0
+ attn_drop_rate (float): Attention dropout rate. Default: 0
+ drop_path_rate (float): Stochastic depth rate. Default: 0.1
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+ upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+ img_range: Image range. 1. or 255.
+ upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+ resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+ """
+
+ def __init__(self, img_size=64, patch_size=1, in_chans=3,
+ embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
+ window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+ norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+ use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+ **kwargs):
+ super(SwinIR, self).__init__()
+ num_in_ch = in_chans
+ num_out_ch = in_chans
+ num_feat = 64
+ self.img_range = img_range
+ if in_chans == 3:
+ rgb_mean = (0.4488, 0.4371, 0.4040)
+ self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+ else:
+ self.mean = torch.zeros(1, 1, 1, 1)
+ self.upscale = upscale
+ self.upsampler = upsampler
+ self.window_size = window_size
+
+ #####################################################################################################
+ ################################### 1, shallow feature extraction ###################################
+ self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+ #####################################################################################################
+ ################################### 2, deep feature extraction ######################################
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.num_features = embed_dim
+ self.mlp_ratio = mlp_ratio
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+ num_patches = self.patch_embed.num_patches
+ patches_resolution = self.patch_embed.patches_resolution
+ self.patches_resolution = patches_resolution
+
+ # merge non-overlapping patches into image
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+
+ # absolute position embedding
+ if self.ape:
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+ trunc_normal_(self.absolute_pos_embed, std=.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
+
+ # build Residual Swin Transformer blocks (RSTB)
+ self.layers = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers.append(layer)
+ self.norm = norm_layer(self.num_features)
+
+ # build the last conv layer in deep feature extraction
+ if resi_connection == '1conv':
+ self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+ #####################################################################################################
+ ################################ 3, high quality image reconstruction ################################
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR (to save parameters)
+ self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+ (patches_resolution[0], patches_resolution[1]))
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR (less artifacts)
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ if self.upscale == 4:
+ self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ @torch.jit.ignore
+ def no_weight_decay(self):
+ return {'absolute_pos_embed'}
+
+ @torch.jit.ignore
+ def no_weight_decay_keywords(self):
+ return {'relative_position_bias_table'}
+
+ def check_image_size(self, x):
+ _, _, h, w = x.size()
+ mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+ mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+ x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+ return x
+
+ def forward_features(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward(self, x):
+ H, W = x.shape[2:]
+ x = self.check_image_size(x)
+
+ self.mean = self.mean.type_as(x)
+ x = (x - self.mean) * self.img_range
+
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.conv_last(self.upsample(x))
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.upsample(x)
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ if self.upscale == 4:
+ x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.conv_last(self.lrelu(self.conv_hr(x)))
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ x_first = self.conv_first(x)
+ res = self.conv_after_body(self.forward_features(x_first)) + x_first
+ x = x + self.conv_last(res)
+
+ x = x / self.img_range + self.mean
+
+ return x[:, :, :H*self.upscale, :W*self.upscale]
+
+ def flops(self):
+ flops = 0
+ H, W = self.patches_resolution
+ flops += H * W * 3 * self.embed_dim * 9
+ flops += self.patch_embed.flops()
+ for layer in self.layers:
+ flops += layer.flops()
+ flops += H * W * 3 * self.embed_dim * self.embed_dim
+ flops += self.upsample.flops()
+ return flops
+
+
+if __name__ == '__main__':
+ upscale = 4
+ window_size = 8
+ height = (1024 // upscale // window_size + 1) * window_size
+ width = (720 // upscale // window_size + 1) * window_size
+ model = SwinIR(upscale=2, img_size=(height, width),
+ window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+ embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+ print(model)
+ print(height, width, model.flops() / 1e9)
+
+ x = torch.randn((1, 3, height, width))
+ x = model(x)
+ print(x.shape)
diff --git a/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..59219f69a9a7f8365628cb2f4f57f5cd0104147a
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/SwinIR/swinir_model_arch_v2.py
@@ -0,0 +1,1017 @@
+# -----------------------------------------------------------------------------------
+# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
+# Written by Conde and Choi et al.
+# -----------------------------------------------------------------------------------
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+class WindowAttention(nn.Module):
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
+ """
+
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
+ pretrained_window_size=(0, 0)):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.pretrained_window_size = pretrained_window_size
+ self.num_heads = num_heads
+
+ self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
+
+ # mlp to generate continuous relative position bias
+ self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
+ nn.ReLU(inplace=True),
+ nn.Linear(512, num_heads, bias=False))
+
+ # get relative_coords_table
+ relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
+ relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
+ relative_coords_table = torch.stack(
+ torch.meshgrid([relative_coords_h,
+ relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
+ if pretrained_window_size[0] > 0:
+ relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
+ relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
+ else:
+ relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
+ relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
+ relative_coords_table *= 8 # normalize to -8, 8
+ relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
+ torch.abs(relative_coords_table) + 1.0) / np.log2(8)
+
+ self.register_buffer("relative_coords_table", relative_coords_table)
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=False)
+ if qkv_bias:
+ self.q_bias = nn.Parameter(torch.zeros(dim))
+ self.v_bias = nn.Parameter(torch.zeros(dim))
+ else:
+ self.q_bias = None
+ self.v_bias = None
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv_bias = None
+ if self.q_bias is not None:
+ qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+ qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+ qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ # cosine attention
+ attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
+ logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
+ attn = attn * logit_scale
+
+ relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
+ relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
+ relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f'dim={self.dim}, window_size={self.window_size}, ' \
+ f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
+
+ def flops(self, N):
+ # calculate flops for 1 window with token length of N
+ flops = 0
+ # qkv = self.qkv(x)
+ flops += N * self.dim * 3 * self.dim
+ # attn = (q @ k.transpose(-2, -1))
+ flops += self.num_heads * N * (self.dim // self.num_heads) * N
+ # x = (attn @ v)
+ flops += self.num_heads * N * N * (self.dim // self.num_heads)
+ # x = self.proj(x)
+ flops += N * self.dim * self.dim
+ return flops
+
+class SwinTransformerBlock(nn.Module):
+ r""" Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resulotion.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ pretrained_window_size (int): Window size in pre-training.
+ """
+
+ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ if min(self.input_resolution) <= self.window_size:
+ # if window size is larger than input resolution, we don't partition windows
+ self.shift_size = 0
+ self.window_size = min(self.input_resolution)
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+ qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
+ pretrained_window_size=to_2tuple(pretrained_window_size))
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ if self.shift_size > 0:
+ attn_mask = self.calculate_mask(self.input_resolution)
+ else:
+ attn_mask = None
+
+ self.register_buffer("attn_mask", attn_mask)
+
+ def calculate_mask(self, x_size):
+ # calculate attention mask for SW-MSA
+ H, W = x_size
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
+ h_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ w_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+ return attn_mask
+
+ def forward(self, x, x_size):
+ H, W = x_size
+ B, L, C = x.shape
+ #assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = x.view(B, H, W, C)
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ else:
+ shifted_x = x
+
+ # partition windows
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+ if self.input_resolution == x_size:
+ attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
+ else:
+ attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+ x = x.view(B, H * W, C)
+ x = shortcut + self.drop_path(self.norm1(x))
+
+ # FFN
+ x = x + self.drop_path(self.norm2(self.mlp(x)))
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
+ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
+
+ def flops(self):
+ flops = 0
+ H, W = self.input_resolution
+ # norm1
+ flops += self.dim * H * W
+ # W-MSA/SW-MSA
+ nW = H * W / self.window_size / self.window_size
+ flops += nW * self.attn.flops(self.window_size * self.window_size)
+ # mlp
+ flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
+ # norm2
+ flops += self.dim * H * W
+ return flops
+
+class PatchMerging(nn.Module):
+ r""" Patch Merging Layer.
+ Args:
+ input_resolution (tuple[int]): Resolution of input feature.
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.input_resolution = input_resolution
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(2 * dim)
+
+ def forward(self, x):
+ """
+ x: B, H*W, C
+ """
+ H, W = self.input_resolution
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+ assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
+
+ x = x.view(B, H, W, C)
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.reduction(x)
+ x = self.norm(x)
+
+ return x
+
+ def extra_repr(self) -> str:
+ return f"input_resolution={self.input_resolution}, dim={self.dim}"
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
+ flops += H * W * self.dim // 2
+ return flops
+
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ pretrained_window_size (int): Local window size in pre-training.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ pretrained_window_size=0):
+
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+ num_heads=num_heads, window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer,
+ pretrained_window_size=pretrained_window_size)
+ for i in range(depth)])
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, x_size):
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, x_size)
+ else:
+ x = blk(x, x_size)
+ if self.downsample is not None:
+ x = self.downsample(x)
+ return x
+
+ def extra_repr(self) -> str:
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
+
+ def flops(self):
+ flops = 0
+ for blk in self.blocks:
+ flops += blk.flops()
+ if self.downsample is not None:
+ flops += self.downsample.flops()
+ return flops
+
+ def _init_respostnorm(self):
+ for blk in self.blocks:
+ nn.init.constant_(blk.norm1.bias, 0)
+ nn.init.constant_(blk.norm1.weight, 0)
+ nn.init.constant_(blk.norm2.bias, 0)
+ nn.init.constant_(blk.norm2.weight, 0)
+
+class PatchEmbed(nn.Module):
+ r""" Image to Patch Embedding
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ B, C, H, W = x.shape
+ # FIXME look at relaxing size constraints
+ # assert H == self.img_size[0] and W == self.img_size[1],
+ # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+ x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
+ if self.norm is not None:
+ x = self.norm(x)
+ return x
+
+ def flops(self):
+ Ho, Wo = self.patches_resolution
+ flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+ if self.norm is not None:
+ flops += Ho * Wo * self.embed_dim
+ return flops
+
+class RSTB(nn.Module):
+ """Residual Swin Transformer Block (RSTB).
+
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ img_size: Input image size.
+ patch_size: Patch size.
+ resi_connection: The convolutional block before residual connection.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
+ img_size=224, patch_size=4, resi_connection='1conv'):
+ super(RSTB, self).__init__()
+
+ self.dim = dim
+ self.input_resolution = input_resolution
+
+ self.residual_group = BasicLayer(dim=dim,
+ input_resolution=input_resolution,
+ depth=depth,
+ num_heads=num_heads,
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path,
+ norm_layer=norm_layer,
+ downsample=downsample,
+ use_checkpoint=use_checkpoint)
+
+ if resi_connection == '1conv':
+ self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(dim // 4, dim, 3, 1, 1))
+
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+ norm_layer=None)
+
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
+ norm_layer=None)
+
+ def forward(self, x, x_size):
+ return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
+
+ def flops(self):
+ flops = 0
+ flops += self.residual_group.flops()
+ H, W = self.input_resolution
+ flops += H * W * self.dim * self.dim * 9
+ flops += self.patch_embed.flops()
+ flops += self.patch_unembed.flops()
+
+ return flops
+
+class PatchUnEmbed(nn.Module):
+ r""" Image to Patch Unembedding
+
+ Args:
+ img_size (int): Image size. Default: 224.
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ img_size = to_2tuple(img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
+ self.img_size = img_size
+ self.patch_size = patch_size
+ self.patches_resolution = patches_resolution
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ def forward(self, x, x_size):
+ B, HW, C = x.shape
+ x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
+ return x
+
+ def flops(self):
+ flops = 0
+ return flops
+
+
+class Upsample(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+ super(Upsample, self).__init__(*m)
+
+class Upsample_hf(nn.Sequential):
+ """Upsample module.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+ """
+
+ def __init__(self, scale, num_feat):
+ m = []
+ if (scale & (scale - 1)) == 0: # scale = 2^n
+ for _ in range(int(math.log(scale, 2))):
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(2))
+ elif scale == 3:
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
+ m.append(nn.PixelShuffle(3))
+ else:
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
+ super(Upsample_hf, self).__init__(*m)
+
+
+class UpsampleOneStep(nn.Sequential):
+ """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
+ Used in lightweight SR to save parameters.
+
+ Args:
+ scale (int): Scale factor. Supported scales: 2^n and 3.
+ num_feat (int): Channel number of intermediate features.
+
+ """
+
+ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
+ self.num_feat = num_feat
+ self.input_resolution = input_resolution
+ m = []
+ m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
+ m.append(nn.PixelShuffle(scale))
+ super(UpsampleOneStep, self).__init__(*m)
+
+ def flops(self):
+ H, W = self.input_resolution
+ flops = H * W * self.num_feat * 3 * 9
+ return flops
+
+
+
+class Swin2SR(nn.Module):
+ r""" Swin2SR
+ A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
+
+ Args:
+ img_size (int | tuple(int)): Input image size. Default 64
+ patch_size (int | tuple(int)): Patch size. Default: 1
+ in_chans (int): Number of input image channels. Default: 3
+ embed_dim (int): Patch embedding dimension. Default: 96
+ depths (tuple(int)): Depth of each Swin Transformer layer.
+ num_heads (tuple(int)): Number of attention heads in different layers.
+ window_size (int): Window size. Default: 7
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ drop_rate (float): Dropout rate. Default: 0
+ attn_drop_rate (float): Attention dropout rate. Default: 0
+ drop_path_rate (float): Stochastic depth rate. Default: 0.1
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
+ upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
+ img_range: Image range. 1. or 255.
+ upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
+ resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
+ """
+
+ def __init__(self, img_size=64, patch_size=1, in_chans=3,
+ embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
+ window_size=7, mlp_ratio=4., qkv_bias=True,
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+ norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
+ use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
+ **kwargs):
+ super(Swin2SR, self).__init__()
+ num_in_ch = in_chans
+ num_out_ch = in_chans
+ num_feat = 64
+ self.img_range = img_range
+ if in_chans == 3:
+ rgb_mean = (0.4488, 0.4371, 0.4040)
+ self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
+ else:
+ self.mean = torch.zeros(1, 1, 1, 1)
+ self.upscale = upscale
+ self.upsampler = upsampler
+ self.window_size = window_size
+
+ #####################################################################################################
+ ################################### 1, shallow feature extraction ###################################
+ self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
+
+ #####################################################################################################
+ ################################### 2, deep feature extraction ######################################
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.num_features = embed_dim
+ self.mlp_ratio = mlp_ratio
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+ num_patches = self.patch_embed.num_patches
+ patches_resolution = self.patch_embed.patches_resolution
+ self.patches_resolution = patches_resolution
+
+ # merge non-overlapping patches into image
+ self.patch_unembed = PatchUnEmbed(
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None)
+
+ # absolute position embedding
+ if self.ape:
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+ trunc_normal_(self.absolute_pos_embed, std=.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
+
+ # build Residual Swin Transformer blocks (RSTB)
+ self.layers = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers.append(layer)
+
+ if self.upsampler == 'pixelshuffle_hf':
+ self.layers_hf = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = RSTB(dim=embed_dim,
+ input_resolution=(patches_resolution[0],
+ patches_resolution[1]),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
+ norm_layer=norm_layer,
+ downsample=None,
+ use_checkpoint=use_checkpoint,
+ img_size=img_size,
+ patch_size=patch_size,
+ resi_connection=resi_connection
+
+ )
+ self.layers_hf.append(layer)
+
+ self.norm = norm_layer(self.num_features)
+
+ # build the last conv layer in deep feature extraction
+ if resi_connection == '1conv':
+ self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ elif resi_connection == '3conv':
+ # to save parameters and memory
+ self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
+
+ #####################################################################################################
+ ################################ 3, high quality image reconstruction ################################
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ elif self.upsampler == 'pixelshuffle_aux':
+ self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+ self.conv_before_upsample = nn.Sequential(
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.conv_after_aux = nn.Sequential(
+ nn.Conv2d(3, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+ elif self.upsampler == 'pixelshuffle_hf':
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.upsample = Upsample(upscale, num_feat)
+ self.upsample_hf = Upsample_hf(upscale, num_feat)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
+ self.conv_before_upsample_hf = nn.Sequential(
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR (to save parameters)
+ self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
+ (patches_resolution[0], patches_resolution[1]))
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR (less artifacts)
+ assert self.upscale == 4, 'only support x4 now.'
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
+ nn.LeakyReLU(inplace=True))
+ self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
+
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ @torch.jit.ignore
+ def no_weight_decay(self):
+ return {'absolute_pos_embed'}
+
+ @torch.jit.ignore
+ def no_weight_decay_keywords(self):
+ return {'relative_position_bias_table'}
+
+ def check_image_size(self, x):
+ _, _, h, w = x.size()
+ mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
+ mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
+ x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
+ return x
+
+ def forward_features(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward_features_hf(self, x):
+ x_size = (x.shape[2], x.shape[3])
+ x = self.patch_embed(x)
+ if self.ape:
+ x = x + self.absolute_pos_embed
+ x = self.pos_drop(x)
+
+ for layer in self.layers_hf:
+ x = layer(x, x_size)
+
+ x = self.norm(x) # B L C
+ x = self.patch_unembed(x, x_size)
+
+ return x
+
+ def forward(self, x):
+ H, W = x.shape[2:]
+ x = self.check_image_size(x)
+
+ self.mean = self.mean.type_as(x)
+ x = (x - self.mean) * self.img_range
+
+ if self.upsampler == 'pixelshuffle':
+ # for classical SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.conv_last(self.upsample(x))
+ elif self.upsampler == 'pixelshuffle_aux':
+ bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
+ bicubic = self.conv_bicubic(bicubic)
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ aux = self.conv_aux(x) # b, 3, LR_H, LR_W
+ x = self.conv_after_aux(aux)
+ x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
+ x = self.conv_last(x)
+ aux = aux / self.img_range + self.mean
+ elif self.upsampler == 'pixelshuffle_hf':
+ # for classical SR with HF
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x_before = self.conv_before_upsample(x)
+ x_out = self.conv_last(self.upsample(x_before))
+
+ x_hf = self.conv_first_hf(x_before)
+ x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
+ x_hf = self.conv_before_upsample_hf(x_hf)
+ x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
+ x = x_out + x_hf
+ x_hf = x_hf / self.img_range + self.mean
+
+ elif self.upsampler == 'pixelshuffledirect':
+ # for lightweight SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.upsample(x)
+ elif self.upsampler == 'nearest+conv':
+ # for real-world SR
+ x = self.conv_first(x)
+ x = self.conv_after_body(self.forward_features(x)) + x
+ x = self.conv_before_upsample(x)
+ x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
+ x = self.conv_last(self.lrelu(self.conv_hr(x)))
+ else:
+ # for image denoising and JPEG compression artifact reduction
+ x_first = self.conv_first(x)
+ res = self.conv_after_body(self.forward_features(x_first)) + x_first
+ x = x + self.conv_last(res)
+
+ x = x / self.img_range + self.mean
+ if self.upsampler == "pixelshuffle_aux":
+ return x[:, :, :H*self.upscale, :W*self.upscale], aux
+
+ elif self.upsampler == "pixelshuffle_hf":
+ x_out = x_out / self.img_range + self.mean
+ return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
+
+ else:
+ return x[:, :, :H*self.upscale, :W*self.upscale]
+
+ def flops(self):
+ flops = 0
+ H, W = self.patches_resolution
+ flops += H * W * 3 * self.embed_dim * 9
+ flops += self.patch_embed.flops()
+ for layer in self.layers:
+ flops += layer.flops()
+ flops += H * W * 3 * self.embed_dim * self.embed_dim
+ flops += self.upsample.flops()
+ return flops
+
+
+if __name__ == '__main__':
+ upscale = 4
+ window_size = 8
+ height = (1024 // upscale // window_size + 1) * window_size
+ width = (720 // upscale // window_size + 1) * window_size
+ model = Swin2SR(upscale=2, img_size=(height, width),
+ window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
+ embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
+ print(model)
+ print(height, width, model.flops() / 1e9)
+
+ x = torch.randn((1, 3, height, width))
+ x = model(x)
+ print(x.shape)
diff --git a/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
new file mode 100644
index 0000000000000000000000000000000000000000..45c7600ac5f81bc8c7b233162d73f6551c8b5e8d
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js
@@ -0,0 +1,962 @@
+onUiLoaded(async() => {
+ const elementIDs = {
+ img2imgTabs: "#mode_img2img .tab-nav",
+ inpaint: "#img2maskimg",
+ inpaintSketch: "#inpaint_sketch",
+ rangeGroup: "#img2img_column_size",
+ sketch: "#img2img_sketch"
+ };
+ const tabNameToElementId = {
+ "Inpaint sketch": elementIDs.inpaintSketch,
+ "Inpaint": elementIDs.inpaint,
+ "Sketch": elementIDs.sketch
+ };
+
+
+ // Helper functions
+ // Get active tab
+
+ /**
+ * Waits for an element to be present in the DOM.
+ */
+ const waitForElement = (id) => new Promise(resolve => {
+ const checkForElement = () => {
+ const element = document.querySelector(id);
+ if (element) return resolve(element);
+ setTimeout(checkForElement, 100);
+ };
+ checkForElement();
+ });
+
+ function getActiveTab(elements, all = false) {
+ const tabs = elements.img2imgTabs.querySelectorAll("button");
+
+ if (all) return tabs;
+
+ for (let tab of tabs) {
+ if (tab.classList.contains("selected")) {
+ return tab;
+ }
+ }
+ }
+
+ // Get tab ID
+ function getTabId(elements) {
+ const activeTab = getActiveTab(elements);
+ return tabNameToElementId[activeTab.innerText];
+ }
+
+ // Wait until opts loaded
+ async function waitForOpts() {
+ for (; ;) {
+ if (window.opts && Object.keys(window.opts).length) {
+ return window.opts;
+ }
+ await new Promise(resolve => setTimeout(resolve, 100));
+ }
+ }
+
+ // Detect whether the element has a horizontal scroll bar
+ function hasHorizontalScrollbar(element) {
+ return element.scrollWidth > element.clientWidth;
+ }
+
+ // Function for defining the "Ctrl", "Shift" and "Alt" keys
+ function isModifierKey(event, key) {
+ switch (key) {
+ case "Ctrl":
+ return event.ctrlKey;
+ case "Shift":
+ return event.shiftKey;
+ case "Alt":
+ return event.altKey;
+ default:
+ return false;
+ }
+ }
+
+ // Check if hotkey is valid
+ function isValidHotkey(value) {
+ const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
+ return (
+ (typeof value === "string" &&
+ value.length === 1 &&
+ /[a-z]/i.test(value)) ||
+ specialKeys.includes(value)
+ );
+ }
+
+ // Normalize hotkey
+ function normalizeHotkey(hotkey) {
+ return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
+ }
+
+ // Format hotkey for display
+ function formatHotkeyForDisplay(hotkey) {
+ return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
+ }
+
+ // Create hotkey configuration with the provided options
+ function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) {
+ const result = {}; // Resulting hotkey configuration
+ const usedKeys = new Set(); // Set of used hotkeys
+
+ // Iterate through defaultHotkeysConfig keys
+ for (const key in defaultHotkeysConfig) {
+ const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value
+ const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value
+
+ // Apply appropriate value for undefined, boolean, or object userValue
+ if (
+ userValue === undefined ||
+ typeof userValue === "boolean" ||
+ typeof userValue === "object" ||
+ userValue === "disable"
+ ) {
+ result[key] =
+ userValue === undefined ? defaultValue : userValue;
+ } else if (isValidHotkey(userValue)) {
+ const normalizedUserValue = normalizeHotkey(userValue);
+
+ // Check for conflicting hotkeys
+ if (!usedKeys.has(normalizedUserValue)) {
+ usedKeys.add(normalizedUserValue);
+ result[key] = normalizedUserValue;
+ } else {
+ console.error(
+ `Hotkey: ${formatHotkeyForDisplay(
+ userValue
+ )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay(
+ defaultValue
+ )}`
+ );
+ result[key] = defaultValue;
+ }
+ } else {
+ console.error(
+ `Hotkey: ${formatHotkeyForDisplay(
+ userValue
+ )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay(
+ defaultValue
+ )}`
+ );
+ result[key] = defaultValue;
+ }
+ }
+
+ return result;
+ }
+
+ // Disables functions in the config object based on the provided list of function names
+ function disableFunctions(config, disabledFunctions) {
+ // Bind the hasOwnProperty method to the functionMap object to avoid errors
+ const hasOwnProperty =
+ Object.prototype.hasOwnProperty.bind(functionMap);
+
+ // Loop through the disabledFunctions array and disable the corresponding functions in the config object
+ disabledFunctions.forEach(funcName => {
+ if (hasOwnProperty(funcName)) {
+ const key = functionMap[funcName];
+ config[key] = "disable";
+ }
+ });
+
+ // Return the updated config object
+ return config;
+ }
+
+ /**
+ * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
+ * If the image display property is set to 'none', the mask breaks. To fix this, the function
+ * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds
+ * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on
+ * very long images.
+ */
+ function restoreImgRedMask(elements) {
+ const mainTabId = getTabId(elements);
+
+ if (!mainTabId) return;
+
+ const mainTab = gradioApp().querySelector(mainTabId);
+ const img = mainTab.querySelector("img");
+ const imageARPreview = gradioApp().querySelector("#imageARPreview");
+
+ if (!img || !imageARPreview) return;
+
+ imageARPreview.style.transform = "";
+ if (parseFloat(mainTab.style.width) > 865) {
+ const transformString = mainTab.style.transform;
+ const scaleMatch = transformString.match(
+ /scale\(([-+]?[0-9]*\.?[0-9]+)\)/
+ );
+ let zoom = 1; // default zoom
+
+ if (scaleMatch && scaleMatch[1]) {
+ zoom = Number(scaleMatch[1]);
+ }
+
+ imageARPreview.style.transformOrigin = "0 0";
+ imageARPreview.style.transform = `scale(${zoom})`;
+ }
+
+ if (img.style.display !== "none") return;
+
+ img.style.display = "block";
+
+ setTimeout(() => {
+ img.style.display = "none";
+ }, 400);
+ }
+
+ const hotkeysConfigOpts = await waitForOpts();
+
+ // Default config
+ const defaultHotkeysConfig = {
+ canvas_hotkey_zoom: "Alt",
+ canvas_hotkey_adjust: "Ctrl",
+ canvas_hotkey_reset: "KeyR",
+ canvas_hotkey_fullscreen: "KeyS",
+ canvas_hotkey_move: "KeyF",
+ canvas_hotkey_overlap: "KeyO",
+ canvas_disabled_functions: [],
+ canvas_show_tooltip: true,
+ canvas_auto_expand: true,
+ canvas_blur_prompt: false,
+ };
+
+ const functionMap = {
+ "Zoom": "canvas_hotkey_zoom",
+ "Adjust brush size": "canvas_hotkey_adjust",
+ "Moving canvas": "canvas_hotkey_move",
+ "Fullscreen": "canvas_hotkey_fullscreen",
+ "Reset Zoom": "canvas_hotkey_reset",
+ "Overlap": "canvas_hotkey_overlap"
+ };
+
+ // Loading the configuration from opts
+ const preHotkeysConfig = createHotkeyConfig(
+ defaultHotkeysConfig,
+ hotkeysConfigOpts
+ );
+
+ // Disable functions that are not needed by the user
+ const hotkeysConfig = disableFunctions(
+ preHotkeysConfig,
+ preHotkeysConfig.canvas_disabled_functions
+ );
+
+ let isMoving = false;
+ let mouseX, mouseY;
+ let activeElement;
+
+ const elements = Object.fromEntries(
+ Object.keys(elementIDs).map(id => [
+ id,
+ gradioApp().querySelector(elementIDs[id])
+ ])
+ );
+ const elemData = {};
+
+ // Apply functionality to the range inputs. Restore redmask and correct for long images.
+ const rangeInputs = elements.rangeGroup ?
+ Array.from(elements.rangeGroup.querySelectorAll("input")) :
+ [
+ gradioApp().querySelector("#img2img_width input[type='range']"),
+ gradioApp().querySelector("#img2img_height input[type='range']")
+ ];
+
+ for (const input of rangeInputs) {
+ input?.addEventListener("input", () => restoreImgRedMask(elements));
+ }
+
+ function applyZoomAndPan(elemId, isExtension = true) {
+ const targetElement = gradioApp().querySelector(elemId);
+
+ if (!targetElement) {
+ console.log("Element not found");
+ return;
+ }
+
+ targetElement.style.transformOrigin = "0 0";
+
+ elemData[elemId] = {
+ zoom: 1,
+ panX: 0,
+ panY: 0
+ };
+ let fullScreenMode = false;
+
+ // Create tooltip
+ function createTooltip() {
+ const toolTipElemnt =
+ targetElement.querySelector(".image-container");
+ const tooltip = document.createElement("div");
+ tooltip.className = "canvas-tooltip";
+
+ // Creating an item of information
+ const info = document.createElement("i");
+ info.className = "canvas-tooltip-info";
+ info.textContent = "";
+
+ // Create a container for the contents of the tooltip
+ const tooltipContent = document.createElement("div");
+ tooltipContent.className = "canvas-tooltip-content";
+
+ // Define an array with hotkey information and their actions
+ const hotkeysInfo = [
+ {
+ configKey: "canvas_hotkey_zoom",
+ action: "Zoom canvas",
+ keySuffix: " + wheel"
+ },
+ {
+ configKey: "canvas_hotkey_adjust",
+ action: "Adjust brush size",
+ keySuffix: " + wheel"
+ },
+ {configKey: "canvas_hotkey_reset", action: "Reset zoom"},
+ {
+ configKey: "canvas_hotkey_fullscreen",
+ action: "Fullscreen mode"
+ },
+ {configKey: "canvas_hotkey_move", action: "Move canvas"},
+ {configKey: "canvas_hotkey_overlap", action: "Overlap"}
+ ];
+
+ // Create hotkeys array with disabled property based on the config values
+ const hotkeys = hotkeysInfo.map(info => {
+ const configValue = hotkeysConfig[info.configKey];
+ const key = info.keySuffix ?
+ `${configValue}${info.keySuffix}` :
+ configValue.charAt(configValue.length - 1);
+ return {
+ key,
+ action: info.action,
+ disabled: configValue === "disable"
+ };
+ });
+
+ for (const hotkey of hotkeys) {
+ if (hotkey.disabled) {
+ continue;
+ }
+
+ const p = document.createElement("p");
+ p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
+ tooltipContent.appendChild(p);
+ }
+
+ // Add information and content elements to the tooltip element
+ tooltip.appendChild(info);
+ tooltip.appendChild(tooltipContent);
+
+ // Add a hint element to the target element
+ toolTipElemnt.appendChild(tooltip);
+ }
+
+ //Show tool tip if setting enable
+ if (hotkeysConfig.canvas_show_tooltip) {
+ createTooltip();
+ }
+
+ // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui.
+ function fixCanvas() {
+ const activeTab = getActiveTab(elements).textContent.trim();
+
+ if (activeTab !== "img2img") {
+ const img = targetElement.querySelector(`${elemId} img`);
+
+ if (img && img.style.display !== "none") {
+ img.style.display = "none";
+ img.style.visibility = "hidden";
+ }
+ }
+ }
+
+ // Reset the zoom level and pan position of the target element to their initial values
+ function resetZoom() {
+ elemData[elemId] = {
+ zoomLevel: 1,
+ panX: 0,
+ panY: 0
+ };
+
+ if (isExtension) {
+ targetElement.style.overflow = "hidden";
+ }
+
+ targetElement.isZoomed = false;
+
+ fixCanvas();
+ targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;
+
+ const canvas = gradioApp().querySelector(
+ `${elemId} canvas[key="interface"]`
+ );
+
+ toggleOverlap("off");
+ fullScreenMode = false;
+
+ const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']");
+ if (closeBtn) {
+ closeBtn.addEventListener("click", resetZoom);
+ }
+
+ if (canvas && isExtension) {
+ const parentElement = targetElement.closest('[id^="component-"]');
+ if (
+ canvas &&
+ parseFloat(canvas.style.width) > parentElement.offsetWidth &&
+ parseFloat(targetElement.style.width) > parentElement.offsetWidth
+ ) {
+ fitToElement();
+ return;
+ }
+
+ }
+
+ if (
+ canvas &&
+ !isExtension &&
+ parseFloat(canvas.style.width) > 865 &&
+ parseFloat(targetElement.style.width) > 865
+ ) {
+ fitToElement();
+ return;
+ }
+
+ targetElement.style.width = "";
+ }
+
+ // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
+ function toggleOverlap(forced = "") {
+ const zIndex1 = "0";
+ const zIndex2 = "998";
+
+ targetElement.style.zIndex =
+ targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1;
+
+ if (forced === "off") {
+ targetElement.style.zIndex = zIndex1;
+ } else if (forced === "on") {
+ targetElement.style.zIndex = zIndex2;
+ }
+ }
+
+ // Adjust the brush size based on the deltaY value from a mouse wheel event
+ function adjustBrushSize(
+ elemId,
+ deltaY,
+ withoutValue = false,
+ percentage = 5
+ ) {
+ const input =
+ gradioApp().querySelector(
+ `${elemId} input[aria-label='Brush radius']`
+ ) ||
+ gradioApp().querySelector(
+ `${elemId} button[aria-label="Use brush"]`
+ );
+
+ if (input) {
+ input.click();
+ if (!withoutValue) {
+ const maxValue =
+ parseFloat(input.getAttribute("max")) || 100;
+ const changeAmount = maxValue * (percentage / 100);
+ const newValue =
+ parseFloat(input.value) +
+ (deltaY > 0 ? -changeAmount : changeAmount);
+ input.value = Math.min(Math.max(newValue, 0), maxValue);
+ input.dispatchEvent(new Event("change"));
+ }
+ }
+ }
+
+ // Reset zoom when uploading a new image
+ const fileInput = gradioApp().querySelector(
+ `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv`
+ );
+ fileInput.addEventListener("click", resetZoom);
+
+ // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
+ function updateZoom(newZoomLevel, mouseX, mouseY) {
+ newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15));
+
+ elemData[elemId].panX +=
+ mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;
+ elemData[elemId].panY +=
+ mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel;
+
+ targetElement.style.transformOrigin = "0 0";
+ targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
+
+ toggleOverlap("on");
+ if (isExtension) {
+ targetElement.style.overflow = "visible";
+ }
+
+ return newZoomLevel;
+ }
+
+ // Change the zoom level based on user interaction
+ function changeZoomLevel(operation, e) {
+ if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) {
+ e.preventDefault();
+
+ let zoomPosX, zoomPosY;
+ let delta = 0.2;
+ if (elemData[elemId].zoomLevel > 7) {
+ delta = 0.9;
+ } else if (elemData[elemId].zoomLevel > 2) {
+ delta = 0.6;
+ }
+
+ zoomPosX = e.clientX;
+ zoomPosY = e.clientY;
+
+ fullScreenMode = false;
+ elemData[elemId].zoomLevel = updateZoom(
+ elemData[elemId].zoomLevel +
+ (operation === "+" ? delta : -delta),
+ zoomPosX - targetElement.getBoundingClientRect().left,
+ zoomPosY - targetElement.getBoundingClientRect().top
+ );
+
+ targetElement.isZoomed = true;
+ }
+ }
+
+ /**
+ * This function fits the target element to the screen by calculating
+ * the required scale and offsets. It also updates the global variables
+ * zoomLevel, panX, and panY to reflect the new state.
+ */
+
+ function fitToElement() {
+ //Reset Zoom
+ targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
+
+ let parentElement;
+
+ if (isExtension) {
+ parentElement = targetElement.closest('[id^="component-"]');
+ } else {
+ parentElement = targetElement.parentElement;
+ }
+
+
+ // Get element and screen dimensions
+ const elementWidth = targetElement.offsetWidth;
+ const elementHeight = targetElement.offsetHeight;
+
+ const screenWidth = parentElement.clientWidth;
+ const screenHeight = parentElement.clientHeight;
+
+ // Get element's coordinates relative to the parent element
+ const elementRect = targetElement.getBoundingClientRect();
+ const parentRect = parentElement.getBoundingClientRect();
+ const elementX = elementRect.x - parentRect.x;
+
+ // Calculate scale and offsets
+ const scaleX = screenWidth / elementWidth;
+ const scaleY = screenHeight / elementHeight;
+ const scale = Math.min(scaleX, scaleY);
+
+ const transformOrigin =
+ window.getComputedStyle(targetElement).transformOrigin;
+ const [originX, originY] = transformOrigin.split(" ");
+ const originXValue = parseFloat(originX);
+ const originYValue = parseFloat(originY);
+
+ const offsetX =
+ (screenWidth - elementWidth * scale) / 2 -
+ originXValue * (1 - scale);
+ const offsetY =
+ (screenHeight - elementHeight * scale) / 2.5 -
+ originYValue * (1 - scale);
+
+ // Apply scale and offsets to the element
+ targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
+
+ // Update global variables
+ elemData[elemId].zoomLevel = scale;
+ elemData[elemId].panX = offsetX;
+ elemData[elemId].panY = offsetY;
+
+ fullScreenMode = false;
+ toggleOverlap("off");
+ }
+
+ /**
+ * This function fits the target element to the screen by calculating
+ * the required scale and offsets. It also updates the global variables
+ * zoomLevel, panX, and panY to reflect the new state.
+ */
+
+ // Fullscreen mode
+ function fitToScreen() {
+ const canvas = gradioApp().querySelector(
+ `${elemId} canvas[key="interface"]`
+ );
+
+ if (!canvas) return;
+
+ if (canvas.offsetWidth > 862 || isExtension) {
+ targetElement.style.width = (canvas.offsetWidth + 2) + "px";
+ }
+
+ if (isExtension) {
+ targetElement.style.overflow = "visible";
+ }
+
+ if (fullScreenMode) {
+ resetZoom();
+ fullScreenMode = false;
+ return;
+ }
+
+ //Reset Zoom
+ targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
+
+ // Get scrollbar width to right-align the image
+ const scrollbarWidth =
+ window.innerWidth - document.documentElement.clientWidth;
+
+ // Get element and screen dimensions
+ const elementWidth = targetElement.offsetWidth;
+ const elementHeight = targetElement.offsetHeight;
+ const screenWidth = window.innerWidth - scrollbarWidth;
+ const screenHeight = window.innerHeight;
+
+ // Get element's coordinates relative to the page
+ const elementRect = targetElement.getBoundingClientRect();
+ const elementY = elementRect.y;
+ const elementX = elementRect.x;
+
+ // Calculate scale and offsets
+ const scaleX = screenWidth / elementWidth;
+ const scaleY = screenHeight / elementHeight;
+ const scale = Math.min(scaleX, scaleY);
+
+ // Get the current transformOrigin
+ const computedStyle = window.getComputedStyle(targetElement);
+ const transformOrigin = computedStyle.transformOrigin;
+ const [originX, originY] = transformOrigin.split(" ");
+ const originXValue = parseFloat(originX);
+ const originYValue = parseFloat(originY);
+
+ // Calculate offsets with respect to the transformOrigin
+ const offsetX =
+ (screenWidth - elementWidth * scale) / 2 -
+ elementX -
+ originXValue * (1 - scale);
+ const offsetY =
+ (screenHeight - elementHeight * scale) / 2 -
+ elementY -
+ originYValue * (1 - scale);
+
+ // Apply scale and offsets to the element
+ targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
+
+ // Update global variables
+ elemData[elemId].zoomLevel = scale;
+ elemData[elemId].panX = offsetX;
+ elemData[elemId].panY = offsetY;
+
+ fullScreenMode = true;
+ toggleOverlap("on");
+ }
+
+ // Handle keydown events
+ function handleKeyDown(event) {
+ // Disable key locks to make pasting from the buffer work correctly
+ if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") {
+ return;
+ }
+
+ // before activating shortcut, ensure user is not actively typing in an input field
+ if (!hotkeysConfig.canvas_blur_prompt) {
+ if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') {
+ return;
+ }
+ }
+
+
+ const hotkeyActions = {
+ [hotkeysConfig.canvas_hotkey_reset]: resetZoom,
+ [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
+ [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
+ };
+
+ const action = hotkeyActions[event.code];
+ if (action) {
+ event.preventDefault();
+ action(event);
+ }
+
+ if (
+ isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) ||
+ isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust)
+ ) {
+ event.preventDefault();
+ }
+ }
+
+ // Get Mouse position
+ function getMousePosition(e) {
+ mouseX = e.offsetX;
+ mouseY = e.offsetY;
+ }
+
+ // Simulation of the function to put a long image into the screen.
+ // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element.
+ // We hide the image and show it to the user when it is ready.
+
+ targetElement.isExpanded = false;
+ function autoExpand() {
+ const canvas = document.querySelector(`${elemId} canvas[key="interface"]`);
+ if (canvas) {
+ if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) {
+ targetElement.style.visibility = "hidden";
+ setTimeout(() => {
+ fitToScreen();
+ resetZoom();
+ targetElement.style.visibility = "visible";
+ targetElement.isExpanded = true;
+ }, 10);
+ }
+ }
+ }
+
+ targetElement.addEventListener("mousemove", getMousePosition);
+
+ //observers
+ // Creating an observer with a callback function to handle DOM changes
+ const observer = new MutationObserver((mutationsList, observer) => {
+ for (let mutation of mutationsList) {
+ // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
+ if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
+ mutation.target.tagName.toLowerCase() === 'canvas') {
+ targetElement.isExpanded = false;
+ setTimeout(resetZoom, 10);
+ }
+ }
+ });
+
+ // Apply auto expand if enabled
+ if (hotkeysConfig.canvas_auto_expand) {
+ targetElement.addEventListener("mousemove", autoExpand);
+ // Set up an observer to track attribute changes
+ observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
+ }
+
+ // Handle events only inside the targetElement
+ let isKeyDownHandlerAttached = false;
+
+ function handleMouseMove() {
+ if (!isKeyDownHandlerAttached) {
+ document.addEventListener("keydown", handleKeyDown);
+ isKeyDownHandlerAttached = true;
+
+ activeElement = elemId;
+ }
+ }
+
+ function handleMouseLeave() {
+ if (isKeyDownHandlerAttached) {
+ document.removeEventListener("keydown", handleKeyDown);
+ isKeyDownHandlerAttached = false;
+
+ activeElement = null;
+ }
+ }
+
+ // Add mouse event handlers
+ targetElement.addEventListener("mousemove", handleMouseMove);
+ targetElement.addEventListener("mouseleave", handleMouseLeave);
+
+ // Reset zoom when click on another tab
+ elements.img2imgTabs.addEventListener("click", resetZoom);
+ elements.img2imgTabs.addEventListener("click", () => {
+ // targetElement.style.width = "";
+ if (parseInt(targetElement.style.width) > 865) {
+ setTimeout(fitToElement, 0);
+ }
+ });
+
+ targetElement.addEventListener("wheel", e => {
+ // change zoom level
+ const operation = e.deltaY > 0 ? "-" : "+";
+ changeZoomLevel(operation, e);
+
+ // Handle brush size adjustment with ctrl key pressed
+ if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) {
+ e.preventDefault();
+
+ // Increase or decrease brush size based on scroll direction
+ adjustBrushSize(elemId, e.deltaY);
+ }
+ });
+
+ // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
+ function handleMoveKeyDown(e) {
+
+ // Disable key locks to make pasting from the buffer work correctly
+ if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") {
+ return;
+ }
+
+ // before activating shortcut, ensure user is not actively typing in an input field
+ if (!hotkeysConfig.canvas_blur_prompt) {
+ if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') {
+ return;
+ }
+ }
+
+
+ if (e.code === hotkeysConfig.canvas_hotkey_move) {
+ if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) {
+ e.preventDefault();
+ document.activeElement.blur();
+ isMoving = true;
+ }
+ }
+ }
+
+ function handleMoveKeyUp(e) {
+ if (e.code === hotkeysConfig.canvas_hotkey_move) {
+ isMoving = false;
+ }
+ }
+
+ document.addEventListener("keydown", handleMoveKeyDown);
+ document.addEventListener("keyup", handleMoveKeyUp);
+
+ // Detect zoom level and update the pan speed.
+ function updatePanPosition(movementX, movementY) {
+ let panSpeed = 2;
+
+ if (elemData[elemId].zoomLevel > 8) {
+ panSpeed = 3.5;
+ }
+
+ elemData[elemId].panX += movementX * panSpeed;
+ elemData[elemId].panY += movementY * panSpeed;
+
+ // Delayed redraw of an element
+ requestAnimationFrame(() => {
+ targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`;
+ toggleOverlap("on");
+ });
+ }
+
+ function handleMoveByKey(e) {
+ if (isMoving && elemId === activeElement) {
+ updatePanPosition(e.movementX, e.movementY);
+ targetElement.style.pointerEvents = "none";
+
+ if (isExtension) {
+ targetElement.style.overflow = "visible";
+ }
+
+ } else {
+ targetElement.style.pointerEvents = "auto";
+ }
+ }
+
+ // Prevents sticking to the mouse
+ window.onblur = function() {
+ isMoving = false;
+ };
+
+ // Checks for extension
+ function checkForOutBox() {
+ const parentElement = targetElement.closest('[id^="component-"]');
+ if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) {
+ resetZoom();
+ targetElement.isExpanded = true;
+ }
+
+ if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) {
+ resetZoom();
+ }
+
+ if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) {
+ resetZoom();
+ }
+ }
+
+ if (isExtension) {
+ targetElement.addEventListener("mousemove", checkForOutBox);
+ }
+
+
+ window.addEventListener('resize', (e) => {
+ resetZoom();
+
+ if (isExtension) {
+ targetElement.isExpanded = false;
+ targetElement.isZoomed = false;
+ }
+ });
+
+ gradioApp().addEventListener("mousemove", handleMoveByKey);
+
+
+ }
+
+ applyZoomAndPan(elementIDs.sketch, false);
+ applyZoomAndPan(elementIDs.inpaint, false);
+ applyZoomAndPan(elementIDs.inpaintSketch, false);
+
+ // Make the function global so that other extensions can take advantage of this solution
+ const applyZoomAndPanIntegration = async(id, elementIDs) => {
+ const mainEl = document.querySelector(id);
+ if (id.toLocaleLowerCase() === "none") {
+ for (const elementID of elementIDs) {
+ const el = await waitForElement(elementID);
+ if (!el) break;
+ applyZoomAndPan(elementID);
+ }
+ return;
+ }
+
+ if (!mainEl) return;
+ mainEl.addEventListener("click", async() => {
+ for (const elementID of elementIDs) {
+ const el = await waitForElement(elementID);
+ if (!el) break;
+ applyZoomAndPan(elementID);
+ }
+ }, {once: true});
+ };
+
+ window.applyZoomAndPan = applyZoomAndPan; // Only 1 elements, argument elementID, for example applyZoomAndPan("#txt2img_controlnet_ControlNet_input_image")
+
+ window.applyZoomAndPanIntegration = applyZoomAndPanIntegration; // for any extension
+
+ /*
+ The function `applyZoomAndPanIntegration` takes two arguments:
+
+ 1. `id`: A string identifier for the element to which zoom and pan functionality will be applied on click.
+ If the `id` value is "none", the functionality will be applied to all elements specified in the second argument without a click event.
+
+ 2. `elementIDs`: An array of string identifiers for elements. Zoom and pan functionality will be applied to each of these elements on click of the element specified by the first argument.
+ If "none" is specified in the first argument, the functionality will be applied to each of these elements without a click event.
+
+ Example usage:
+ applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
+ In this example, zoom and pan functionality will be applied to the element with the identifier "txt2img_controlnet_ControlNet_input_image" upon clicking the element with the identifier "txt2img_controlnet".
+ */
+
+ // More examples
+ // Add integration with ControlNet txt2img One TAB
+ // applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
+
+ // Add integration with ControlNet txt2img Tabs
+ // applyZoomAndPanIntegration("#txt2img_controlnet",Array.from({ length: 10 }, (_, i) => `#txt2img_controlnet_ControlNet-${i}_input_image`));
+
+ // Add integration with Inpaint Anything
+ // applyZoomAndPanIntegration("None", ["#ia_sam_image", "#ia_sel_mask"]);
+});
diff --git a/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d8d2d1c014be5dc1bac24b2c71079351fe1177e
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py
@@ -0,0 +1,15 @@
+import gradio as gr
+from modules import shared
+
+shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
+ "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+ "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
+ "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
+ "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
+ "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
+ "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
+ "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
+ "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
+ "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
+ "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
+}))
diff --git a/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/style.css b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..5d8054e65196408c97791727088088650f102b21
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/canvas-zoom-and-pan/style.css
@@ -0,0 +1,66 @@
+.canvas-tooltip-info {
+ position: absolute;
+ top: 10px;
+ left: 10px;
+ cursor: help;
+ background-color: rgba(0, 0, 0, 0.3);
+ width: 20px;
+ height: 20px;
+ border-radius: 50%;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ flex-direction: column;
+
+ z-index: 100;
+}
+
+.canvas-tooltip-info::after {
+ content: '';
+ display: block;
+ width: 2px;
+ height: 7px;
+ background-color: white;
+ margin-top: 2px;
+}
+
+.canvas-tooltip-info::before {
+ content: '';
+ display: block;
+ width: 2px;
+ height: 2px;
+ background-color: white;
+}
+
+.canvas-tooltip-content {
+ display: none;
+ background-color: #f9f9f9;
+ color: #333;
+ border: 1px solid #ddd;
+ padding: 15px;
+ position: absolute;
+ top: 40px;
+ left: 10px;
+ width: 250px;
+ font-size: 16px;
+ opacity: 0;
+ border-radius: 8px;
+ box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
+
+ z-index: 100;
+}
+
+.canvas-tooltip:hover .canvas-tooltip-content {
+ display: block;
+ animation: fadeIn 0.5s;
+ opacity: 1;
+}
+
+@keyframes fadeIn {
+ from {opacity: 0;}
+ to {opacity: 1;}
+}
+
+.styler {
+ overflow:inherit !important;
+}
\ No newline at end of file
diff --git a/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/extra_options_section.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff8c9fc2c24e11a7dbed213bc749836ba9d194de
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -0,0 +1,74 @@
+import math
+
+import gradio as gr
+from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
+from modules.ui_components import FormColumn
+
+
+class ExtraOptionsSection(scripts.Script):
+ section = "extra_options"
+
+ def __init__(self):
+ self.comps = None
+ self.setting_names = None
+
+ def title(self):
+ return "Extra options"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ self.comps = []
+ self.setting_names = []
+ self.infotext_fields = []
+ extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
+
+ mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
+
+ with gr.Blocks() as interface:
+ with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
+
+ row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
+
+ for row in range(row_count):
+ with gr.Row():
+ for col in range(shared.opts.extra_options_cols):
+ index = row * shared.opts.extra_options_cols + col
+ if index >= len(extra_options):
+ break
+
+ setting_name = extra_options[index]
+
+ with FormColumn():
+ comp = ui_settings.create_setting_component(setting_name)
+
+ self.comps.append(comp)
+ self.setting_names.append(setting_name)
+
+ setting_infotext_name = mapping.get(setting_name)
+ if setting_infotext_name is not None:
+ self.infotext_fields.append((comp, setting_infotext_name))
+
+ def get_settings_values():
+ res = [ui_settings.get_value_for_setting(key) for key in self.setting_names]
+ return res[0] if len(res) == 1 else res
+
+ interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)
+
+ return self.comps
+
+ def before_process(self, p, *args):
+ for name, value in zip(self.setting_names, args):
+ if name not in p.override_settings:
+ p.override_settings[name] = value
+
+
+shared.options_templates.update(shared.options_section(('ui', "User interface"), {
+ "extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
+ "extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
+ "extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
+ "extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
+}))
+
+
diff --git a/stable-diffusion-webui/extensions-builtin/mobile/javascript/mobile.js b/stable-diffusion-webui/extensions-builtin/mobile/javascript/mobile.js
new file mode 100644
index 0000000000000000000000000000000000000000..652f07ac7eceb7ac780d6c19c1be85480471491a
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/mobile/javascript/mobile.js
@@ -0,0 +1,32 @@
+var isSetupForMobile = false;
+
+function isMobile() {
+ for (var tab of ["txt2img", "img2img"]) {
+ var imageTab = gradioApp().getElementById(tab + '_results');
+ if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) {
+ return true;
+ }
+ }
+
+ return false;
+}
+
+function reportWindowSize() {
+ var currentlyMobile = isMobile();
+ if (currentlyMobile == isSetupForMobile) return;
+ isSetupForMobile = currentlyMobile;
+
+ for (var tab of ["txt2img", "img2img"]) {
+ var button = gradioApp().getElementById(tab + '_generate_box');
+ var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column');
+ target.insertBefore(button, target.firstElementChild);
+
+ gradioApp().getElementById(tab + '_results').classList.toggle('mobile', currentlyMobile);
+ }
+}
+
+window.addEventListener("resize", reportWindowSize);
+
+onUiLoaded(function() {
+ reportWindowSize();
+});
diff --git a/stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
new file mode 100644
index 0000000000000000000000000000000000000000..114cf94ccbf69b473757f2fc46443a39723a9269
--- /dev/null
+++ b/stable-diffusion-webui/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js
@@ -0,0 +1,42 @@
+// Stable Diffusion WebUI - Bracket checker
+// By Hingashi no Florin/Bwin4L & @akx
+// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
+// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
+
+function checkBrackets(textArea, counterElt) {
+ var counts = {};
+ (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
+ counts[bracket] = (counts[bracket] || 0) + 1;
+ });
+ var errors = [];
+
+ function checkPair(open, close, kind) {
+ if (counts[open] !== counts[close]) {
+ errors.push(
+ `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+ );
+ }
+ }
+
+ checkPair('(', ')', 'round brackets');
+ checkPair('[', ']', 'square brackets');
+ checkPair('{', '}', 'curly brackets');
+ counterElt.title = errors.join('\n');
+ counterElt.classList.toggle('error', errors.length !== 0);
+}
+
+function setupBracketChecking(id_prompt, id_counter) {
+ var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
+ var counter = gradioApp().getElementById(id_counter);
+
+ if (textarea && counter) {
+ textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+ }
+}
+
+onUiLoaded(function() {
+ setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
+ setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
+ setupBracketChecking('img2img_prompt', 'img2img_token_counter');
+ setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
+});
diff --git a/stable-diffusion-webui/extensions/put extensions here.txt b/stable-diffusion-webui/extensions/put extensions here.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/html/card-no-preview.png b/stable-diffusion-webui/html/card-no-preview.png
new file mode 100644
index 0000000000000000000000000000000000000000..f135fc4ec1e9a9f0850a310fbfde4b8811232437
--- /dev/null
+++ b/stable-diffusion-webui/html/card-no-preview.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab5740a5ae4494cd483daa1c5ba577b62260acd19cf94198c527ae05650f32dd
+size 84440
diff --git a/stable-diffusion-webui/html/extra-networks-card.html b/stable-diffusion-webui/html/extra-networks-card.html
new file mode 100644
index 0000000000000000000000000000000000000000..39674666f1e336d9bf61d2a6986721cf8591eeee
--- /dev/null
+++ b/stable-diffusion-webui/html/extra-networks-card.html
@@ -0,0 +1,14 @@
+<div class='card' style={style} onclick={card_clicked} data-name="{name}" {sort_keys}>
+ {background_image}
+ <div class="button-row">
+ {metadata_button}
+ {edit_button}
+ </div>
+ <div class='actions'>
+ <div class='additional'>
+ <span style="display:none" class='search_term{search_only}'>{search_term}</span>
+ </div>
+ <span class='name'>{name}</span>
+ <span class='description'>{description}</span>
+ </div>
+</div>
diff --git a/stable-diffusion-webui/html/extra-networks-no-cards.html b/stable-diffusion-webui/html/extra-networks-no-cards.html
new file mode 100644
index 0000000000000000000000000000000000000000..389358d6c4b383fdc3c5686e029e7b3b1ae9a493
--- /dev/null
+++ b/stable-diffusion-webui/html/extra-networks-no-cards.html
@@ -0,0 +1,8 @@
+<div class='nocards'>
+<h1>Nothing here. Add some content to the following directories:</h1>
+
+<ul>
+{dirs}
+</ul>
+</div>
+
diff --git a/stable-diffusion-webui/html/footer.html b/stable-diffusion-webui/html/footer.html
new file mode 100644
index 0000000000000000000000000000000000000000..8739a0f4752fd00b941d888d9a676158a3ba31a2
--- /dev/null
+++ b/stable-diffusion-webui/html/footer.html
@@ -0,0 +1,15 @@
+<div>
+ <a href="{api_docs}">API</a>
+ •
+ <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
+ •
+ <a href="https://gradio.app">Gradio</a>
+ •
+ <a href="#" onclick="showProfile('./internal/profile-startup'); return false;">Startup profile</a>
+ •
+ <a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
+</div>
+<br />
+<div class="versions">
+{versions}
+</div>
diff --git a/stable-diffusion-webui/html/licenses.html b/stable-diffusion-webui/html/licenses.html
new file mode 100644
index 0000000000000000000000000000000000000000..ca44deddd3663514962493c06a42a38d608c1229
--- /dev/null
+++ b/stable-diffusion-webui/html/licenses.html
@@ -0,0 +1,690 @@
+<style>
+ #licenses h2 {font-size: 1.2em; font-weight: bold; margin-bottom: 0.2em;}
+ #licenses small {font-size: 0.95em; opacity: 0.85;}
+ #licenses pre { margin: 1em 0 2em 0;}
+</style>
+
+<h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
+<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
+<pre>
+S-Lab License 1.0
+
+Copyright 2022 S-Lab
+
+Redistribution and use for non-commercial purpose in source and
+binary forms, with or without modification, are permitted provided
+that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
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+
+2. Redistributions in binary form must reproduce the above copyright
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+ the documentation and/or other materials provided with the
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+
+3. Neither the name of the copyright holder nor the names of its
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+
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+</pre>
+
+
+<h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
+<small>Code for architecture and reading models copied.</small>
+<pre>
+MIT License
+
+Copyright (c) 2021 victorca25
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+copies of the Software, and to permit persons to whom the Software is
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+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
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+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
+<small>Some code is copied to support ESRGAN models.</small>
+<pre>
+BSD 3-Clause License
+
+Copyright (c) 2021, Xintao Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
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+2. Redistributions in binary form must reproduce the above copyright notice,
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+</pre>
+
+<h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
+<small>Some code for compatibility with OSX is taken from lstein's repository.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 InvokeAI Team
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+</pre>
+
+<h2><a href="https://github.com/Hafiidz/latent-diffusion/blob/main/LICENSE">LDSR</a></h2>
+<small>Code added by contirubtors, most likely copied from this repository.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+
+<h2><a href="https://github.com/pharmapsychotic/clip-interrogator/blob/main/LICENSE">CLIP Interrogator</a></h2>
+<small>Some small amounts of code borrowed and reworked.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 pharmapsychotic
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+</pre>
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+<small>Code added by contributors, most likely copied from this repository.</small>
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+<h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>
+<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>
+<pre>
+MIT License
+
+Copyright (c) 2023 Alex Birch
+Copyright (c) 2023 Amin Rezaei
+
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+</pre>
+
+<h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
+<small>Some small amounts of code borrowed and reworked.</small>
+<pre>
+ Copyright 2023 The HuggingFace Team. All rights reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
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+
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+ License. However, in accepting such obligations, You may act only
+ on Your own behalf and on Your sole responsibility, not on behalf
+ of any other Contributor, and only if You agree to indemnify,
+ defend, and hold each Contributor harmless for any liability
+ incurred by, or claims asserted against, such Contributor by reason
+ of your accepting any such warranty or additional liability.
+
+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
+ replaced with your own identifying information. (Don't include
+ the brackets!) The text should be enclosed in the appropriate
+ comment syntax for the file format. We also recommend that a
+ file or class name and description of purpose be included on the
+ same "printed page" as the copyright notice for easier
+ identification within third-party archives.
+
+ Copyright [yyyy] [name of copyright owner]
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+</pre>
+
+<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
+<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
+<pre>
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/madebyollin/taesd/blob/main/LICENSE">TAESD</a></h2>
+<small>Tiny AutoEncoder for Stable Diffusion option for live previews</small>
+<pre>
+MIT License
+
+Copyright (c) 2023 Ollin Boer Bohan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
\ No newline at end of file
diff --git a/stable-diffusion-webui/javascript/aspectRatioOverlay.js b/stable-diffusion-webui/javascript/aspectRatioOverlay.js
new file mode 100644
index 0000000000000000000000000000000000000000..2cf2d571fc02a026b6cdedcf589a217ef0d65d27
--- /dev/null
+++ b/stable-diffusion-webui/javascript/aspectRatioOverlay.js
@@ -0,0 +1,113 @@
+
+let currentWidth = null;
+let currentHeight = null;
+let arFrameTimeout = setTimeout(function() {}, 0);
+
+function dimensionChange(e, is_width, is_height) {
+
+ if (is_width) {
+ currentWidth = e.target.value * 1.0;
+ }
+ if (is_height) {
+ currentHeight = e.target.value * 1.0;
+ }
+
+ var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
+
+ if (!inImg2img) {
+ return;
+ }
+
+ var targetElement = null;
+
+ var tabIndex = get_tab_index('mode_img2img');
+ if (tabIndex == 0) { // img2img
+ targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
+ } else if (tabIndex == 1) { //Sketch
+ targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
+ } else if (tabIndex == 2) { // Inpaint
+ targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
+ } else if (tabIndex == 3) { // Inpaint sketch
+ targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
+ }
+
+
+ if (targetElement) {
+
+ var arPreviewRect = gradioApp().querySelector('#imageARPreview');
+ if (!arPreviewRect) {
+ arPreviewRect = document.createElement('div');
+ arPreviewRect.id = "imageARPreview";
+ gradioApp().appendChild(arPreviewRect);
+ }
+
+
+
+ var viewportOffset = targetElement.getBoundingClientRect();
+
+ var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight);
+
+ var scaledx = targetElement.naturalWidth * viewportscale;
+ var scaledy = targetElement.naturalHeight * viewportscale;
+
+ var cleintRectTop = (viewportOffset.top + window.scrollY);
+ var cleintRectLeft = (viewportOffset.left + window.scrollX);
+ var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2);
+ var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2);
+
+ var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight);
+ var arscaledx = currentWidth * arscale;
+ var arscaledy = currentHeight * arscale;
+
+ var arRectTop = cleintRectCentreY - (arscaledy / 2);
+ var arRectLeft = cleintRectCentreX - (arscaledx / 2);
+ var arRectWidth = arscaledx;
+ var arRectHeight = arscaledy;
+
+ arPreviewRect.style.top = arRectTop + 'px';
+ arPreviewRect.style.left = arRectLeft + 'px';
+ arPreviewRect.style.width = arRectWidth + 'px';
+ arPreviewRect.style.height = arRectHeight + 'px';
+
+ clearTimeout(arFrameTimeout);
+ arFrameTimeout = setTimeout(function() {
+ arPreviewRect.style.display = 'none';
+ }, 2000);
+
+ arPreviewRect.style.display = 'block';
+
+ }
+
+}
+
+
+onAfterUiUpdate(function() {
+ var arPreviewRect = gradioApp().querySelector('#imageARPreview');
+ if (arPreviewRect) {
+ arPreviewRect.style.display = 'none';
+ }
+ var tabImg2img = gradioApp().querySelector("#tab_img2img");
+ if (tabImg2img) {
+ var inImg2img = tabImg2img.style.display == "block";
+ if (inImg2img) {
+ let inputs = gradioApp().querySelectorAll('input');
+ inputs.forEach(function(e) {
+ var is_width = e.parentElement.id == "img2img_width";
+ var is_height = e.parentElement.id == "img2img_height";
+
+ if ((is_width || is_height) && !e.classList.contains('scrollwatch')) {
+ e.addEventListener('input', function(e) {
+ dimensionChange(e, is_width, is_height);
+ });
+ e.classList.add('scrollwatch');
+ }
+ if (is_width) {
+ currentWidth = e.value * 1.0;
+ }
+ if (is_height) {
+ currentHeight = e.value * 1.0;
+ }
+ });
+ }
+ }
+});
diff --git a/stable-diffusion-webui/javascript/contextMenus.js b/stable-diffusion-webui/javascript/contextMenus.js
new file mode 100644
index 0000000000000000000000000000000000000000..ccae242f2b6a731e89d8752814aae6b78e143482
--- /dev/null
+++ b/stable-diffusion-webui/javascript/contextMenus.js
@@ -0,0 +1,176 @@
+
+var contextMenuInit = function() {
+ let eventListenerApplied = false;
+ let menuSpecs = new Map();
+
+ const uid = function() {
+ return Date.now().toString(36) + Math.random().toString(36).substring(2);
+ };
+
+ function showContextMenu(event, element, menuEntries) {
+ let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
+ let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
+
+ let oldMenu = gradioApp().querySelector('#context-menu');
+ if (oldMenu) {
+ oldMenu.remove();
+ }
+
+ let baseStyle = window.getComputedStyle(uiCurrentTab);
+
+ const contextMenu = document.createElement('nav');
+ contextMenu.id = "context-menu";
+ contextMenu.style.background = baseStyle.background;
+ contextMenu.style.color = baseStyle.color;
+ contextMenu.style.fontFamily = baseStyle.fontFamily;
+ contextMenu.style.top = posy + 'px';
+ contextMenu.style.left = posx + 'px';
+
+
+
+ const contextMenuList = document.createElement('ul');
+ contextMenuList.className = 'context-menu-items';
+ contextMenu.append(contextMenuList);
+
+ menuEntries.forEach(function(entry) {
+ let contextMenuEntry = document.createElement('a');
+ contextMenuEntry.innerHTML = entry['name'];
+ contextMenuEntry.addEventListener("click", function() {
+ entry['func']();
+ });
+ contextMenuList.append(contextMenuEntry);
+
+ });
+
+ gradioApp().appendChild(contextMenu);
+
+ let menuWidth = contextMenu.offsetWidth + 4;
+ let menuHeight = contextMenu.offsetHeight + 4;
+
+ let windowWidth = window.innerWidth;
+ let windowHeight = window.innerHeight;
+
+ if ((windowWidth - posx) < menuWidth) {
+ contextMenu.style.left = windowWidth - menuWidth + "px";
+ }
+
+ if ((windowHeight - posy) < menuHeight) {
+ contextMenu.style.top = windowHeight - menuHeight + "px";
+ }
+
+ }
+
+ function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {
+
+ var currentItems = menuSpecs.get(targetElementSelector);
+
+ if (!currentItems) {
+ currentItems = [];
+ menuSpecs.set(targetElementSelector, currentItems);
+ }
+ let newItem = {
+ id: targetElementSelector + '_' + uid(),
+ name: entryName,
+ func: entryFunction,
+ isNew: true
+ };
+
+ currentItems.push(newItem);
+ return newItem['id'];
+ }
+
+ function removeContextMenuOption(uid) {
+ menuSpecs.forEach(function(v) {
+ let index = -1;
+ v.forEach(function(e, ei) {
+ if (e['id'] == uid) {
+ index = ei;
+ }
+ });
+ if (index >= 0) {
+ v.splice(index, 1);
+ }
+ });
+ }
+
+ function addContextMenuEventListener() {
+ if (eventListenerApplied) {
+ return;
+ }
+ gradioApp().addEventListener("click", function(e) {
+ if (!e.isTrusted) {
+ return;
+ }
+
+ let oldMenu = gradioApp().querySelector('#context-menu');
+ if (oldMenu) {
+ oldMenu.remove();
+ }
+ });
+ gradioApp().addEventListener("contextmenu", function(e) {
+ let oldMenu = gradioApp().querySelector('#context-menu');
+ if (oldMenu) {
+ oldMenu.remove();
+ }
+ menuSpecs.forEach(function(v, k) {
+ if (e.composedPath()[0].matches(k)) {
+ showContextMenu(e, e.composedPath()[0], v);
+ e.preventDefault();
+ }
+ });
+ });
+ eventListenerApplied = true;
+
+ }
+
+ return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener];
+};
+
+var initResponse = contextMenuInit();
+var appendContextMenuOption = initResponse[0];
+var removeContextMenuOption = initResponse[1];
+var addContextMenuEventListener = initResponse[2];
+
+(function() {
+ //Start example Context Menu Items
+ let generateOnRepeat = function(genbuttonid, interruptbuttonid) {
+ let genbutton = gradioApp().querySelector(genbuttonid);
+ let interruptbutton = gradioApp().querySelector(interruptbuttonid);
+ if (!interruptbutton.offsetParent) {
+ genbutton.click();
+ }
+ clearInterval(window.generateOnRepeatInterval);
+ window.generateOnRepeatInterval = setInterval(function() {
+ if (!interruptbutton.offsetParent) {
+ genbutton.click();
+ }
+ },
+ 500);
+ };
+
+ let generateOnRepeat_txt2img = function() {
+ generateOnRepeat('#txt2img_generate', '#txt2img_interrupt');
+ };
+
+ let generateOnRepeat_img2img = function() {
+ generateOnRepeat('#img2img_generate', '#img2img_interrupt');
+ };
+
+ appendContextMenuOption('#txt2img_generate', 'Generate forever', generateOnRepeat_txt2img);
+ appendContextMenuOption('#txt2img_interrupt', 'Generate forever', generateOnRepeat_txt2img);
+ appendContextMenuOption('#img2img_generate', 'Generate forever', generateOnRepeat_img2img);
+ appendContextMenuOption('#img2img_interrupt', 'Generate forever', generateOnRepeat_img2img);
+
+ let cancelGenerateForever = function() {
+ clearInterval(window.generateOnRepeatInterval);
+ };
+
+ appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
+ appendContextMenuOption('#txt2img_generate', 'Cancel generate forever', cancelGenerateForever);
+ appendContextMenuOption('#img2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
+ appendContextMenuOption('#img2img_generate', 'Cancel generate forever', cancelGenerateForever);
+
+})();
+//End example Context Menu Items
+
+onAfterUiUpdate(addContextMenuEventListener);
diff --git a/stable-diffusion-webui/javascript/dragdrop.js b/stable-diffusion-webui/javascript/dragdrop.js
new file mode 100644
index 0000000000000000000000000000000000000000..5803daea5ef33341b5307e03a7ebbadc7c324ed7
--- /dev/null
+++ b/stable-diffusion-webui/javascript/dragdrop.js
@@ -0,0 +1,130 @@
+// allows drag-dropping files into gradio image elements, and also pasting images from clipboard
+
+function isValidImageList(files) {
+ return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
+}
+
+function dropReplaceImage(imgWrap, files) {
+ if (!isValidImageList(files)) {
+ return;
+ }
+
+ const tmpFile = files[0];
+
+ imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
+ const callback = () => {
+ const fileInput = imgWrap.querySelector('input[type="file"]');
+ if (fileInput) {
+ if (files.length === 0) {
+ files = new DataTransfer();
+ files.items.add(tmpFile);
+ fileInput.files = files.files;
+ } else {
+ fileInput.files = files;
+ }
+ fileInput.dispatchEvent(new Event('change'));
+ }
+ };
+
+ if (imgWrap.closest('#pnginfo_image')) {
+ // special treatment for PNG Info tab, wait for fetch request to finish
+ const oldFetch = window.fetch;
+ window.fetch = async(input, options) => {
+ const response = await oldFetch(input, options);
+ if ('api/predict/' === input) {
+ const content = await response.text();
+ window.fetch = oldFetch;
+ window.requestAnimationFrame(() => callback());
+ return new Response(content, {
+ status: response.status,
+ statusText: response.statusText,
+ headers: response.headers
+ });
+ }
+ return response;
+ };
+ } else {
+ window.requestAnimationFrame(() => callback());
+ }
+}
+
+function eventHasFiles(e) {
+ if (!e.dataTransfer || !e.dataTransfer.files) return false;
+ if (e.dataTransfer.files.length > 0) return true;
+ if (e.dataTransfer.items.length > 0 && e.dataTransfer.items[0].kind == "file") return true;
+
+ return false;
+}
+
+function dragDropTargetIsPrompt(target) {
+ if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true;
+ if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true;
+ return false;
+}
+
+window.document.addEventListener('dragover', e => {
+ const target = e.composedPath()[0];
+ if (!eventHasFiles(e)) return;
+
+ var targetImage = target.closest('[data-testid="image"]');
+ if (!dragDropTargetIsPrompt(target) && !targetImage) return;
+
+ e.stopPropagation();
+ e.preventDefault();
+ e.dataTransfer.dropEffect = 'copy';
+});
+
+window.document.addEventListener('drop', e => {
+ const target = e.composedPath()[0];
+ if (!eventHasFiles(e)) return;
+
+ if (dragDropTargetIsPrompt(target)) {
+ e.stopPropagation();
+ e.preventDefault();
+
+ let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
+
+ const imgParent = gradioApp().getElementById(prompt_target);
+ const files = e.dataTransfer.files;
+ const fileInput = imgParent.querySelector('input[type="file"]');
+ if (fileInput) {
+ fileInput.files = files;
+ fileInput.dispatchEvent(new Event('change'));
+ }
+ }
+
+ var targetImage = target.closest('[data-testid="image"]');
+ if (targetImage) {
+ e.stopPropagation();
+ e.preventDefault();
+ const files = e.dataTransfer.files;
+ dropReplaceImage(targetImage, files);
+ return;
+ }
+});
+
+window.addEventListener('paste', e => {
+ const files = e.clipboardData.files;
+ if (!isValidImageList(files)) {
+ return;
+ }
+
+ const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
+ .filter(el => uiElementIsVisible(el))
+ .sort((a, b) => uiElementInSight(b) - uiElementInSight(a));
+
+
+ if (!visibleImageFields.length) {
+ return;
+ }
+
+ const firstFreeImageField = visibleImageFields
+ .filter(el => el.querySelector('input[type=file]'))?.[0];
+
+ dropReplaceImage(
+ firstFreeImageField ?
+ firstFreeImageField :
+ visibleImageFields[visibleImageFields.length - 1]
+ , files
+ );
+});
diff --git a/stable-diffusion-webui/javascript/edit-attention.js b/stable-diffusion-webui/javascript/edit-attention.js
new file mode 100644
index 0000000000000000000000000000000000000000..8906c8922e17709ebde168f15d3f7c18706e75d4
--- /dev/null
+++ b/stable-diffusion-webui/javascript/edit-attention.js
@@ -0,0 +1,121 @@
+function keyupEditAttention(event) {
+ let target = event.originalTarget || event.composedPath()[0];
+ if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
+ if (!(event.metaKey || event.ctrlKey)) return;
+
+ let isPlus = event.key == "ArrowUp";
+ let isMinus = event.key == "ArrowDown";
+ if (!isPlus && !isMinus) return;
+
+ let selectionStart = target.selectionStart;
+ let selectionEnd = target.selectionEnd;
+ let text = target.value;
+
+ function selectCurrentParenthesisBlock(OPEN, CLOSE) {
+ if (selectionStart !== selectionEnd) return false;
+
+ // Find opening parenthesis around current cursor
+ const before = text.substring(0, selectionStart);
+ let beforeParen = before.lastIndexOf(OPEN);
+ if (beforeParen == -1) return false;
+ let beforeParenClose = before.lastIndexOf(CLOSE);
+ while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
+ beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
+ beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
+ }
+
+ // Find closing parenthesis around current cursor
+ const after = text.substring(selectionStart);
+ let afterParen = after.indexOf(CLOSE);
+ if (afterParen == -1) return false;
+ let afterParenOpen = after.indexOf(OPEN);
+ while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
+ afterParen = after.indexOf(CLOSE, afterParen + 1);
+ afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
+ }
+ if (beforeParen === -1 || afterParen === -1) return false;
+
+ // Set the selection to the text between the parenthesis
+ const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
+ const lastColon = parenContent.lastIndexOf(":");
+ selectionStart = beforeParen + 1;
+ selectionEnd = selectionStart + lastColon;
+ target.setSelectionRange(selectionStart, selectionEnd);
+ return true;
+ }
+
+ function selectCurrentWord() {
+ if (selectionStart !== selectionEnd) return false;
+ const delimiters = opts.keyedit_delimiters + " \r\n\t";
+
+ // seek backward until to find beggining
+ while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+ selectionStart--;
+ }
+
+ // seek forward to find end
+ while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+ selectionEnd++;
+ }
+
+ target.setSelectionRange(selectionStart, selectionEnd);
+ return true;
+ }
+
+ // If the user hasn't selected anything, let's select their current parenthesis block or word
+ if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+ selectCurrentWord();
+ }
+
+ event.preventDefault();
+
+ var closeCharacter = ')';
+ var delta = opts.keyedit_precision_attention;
+
+ if (selectionStart > 0 && text[selectionStart - 1] == '<') {
+ closeCharacter = '>';
+ delta = opts.keyedit_precision_extra;
+ } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
+
+ // do not include spaces at the end
+ while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
+ selectionEnd -= 1;
+ }
+ if (selectionStart == selectionEnd) {
+ return;
+ }
+
+ text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
+
+ selectionStart += 1;
+ selectionEnd += 1;
+ }
+
+ var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+ var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+ if (isNaN(weight)) return;
+
+ weight += isPlus ? delta : -delta;
+ weight = parseFloat(weight.toPrecision(12));
+ if (String(weight).length == 1) weight += ".0";
+
+ if (closeCharacter == ')' && weight == 1) {
+ var endParenPos = text.substring(selectionEnd).indexOf(')');
+ text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1);
+ selectionStart--;
+ selectionEnd--;
+ } else {
+ text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
+ }
+
+ target.focus();
+ target.value = text;
+ target.selectionStart = selectionStart;
+ target.selectionEnd = selectionEnd;
+
+ updateInput(target);
+}
+
+addEventListener('keydown', (event) => {
+ keyupEditAttention(event);
+});
diff --git a/stable-diffusion-webui/javascript/edit-order.js b/stable-diffusion-webui/javascript/edit-order.js
new file mode 100644
index 0000000000000000000000000000000000000000..ed4ef9ac399a6d0bd83435958dc4d46837760c6a
--- /dev/null
+++ b/stable-diffusion-webui/javascript/edit-order.js
@@ -0,0 +1,41 @@
+/* alt+left/right moves text in prompt */
+
+function keyupEditOrder(event) {
+ if (!opts.keyedit_move) return;
+
+ let target = event.originalTarget || event.composedPath()[0];
+ if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
+ if (!event.altKey) return;
+
+ let isLeft = event.key == "ArrowLeft";
+ let isRight = event.key == "ArrowRight";
+ if (!isLeft && !isRight) return;
+ event.preventDefault();
+
+ let selectionStart = target.selectionStart;
+ let selectionEnd = target.selectionEnd;
+ let text = target.value;
+ let items = text.split(",");
+ let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length;
+ let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length;
+ let range = indexEnd - indexStart + 1;
+
+ if (isLeft && indexStart > 0) {
+ items.splice(indexStart - 1, 0, ...items.splice(indexStart, range));
+ target.value = items.join();
+ target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1);
+ target.selectionEnd = items.slice(0, indexEnd).join().length;
+ } else if (isRight && indexEnd < items.length - 1) {
+ items.splice(indexStart + 1, 0, ...items.splice(indexStart, range));
+ target.value = items.join();
+ target.selectionStart = items.slice(0, indexStart + 1).join().length + 1;
+ target.selectionEnd = items.slice(0, indexEnd + 2).join().length;
+ }
+
+ event.preventDefault();
+ updateInput(target);
+}
+
+addEventListener('keydown', (event) => {
+ keyupEditOrder(event);
+});
diff --git a/stable-diffusion-webui/javascript/extensions.js b/stable-diffusion-webui/javascript/extensions.js
new file mode 100644
index 0000000000000000000000000000000000000000..312131b76ebc2eea200698b81d024d98e8af9ea4
--- /dev/null
+++ b/stable-diffusion-webui/javascript/extensions.js
@@ -0,0 +1,92 @@
+
+function extensions_apply(_disabled_list, _update_list, disable_all) {
+ var disable = [];
+ var update = [];
+
+ gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
+ if (x.name.startsWith("enable_") && !x.checked) {
+ disable.push(x.name.substring(7));
+ }
+
+ if (x.name.startsWith("update_") && x.checked) {
+ update.push(x.name.substring(7));
+ }
+ });
+
+ restart_reload();
+
+ return [JSON.stringify(disable), JSON.stringify(update), disable_all];
+}
+
+function extensions_check() {
+ var disable = [];
+
+ gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
+ if (x.name.startsWith("enable_") && !x.checked) {
+ disable.push(x.name.substring(7));
+ }
+ });
+
+ gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
+ x.innerHTML = "Loading...";
+ });
+
+
+ var id = randomId();
+ requestProgress(id, gradioApp().getElementById('extensions_installed_html'), null, function() {
+
+ });
+
+ return [id, JSON.stringify(disable)];
+}
+
+function install_extension_from_index(button, url) {
+ button.disabled = "disabled";
+ button.value = "Installing...";
+
+ var textarea = gradioApp().querySelector('#extension_to_install textarea');
+ textarea.value = url;
+ updateInput(textarea);
+
+ gradioApp().querySelector('#install_extension_button').click();
+}
+
+function config_state_confirm_restore(_, config_state_name, config_restore_type) {
+ if (config_state_name == "Current") {
+ return [false, config_state_name, config_restore_type];
+ }
+ let restored = "";
+ if (config_restore_type == "extensions") {
+ restored = "all saved extension versions";
+ } else if (config_restore_type == "webui") {
+ restored = "the webui version";
+ } else {
+ restored = "the webui version and all saved extension versions";
+ }
+ let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
+ if (confirmed) {
+ restart_reload();
+ gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
+ x.innerHTML = "Loading...";
+ });
+ }
+ return [confirmed, config_state_name, config_restore_type];
+}
+
+function toggle_all_extensions(event) {
+ gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) {
+ checkbox_el.checked = event.target.checked;
+ });
+}
+
+function toggle_extension() {
+ let all_extensions_toggled = true;
+ for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) {
+ if (!checkbox_el.checked) {
+ all_extensions_toggled = false;
+ break;
+ }
+ }
+
+ gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled;
+}
diff --git a/stable-diffusion-webui/javascript/extraNetworks.js b/stable-diffusion-webui/javascript/extraNetworks.js
new file mode 100644
index 0000000000000000000000000000000000000000..493f31af28a0d34e81907c07787717acfc8d9aea
--- /dev/null
+++ b/stable-diffusion-webui/javascript/extraNetworks.js
@@ -0,0 +1,349 @@
+function toggleCss(key, css, enable) {
+ var style = document.getElementById(key);
+ if (enable && !style) {
+ style = document.createElement('style');
+ style.id = key;
+ style.type = 'text/css';
+ document.head.appendChild(style);
+ }
+ if (style && !enable) {
+ document.head.removeChild(style);
+ }
+ if (style) {
+ style.innerHTML == '';
+ style.appendChild(document.createTextNode(css));
+ }
+}
+
+function setupExtraNetworksForTab(tabname) {
+ gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');
+
+ var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
+ var searchDiv = gradioApp().getElementById(tabname + '_extra_search');
+ var search = searchDiv.querySelector('textarea');
+ var sort = gradioApp().getElementById(tabname + '_extra_sort');
+ var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder');
+ var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
+ var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
+ var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');
+
+ sort.dataset.sortkey = 'sortDefault';
+ tabs.appendChild(searchDiv);
+ tabs.appendChild(sort);
+ tabs.appendChild(sortOrder);
+ tabs.appendChild(refresh);
+ tabs.appendChild(showDirsDiv);
+
+ var applyFilter = function() {
+ var searchTerm = search.value.toLowerCase();
+
+ gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) {
+ var searchOnly = elem.querySelector('.search_only');
+ var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase();
+
+ var visible = text.indexOf(searchTerm) != -1;
+
+ if (searchOnly && searchTerm.length < 4) {
+ visible = false;
+ }
+
+ elem.style.display = visible ? "" : "none";
+ });
+ };
+
+ var applySort = function() {
+ var reverse = sortOrder.classList.contains("sortReverse");
+ var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
+ sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
+ var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
+ if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
+ return;
+ }
+
+ sort.dataset.sortkey = sortKeyStore;
+
+ var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
+ cards.forEach(function(card) {
+ card.originalParentElement = card.parentElement;
+ });
+ var sortedCards = Array.from(cards);
+ sortedCards.sort(function(cardA, cardB) {
+ var a = cardA.dataset[sortKey];
+ var b = cardB.dataset[sortKey];
+ if (!isNaN(a) && !isNaN(b)) {
+ return parseInt(a) - parseInt(b);
+ }
+
+ return (a < b ? -1 : (a > b ? 1 : 0));
+ });
+ if (reverse) {
+ sortedCards.reverse();
+ }
+ cards.forEach(function(card) {
+ card.remove();
+ });
+ sortedCards.forEach(function(card) {
+ card.originalParentElement.appendChild(card);
+ });
+ };
+
+ search.addEventListener("input", applyFilter);
+ applyFilter();
+ ["change", "blur", "click"].forEach(function(evt) {
+ sort.querySelector("input").addEventListener(evt, applySort);
+ });
+ sortOrder.addEventListener("click", function() {
+ sortOrder.classList.toggle("sortReverse");
+ applySort();
+ });
+
+ extraNetworksApplyFilter[tabname] = applyFilter;
+
+ var showDirsUpdate = function() {
+ var css = '#' + tabname + '_extra_tabs .extra-network-subdirs { display: none; }';
+ toggleCss(tabname + '_extra_show_dirs_style', css, !showDirs.checked);
+ localSet('extra-networks-show-dirs', showDirs.checked ? 1 : 0);
+ };
+ showDirs.checked = localGet('extra-networks-show-dirs', 1) == 1;
+ showDirs.addEventListener("change", showDirsUpdate);
+ showDirsUpdate();
+}
+
+function applyExtraNetworkFilter(tabname) {
+ setTimeout(extraNetworksApplyFilter[tabname], 1);
+}
+
+var extraNetworksApplyFilter = {};
+var activePromptTextarea = {};
+
+function setupExtraNetworks() {
+ setupExtraNetworksForTab('txt2img');
+ setupExtraNetworksForTab('img2img');
+
+ function registerPrompt(tabname, id) {
+ var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
+
+ if (!activePromptTextarea[tabname]) {
+ activePromptTextarea[tabname] = textarea;
+ }
+
+ textarea.addEventListener("focus", function() {
+ activePromptTextarea[tabname] = textarea;
+ });
+ }
+
+ registerPrompt('txt2img', 'txt2img_prompt');
+ registerPrompt('txt2img', 'txt2img_neg_prompt');
+ registerPrompt('img2img', 'img2img_prompt');
+ registerPrompt('img2img', 'img2img_neg_prompt');
+}
+
+onUiLoaded(setupExtraNetworks);
+
+var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/;
+var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
+
+function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
+ var m = text.match(re_extranet);
+ var replaced = false;
+ var newTextareaText;
+ if (m) {
+ var extraTextAfterNet = m[2];
+ var partToSearch = m[1];
+ var foundAtPosition = -1;
+ newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) {
+ m = found.match(re_extranet);
+ if (m[1] == partToSearch) {
+ replaced = true;
+ foundAtPosition = pos;
+ return "";
+ }
+ return found;
+ });
+
+ if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
+ newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
+ }
+ } else {
+ newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
+ if (found == text) {
+ replaced = true;
+ return "";
+ }
+ return found;
+ });
+ }
+
+ if (replaced) {
+ textarea.value = newTextareaText;
+ return true;
+ }
+
+ return false;
+}
+
+function cardClicked(tabname, textToAdd, allowNegativePrompt) {
+ var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
+
+ if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) {
+ textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd;
+ }
+
+ updateInput(textarea);
+}
+
+function saveCardPreview(event, tabname, filename) {
+ var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea');
+ var button = gradioApp().getElementById(tabname + '_save_preview');
+
+ textarea.value = filename;
+ updateInput(textarea);
+
+ button.click();
+
+ event.stopPropagation();
+ event.preventDefault();
+}
+
+function extraNetworksSearchButton(tabs_id, event) {
+ var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > label > textarea');
+ var button = event.target;
+ var text = button.classList.contains("search-all") ? "" : button.textContent.trim();
+
+ searchTextarea.value = text;
+ updateInput(searchTextarea);
+}
+
+var globalPopup = null;
+var globalPopupInner = null;
+function closePopup() {
+ if (!globalPopup) return;
+
+ globalPopup.style.display = "none";
+}
+function popup(contents) {
+ if (!globalPopup) {
+ globalPopup = document.createElement('div');
+ globalPopup.onclick = closePopup;
+ globalPopup.classList.add('global-popup');
+
+ var close = document.createElement('div');
+ close.classList.add('global-popup-close');
+ close.onclick = closePopup;
+ close.title = "Close";
+ globalPopup.appendChild(close);
+
+ globalPopupInner = document.createElement('div');
+ globalPopupInner.onclick = function(event) {
+ event.stopPropagation(); return false;
+ };
+ globalPopupInner.classList.add('global-popup-inner');
+ globalPopup.appendChild(globalPopupInner);
+
+ gradioApp().querySelector('.main').appendChild(globalPopup);
+ }
+
+ globalPopupInner.innerHTML = '';
+ globalPopupInner.appendChild(contents);
+
+ globalPopup.style.display = "flex";
+}
+
+var storedPopupIds = {};
+function popupId(id) {
+ if (!storedPopupIds[id]) {
+ storedPopupIds[id] = gradioApp().getElementById(id);
+ }
+
+ popup(storedPopupIds[id]);
+}
+
+function extraNetworksShowMetadata(text) {
+ var elem = document.createElement('pre');
+ elem.classList.add('popup-metadata');
+ elem.textContent = text;
+
+ popup(elem);
+}
+
+function requestGet(url, data, handler, errorHandler) {
+ var xhr = new XMLHttpRequest();
+ var args = Object.keys(data).map(function(k) {
+ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]);
+ }).join('&');
+ xhr.open("GET", url + "?" + args, true);
+
+ xhr.onreadystatechange = function() {
+ if (xhr.readyState === 4) {
+ if (xhr.status === 200) {
+ try {
+ var js = JSON.parse(xhr.responseText);
+ handler(js);
+ } catch (error) {
+ console.error(error);
+ errorHandler();
+ }
+ } else {
+ errorHandler();
+ }
+ }
+ };
+ var js = JSON.stringify(data);
+ xhr.send(js);
+}
+
+function extraNetworksRequestMetadata(event, extraPage, cardName) {
+ var showError = function() {
+ extraNetworksShowMetadata("there was an error getting metadata");
+ };
+
+ requestGet("./sd_extra_networks/metadata", {page: extraPage, item: cardName}, function(data) {
+ if (data && data.metadata) {
+ extraNetworksShowMetadata(data.metadata);
+ } else {
+ showError();
+ }
+ }, showError);
+
+ event.stopPropagation();
+}
+
+var extraPageUserMetadataEditors = {};
+
+function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) {
+ var id = tabname + '_' + extraPage + '_edit_user_metadata';
+
+ var editor = extraPageUserMetadataEditors[id];
+ if (!editor) {
+ editor = {};
+ editor.page = gradioApp().getElementById(id);
+ editor.nameTextarea = gradioApp().querySelector("#" + id + "_name" + ' textarea');
+ editor.button = gradioApp().querySelector("#" + id + "_button");
+ extraPageUserMetadataEditors[id] = editor;
+ }
+
+ editor.nameTextarea.value = cardName;
+ updateInput(editor.nameTextarea);
+
+ editor.button.click();
+
+ popup(editor.page);
+
+ event.stopPropagation();
+}
+
+function extraNetworksRefreshSingleCard(page, tabname, name) {
+ requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) {
+ if (data && data.html) {
+ var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function
+
+ var newDiv = document.createElement('DIV');
+ newDiv.innerHTML = data.html;
+ var newCard = newDiv.firstElementChild;
+
+ newCard.style.display = '';
+ card.parentElement.insertBefore(newCard, card);
+ card.parentElement.removeChild(card);
+ }
+ });
+}
diff --git a/stable-diffusion-webui/javascript/generationParams.js b/stable-diffusion-webui/javascript/generationParams.js
new file mode 100644
index 0000000000000000000000000000000000000000..7c0fd221d63313ab063f545570eb0da780b9da3a
--- /dev/null
+++ b/stable-diffusion-webui/javascript/generationParams.js
@@ -0,0 +1,35 @@
+// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
+
+let txt2img_gallery, img2img_gallery, modal = undefined;
+onAfterUiUpdate(function() {
+ if (!txt2img_gallery) {
+ txt2img_gallery = attachGalleryListeners("txt2img");
+ }
+ if (!img2img_gallery) {
+ img2img_gallery = attachGalleryListeners("img2img");
+ }
+ if (!modal) {
+ modal = gradioApp().getElementById('lightboxModal');
+ modalObserver.observe(modal, {attributes: true, attributeFilter: ['style']});
+ }
+});
+
+let modalObserver = new MutationObserver(function(mutations) {
+ mutations.forEach(function(mutationRecord) {
+ let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText;
+ if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) {
+ gradioApp().getElementById(selectedTab + "_generation_info_button")?.click();
+ }
+ });
+});
+
+function attachGalleryListeners(tab_name) {
+ var gallery = gradioApp().querySelector('#' + tab_name + '_gallery');
+ gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name + "_generation_info_button").click());
+ gallery?.addEventListener('keydown', (e) => {
+ if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow
+ gradioApp().getElementById(tab_name + "_generation_info_button").click();
+ }
+ });
+ return gallery;
+}
diff --git a/stable-diffusion-webui/javascript/hints.js b/stable-diffusion-webui/javascript/hints.js
new file mode 100644
index 0000000000000000000000000000000000000000..6de9372e8ea8c9fb032351e241d0f9c265995290
--- /dev/null
+++ b/stable-diffusion-webui/javascript/hints.js
@@ -0,0 +1,203 @@
+// mouseover tooltips for various UI elements
+
+var titles = {
+ "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",
+ "Sampling method": "Which algorithm to use to produce the image",
+ "GFPGAN": "Restore low quality faces using GFPGAN neural network",
+ "Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
+ "DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
+ "UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
+ "DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
+
+ "\u{1F4D0}": "Auto detect size from img2img",
+ "Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
+ "Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
+ "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
+ "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
+ "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
+ "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomized",
+ "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
+ "\u{1f4c2}": "Open images output directory",
+ "\u{1f4be}": "Save style",
+ "\u{1f5d1}\ufe0f": "Clear prompt",
+ "\u{1f4cb}": "Apply selected styles to current prompt",
+ "\u{1f4d2}": "Paste available values into the field",
+ "\u{1f3b4}": "Show/hide extra networks",
+ "\u{1f300}": "Restore progress",
+
+ "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
+ "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
+
+ "Just resize": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.",
+ "Crop and resize": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.",
+ "Resize and fill": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.",
+
+ "Mask blur": "How much to blur the mask before processing, in pixels.",
+ "Masked content": "What to put inside the masked area before processing it with Stable Diffusion.",
+ "fill": "fill it with colors of the image",
+ "original": "keep whatever was there originally",
+ "latent noise": "fill it with latent space noise",
+ "latent nothing": "fill it with latent space zeroes",
+ "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
+
+ "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
+
+ "Skip": "Stop processing current image and continue processing.",
+ "Interrupt": "Stop processing images and return any results accumulated so far.",
+ "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
+
+ "X values": "Separate values for X axis using commas.",
+ "Y values": "Separate values for Y axis using commas.",
+
+ "None": "Do not do anything special",
+ "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)",
+ "X/Y/Z plot": "Create grid(s) where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows",
+ "Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work",
+
+ "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
+ "Prompt order": "Separate a list of words with commas, and the script will make a variation of prompt with those words for their every possible order",
+
+ "Tiling": "Produce an image that can be tiled.",
+ "Tile overlap": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.",
+
+ "Variation seed": "Seed of a different picture to be mixed into the generation.",
+ "Variation strength": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).",
+ "Resize seed from height": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
+ "Resize seed from width": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution",
+
+ "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
+
+ "Images filename pattern": "Use tags like [seed] and [date] to define how filenames for images are chosen. Leave empty for default.",
+ "Directory name pattern": "Use tags like [seed] and [date] to define how subdirectories for images and grids are chosen. Leave empty for default.",
+ "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
+
+ "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
+ "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.",
+ "Final denoising strength": "The denoising strength for the final loop of each image in the batch.",
+ "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.",
+
+ "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+ "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",
+ "Apply style": "Insert selected styles into prompt fields",
+ "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style uses that as a placeholder for your prompt when you use the style in the future.",
+
+ "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
+ "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",
+
+ "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
+
+ "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
+ "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
+
+ "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
+
+ "Weighted sum": "Result = A * (1 - M) + B * M",
+ "Add difference": "Result = A + (B - C) * M",
+ "No interpolation": "Result = A",
+
+ "Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
+ "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+
+ "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
+
+ "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resolution and lower quality.",
+ "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resolution and extremely low quality.",
+
+ "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+ "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",
+ "Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",
+ "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",
+ "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
+ "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
+ "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order listed.",
+ "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
+};
+
+function updateTooltip(element) {
+ if (element.title) return; // already has a title
+
+ let text = element.textContent;
+ let tooltip = localization[titles[text]] || titles[text];
+
+ if (!tooltip) {
+ let value = element.value;
+ if (value) tooltip = localization[titles[value]] || titles[value];
+ }
+
+ if (!tooltip) {
+ // Gradio dropdown options have `data-value`.
+ let dataValue = element.dataset.value;
+ if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue];
+ }
+
+ if (!tooltip) {
+ for (const c of element.classList) {
+ if (c in titles) {
+ tooltip = localization[titles[c]] || titles[c];
+ break;
+ }
+ }
+ }
+
+ if (tooltip) {
+ element.title = tooltip;
+ }
+}
+
+// Nodes to check for adding tooltips.
+const tooltipCheckNodes = new Set();
+// Timer for debouncing tooltip check.
+let tooltipCheckTimer = null;
+
+function processTooltipCheckNodes() {
+ for (const node of tooltipCheckNodes) {
+ updateTooltip(node);
+ }
+ tooltipCheckNodes.clear();
+}
+
+onUiUpdate(function(mutationRecords) {
+ for (const record of mutationRecords) {
+ if (record.type === "childList" && record.target.classList.contains("options")) {
+ // This smells like a Gradio dropdown menu having changed,
+ // so let's enqueue an update for the input element that shows the current value.
+ let wrap = record.target.parentNode;
+ let input = wrap?.querySelector("input");
+ if (input) {
+ input.title = ""; // So we'll even have a chance to update it.
+ tooltipCheckNodes.add(input);
+ }
+ }
+ for (const node of record.addedNodes) {
+ if (node.nodeType === Node.ELEMENT_NODE && !node.classList.contains("hide")) {
+ if (!node.title) {
+ if (
+ node.tagName === "SPAN" ||
+ node.tagName === "BUTTON" ||
+ node.tagName === "P" ||
+ node.tagName === "INPUT" ||
+ (node.tagName === "LI" && node.classList.contains("item")) // Gradio dropdown item
+ ) {
+ tooltipCheckNodes.add(node);
+ }
+ }
+ node.querySelectorAll('span, button, p').forEach(n => tooltipCheckNodes.add(n));
+ }
+ }
+ }
+ if (tooltipCheckNodes.size) {
+ clearTimeout(tooltipCheckTimer);
+ tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000);
+ }
+});
+
+onUiLoaded(function() {
+ for (var comp of window.gradio_config.components) {
+ if (comp.props.webui_tooltip && comp.props.elem_id) {
+ var elem = gradioApp().getElementById(comp.props.elem_id);
+ if (elem) {
+ elem.title = comp.props.webui_tooltip;
+ }
+ }
+ }
+});
diff --git a/stable-diffusion-webui/javascript/hires_fix.js b/stable-diffusion-webui/javascript/hires_fix.js
new file mode 100644
index 0000000000000000000000000000000000000000..0d04ab3b424338634af3e71a2f9d8796a5f00224
--- /dev/null
+++ b/stable-diffusion-webui/javascript/hires_fix.js
@@ -0,0 +1,18 @@
+
+function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y) {
+ function setInactive(elem, inactive) {
+ elem.classList.toggle('inactive', !!inactive);
+ }
+
+ var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale');
+ var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x');
+ var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y');
+
+ gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : "";
+
+ setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0);
+ setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0);
+ setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0);
+
+ return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y];
+}
diff --git a/stable-diffusion-webui/javascript/imageMaskFix.js b/stable-diffusion-webui/javascript/imageMaskFix.js
new file mode 100644
index 0000000000000000000000000000000000000000..900c56f32fdf7128f0433621df25a0fbd14c4e42
--- /dev/null
+++ b/stable-diffusion-webui/javascript/imageMaskFix.js
@@ -0,0 +1,43 @@
+/**
+ * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
+ * @see https://github.com/gradio-app/gradio/issues/1721
+ */
+function imageMaskResize() {
+ const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
+ if (!canvases.length) {
+ window.removeEventListener('resize', imageMaskResize);
+ return;
+ }
+
+ const wrapper = canvases[0].closest('.touch-none');
+ const previewImage = wrapper.previousElementSibling;
+
+ if (!previewImage.complete) {
+ previewImage.addEventListener('load', imageMaskResize);
+ return;
+ }
+
+ const w = previewImage.width;
+ const h = previewImage.height;
+ const nw = previewImage.naturalWidth;
+ const nh = previewImage.naturalHeight;
+ const portrait = nh > nw;
+
+ const wW = Math.min(w, portrait ? h / nh * nw : w / nw * nw);
+ const wH = Math.min(h, portrait ? h / nh * nh : w / nw * nh);
+
+ wrapper.style.width = `${wW}px`;
+ wrapper.style.height = `${wH}px`;
+ wrapper.style.left = `0px`;
+ wrapper.style.top = `0px`;
+
+ canvases.forEach(c => {
+ c.style.width = c.style.height = '';
+ c.style.maxWidth = '100%';
+ c.style.maxHeight = '100%';
+ c.style.objectFit = 'contain';
+ });
+}
+
+onAfterUiUpdate(imageMaskResize);
+window.addEventListener('resize', imageMaskResize);
diff --git a/stable-diffusion-webui/javascript/imageviewer.js b/stable-diffusion-webui/javascript/imageviewer.js
new file mode 100644
index 0000000000000000000000000000000000000000..c21d396eefd5283691091fc5b87aba570a325297
--- /dev/null
+++ b/stable-diffusion-webui/javascript/imageviewer.js
@@ -0,0 +1,259 @@
+// A full size 'lightbox' preview modal shown when left clicking on gallery previews
+function closeModal() {
+ gradioApp().getElementById("lightboxModal").style.display = "none";
+}
+
+function showModal(event) {
+ const source = event.target || event.srcElement;
+ const modalImage = gradioApp().getElementById("modalImage");
+ const lb = gradioApp().getElementById("lightboxModal");
+ modalImage.src = source.src;
+ if (modalImage.style.display === 'none') {
+ lb.style.setProperty('background-image', 'url(' + source.src + ')');
+ }
+ lb.style.display = "flex";
+ lb.focus();
+
+ const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
+ const tabImg2Img = gradioApp().getElementById("tab_img2img");
+ // show the save button in modal only on txt2img or img2img tabs
+ if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
+ gradioApp().getElementById("modal_save").style.display = "inline";
+ } else {
+ gradioApp().getElementById("modal_save").style.display = "none";
+ }
+ event.stopPropagation();
+}
+
+function negmod(n, m) {
+ return ((n % m) + m) % m;
+}
+
+function updateOnBackgroundChange() {
+ const modalImage = gradioApp().getElementById("modalImage");
+ if (modalImage && modalImage.offsetParent) {
+ let currentButton = selected_gallery_button();
+
+ if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+ modalImage.src = currentButton.children[0].src;
+ if (modalImage.style.display === 'none') {
+ const modal = gradioApp().getElementById("lightboxModal");
+ modal.style.setProperty('background-image', `url(${modalImage.src})`);
+ }
+ }
+ }
+}
+
+function modalImageSwitch(offset) {
+ var galleryButtons = all_gallery_buttons();
+
+ if (galleryButtons.length > 1) {
+ var currentButton = selected_gallery_button();
+
+ var result = -1;
+ galleryButtons.forEach(function(v, i) {
+ if (v == currentButton) {
+ result = i;
+ }
+ });
+
+ if (result != -1) {
+ var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];
+ nextButton.click();
+ const modalImage = gradioApp().getElementById("modalImage");
+ const modal = gradioApp().getElementById("lightboxModal");
+ modalImage.src = nextButton.children[0].src;
+ if (modalImage.style.display === 'none') {
+ modal.style.setProperty('background-image', `url(${modalImage.src})`);
+ }
+ setTimeout(function() {
+ modal.focus();
+ }, 10);
+ }
+ }
+}
+
+function saveImage() {
+ const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
+ const tabImg2Img = gradioApp().getElementById("tab_img2img");
+ const saveTxt2Img = "save_txt2img";
+ const saveImg2Img = "save_img2img";
+ if (tabTxt2Img.style.display != "none") {
+ gradioApp().getElementById(saveTxt2Img).click();
+ } else if (tabImg2Img.style.display != "none") {
+ gradioApp().getElementById(saveImg2Img).click();
+ } else {
+ console.error("missing implementation for saving modal of this type");
+ }
+}
+
+function modalSaveImage(event) {
+ saveImage();
+ event.stopPropagation();
+}
+
+function modalNextImage(event) {
+ modalImageSwitch(1);
+ event.stopPropagation();
+}
+
+function modalPrevImage(event) {
+ modalImageSwitch(-1);
+ event.stopPropagation();
+}
+
+function modalKeyHandler(event) {
+ switch (event.key) {
+ case "s":
+ saveImage();
+ break;
+ case "ArrowLeft":
+ modalPrevImage(event);
+ break;
+ case "ArrowRight":
+ modalNextImage(event);
+ break;
+ case "Escape":
+ closeModal();
+ break;
+ }
+}
+
+function setupImageForLightbox(e) {
+ if (e.dataset.modded) {
+ return;
+ }
+
+ e.dataset.modded = true;
+ e.style.cursor = 'pointer';
+ e.style.userSelect = 'none';
+
+ var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;
+
+ // For Firefox, listening on click first switched to next image then shows the lightbox.
+ // If you know how to fix this without switching to mousedown event, please.
+ // For other browsers the event is click to make it possiblr to drag picture.
+ var event = isFirefox ? 'mousedown' : 'click';
+
+ e.addEventListener(event, function(evt) {
+ if (evt.button == 1) {
+ open(evt.target.src);
+ evt.preventDefault();
+ return;
+ }
+ if (!opts.js_modal_lightbox || evt.button != 0) return;
+
+ modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed);
+ evt.preventDefault();
+ showModal(evt);
+ }, true);
+
+}
+
+function modalZoomSet(modalImage, enable) {
+ if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
+}
+
+function modalZoomToggle(event) {
+ var modalImage = gradioApp().getElementById("modalImage");
+ modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'));
+ event.stopPropagation();
+}
+
+function modalTileImageToggle(event) {
+ const modalImage = gradioApp().getElementById("modalImage");
+ const modal = gradioApp().getElementById("lightboxModal");
+ const isTiling = modalImage.style.display === 'none';
+ if (isTiling) {
+ modalImage.style.display = 'block';
+ modal.style.setProperty('background-image', 'none');
+ } else {
+ modalImage.style.display = 'none';
+ modal.style.setProperty('background-image', `url(${modalImage.src})`);
+ }
+
+ event.stopPropagation();
+}
+
+onAfterUiUpdate(function() {
+ var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img');
+ if (fullImg_preview != null) {
+ fullImg_preview.forEach(setupImageForLightbox);
+ }
+ updateOnBackgroundChange();
+});
+
+document.addEventListener("DOMContentLoaded", function() {
+ //const modalFragment = document.createDocumentFragment();
+ const modal = document.createElement('div');
+ modal.onclick = closeModal;
+ modal.id = "lightboxModal";
+ modal.tabIndex = 0;
+ modal.addEventListener('keydown', modalKeyHandler, true);
+
+ const modalControls = document.createElement('div');
+ modalControls.className = 'modalControls gradio-container';
+ modal.append(modalControls);
+
+ const modalZoom = document.createElement('span');
+ modalZoom.className = 'modalZoom cursor';
+ modalZoom.innerHTML = '⤡';
+ modalZoom.addEventListener('click', modalZoomToggle, true);
+ modalZoom.title = "Toggle zoomed view";
+ modalControls.appendChild(modalZoom);
+
+ const modalTileImage = document.createElement('span');
+ modalTileImage.className = 'modalTileImage cursor';
+ modalTileImage.innerHTML = '⊞';
+ modalTileImage.addEventListener('click', modalTileImageToggle, true);
+ modalTileImage.title = "Preview tiling";
+ modalControls.appendChild(modalTileImage);
+
+ const modalSave = document.createElement("span");
+ modalSave.className = "modalSave cursor";
+ modalSave.id = "modal_save";
+ modalSave.innerHTML = "🖫";
+ modalSave.addEventListener("click", modalSaveImage, true);
+ modalSave.title = "Save Image(s)";
+ modalControls.appendChild(modalSave);
+
+ const modalClose = document.createElement('span');
+ modalClose.className = 'modalClose cursor';
+ modalClose.innerHTML = '×';
+ modalClose.onclick = closeModal;
+ modalClose.title = "Close image viewer";
+ modalControls.appendChild(modalClose);
+
+ const modalImage = document.createElement('img');
+ modalImage.id = 'modalImage';
+ modalImage.onclick = closeModal;
+ modalImage.tabIndex = 0;
+ modalImage.addEventListener('keydown', modalKeyHandler, true);
+ modal.appendChild(modalImage);
+
+ const modalPrev = document.createElement('a');
+ modalPrev.className = 'modalPrev';
+ modalPrev.innerHTML = '❮';
+ modalPrev.tabIndex = 0;
+ modalPrev.addEventListener('click', modalPrevImage, true);
+ modalPrev.addEventListener('keydown', modalKeyHandler, true);
+ modal.appendChild(modalPrev);
+
+ const modalNext = document.createElement('a');
+ modalNext.className = 'modalNext';
+ modalNext.innerHTML = '❯';
+ modalNext.tabIndex = 0;
+ modalNext.addEventListener('click', modalNextImage, true);
+ modalNext.addEventListener('keydown', modalKeyHandler, true);
+
+ modal.appendChild(modalNext);
+
+ try {
+ gradioApp().appendChild(modal);
+ } catch (e) {
+ gradioApp().body.appendChild(modal);
+ }
+
+ document.body.appendChild(modal);
+
+});
diff --git a/stable-diffusion-webui/javascript/imageviewerGamepad.js b/stable-diffusion-webui/javascript/imageviewerGamepad.js
new file mode 100644
index 0000000000000000000000000000000000000000..a22c7e6e6435f677c7a86dbbae5da86af8fdc9eb
--- /dev/null
+++ b/stable-diffusion-webui/javascript/imageviewerGamepad.js
@@ -0,0 +1,63 @@
+let gamepads = [];
+
+window.addEventListener('gamepadconnected', (e) => {
+ const index = e.gamepad.index;
+ let isWaiting = false;
+ gamepads[index] = setInterval(async() => {
+ if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
+ const gamepad = navigator.getGamepads()[index];
+ const xValue = gamepad.axes[0];
+ if (xValue <= -0.3) {
+ modalPrevImage(e);
+ isWaiting = true;
+ } else if (xValue >= 0.3) {
+ modalNextImage(e);
+ isWaiting = true;
+ }
+ if (isWaiting) {
+ await sleepUntil(() => {
+ const xValue = navigator.getGamepads()[index].axes[0];
+ if (xValue < 0.3 && xValue > -0.3) {
+ return true;
+ }
+ }, opts.js_modal_lightbox_gamepad_repeat);
+ isWaiting = false;
+ }
+ }, 10);
+});
+
+window.addEventListener('gamepaddisconnected', (e) => {
+ clearInterval(gamepads[e.gamepad.index]);
+});
+
+/*
+Primarily for vr controller type pointer devices.
+I use the wheel event because there's currently no way to do it properly with web xr.
+ */
+let isScrolling = false;
+window.addEventListener('wheel', (e) => {
+ if (!opts.js_modal_lightbox_gamepad || isScrolling) return;
+ isScrolling = true;
+
+ if (e.deltaX <= -0.6) {
+ modalPrevImage(e);
+ } else if (e.deltaX >= 0.6) {
+ modalNextImage(e);
+ }
+
+ setTimeout(() => {
+ isScrolling = false;
+ }, opts.js_modal_lightbox_gamepad_repeat);
+});
+
+function sleepUntil(f, timeout) {
+ return new Promise((resolve) => {
+ const timeStart = new Date();
+ const wait = setInterval(function() {
+ if (f() || new Date() - timeStart > timeout) {
+ clearInterval(wait);
+ resolve();
+ }
+ }, 20);
+ });
+}
diff --git a/stable-diffusion-webui/javascript/inputAccordion.js b/stable-diffusion-webui/javascript/inputAccordion.js
new file mode 100644
index 0000000000000000000000000000000000000000..f2839852ee710bc1f4ae03e6788c1781001006a0
--- /dev/null
+++ b/stable-diffusion-webui/javascript/inputAccordion.js
@@ -0,0 +1,37 @@
+var observerAccordionOpen = new MutationObserver(function(mutations) {
+ mutations.forEach(function(mutationRecord) {
+ var elem = mutationRecord.target;
+ var open = elem.classList.contains('open');
+
+ var accordion = elem.parentNode;
+ accordion.classList.toggle('input-accordion-open', open);
+
+ var checkbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
+ checkbox.checked = open;
+ updateInput(checkbox);
+
+ var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
+ if (extra) {
+ extra.style.display = open ? "" : "none";
+ }
+ });
+});
+
+function inputAccordionChecked(id, checked) {
+ var label = gradioApp().querySelector('#' + id + " .label-wrap");
+ if (label.classList.contains('open') != checked) {
+ label.click();
+ }
+}
+
+onUiLoaded(function() {
+ for (var accordion of gradioApp().querySelectorAll('.input-accordion')) {
+ var labelWrap = accordion.querySelector('.label-wrap');
+ observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
+
+ var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
+ if (extra) {
+ labelWrap.insertBefore(extra, labelWrap.lastElementChild);
+ }
+ }
+});
diff --git a/stable-diffusion-webui/javascript/localStorage.js b/stable-diffusion-webui/javascript/localStorage.js
new file mode 100644
index 0000000000000000000000000000000000000000..dc1a36c328799ea3df1843001d397aa638935952
--- /dev/null
+++ b/stable-diffusion-webui/javascript/localStorage.js
@@ -0,0 +1,26 @@
+
+function localSet(k, v) {
+ try {
+ localStorage.setItem(k, v);
+ } catch (e) {
+ console.warn(`Failed to save ${k} to localStorage: ${e}`);
+ }
+}
+
+function localGet(k, def) {
+ try {
+ return localStorage.getItem(k);
+ } catch (e) {
+ console.warn(`Failed to load ${k} from localStorage: ${e}`);
+ }
+
+ return def;
+}
+
+function localRemove(k) {
+ try {
+ return localStorage.removeItem(k);
+ } catch (e) {
+ console.warn(`Failed to remove ${k} from localStorage: ${e}`);
+ }
+}
diff --git a/stable-diffusion-webui/javascript/localization.js b/stable-diffusion-webui/javascript/localization.js
new file mode 100644
index 0000000000000000000000000000000000000000..8f00c18686057e3e12154f657170b014b13320a5
--- /dev/null
+++ b/stable-diffusion-webui/javascript/localization.js
@@ -0,0 +1,205 @@
+
+// localization = {} -- the dict with translations is created by the backend
+
+var ignore_ids_for_localization = {
+ setting_sd_hypernetwork: 'OPTION',
+ setting_sd_model_checkpoint: 'OPTION',
+ modelmerger_primary_model_name: 'OPTION',
+ modelmerger_secondary_model_name: 'OPTION',
+ modelmerger_tertiary_model_name: 'OPTION',
+ train_embedding: 'OPTION',
+ train_hypernetwork: 'OPTION',
+ txt2img_styles: 'OPTION',
+ img2img_styles: 'OPTION',
+ setting_random_artist_categories: 'OPTION',
+ setting_face_restoration_model: 'OPTION',
+ setting_realesrgan_enabled_models: 'OPTION',
+ extras_upscaler_1: 'OPTION',
+ extras_upscaler_2: 'OPTION',
+};
+
+var re_num = /^[.\d]+$/;
+var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u;
+
+var original_lines = {};
+var translated_lines = {};
+
+function hasLocalization() {
+ return window.localization && Object.keys(window.localization).length > 0;
+}
+
+function textNodesUnder(el) {
+ var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false);
+ while ((n = walk.nextNode())) a.push(n);
+ return a;
+}
+
+function canBeTranslated(node, text) {
+ if (!text) return false;
+ if (!node.parentElement) return false;
+
+ var parentType = node.parentElement.nodeName;
+ if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false;
+
+ if (parentType == 'OPTION' || parentType == 'SPAN') {
+ var pnode = node;
+ for (var level = 0; level < 4; level++) {
+ pnode = pnode.parentElement;
+ if (!pnode) break;
+
+ if (ignore_ids_for_localization[pnode.id] == parentType) return false;
+ }
+ }
+
+ if (re_num.test(text)) return false;
+ if (re_emoji.test(text)) return false;
+ return true;
+}
+
+function getTranslation(text) {
+ if (!text) return undefined;
+
+ if (translated_lines[text] === undefined) {
+ original_lines[text] = 1;
+ }
+
+ var tl = localization[text];
+ if (tl !== undefined) {
+ translated_lines[tl] = 1;
+ }
+
+ return tl;
+}
+
+function processTextNode(node) {
+ var text = node.textContent.trim();
+
+ if (!canBeTranslated(node, text)) return;
+
+ var tl = getTranslation(text);
+ if (tl !== undefined) {
+ node.textContent = tl;
+ }
+}
+
+function processNode(node) {
+ if (node.nodeType == 3) {
+ processTextNode(node);
+ return;
+ }
+
+ if (node.title) {
+ let tl = getTranslation(node.title);
+ if (tl !== undefined) {
+ node.title = tl;
+ }
+ }
+
+ if (node.placeholder) {
+ let tl = getTranslation(node.placeholder);
+ if (tl !== undefined) {
+ node.placeholder = tl;
+ }
+ }
+
+ textNodesUnder(node).forEach(function(node) {
+ processTextNode(node);
+ });
+}
+
+function localizeWholePage() {
+ processNode(gradioApp());
+
+ function elem(comp) {
+ var elem_id = comp.props.elem_id ? comp.props.elem_id : "component-" + comp.id;
+ return gradioApp().getElementById(elem_id);
+ }
+
+ for (var comp of window.gradio_config.components) {
+ if (comp.props.webui_tooltip) {
+ let e = elem(comp);
+
+ let tl = e ? getTranslation(e.title) : undefined;
+ if (tl !== undefined) {
+ e.title = tl;
+ }
+ }
+ if (comp.props.placeholder) {
+ let e = elem(comp);
+ let textbox = e ? e.querySelector('[placeholder]') : null;
+
+ let tl = textbox ? getTranslation(textbox.placeholder) : undefined;
+ if (tl !== undefined) {
+ textbox.placeholder = tl;
+ }
+ }
+ }
+}
+
+function dumpTranslations() {
+ if (!hasLocalization()) {
+ // If we don't have any localization,
+ // we will not have traversed the app to find
+ // original_lines, so do that now.
+ localizeWholePage();
+ }
+ var dumped = {};
+ if (localization.rtl) {
+ dumped.rtl = true;
+ }
+
+ for (const text in original_lines) {
+ if (dumped[text] !== undefined) continue;
+ dumped[text] = localization[text] || text;
+ }
+
+ return dumped;
+}
+
+function download_localization() {
+ var text = JSON.stringify(dumpTranslations(), null, 4);
+
+ var element = document.createElement('a');
+ element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
+ element.setAttribute('download', "localization.json");
+ element.style.display = 'none';
+ document.body.appendChild(element);
+
+ element.click();
+
+ document.body.removeChild(element);
+}
+
+document.addEventListener("DOMContentLoaded", function() {
+ if (!hasLocalization()) {
+ return;
+ }
+
+ onUiUpdate(function(m) {
+ m.forEach(function(mutation) {
+ mutation.addedNodes.forEach(function(node) {
+ processNode(node);
+ });
+ });
+ });
+
+ localizeWholePage();
+
+ if (localization.rtl) { // if the language is from right to left,
+ (new MutationObserver((mutations, observer) => { // wait for the style to load
+ mutations.forEach(mutation => {
+ mutation.addedNodes.forEach(node => {
+ if (node.tagName === 'STYLE') {
+ observer.disconnect();
+
+ for (const x of node.sheet.rules) { // find all rtl media rules
+ if (Array.from(x.media || []).includes('rtl')) {
+ x.media.appendMedium('all'); // enable them
+ }
+ }
+ }
+ });
+ });
+ })).observe(gradioApp(), {childList: true});
+ }
+});
diff --git a/stable-diffusion-webui/javascript/notification.js b/stable-diffusion-webui/javascript/notification.js
new file mode 100644
index 0000000000000000000000000000000000000000..6d79956125c383b963ea0e6a16079a253a666c55
--- /dev/null
+++ b/stable-diffusion-webui/javascript/notification.js
@@ -0,0 +1,49 @@
+// Monitors the gallery and sends a browser notification when the leading image is new.
+
+let lastHeadImg = null;
+
+let notificationButton = null;
+
+onAfterUiUpdate(function() {
+ if (notificationButton == null) {
+ notificationButton = gradioApp().getElementById('request_notifications');
+
+ if (notificationButton != null) {
+ notificationButton.addEventListener('click', () => {
+ void Notification.requestPermission();
+ }, true);
+ }
+ }
+
+ const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"] div[id$="_results"] .thumbnail-item > img');
+
+ if (galleryPreviews == null) return;
+
+ const headImg = galleryPreviews[0]?.src;
+
+ if (headImg == null || headImg == lastHeadImg) return;
+
+ lastHeadImg = headImg;
+
+ // play notification sound if available
+ gradioApp().querySelector('#audio_notification audio')?.play();
+
+ if (document.hasFocus()) return;
+
+ // Multiple copies of the images are in the DOM when one is selected. Dedup with a Set to get the real number generated.
+ const imgs = new Set(Array.from(galleryPreviews).map(img => img.src));
+
+ const notification = new Notification(
+ 'Stable Diffusion',
+ {
+ body: `Generated ${imgs.size > 1 ? imgs.size - opts.return_grid : 1} image${imgs.size > 1 ? 's' : ''}`,
+ icon: headImg,
+ image: headImg,
+ }
+ );
+
+ notification.onclick = function(_) {
+ parent.focus();
+ this.close();
+ };
+});
diff --git a/stable-diffusion-webui/javascript/profilerVisualization.js b/stable-diffusion-webui/javascript/profilerVisualization.js
new file mode 100644
index 0000000000000000000000000000000000000000..9d8e5f42f327f93db42773ebf0b97ee1e9671806
--- /dev/null
+++ b/stable-diffusion-webui/javascript/profilerVisualization.js
@@ -0,0 +1,153 @@
+
+function createRow(table, cellName, items) {
+ var tr = document.createElement('tr');
+ var res = [];
+
+ items.forEach(function(x, i) {
+ if (x === undefined) {
+ res.push(null);
+ return;
+ }
+
+ var td = document.createElement(cellName);
+ td.textContent = x;
+ tr.appendChild(td);
+ res.push(td);
+
+ var colspan = 1;
+ for (var n = i + 1; n < items.length; n++) {
+ if (items[n] !== undefined) {
+ break;
+ }
+
+ colspan += 1;
+ }
+
+ if (colspan > 1) {
+ td.colSpan = colspan;
+ }
+ });
+
+ table.appendChild(tr);
+
+ return res;
+}
+
+function showProfile(path, cutoff = 0.05) {
+ requestGet(path, {}, function(data) {
+ var table = document.createElement('table');
+ table.className = 'popup-table';
+
+ data.records['total'] = data.total;
+ var keys = Object.keys(data.records).sort(function(a, b) {
+ return data.records[b] - data.records[a];
+ });
+ var items = keys.map(function(x) {
+ return {key: x, parts: x.split('/'), time: data.records[x]};
+ });
+ var maxLength = items.reduce(function(a, b) {
+ return Math.max(a, b.parts.length);
+ }, 0);
+
+ var cols = createRow(table, 'th', ['record', 'seconds']);
+ cols[0].colSpan = maxLength;
+
+ function arraysEqual(a, b) {
+ return !(a < b || b < a);
+ }
+
+ var addLevel = function(level, parent, hide) {
+ var matching = items.filter(function(x) {
+ return x.parts[level] && !x.parts[level + 1] && arraysEqual(x.parts.slice(0, level), parent);
+ });
+ var sorted = matching.sort(function(a, b) {
+ return b.time - a.time;
+ });
+ var othersTime = 0;
+ var othersList = [];
+ var othersRows = [];
+ var childrenRows = [];
+ sorted.forEach(function(x) {
+ var visible = x.time >= cutoff && !hide;
+
+ var cells = [];
+ for (var i = 0; i < maxLength; i++) {
+ cells.push(x.parts[i]);
+ }
+ cells.push(x.time.toFixed(3));
+ var cols = createRow(table, 'td', cells);
+ for (i = 0; i < level; i++) {
+ cols[i].className = 'muted';
+ }
+
+ var tr = cols[0].parentNode;
+ if (!visible) {
+ tr.classList.add("hidden");
+ }
+
+ if (x.time >= cutoff) {
+ childrenRows.push(tr);
+ } else {
+ othersTime += x.time;
+ othersList.push(x.parts[level]);
+ othersRows.push(tr);
+ }
+
+ var children = addLevel(level + 1, parent.concat([x.parts[level]]), true);
+ if (children.length > 0) {
+ var cell = cols[level];
+ var onclick = function() {
+ cell.classList.remove("link");
+ cell.removeEventListener("click", onclick);
+ children.forEach(function(x) {
+ x.classList.remove("hidden");
+ });
+ };
+ cell.classList.add("link");
+ cell.addEventListener("click", onclick);
+ }
+ });
+
+ if (othersTime > 0) {
+ var cells = [];
+ for (var i = 0; i < maxLength; i++) {
+ cells.push(parent[i]);
+ }
+ cells.push(othersTime.toFixed(3));
+ cells[level] = 'others';
+ var cols = createRow(table, 'td', cells);
+ for (i = 0; i < level; i++) {
+ cols[i].className = 'muted';
+ }
+
+ var cell = cols[level];
+ var tr = cell.parentNode;
+ var onclick = function() {
+ tr.classList.add("hidden");
+ cell.classList.remove("link");
+ cell.removeEventListener("click", onclick);
+ othersRows.forEach(function(x) {
+ x.classList.remove("hidden");
+ });
+ };
+
+ cell.title = othersList.join(", ");
+ cell.classList.add("link");
+ cell.addEventListener("click", onclick);
+
+ if (hide) {
+ tr.classList.add("hidden");
+ }
+
+ childrenRows.push(tr);
+ }
+
+ return childrenRows;
+ };
+
+ addLevel(0, []);
+
+ popup(table);
+ });
+}
+
diff --git a/stable-diffusion-webui/javascript/progressbar.js b/stable-diffusion-webui/javascript/progressbar.js
new file mode 100644
index 0000000000000000000000000000000000000000..777614954b2d489df32813fb27911dd9bbcd9c9a
--- /dev/null
+++ b/stable-diffusion-webui/javascript/progressbar.js
@@ -0,0 +1,186 @@
+// code related to showing and updating progressbar shown as the image is being made
+
+function rememberGallerySelection() {
+
+}
+
+function getGallerySelectedIndex() {
+
+}
+
+function request(url, data, handler, errorHandler) {
+ var xhr = new XMLHttpRequest();
+ xhr.open("POST", url, true);
+ xhr.setRequestHeader("Content-Type", "application/json");
+ xhr.onreadystatechange = function() {
+ if (xhr.readyState === 4) {
+ if (xhr.status === 200) {
+ try {
+ var js = JSON.parse(xhr.responseText);
+ handler(js);
+ } catch (error) {
+ console.error(error);
+ errorHandler();
+ }
+ } else {
+ errorHandler();
+ }
+ }
+ };
+ var js = JSON.stringify(data);
+ xhr.send(js);
+}
+
+function pad2(x) {
+ return x < 10 ? '0' + x : x;
+}
+
+function formatTime(secs) {
+ if (secs > 3600) {
+ return pad2(Math.floor(secs / 60 / 60)) + ":" + pad2(Math.floor(secs / 60) % 60) + ":" + pad2(Math.floor(secs) % 60);
+ } else if (secs > 60) {
+ return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60);
+ } else {
+ return Math.floor(secs) + "s";
+ }
+}
+
+function setTitle(progress) {
+ var title = 'Stable Diffusion';
+
+ if (opts.show_progress_in_title && progress) {
+ title = '[' + progress.trim() + '] ' + title;
+ }
+
+ if (document.title != title) {
+ document.title = title;
+ }
+}
+
+
+function randomId() {
+ return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + ")";
+}
+
+// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and
+// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd.
+// calls onProgress every time there is a progress update
+function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout = 40) {
+ var dateStart = new Date();
+ var wasEverActive = false;
+ var parentProgressbar = progressbarContainer.parentNode;
+
+ var divProgress = document.createElement('div');
+ divProgress.className = 'progressDiv';
+ divProgress.style.display = opts.show_progressbar ? "block" : "none";
+ var divInner = document.createElement('div');
+ divInner.className = 'progress';
+
+ divProgress.appendChild(divInner);
+ parentProgressbar.insertBefore(divProgress, progressbarContainer);
+
+ var livePreview = null;
+
+ var removeProgressBar = function() {
+ if (!divProgress) return;
+
+ setTitle("");
+ parentProgressbar.removeChild(divProgress);
+ if (gallery && livePreview) gallery.removeChild(livePreview);
+ atEnd();
+
+ divProgress = null;
+ };
+
+ var funProgress = function(id_task) {
+ request("./internal/progress", {id_task: id_task, live_preview: false}, function(res) {
+ if (res.completed) {
+ removeProgressBar();
+ return;
+ }
+
+ let progressText = "";
+
+ divInner.style.width = ((res.progress || 0) * 100.0) + '%';
+ divInner.style.background = res.progress ? "" : "transparent";
+
+ if (res.progress > 0) {
+ progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%';
+ }
+
+ if (res.eta) {
+ progressText += " ETA: " + formatTime(res.eta);
+ }
+
+ setTitle(progressText);
+
+ if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
+ progressText = res.textinfo + " " + progressText;
+ }
+
+ divInner.textContent = progressText;
+
+ var elapsedFromStart = (new Date() - dateStart) / 1000;
+
+ if (res.active) wasEverActive = true;
+
+ if (!res.active && wasEverActive) {
+ removeProgressBar();
+ return;
+ }
+
+ if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) {
+ removeProgressBar();
+ return;
+ }
+
+ if (onProgress) {
+ onProgress(res);
+ }
+
+ setTimeout(() => {
+ funProgress(id_task, res.id_live_preview);
+ }, opts.live_preview_refresh_period || 500);
+ }, function() {
+ removeProgressBar();
+ });
+ };
+
+ var funLivePreview = function(id_task, id_live_preview) {
+ request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
+ if (!divProgress) {
+ return;
+ }
+
+ if (res.live_preview && gallery) {
+ var img = new Image();
+ img.onload = function() {
+ if (!livePreview) {
+ livePreview = document.createElement('div');
+ livePreview.className = 'livePreview';
+ gallery.insertBefore(livePreview, gallery.firstElementChild);
+ }
+
+ livePreview.appendChild(img);
+ if (livePreview.childElementCount > 2) {
+ livePreview.removeChild(livePreview.firstElementChild);
+ }
+ };
+ img.src = res.live_preview;
+ }
+
+ setTimeout(() => {
+ funLivePreview(id_task, res.id_live_preview);
+ }, opts.live_preview_refresh_period || 500);
+ }, function() {
+ removeProgressBar();
+ });
+ };
+
+ funProgress(id_task, 0);
+
+ if (gallery) {
+ funLivePreview(id_task, 0);
+ }
+
+}
diff --git a/stable-diffusion-webui/javascript/resizeHandle.js b/stable-diffusion-webui/javascript/resizeHandle.js
new file mode 100644
index 0000000000000000000000000000000000000000..8c5c5169210603ea229b96b746f9eb16ee4bfe56
--- /dev/null
+++ b/stable-diffusion-webui/javascript/resizeHandle.js
@@ -0,0 +1,141 @@
+(function() {
+ const GRADIO_MIN_WIDTH = 320;
+ const GRID_TEMPLATE_COLUMNS = '1fr 16px 1fr';
+ const PAD = 16;
+ const DEBOUNCE_TIME = 100;
+
+ const R = {
+ tracking: false,
+ parent: null,
+ parentWidth: null,
+ leftCol: null,
+ leftColStartWidth: null,
+ screenX: null,
+ };
+
+ let resizeTimer;
+ let parents = [];
+
+ function setLeftColGridTemplate(el, width) {
+ el.style.gridTemplateColumns = `${width}px 16px 1fr`;
+ }
+
+ function displayResizeHandle(parent) {
+ if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) {
+ parent.style.display = 'flex';
+ if (R.handle != null) {
+ R.handle.style.opacity = '0';
+ }
+ return false;
+ } else {
+ parent.style.display = 'grid';
+ if (R.handle != null) {
+ R.handle.style.opacity = '100';
+ }
+ return true;
+ }
+ }
+
+ function afterResize(parent) {
+ if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != GRID_TEMPLATE_COLUMNS) {
+ const oldParentWidth = R.parentWidth;
+ const newParentWidth = parent.offsetWidth;
+ const widthL = parseInt(parent.style.gridTemplateColumns.split(' ')[0]);
+
+ const ratio = newParentWidth / oldParentWidth;
+
+ const newWidthL = Math.max(Math.floor(ratio * widthL), GRADIO_MIN_WIDTH);
+ setLeftColGridTemplate(parent, newWidthL);
+
+ R.parentWidth = newParentWidth;
+ }
+ }
+
+ function setup(parent) {
+ const leftCol = parent.firstElementChild;
+ const rightCol = parent.lastElementChild;
+
+ parents.push(parent);
+
+ parent.style.display = 'grid';
+ parent.style.gap = '0';
+ parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
+
+ const resizeHandle = document.createElement('div');
+ resizeHandle.classList.add('resize-handle');
+ parent.insertBefore(resizeHandle, rightCol);
+
+ resizeHandle.addEventListener('mousedown', (evt) => {
+ if (evt.button !== 0) return;
+
+ evt.preventDefault();
+ evt.stopPropagation();
+
+ document.body.classList.add('resizing');
+
+ R.tracking = true;
+ R.parent = parent;
+ R.parentWidth = parent.offsetWidth;
+ R.handle = resizeHandle;
+ R.leftCol = leftCol;
+ R.leftColStartWidth = leftCol.offsetWidth;
+ R.screenX = evt.screenX;
+ });
+
+ resizeHandle.addEventListener('dblclick', (evt) => {
+ evt.preventDefault();
+ evt.stopPropagation();
+
+ parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
+ });
+
+ afterResize(parent);
+ }
+
+ window.addEventListener('mousemove', (evt) => {
+ if (evt.button !== 0) return;
+
+ if (R.tracking) {
+ evt.preventDefault();
+ evt.stopPropagation();
+
+ const delta = R.screenX - evt.screenX;
+ const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH);
+ setLeftColGridTemplate(R.parent, leftColWidth);
+ }
+ });
+
+ window.addEventListener('mouseup', (evt) => {
+ if (evt.button !== 0) return;
+
+ if (R.tracking) {
+ evt.preventDefault();
+ evt.stopPropagation();
+
+ R.tracking = false;
+
+ document.body.classList.remove('resizing');
+ }
+ });
+
+
+ window.addEventListener('resize', () => {
+ clearTimeout(resizeTimer);
+
+ resizeTimer = setTimeout(function() {
+ for (const parent of parents) {
+ afterResize(parent);
+ }
+ }, DEBOUNCE_TIME);
+ });
+
+ setupResizeHandle = setup;
+})();
+
+onUiLoaded(function() {
+ for (var elem of gradioApp().querySelectorAll('.resize-handle-row')) {
+ if (!elem.querySelector('.resize-handle')) {
+ setupResizeHandle(elem);
+ }
+ }
+});
diff --git a/stable-diffusion-webui/javascript/textualInversion.js b/stable-diffusion-webui/javascript/textualInversion.js
new file mode 100644
index 0000000000000000000000000000000000000000..20443fcca01bbba6712e40136c57dbcdb78ca945
--- /dev/null
+++ b/stable-diffusion-webui/javascript/textualInversion.js
@@ -0,0 +1,17 @@
+
+
+
+function start_training_textual_inversion() {
+ gradioApp().querySelector('#ti_error').innerHTML = '';
+
+ var id = randomId();
+ requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) {
+ gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo;
+ });
+
+ var res = Array.from(arguments);
+
+ res[0] = id;
+
+ return res;
+}
diff --git a/stable-diffusion-webui/javascript/token-counters.js b/stable-diffusion-webui/javascript/token-counters.js
new file mode 100644
index 0000000000000000000000000000000000000000..9d81a723b01f8b6e3c0894b7a5191dc6b1614c2d
--- /dev/null
+++ b/stable-diffusion-webui/javascript/token-counters.js
@@ -0,0 +1,83 @@
+let promptTokenCountDebounceTime = 800;
+let promptTokenCountTimeouts = {};
+var promptTokenCountUpdateFunctions = {};
+
+function update_txt2img_tokens(...args) {
+ // Called from Gradio
+ update_token_counter("txt2img_token_button");
+ if (args.length == 2) {
+ return args[0];
+ }
+ return args;
+}
+
+function update_img2img_tokens(...args) {
+ // Called from Gradio
+ update_token_counter("img2img_token_button");
+ if (args.length == 2) {
+ return args[0];
+ }
+ return args;
+}
+
+function update_token_counter(button_id) {
+ if (opts.disable_token_counters) {
+ return;
+ }
+ if (promptTokenCountTimeouts[button_id]) {
+ clearTimeout(promptTokenCountTimeouts[button_id]);
+ }
+ promptTokenCountTimeouts[button_id] = setTimeout(
+ () => gradioApp().getElementById(button_id)?.click(),
+ promptTokenCountDebounceTime,
+ );
+}
+
+
+function recalculatePromptTokens(name) {
+ promptTokenCountUpdateFunctions[name]?.();
+}
+
+function recalculate_prompts_txt2img() {
+ // Called from Gradio
+ recalculatePromptTokens('txt2img_prompt');
+ recalculatePromptTokens('txt2img_neg_prompt');
+ return Array.from(arguments);
+}
+
+function recalculate_prompts_img2img() {
+ // Called from Gradio
+ recalculatePromptTokens('img2img_prompt');
+ recalculatePromptTokens('img2img_neg_prompt');
+ return Array.from(arguments);
+}
+
+function setupTokenCounting(id, id_counter, id_button) {
+ var prompt = gradioApp().getElementById(id);
+ var counter = gradioApp().getElementById(id_counter);
+ var textarea = gradioApp().querySelector(`#${id} > label > textarea`);
+
+ if (opts.disable_token_counters) {
+ counter.style.display = "none";
+ return;
+ }
+
+ if (counter.parentElement == prompt.parentElement) {
+ return;
+ }
+
+ prompt.parentElement.insertBefore(counter, prompt);
+ prompt.parentElement.style.position = "relative";
+
+ promptTokenCountUpdateFunctions[id] = function() {
+ update_token_counter(id_button);
+ };
+ textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
+}
+
+function setupTokenCounters() {
+ setupTokenCounting('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
+ setupTokenCounting('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
+ setupTokenCounting('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
+ setupTokenCounting('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
+}
diff --git a/stable-diffusion-webui/javascript/ui.js b/stable-diffusion-webui/javascript/ui.js
new file mode 100644
index 0000000000000000000000000000000000000000..bedcbf3e211f5bc1222f2ad2f28c4622614e32a5
--- /dev/null
+++ b/stable-diffusion-webui/javascript/ui.js
@@ -0,0 +1,368 @@
+// various functions for interaction with ui.py not large enough to warrant putting them in separate files
+
+function set_theme(theme) {
+ var gradioURL = window.location.href;
+ if (!gradioURL.includes('?__theme=')) {
+ window.location.replace(gradioURL + '?__theme=' + theme);
+ }
+}
+
+function all_gallery_buttons() {
+ var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small');
+ var visibleGalleryButtons = [];
+ allGalleryButtons.forEach(function(elem) {
+ if (elem.parentElement.offsetParent) {
+ visibleGalleryButtons.push(elem);
+ }
+ });
+ return visibleGalleryButtons;
+}
+
+function selected_gallery_button() {
+ return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null;
+}
+
+function selected_gallery_index() {
+ return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected'));
+}
+
+function extract_image_from_gallery(gallery) {
+ if (gallery.length == 0) {
+ return [null];
+ }
+ if (gallery.length == 1) {
+ return [gallery[0]];
+ }
+
+ var index = selected_gallery_index();
+
+ if (index < 0 || index >= gallery.length) {
+ // Use the first image in the gallery as the default
+ index = 0;
+ }
+
+ return [gallery[index]];
+}
+
+window.args_to_array = Array.from; // Compatibility with e.g. extensions that may expect this to be around
+
+function switch_to_txt2img() {
+ gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click();
+
+ return Array.from(arguments);
+}
+
+function switch_to_img2img_tab(no) {
+ gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
+ gradioApp().getElementById('mode_img2img').querySelectorAll('button')[no].click();
+}
+function switch_to_img2img() {
+ switch_to_img2img_tab(0);
+ return Array.from(arguments);
+}
+
+function switch_to_sketch() {
+ switch_to_img2img_tab(1);
+ return Array.from(arguments);
+}
+
+function switch_to_inpaint() {
+ switch_to_img2img_tab(2);
+ return Array.from(arguments);
+}
+
+function switch_to_inpaint_sketch() {
+ switch_to_img2img_tab(3);
+ return Array.from(arguments);
+}
+
+function switch_to_extras() {
+ gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
+
+ return Array.from(arguments);
+}
+
+function get_tab_index(tabId) {
+ let buttons = gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button');
+ for (let i = 0; i < buttons.length; i++) {
+ if (buttons[i].classList.contains('selected')) {
+ return i;
+ }
+ }
+ return 0;
+}
+
+function create_tab_index_args(tabId, args) {
+ var res = Array.from(args);
+ res[0] = get_tab_index(tabId);
+ return res;
+}
+
+function get_img2img_tab_index() {
+ let res = Array.from(arguments);
+ res.splice(-2);
+ res[0] = get_tab_index('mode_img2img');
+ return res;
+}
+
+function create_submit_args(args) {
+ var res = Array.from(args);
+
+ // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
+ // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
+ // I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
+ // If gradio at some point stops sending outputs, this may break something
+ if (Array.isArray(res[res.length - 3])) {
+ res[res.length - 3] = null;
+ }
+
+ return res;
+}
+
+function showSubmitButtons(tabname, show) {
+ gradioApp().getElementById(tabname + '_interrupt').style.display = show ? "none" : "block";
+ gradioApp().getElementById(tabname + '_skip').style.display = show ? "none" : "block";
+}
+
+function showRestoreProgressButton(tabname, show) {
+ var button = gradioApp().getElementById(tabname + "_restore_progress");
+ if (!button) return;
+
+ button.style.display = show ? "flex" : "none";
+}
+
+function submit() {
+ showSubmitButtons('txt2img', false);
+
+ var id = randomId();
+ localSet("txt2img_task_id", id);
+
+ requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
+ showSubmitButtons('txt2img', true);
+ localRemove("txt2img_task_id");
+ showRestoreProgressButton('txt2img', false);
+ });
+
+ var res = create_submit_args(arguments);
+
+ res[0] = id;
+
+ return res;
+}
+
+function submit_img2img() {
+ showSubmitButtons('img2img', false);
+
+ var id = randomId();
+ localSet("img2img_task_id", id);
+
+ requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
+ showSubmitButtons('img2img', true);
+ localRemove("img2img_task_id");
+ showRestoreProgressButton('img2img', false);
+ });
+
+ var res = create_submit_args(arguments);
+
+ res[0] = id;
+ res[1] = get_tab_index('mode_img2img');
+
+ return res;
+}
+
+function restoreProgressTxt2img() {
+ showRestoreProgressButton("txt2img", false);
+ var id = localGet("txt2img_task_id");
+
+ if (id) {
+ requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
+ showSubmitButtons('txt2img', true);
+ }, null, 0);
+ }
+
+ return id;
+}
+
+function restoreProgressImg2img() {
+ showRestoreProgressButton("img2img", false);
+
+ var id = localGet("img2img_task_id");
+
+ if (id) {
+ requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
+ showSubmitButtons('img2img', true);
+ }, null, 0);
+ }
+
+ return id;
+}
+
+
+onUiLoaded(function() {
+ showRestoreProgressButton('txt2img', localGet("txt2img_task_id"));
+ showRestoreProgressButton('img2img', localGet("img2img_task_id"));
+});
+
+
+function modelmerger() {
+ var id = randomId();
+ requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function() {});
+
+ var res = create_submit_args(arguments);
+ res[0] = id;
+ return res;
+}
+
+
+function ask_for_style_name(_, prompt_text, negative_prompt_text) {
+ var name_ = prompt('Style name:');
+ return [name_, prompt_text, negative_prompt_text];
+}
+
+function confirm_clear_prompt(prompt, negative_prompt) {
+ if (confirm("Delete prompt?")) {
+ prompt = "";
+ negative_prompt = "";
+ }
+
+ return [prompt, negative_prompt];
+}
+
+
+var opts = {};
+onAfterUiUpdate(function() {
+ if (Object.keys(opts).length != 0) return;
+
+ var json_elem = gradioApp().getElementById('settings_json');
+ if (json_elem == null) return;
+
+ var textarea = json_elem.querySelector('textarea');
+ var jsdata = textarea.value;
+ opts = JSON.parse(jsdata);
+
+ executeCallbacks(optionsChangedCallbacks); /*global optionsChangedCallbacks*/
+
+ Object.defineProperty(textarea, 'value', {
+ set: function(newValue) {
+ var valueProp = Object.getOwnPropertyDescriptor(HTMLTextAreaElement.prototype, 'value');
+ var oldValue = valueProp.get.call(textarea);
+ valueProp.set.call(textarea, newValue);
+
+ if (oldValue != newValue) {
+ opts = JSON.parse(textarea.value);
+ }
+
+ executeCallbacks(optionsChangedCallbacks);
+ },
+ get: function() {
+ var valueProp = Object.getOwnPropertyDescriptor(HTMLTextAreaElement.prototype, 'value');
+ return valueProp.get.call(textarea);
+ }
+ });
+
+ json_elem.parentElement.style.display = "none";
+
+ setupTokenCounters();
+
+ var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
+ var settings_tabs = gradioApp().querySelector('#settings div');
+ if (show_all_pages && settings_tabs) {
+ settings_tabs.appendChild(show_all_pages);
+ show_all_pages.onclick = function() {
+ gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
+ if (elem.id == "settings_tab_licenses") {
+ return;
+ }
+
+ elem.style.display = "block";
+ });
+ };
+ }
+});
+
+onOptionsChanged(function() {
+ var elem = gradioApp().getElementById('sd_checkpoint_hash');
+ var sd_checkpoint_hash = opts.sd_checkpoint_hash || "";
+ var shorthash = sd_checkpoint_hash.substring(0, 10);
+
+ if (elem && elem.textContent != shorthash) {
+ elem.textContent = shorthash;
+ elem.title = sd_checkpoint_hash;
+ elem.href = "https://google.com/search?q=" + sd_checkpoint_hash;
+ }
+});
+
+let txt2img_textarea, img2img_textarea = undefined;
+
+function restart_reload() {
+ document.body.innerHTML = '<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
+
+ var requestPing = function() {
+ requestGet("./internal/ping", {}, function(data) {
+ location.reload();
+ }, function() {
+ setTimeout(requestPing, 500);
+ });
+ };
+
+ setTimeout(requestPing, 2000);
+
+ return [];
+}
+
+// Simulate an `input` DOM event for Gradio Textbox component. Needed after you edit its contents in javascript, otherwise your edits
+// will only visible on web page and not sent to python.
+function updateInput(target) {
+ let e = new Event("input", {bubbles: true});
+ Object.defineProperty(e, "target", {value: target});
+ target.dispatchEvent(e);
+}
+
+
+var desiredCheckpointName = null;
+function selectCheckpoint(name) {
+ desiredCheckpointName = name;
+ gradioApp().getElementById('change_checkpoint').click();
+}
+
+function currentImg2imgSourceResolution(w, h, scaleBy) {
+ var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img');
+ return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy];
+}
+
+function updateImg2imgResizeToTextAfterChangingImage() {
+ // At the time this is called from gradio, the image has no yet been replaced.
+ // There may be a better solution, but this is simple and straightforward so I'm going with it.
+
+ setTimeout(function() {
+ gradioApp().getElementById('img2img_update_resize_to').click();
+ }, 500);
+
+ return [];
+
+}
+
+
+
+function setRandomSeed(elem_id) {
+ var input = gradioApp().querySelector("#" + elem_id + " input");
+ if (!input) return [];
+
+ input.value = "-1";
+ updateInput(input);
+ return [];
+}
+
+function switchWidthHeight(tabname) {
+ var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]");
+ var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]");
+ if (!width || !height) return [];
+
+ var tmp = width.value;
+ width.value = height.value;
+ height.value = tmp;
+
+ updateInput(width);
+ updateInput(height);
+ return [];
+}
diff --git a/stable-diffusion-webui/javascript/ui_settings_hints.js b/stable-diffusion-webui/javascript/ui_settings_hints.js
new file mode 100644
index 0000000000000000000000000000000000000000..d088f9494f826d9534dc105ac2f99bda702d22c0
--- /dev/null
+++ b/stable-diffusion-webui/javascript/ui_settings_hints.js
@@ -0,0 +1,62 @@
+// various hints and extra info for the settings tab
+
+var settingsHintsSetup = false;
+
+onOptionsChanged(function() {
+ if (settingsHintsSetup) return;
+ settingsHintsSetup = true;
+
+ gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div) {
+ var name = div.id.substr(8);
+ var commentBefore = opts._comments_before[name];
+ var commentAfter = opts._comments_after[name];
+
+ if (!commentBefore && !commentAfter) return;
+
+ var span = null;
+ if (div.classList.contains('gradio-checkbox')) span = div.querySelector('label span');
+ else if (div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild;
+ else if (div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild;
+ else span = div.querySelector('label span').firstChild;
+
+ if (!span) return;
+
+ if (commentBefore) {
+ var comment = document.createElement('DIV');
+ comment.className = 'settings-comment';
+ comment.innerHTML = commentBefore;
+ span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
+ span.parentElement.insertBefore(comment, span);
+ span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
+ }
+ if (commentAfter) {
+ comment = document.createElement('DIV');
+ comment.className = 'settings-comment';
+ comment.innerHTML = commentAfter;
+ span.parentElement.insertBefore(comment, span.nextSibling);
+ span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling);
+ }
+ });
+});
+
+function settingsHintsShowQuicksettings() {
+ requestGet("./internal/quicksettings-hint", {}, function(data) {
+ var table = document.createElement('table');
+ table.className = 'popup-table';
+
+ data.forEach(function(obj) {
+ var tr = document.createElement('tr');
+ var td = document.createElement('td');
+ td.textContent = obj.name;
+ tr.appendChild(td);
+
+ td = document.createElement('td');
+ td.textContent = obj.label;
+ tr.appendChild(td);
+
+ table.appendChild(tr);
+ });
+
+ popup(table);
+ });
+}
diff --git a/stable-diffusion-webui/launch.py b/stable-diffusion-webui/launch.py
new file mode 100644
index 0000000000000000000000000000000000000000..cafab78060f727848ac47bd041b1c51d69875c33
--- /dev/null
+++ b/stable-diffusion-webui/launch.py
@@ -0,0 +1,48 @@
+from modules import launch_utils
+
+args = launch_utils.args
+python = launch_utils.python
+git = launch_utils.git
+index_url = launch_utils.index_url
+dir_repos = launch_utils.dir_repos
+
+commit_hash = launch_utils.commit_hash
+git_tag = launch_utils.git_tag
+
+run = launch_utils.run
+is_installed = launch_utils.is_installed
+repo_dir = launch_utils.repo_dir
+
+run_pip = launch_utils.run_pip
+check_run_python = launch_utils.check_run_python
+git_clone = launch_utils.git_clone
+git_pull_recursive = launch_utils.git_pull_recursive
+list_extensions = launch_utils.list_extensions
+run_extension_installer = launch_utils.run_extension_installer
+prepare_environment = launch_utils.prepare_environment
+configure_for_tests = launch_utils.configure_for_tests
+start = launch_utils.start
+
+
+def main():
+ if args.dump_sysinfo:
+ filename = launch_utils.dump_sysinfo()
+
+ print(f"Sysinfo saved as {filename}. Exiting...")
+
+ exit(0)
+
+ launch_utils.startup_timer.record("initial startup")
+
+ with launch_utils.startup_timer.subcategory("prepare environment"):
+ if not args.skip_prepare_environment:
+ prepare_environment()
+
+ if args.test_server:
+ configure_for_tests()
+
+ start()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/stable-diffusion-webui/localizations/Put localization files here.txt b/stable-diffusion-webui/localizations/Put localization files here.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/models/Stable-diffusion/Put Stable Diffusion checkpoints here.txt b/stable-diffusion-webui/models/Stable-diffusion/Put Stable Diffusion checkpoints here.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/models/VAE-approx/model.pt b/stable-diffusion-webui/models/VAE-approx/model.pt
new file mode 100644
index 0000000000000000000000000000000000000000..09c6b8f7fda5e15495c6203ca323d6573745d0af
--- /dev/null
+++ b/stable-diffusion-webui/models/VAE-approx/model.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f88c9078bb2238cdd0d8864671dd33e3f42e091e41f08903f3c15e4a54a9b39
+size 213777
diff --git a/stable-diffusion-webui/models/VAE/Put VAE here.txt b/stable-diffusion-webui/models/VAE/Put VAE here.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/models/deepbooru/Put your deepbooru release project folder here.txt b/stable-diffusion-webui/models/deepbooru/Put your deepbooru release project folder here.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/models/karlo/ViT-L-14_stats.th b/stable-diffusion-webui/models/karlo/ViT-L-14_stats.th
new file mode 100644
index 0000000000000000000000000000000000000000..a6a06e94ecaa4f2977972ff991f75db6c90403ea
Binary files /dev/null and b/stable-diffusion-webui/models/karlo/ViT-L-14_stats.th differ
diff --git a/stable-diffusion-webui/modules/Roboto-Regular.ttf b/stable-diffusion-webui/modules/Roboto-Regular.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..500b1045b0c94d83d2e6798aaf1faa55a2dab6fc
Binary files /dev/null and b/stable-diffusion-webui/modules/Roboto-Regular.ttf differ
diff --git a/stable-diffusion-webui/modules/api/api.py b/stable-diffusion-webui/modules/api/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6edffe7144e539ab970bf85a0bc10e254821ce3
--- /dev/null
+++ b/stable-diffusion-webui/modules/api/api.py
@@ -0,0 +1,788 @@
+import base64
+import io
+import os
+import time
+import datetime
+import uvicorn
+import ipaddress
+import requests
+import gradio as gr
+from threading import Lock
+from io import BytesIO
+from fastapi import APIRouter, Depends, FastAPI, Request, Response
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from fastapi.exceptions import HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
+from secrets import compare_digest
+
+import modules.shared as shared
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items
+from modules.api import models
+from modules.shared import opts
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
+from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
+from modules.textual_inversion.preprocess import preprocess
+from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
+from PIL import PngImagePlugin,Image
+from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
+from modules.sd_models_config import find_checkpoint_config_near_filename
+from modules.realesrgan_model import get_realesrgan_models
+from modules import devices
+from typing import Dict, List, Any
+import piexif
+import piexif.helper
+from contextlib import closing
+
+
+def script_name_to_index(name, scripts):
+ try:
+ return [script.title().lower() for script in scripts].index(name.lower())
+ except Exception as e:
+ raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
+
+
+def validate_sampler_name(name):
+ config = sd_samplers.all_samplers_map.get(name, None)
+ if config is None:
+ raise HTTPException(status_code=404, detail="Sampler not found")
+
+ return name
+
+
+def setUpscalers(req: dict):
+ reqDict = vars(req)
+ reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
+ reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
+ return reqDict
+
+
+def verify_url(url):
+ """Returns True if the url refers to a global resource."""
+
+ import socket
+ from urllib.parse import urlparse
+ try:
+ parsed_url = urlparse(url)
+ domain_name = parsed_url.netloc
+ host = socket.gethostbyname_ex(domain_name)
+ for ip in host[2]:
+ ip_addr = ipaddress.ip_address(ip)
+ if not ip_addr.is_global:
+ return False
+ except Exception:
+ return False
+
+ return True
+
+
+def decode_base64_to_image(encoding):
+ if encoding.startswith("http://") or encoding.startswith("https://"):
+ if not opts.api_enable_requests:
+ raise HTTPException(status_code=500, detail="Requests not allowed")
+
+ if opts.api_forbid_local_requests and not verify_url(encoding):
+ raise HTTPException(status_code=500, detail="Request to local resource not allowed")
+
+ headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {}
+ response = requests.get(encoding, timeout=30, headers=headers)
+ try:
+ image = Image.open(BytesIO(response.content))
+ return image
+ except Exception as e:
+ raise HTTPException(status_code=500, detail="Invalid image url") from e
+
+ if encoding.startswith("data:image/"):
+ encoding = encoding.split(";")[1].split(",")[1]
+ try:
+ image = Image.open(BytesIO(base64.b64decode(encoding)))
+ return image
+ except Exception as e:
+ raise HTTPException(status_code=500, detail="Invalid encoded image") from e
+
+
+def encode_pil_to_base64(image):
+ with io.BytesIO() as output_bytes:
+
+ if opts.samples_format.lower() == 'png':
+ use_metadata = False
+ metadata = PngImagePlugin.PngInfo()
+ for key, value in image.info.items():
+ if isinstance(key, str) and isinstance(value, str):
+ metadata.add_text(key, value)
+ use_metadata = True
+ image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
+
+ elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
+ if image.mode == "RGBA":
+ image = image.convert("RGB")
+ parameters = image.info.get('parameters', None)
+ exif_bytes = piexif.dump({
+ "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
+ })
+ if opts.samples_format.lower() in ("jpg", "jpeg"):
+ image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality)
+ else:
+ image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality)
+
+ else:
+ raise HTTPException(status_code=500, detail="Invalid image format")
+
+ bytes_data = output_bytes.getvalue()
+
+ return base64.b64encode(bytes_data)
+
+
+def api_middleware(app: FastAPI):
+ rich_available = False
+ try:
+ if os.environ.get('WEBUI_RICH_EXCEPTIONS', None) is not None:
+ import anyio # importing just so it can be placed on silent list
+ import starlette # importing just so it can be placed on silent list
+ from rich.console import Console
+ console = Console()
+ rich_available = True
+ except Exception:
+ pass
+
+ @app.middleware("http")
+ async def log_and_time(req: Request, call_next):
+ ts = time.time()
+ res: Response = await call_next(req)
+ duration = str(round(time.time() - ts, 4))
+ res.headers["X-Process-Time"] = duration
+ endpoint = req.scope.get('path', 'err')
+ if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
+ print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
+ t=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
+ code=res.status_code,
+ ver=req.scope.get('http_version', '0.0'),
+ cli=req.scope.get('client', ('0:0.0.0', 0))[0],
+ prot=req.scope.get('scheme', 'err'),
+ method=req.scope.get('method', 'err'),
+ endpoint=endpoint,
+ duration=duration,
+ ))
+ return res
+
+ def handle_exception(request: Request, e: Exception):
+ err = {
+ "error": type(e).__name__,
+ "detail": vars(e).get('detail', ''),
+ "body": vars(e).get('body', ''),
+ "errors": str(e),
+ }
+ if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
+ message = f"API error: {request.method}: {request.url} {err}"
+ if rich_available:
+ print(message)
+ console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
+ else:
+ errors.report(message, exc_info=True)
+ return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
+
+ @app.middleware("http")
+ async def exception_handling(request: Request, call_next):
+ try:
+ return await call_next(request)
+ except Exception as e:
+ return handle_exception(request, e)
+
+ @app.exception_handler(Exception)
+ async def fastapi_exception_handler(request: Request, e: Exception):
+ return handle_exception(request, e)
+
+ @app.exception_handler(HTTPException)
+ async def http_exception_handler(request: Request, e: HTTPException):
+ return handle_exception(request, e)
+
+
+class Api:
+ def __init__(self, app: FastAPI, queue_lock: Lock):
+ if shared.cmd_opts.api_auth:
+ self.credentials = {}
+ for auth in shared.cmd_opts.api_auth.split(","):
+ user, password = auth.split(":")
+ self.credentials[user] = password
+
+ self.router = APIRouter()
+ self.app = app
+ self.queue_lock = queue_lock
+ api_middleware(self.app)
+ self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse)
+ self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse)
+ self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
+ self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
+ self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
+ self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse)
+ self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
+ self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+ self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
+ self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
+ self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
+ self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
+ self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
+ self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
+ self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
+ self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
+ self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
+ self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
+ self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
+ self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
+ self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
+ self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
+ self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
+ self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
+ self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
+ self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
+ self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
+ self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
+ self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
+ self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
+ self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
+ self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
+ self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
+ self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
+
+ if shared.cmd_opts.api_server_stop:
+ self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
+ self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"])
+ self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"])
+
+ self.default_script_arg_txt2img = []
+ self.default_script_arg_img2img = []
+
+ def add_api_route(self, path: str, endpoint, **kwargs):
+ if shared.cmd_opts.api_auth:
+ return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
+ return self.app.add_api_route(path, endpoint, **kwargs)
+
+ def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
+ if credentials.username in self.credentials:
+ if compare_digest(credentials.password, self.credentials[credentials.username]):
+ return True
+
+ raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
+
+ def get_selectable_script(self, script_name, script_runner):
+ if script_name is None or script_name == "":
+ return None, None
+
+ script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
+ script = script_runner.selectable_scripts[script_idx]
+ return script, script_idx
+
+ def get_scripts_list(self):
+ t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None]
+ i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None]
+
+ return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist)
+
+ def get_script_info(self):
+ res = []
+
+ for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]:
+ res += [script.api_info for script in script_list if script.api_info is not None]
+
+ return res
+
+ def get_script(self, script_name, script_runner):
+ if script_name is None or script_name == "":
+ return None, None
+
+ script_idx = script_name_to_index(script_name, script_runner.scripts)
+ return script_runner.scripts[script_idx]
+
+ def init_default_script_args(self, script_runner):
+ #find max idx from the scripts in runner and generate a none array to init script_args
+ last_arg_index = 1
+ for script in script_runner.scripts:
+ if last_arg_index < script.args_to:
+ last_arg_index = script.args_to
+ # None everywhere except position 0 to initialize script args
+ script_args = [None]*last_arg_index
+ script_args[0] = 0
+
+ # get default values
+ with gr.Blocks(): # will throw errors calling ui function without this
+ for script in script_runner.scripts:
+ if script.ui(script.is_img2img):
+ ui_default_values = []
+ for elem in script.ui(script.is_img2img):
+ ui_default_values.append(elem.value)
+ script_args[script.args_from:script.args_to] = ui_default_values
+ return script_args
+
+ def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner):
+ script_args = default_script_args.copy()
+ # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
+ if selectable_scripts:
+ script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
+ script_args[0] = selectable_idx + 1
+
+ # Now check for always on scripts
+ if request.alwayson_scripts:
+ for alwayson_script_name in request.alwayson_scripts.keys():
+ alwayson_script = self.get_script(alwayson_script_name, script_runner)
+ if alwayson_script is None:
+ raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
+ # Selectable script in always on script param check
+ if alwayson_script.alwayson is False:
+ raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params")
+ # always on script with no arg should always run so you don't really need to add them to the requests
+ if "args" in request.alwayson_scripts[alwayson_script_name]:
+ # min between arg length in scriptrunner and arg length in the request
+ for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
+ script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
+ return script_args
+
+ def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
+ script_runner = scripts.scripts_txt2img
+ if not script_runner.scripts:
+ script_runner.initialize_scripts(False)
+ ui.create_ui()
+ if not self.default_script_arg_txt2img:
+ self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
+ selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
+
+ populate = txt2imgreq.copy(update={ # Override __init__ params
+ "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
+ "do_not_save_samples": not txt2imgreq.save_images,
+ "do_not_save_grid": not txt2imgreq.save_images,
+ })
+ if populate.sampler_name:
+ populate.sampler_index = None # prevent a warning later on
+
+ args = vars(populate)
+ args.pop('script_name', None)
+ args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
+ args.pop('alwayson_scripts', None)
+
+ script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
+
+ send_images = args.pop('send_images', True)
+ args.pop('save_images', None)
+
+ with self.queue_lock:
+ with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
+ p.is_api = True
+ p.scripts = script_runner
+ p.outpath_grids = opts.outdir_txt2img_grids
+ p.outpath_samples = opts.outdir_txt2img_samples
+
+ try:
+ shared.state.begin(job="scripts_txt2img")
+ if selectable_scripts is not None:
+ p.script_args = script_args
+ processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+ else:
+ p.script_args = tuple(script_args) # Need to pass args as tuple here
+ processed = process_images(p)
+ finally:
+ shared.state.end()
+ shared.total_tqdm.clear()
+
+ b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
+
+ return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
+
+ def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
+ init_images = img2imgreq.init_images
+ if init_images is None:
+ raise HTTPException(status_code=404, detail="Init image not found")
+
+ mask = img2imgreq.mask
+ if mask:
+ mask = decode_base64_to_image(mask)
+
+ script_runner = scripts.scripts_img2img
+ if not script_runner.scripts:
+ script_runner.initialize_scripts(True)
+ ui.create_ui()
+ if not self.default_script_arg_img2img:
+ self.default_script_arg_img2img = self.init_default_script_args(script_runner)
+ selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
+
+ populate = img2imgreq.copy(update={ # Override __init__ params
+ "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
+ "do_not_save_samples": not img2imgreq.save_images,
+ "do_not_save_grid": not img2imgreq.save_images,
+ "mask": mask,
+ })
+ if populate.sampler_name:
+ populate.sampler_index = None # prevent a warning later on
+
+ args = vars(populate)
+ args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
+ args.pop('script_name', None)
+ args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
+ args.pop('alwayson_scripts', None)
+
+ script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
+
+ send_images = args.pop('send_images', True)
+ args.pop('save_images', None)
+
+ with self.queue_lock:
+ with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
+ p.init_images = [decode_base64_to_image(x) for x in init_images]
+ p.is_api = True
+ p.scripts = script_runner
+ p.outpath_grids = opts.outdir_img2img_grids
+ p.outpath_samples = opts.outdir_img2img_samples
+
+ try:
+ shared.state.begin(job="scripts_img2img")
+ if selectable_scripts is not None:
+ p.script_args = script_args
+ processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+ else:
+ p.script_args = tuple(script_args) # Need to pass args as tuple here
+ processed = process_images(p)
+ finally:
+ shared.state.end()
+ shared.total_tqdm.clear()
+
+ b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
+
+ if not img2imgreq.include_init_images:
+ img2imgreq.init_images = None
+ img2imgreq.mask = None
+
+ return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
+
+ def extras_single_image_api(self, req: models.ExtrasSingleImageRequest):
+ reqDict = setUpscalers(req)
+
+ reqDict['image'] = decode_base64_to_image(reqDict['image'])
+
+ with self.queue_lock:
+ result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
+
+ return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
+
+ def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest):
+ reqDict = setUpscalers(req)
+
+ image_list = reqDict.pop('imageList', [])
+ image_folder = [decode_base64_to_image(x.data) for x in image_list]
+
+ with self.queue_lock:
+ result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
+
+ return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
+
+ def pnginfoapi(self, req: models.PNGInfoRequest):
+ if(not req.image.strip()):
+ return models.PNGInfoResponse(info="")
+
+ image = decode_base64_to_image(req.image.strip())
+ if image is None:
+ return models.PNGInfoResponse(info="")
+
+ geninfo, items = images.read_info_from_image(image)
+ if geninfo is None:
+ geninfo = ""
+
+ items = {**{'parameters': geninfo}, **items}
+
+ return models.PNGInfoResponse(info=geninfo, items=items)
+
+ def progressapi(self, req: models.ProgressRequest = Depends()):
+ # copy from check_progress_call of ui.py
+
+ if shared.state.job_count == 0:
+ return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
+
+ # avoid dividing zero
+ progress = 0.01
+
+ if shared.state.job_count > 0:
+ progress += shared.state.job_no / shared.state.job_count
+ if shared.state.sampling_steps > 0:
+ progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
+
+ time_since_start = time.time() - shared.state.time_start
+ eta = (time_since_start/progress)
+ eta_relative = eta-time_since_start
+
+ progress = min(progress, 1)
+
+ shared.state.set_current_image()
+
+ current_image = None
+ if shared.state.current_image and not req.skip_current_image:
+ current_image = encode_pil_to_base64(shared.state.current_image)
+
+ return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
+
+ def interrogateapi(self, interrogatereq: models.InterrogateRequest):
+ image_b64 = interrogatereq.image
+ if image_b64 is None:
+ raise HTTPException(status_code=404, detail="Image not found")
+
+ img = decode_base64_to_image(image_b64)
+ img = img.convert('RGB')
+
+ # Override object param
+ with self.queue_lock:
+ if interrogatereq.model == "clip":
+ processed = shared.interrogator.interrogate(img)
+ elif interrogatereq.model == "deepdanbooru":
+ processed = deepbooru.model.tag(img)
+ else:
+ raise HTTPException(status_code=404, detail="Model not found")
+
+ return models.InterrogateResponse(caption=processed)
+
+ def interruptapi(self):
+ shared.state.interrupt()
+
+ return {}
+
+ def unloadapi(self):
+ unload_model_weights()
+
+ return {}
+
+ def reloadapi(self):
+ reload_model_weights()
+
+ return {}
+
+ def skip(self):
+ shared.state.skip()
+
+ def get_config(self):
+ options = {}
+ for key in shared.opts.data.keys():
+ metadata = shared.opts.data_labels.get(key)
+ if(metadata is not None):
+ options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
+ else:
+ options.update({key: shared.opts.data.get(key, None)})
+
+ return options
+
+ def set_config(self, req: Dict[str, Any]):
+ checkpoint_name = req.get("sd_model_checkpoint", None)
+ if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
+ raise RuntimeError(f"model {checkpoint_name!r} not found")
+
+ for k, v in req.items():
+ shared.opts.set(k, v, is_api=True)
+
+ shared.opts.save(shared.config_filename)
+ return
+
+ def get_cmd_flags(self):
+ return vars(shared.cmd_opts)
+
+ def get_samplers(self):
+ return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
+
+ def get_upscalers(self):
+ return [
+ {
+ "name": upscaler.name,
+ "model_name": upscaler.scaler.model_name,
+ "model_path": upscaler.data_path,
+ "model_url": None,
+ "scale": upscaler.scale,
+ }
+ for upscaler in shared.sd_upscalers
+ ]
+
+ def get_latent_upscale_modes(self):
+ return [
+ {
+ "name": upscale_mode,
+ }
+ for upscale_mode in [*(shared.latent_upscale_modes or {})]
+ ]
+
+ def get_sd_models(self):
+ import modules.sd_models as sd_models
+ return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in sd_models.checkpoints_list.values()]
+
+ def get_sd_vaes(self):
+ import modules.sd_vae as sd_vae
+ return [{"model_name": x, "filename": sd_vae.vae_dict[x]} for x in sd_vae.vae_dict.keys()]
+
+ def get_hypernetworks(self):
+ return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
+
+ def get_face_restorers(self):
+ return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
+
+ def get_realesrgan_models(self):
+ return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
+
+ def get_prompt_styles(self):
+ styleList = []
+ for k in shared.prompt_styles.styles:
+ style = shared.prompt_styles.styles[k]
+ styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]})
+
+ return styleList
+
+ def get_embeddings(self):
+ db = sd_hijack.model_hijack.embedding_db
+
+ def convert_embedding(embedding):
+ return {
+ "step": embedding.step,
+ "sd_checkpoint": embedding.sd_checkpoint,
+ "sd_checkpoint_name": embedding.sd_checkpoint_name,
+ "shape": embedding.shape,
+ "vectors": embedding.vectors,
+ }
+
+ def convert_embeddings(embeddings):
+ return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
+
+ return {
+ "loaded": convert_embeddings(db.word_embeddings),
+ "skipped": convert_embeddings(db.skipped_embeddings),
+ }
+
+ def refresh_checkpoints(self):
+ with self.queue_lock:
+ shared.refresh_checkpoints()
+
+ def refresh_vae(self):
+ with self.queue_lock:
+ shared_items.refresh_vae_list()
+
+ def create_embedding(self, args: dict):
+ try:
+ shared.state.begin(job="create_embedding")
+ filename = create_embedding(**args) # create empty embedding
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
+ return models.CreateResponse(info=f"create embedding filename: {filename}")
+ except AssertionError as e:
+ return models.TrainResponse(info=f"create embedding error: {e}")
+ finally:
+ shared.state.end()
+
+
+ def create_hypernetwork(self, args: dict):
+ try:
+ shared.state.begin(job="create_hypernetwork")
+ filename = create_hypernetwork(**args) # create empty embedding
+ return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
+ except AssertionError as e:
+ return models.TrainResponse(info=f"create hypernetwork error: {e}")
+ finally:
+ shared.state.end()
+
+ def preprocess(self, args: dict):
+ try:
+ shared.state.begin(job="preprocess")
+ preprocess(**args) # quick operation unless blip/booru interrogation is enabled
+ shared.state.end()
+ return models.PreprocessResponse(info='preprocess complete')
+ except KeyError as e:
+ return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
+ except Exception as e:
+ return models.PreprocessResponse(info=f"preprocess error: {e}")
+ finally:
+ shared.state.end()
+
+ def train_embedding(self, args: dict):
+ try:
+ shared.state.begin(job="train_embedding")
+ apply_optimizations = shared.opts.training_xattention_optimizations
+ error = None
+ filename = ''
+ if not apply_optimizations:
+ sd_hijack.undo_optimizations()
+ try:
+ embedding, filename = train_embedding(**args) # can take a long time to complete
+ except Exception as e:
+ error = e
+ finally:
+ if not apply_optimizations:
+ sd_hijack.apply_optimizations()
+ return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
+ except Exception as msg:
+ return models.TrainResponse(info=f"train embedding error: {msg}")
+ finally:
+ shared.state.end()
+
+ def train_hypernetwork(self, args: dict):
+ try:
+ shared.state.begin(job="train_hypernetwork")
+ shared.loaded_hypernetworks = []
+ apply_optimizations = shared.opts.training_xattention_optimizations
+ error = None
+ filename = ''
+ if not apply_optimizations:
+ sd_hijack.undo_optimizations()
+ try:
+ hypernetwork, filename = train_hypernetwork(**args)
+ except Exception as e:
+ error = e
+ finally:
+ shared.sd_model.cond_stage_model.to(devices.device)
+ shared.sd_model.first_stage_model.to(devices.device)
+ if not apply_optimizations:
+ sd_hijack.apply_optimizations()
+ shared.state.end()
+ return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
+ except Exception as exc:
+ return models.TrainResponse(info=f"train embedding error: {exc}")
+ finally:
+ shared.state.end()
+
+ def get_memory(self):
+ try:
+ import os
+ import psutil
+ process = psutil.Process(os.getpid())
+ res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values
+ ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe
+ ram = { 'free': ram_total - res.rss, 'used': res.rss, 'total': ram_total }
+ except Exception as err:
+ ram = { 'error': f'{err}' }
+ try:
+ import torch
+ if torch.cuda.is_available():
+ s = torch.cuda.mem_get_info()
+ system = { 'free': s[0], 'used': s[1] - s[0], 'total': s[1] }
+ s = dict(torch.cuda.memory_stats(shared.device))
+ allocated = { 'current': s['allocated_bytes.all.current'], 'peak': s['allocated_bytes.all.peak'] }
+ reserved = { 'current': s['reserved_bytes.all.current'], 'peak': s['reserved_bytes.all.peak'] }
+ active = { 'current': s['active_bytes.all.current'], 'peak': s['active_bytes.all.peak'] }
+ inactive = { 'current': s['inactive_split_bytes.all.current'], 'peak': s['inactive_split_bytes.all.peak'] }
+ warnings = { 'retries': s['num_alloc_retries'], 'oom': s['num_ooms'] }
+ cuda = {
+ 'system': system,
+ 'active': active,
+ 'allocated': allocated,
+ 'reserved': reserved,
+ 'inactive': inactive,
+ 'events': warnings,
+ }
+ else:
+ cuda = {'error': 'unavailable'}
+ except Exception as err:
+ cuda = {'error': f'{err}'}
+ return models.MemoryResponse(ram=ram, cuda=cuda)
+
+ def launch(self, server_name, port, root_path):
+ self.app.include_router(self.router)
+ uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
+
+ def kill_webui(self):
+ restart.stop_program()
+
+ def restart_webui(self):
+ if restart.is_restartable():
+ restart.restart_program()
+ return Response(status_code=501)
+
+ def stop_webui(request):
+ shared.state.server_command = "stop"
+ return Response("Stopping.")
+
diff --git a/stable-diffusion-webui/modules/api/models.py b/stable-diffusion-webui/modules/api/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a574771c3346456b8cdf0d6e6a2d75fb9f3084f
--- /dev/null
+++ b/stable-diffusion-webui/modules/api/models.py
@@ -0,0 +1,313 @@
+import inspect
+
+from pydantic import BaseModel, Field, create_model
+from typing import Any, Optional
+from typing_extensions import Literal
+from inflection import underscore
+from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
+from modules.shared import sd_upscalers, opts, parser
+from typing import Dict, List
+
+API_NOT_ALLOWED = [
+ "self",
+ "kwargs",
+ "sd_model",
+ "outpath_samples",
+ "outpath_grids",
+ "sampler_index",
+ # "do_not_save_samples",
+ # "do_not_save_grid",
+ "extra_generation_params",
+ "overlay_images",
+ "do_not_reload_embeddings",
+ "seed_enable_extras",
+ "prompt_for_display",
+ "sampler_noise_scheduler_override",
+ "ddim_discretize"
+]
+
+class ModelDef(BaseModel):
+ """Assistance Class for Pydantic Dynamic Model Generation"""
+
+ field: str
+ field_alias: str
+ field_type: Any
+ field_value: Any
+ field_exclude: bool = False
+
+
+class PydanticModelGenerator:
+ """
+ Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
+ source_data is a snapshot of the default values produced by the class
+ params are the names of the actual keys required by __init__
+ """
+
+ def __init__(
+ self,
+ model_name: str = None,
+ class_instance = None,
+ additional_fields = None,
+ ):
+ def field_type_generator(k, v):
+ field_type = v.annotation
+
+ if field_type == 'Image':
+ # images are sent as base64 strings via API
+ field_type = 'str'
+
+ return Optional[field_type]
+
+ def merge_class_params(class_):
+ all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
+ parameters = {}
+ for classes in all_classes:
+ parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
+ return parameters
+
+ self._model_name = model_name
+ self._class_data = merge_class_params(class_instance)
+
+ self._model_def = [
+ ModelDef(
+ field=underscore(k),
+ field_alias=k,
+ field_type=field_type_generator(k, v),
+ field_value=None if isinstance(v.default, property) else v.default
+ )
+ for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
+ ]
+
+ for fields in additional_fields:
+ self._model_def.append(ModelDef(
+ field=underscore(fields["key"]),
+ field_alias=fields["key"],
+ field_type=fields["type"],
+ field_value=fields["default"],
+ field_exclude=fields["exclude"] if "exclude" in fields else False))
+
+ def generate_model(self):
+ """
+ Creates a pydantic BaseModel
+ from the json and overrides provided at initialization
+ """
+ fields = {
+ d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
+ }
+ DynamicModel = create_model(self._model_name, **fields)
+ DynamicModel.__config__.allow_population_by_field_name = True
+ DynamicModel.__config__.allow_mutation = True
+ return DynamicModel
+
+StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
+ "StableDiffusionProcessingTxt2Img",
+ StableDiffusionProcessingTxt2Img,
+ [
+ {"key": "sampler_index", "type": str, "default": "Euler"},
+ {"key": "script_name", "type": str, "default": None},
+ {"key": "script_args", "type": list, "default": []},
+ {"key": "send_images", "type": bool, "default": True},
+ {"key": "save_images", "type": bool, "default": False},
+ {"key": "alwayson_scripts", "type": dict, "default": {}},
+ ]
+).generate_model()
+
+StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
+ "StableDiffusionProcessingImg2Img",
+ StableDiffusionProcessingImg2Img,
+ [
+ {"key": "sampler_index", "type": str, "default": "Euler"},
+ {"key": "init_images", "type": list, "default": None},
+ {"key": "denoising_strength", "type": float, "default": 0.75},
+ {"key": "mask", "type": str, "default": None},
+ {"key": "include_init_images", "type": bool, "default": False, "exclude" : True},
+ {"key": "script_name", "type": str, "default": None},
+ {"key": "script_args", "type": list, "default": []},
+ {"key": "send_images", "type": bool, "default": True},
+ {"key": "save_images", "type": bool, "default": False},
+ {"key": "alwayson_scripts", "type": dict, "default": {}},
+ ]
+).generate_model()
+
+class TextToImageResponse(BaseModel):
+ images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+ parameters: dict
+ info: str
+
+class ImageToImageResponse(BaseModel):
+ images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+ parameters: dict
+ info: str
+
+class ExtrasBaseRequest(BaseModel):
+ resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
+ show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
+ gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
+ codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
+ codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
+ upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
+ upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
+ upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
+ upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
+ upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+ upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
+ extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+ upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
+
+class ExtraBaseResponse(BaseModel):
+ html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
+
+class ExtrasSingleImageRequest(ExtrasBaseRequest):
+ image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+
+class ExtrasSingleImageResponse(ExtraBaseResponse):
+ image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
+
+class FileData(BaseModel):
+ data: str = Field(title="File data", description="Base64 representation of the file")
+ name: str = Field(title="File name")
+
+class ExtrasBatchImagesRequest(ExtrasBaseRequest):
+ imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+
+class ExtrasBatchImagesResponse(ExtraBaseResponse):
+ images: List[str] = Field(title="Images", description="The generated images in base64 format.")
+
+class PNGInfoRequest(BaseModel):
+ image: str = Field(title="Image", description="The base64 encoded PNG image")
+
+class PNGInfoResponse(BaseModel):
+ info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
+ items: dict = Field(title="Items", description="An object containing all the info the image had")
+
+class ProgressRequest(BaseModel):
+ skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
+
+class ProgressResponse(BaseModel):
+ progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
+ eta_relative: float = Field(title="ETA in secs")
+ state: dict = Field(title="State", description="The current state snapshot")
+ current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
+ textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
+
+class InterrogateRequest(BaseModel):
+ image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+ model: str = Field(default="clip", title="Model", description="The interrogate model used.")
+
+class InterrogateResponse(BaseModel):
+ caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
+
+class TrainResponse(BaseModel):
+ info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
+
+class CreateResponse(BaseModel):
+ info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
+
+class PreprocessResponse(BaseModel):
+ info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
+
+fields = {}
+for key, metadata in opts.data_labels.items():
+ value = opts.data.get(key)
+ optType = opts.typemap.get(type(metadata.default), type(metadata.default)) if metadata.default else Any
+
+ if metadata is not None:
+ fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))})
+ else:
+ fields.update({key: (Optional[optType], Field())})
+
+OptionsModel = create_model("Options", **fields)
+
+flags = {}
+_options = vars(parser)['_option_string_actions']
+for key in _options:
+ if(_options[key].dest != 'help'):
+ flag = _options[key]
+ _type = str
+ if _options[key].default is not None:
+ _type = type(_options[key].default)
+ flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
+
+FlagsModel = create_model("Flags", **flags)
+
+class SamplerItem(BaseModel):
+ name: str = Field(title="Name")
+ aliases: List[str] = Field(title="Aliases")
+ options: Dict[str, str] = Field(title="Options")
+
+class UpscalerItem(BaseModel):
+ name: str = Field(title="Name")
+ model_name: Optional[str] = Field(title="Model Name")
+ model_path: Optional[str] = Field(title="Path")
+ model_url: Optional[str] = Field(title="URL")
+ scale: Optional[float] = Field(title="Scale")
+
+class LatentUpscalerModeItem(BaseModel):
+ name: str = Field(title="Name")
+
+class SDModelItem(BaseModel):
+ title: str = Field(title="Title")
+ model_name: str = Field(title="Model Name")
+ hash: Optional[str] = Field(title="Short hash")
+ sha256: Optional[str] = Field(title="sha256 hash")
+ filename: str = Field(title="Filename")
+ config: Optional[str] = Field(title="Config file")
+
+class SDVaeItem(BaseModel):
+ model_name: str = Field(title="Model Name")
+ filename: str = Field(title="Filename")
+
+class HypernetworkItem(BaseModel):
+ name: str = Field(title="Name")
+ path: Optional[str] = Field(title="Path")
+
+class FaceRestorerItem(BaseModel):
+ name: str = Field(title="Name")
+ cmd_dir: Optional[str] = Field(title="Path")
+
+class RealesrganItem(BaseModel):
+ name: str = Field(title="Name")
+ path: Optional[str] = Field(title="Path")
+ scale: Optional[int] = Field(title="Scale")
+
+class PromptStyleItem(BaseModel):
+ name: str = Field(title="Name")
+ prompt: Optional[str] = Field(title="Prompt")
+ negative_prompt: Optional[str] = Field(title="Negative Prompt")
+
+
+class EmbeddingItem(BaseModel):
+ step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
+ sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
+ sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
+ shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
+ vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
+
+class EmbeddingsResponse(BaseModel):
+ loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+ skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
+
+class MemoryResponse(BaseModel):
+ ram: dict = Field(title="RAM", description="System memory stats")
+ cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
+
+
+class ScriptsList(BaseModel):
+ txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
+ img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
+
+
+class ScriptArg(BaseModel):
+ label: str = Field(default=None, title="Label", description="Name of the argument in UI")
+ value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
+ minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
+ maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
+ step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
+ choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
+
+
+class ScriptInfo(BaseModel):
+ name: str = Field(default=None, title="Name", description="Script name")
+ is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
+ is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
+ args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
diff --git a/stable-diffusion-webui/modules/cache.py b/stable-diffusion-webui/modules/cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..d23419c4e9b0eb32a5cc9c1a75492f85eac1db6e
--- /dev/null
+++ b/stable-diffusion-webui/modules/cache.py
@@ -0,0 +1,124 @@
+import json
+import os
+import os.path
+import threading
+import time
+
+from modules.paths import data_path, script_path
+
+cache_filename = os.environ.get('SD_WEBUI_CACHE_FILE', os.path.join(data_path, "cache.json"))
+cache_data = None
+cache_lock = threading.Lock()
+
+dump_cache_after = None
+dump_cache_thread = None
+
+
+def dump_cache():
+ """
+ Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written.
+ """
+
+ global dump_cache_after
+ global dump_cache_thread
+
+ def thread_func():
+ global dump_cache_after
+ global dump_cache_thread
+
+ while dump_cache_after is not None and time.time() < dump_cache_after:
+ time.sleep(1)
+
+ with cache_lock:
+ cache_filename_tmp = cache_filename + "-"
+ with open(cache_filename_tmp, "w", encoding="utf8") as file:
+ json.dump(cache_data, file, indent=4)
+
+ os.replace(cache_filename_tmp, cache_filename)
+
+ dump_cache_after = None
+ dump_cache_thread = None
+
+ with cache_lock:
+ dump_cache_after = time.time() + 5
+ if dump_cache_thread is None:
+ dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func)
+ dump_cache_thread.start()
+
+
+def cache(subsection):
+ """
+ Retrieves or initializes a cache for a specific subsection.
+
+ Parameters:
+ subsection (str): The subsection identifier for the cache.
+
+ Returns:
+ dict: The cache data for the specified subsection.
+ """
+
+ global cache_data
+
+ if cache_data is None:
+ with cache_lock:
+ if cache_data is None:
+ if not os.path.isfile(cache_filename):
+ cache_data = {}
+ else:
+ try:
+ with open(cache_filename, "r", encoding="utf8") as file:
+ cache_data = json.load(file)
+ except Exception:
+ os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
+ print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
+ cache_data = {}
+
+ s = cache_data.get(subsection, {})
+ cache_data[subsection] = s
+
+ return s
+
+
+def cached_data_for_file(subsection, title, filename, func):
+ """
+ Retrieves or generates data for a specific file, using a caching mechanism.
+
+ Parameters:
+ subsection (str): The subsection of the cache to use.
+ title (str): The title of the data entry in the subsection of the cache.
+ filename (str): The path to the file to be checked for modifications.
+ func (callable): A function that generates the data if it is not available in the cache.
+
+ Returns:
+ dict or None: The cached or generated data, or None if data generation fails.
+
+ The `cached_data_for_file` function implements a caching mechanism for data stored in files.
+ It checks if the data associated with the given `title` is present in the cache and compares the
+ modification time of the file with the cached modification time. If the file has been modified,
+ the cache is considered invalid and the data is regenerated using the provided `func`.
+ Otherwise, the cached data is returned.
+
+ If the data generation fails, None is returned to indicate the failure. Otherwise, the generated
+ or cached data is returned as a dictionary.
+ """
+
+ existing_cache = cache(subsection)
+ ondisk_mtime = os.path.getmtime(filename)
+
+ entry = existing_cache.get(title)
+ if entry:
+ cached_mtime = entry.get("mtime", 0)
+ if ondisk_mtime > cached_mtime:
+ entry = None
+
+ if not entry or 'value' not in entry:
+ value = func()
+ if value is None:
+ return None
+
+ entry = {'mtime': ondisk_mtime, 'value': value}
+ existing_cache[title] = entry
+
+ dump_cache()
+
+ return entry['value']
diff --git a/stable-diffusion-webui/modules/call_queue.py b/stable-diffusion-webui/modules/call_queue.py
new file mode 100644
index 0000000000000000000000000000000000000000..396501918884db3afd352eb4dca4febd5682f4c4
--- /dev/null
+++ b/stable-diffusion-webui/modules/call_queue.py
@@ -0,0 +1,118 @@
+from functools import wraps
+import html
+import time
+
+from modules import shared, progress, errors, devices, fifo_lock
+
+queue_lock = fifo_lock.FIFOLock()
+
+
+def wrap_queued_call(func):
+ def f(*args, **kwargs):
+ with queue_lock:
+ res = func(*args, **kwargs)
+
+ return res
+
+ return f
+
+
+def wrap_gradio_gpu_call(func, extra_outputs=None):
+ @wraps(func)
+ def f(*args, **kwargs):
+
+ # if the first argument is a string that says "task(...)", it is treated as a job id
+ if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"):
+ id_task = args[0]
+ progress.add_task_to_queue(id_task)
+ else:
+ id_task = None
+
+ with queue_lock:
+ shared.state.begin(job=id_task)
+ progress.start_task(id_task)
+
+ try:
+ res = func(*args, **kwargs)
+ progress.record_results(id_task, res)
+ finally:
+ progress.finish_task(id_task)
+
+ shared.state.end()
+
+ return res
+
+ return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
+
+
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
+ @wraps(func)
+ def f(*args, extra_outputs_array=extra_outputs, **kwargs):
+ run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
+ if run_memmon:
+ shared.mem_mon.monitor()
+ t = time.perf_counter()
+
+ try:
+ res = list(func(*args, **kwargs))
+ except Exception as e:
+ # When printing out our debug argument list,
+ # do not print out more than a 100 KB of text
+ max_debug_str_len = 131072
+ message = "Error completing request"
+ arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len]
+ if len(arg_str) > max_debug_str_len:
+ arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
+ errors.report(f"{message}\n{arg_str}", exc_info=True)
+
+ shared.state.job = ""
+ shared.state.job_count = 0
+
+ if extra_outputs_array is None:
+ extra_outputs_array = [None, '']
+
+ error_message = f'{type(e).__name__}: {e}'
+ res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
+
+ devices.torch_gc()
+
+ shared.state.skipped = False
+ shared.state.interrupted = False
+ shared.state.job_count = 0
+
+ if not add_stats:
+ return tuple(res)
+
+ elapsed = time.perf_counter() - t
+ elapsed_m = int(elapsed // 60)
+ elapsed_s = elapsed % 60
+ elapsed_text = f"{elapsed_s:.1f} sec."
+ if elapsed_m > 0:
+ elapsed_text = f"{elapsed_m} min. "+elapsed_text
+
+ if run_memmon:
+ mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
+ active_peak = mem_stats['active_peak']
+ reserved_peak = mem_stats['reserved_peak']
+ sys_peak = mem_stats['system_peak']
+ sys_total = mem_stats['total']
+ sys_pct = sys_peak/max(sys_total, 1) * 100
+
+ toltip_a = "Active: peak amount of video memory used during generation (excluding cached data)"
+ toltip_r = "Reserved: total amout of video memory allocated by the Torch library "
+ toltip_sys = "System: peak amout of video memory allocated by all running programs, out of total capacity"
+
+ text_a = f"<abbr title='{toltip_a}'>A</abbr>: <span class='measurement'>{active_peak/1024:.2f} GB</span>"
+ text_r = f"<abbr title='{toltip_r}'>R</abbr>: <span class='measurement'>{reserved_peak/1024:.2f} GB</span>"
+ text_sys = f"<abbr title='{toltip_sys}'>Sys</abbr>: <span class='measurement'>{sys_peak/1024:.1f}/{sys_total/1024:g} GB</span> ({sys_pct:.1f}%)"
+
+ vram_html = f"<p class='vram'>{text_a}, <wbr>{text_r}, <wbr>{text_sys}</p>"
+ else:
+ vram_html = ''
+
+ # last item is always HTML
+ res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr><span class='measurement'>{elapsed_text}</span></p>{vram_html}</div>"
+
+ return tuple(res)
+
+ return f
diff --git a/stable-diffusion-webui/modules/cmd_args.py b/stable-diffusion-webui/modules/cmd_args.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3ed9dec078dca15e2039a75679ab3758cd2f0f8
--- /dev/null
+++ b/stable-diffusion-webui/modules/cmd_args.py
@@ -0,0 +1,119 @@
+import argparse
+import json
+import os
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui
+parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program")
+parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version")
+parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
+parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
+parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
+parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
+parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
+parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup")
+parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
+parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
+parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit")
+parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
+parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
+parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
+parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
+parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
+parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
+parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
+parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
+parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
+parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
+parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
+parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
+parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
+parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
+parser.add_argument("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models")
+parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
+parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything")
+parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
+parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
+parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
+parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
+parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
+parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="prefer InvokeAI's cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
+parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
+parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
+parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
+parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
+parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
+parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
+parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
+parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
+parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
+parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
+parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
+parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
+parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
+parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path])
+parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
+parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
+parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
+parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
+parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
+parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
+parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
+parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
+parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
+parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
+parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
+parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
+parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
+parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
+parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
+parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
+parser.add_argument("--disable-extra-extensions", action='store_true', help=" prevent all extensions except built-in from running regardless of any other settings", default=False)
diff --git a/stable-diffusion-webui/modules/codeformer/codeformer_arch.py b/stable-diffusion-webui/modules/codeformer/codeformer_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..12db6814268fdba5a3025f44d1bb24e93d280a69
--- /dev/null
+++ b/stable-diffusion-webui/modules/codeformer/codeformer_arch.py
@@ -0,0 +1,276 @@
+# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
+
+import math
+import torch
+from torch import nn, Tensor
+import torch.nn.functional as F
+from typing import Optional
+
+from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
+from basicsr.utils.registry import ARCH_REGISTRY
+
+def calc_mean_std(feat, eps=1e-5):
+ """Calculate mean and std for adaptive_instance_normalization.
+
+ Args:
+ feat (Tensor): 4D tensor.
+ eps (float): A small value added to the variance to avoid
+ divide-by-zero. Default: 1e-5.
+ """
+ size = feat.size()
+ assert len(size) == 4, 'The input feature should be 4D tensor.'
+ b, c = size[:2]
+ feat_var = feat.view(b, c, -1).var(dim=2) + eps
+ feat_std = feat_var.sqrt().view(b, c, 1, 1)
+ feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
+ return feat_mean, feat_std
+
+
+def adaptive_instance_normalization(content_feat, style_feat):
+ """Adaptive instance normalization.
+
+ Adjust the reference features to have the similar color and illuminations
+ as those in the degradate features.
+
+ Args:
+ content_feat (Tensor): The reference feature.
+ style_feat (Tensor): The degradate features.
+ """
+ size = content_feat.size()
+ style_mean, style_std = calc_mean_std(style_feat)
+ content_mean, content_std = calc_mean_std(content_feat)
+ normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+ return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+class PositionEmbeddingSine(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one
+ used by the Attention is all you need paper, generalized to work on images.
+ """
+
+ def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+ super().__init__()
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ def forward(self, x, mask=None):
+ if mask is None:
+ mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
+ not_mask = ~mask
+ y_embed = not_mask.cumsum(1, dtype=torch.float32)
+ x_embed = not_mask.cumsum(2, dtype=torch.float32)
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack(
+ (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos_y = torch.stack(
+ (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+def _get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class TransformerSALayer(nn.Module):
+ def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
+ # Implementation of Feedforward model - MLP
+ self.linear1 = nn.Linear(embed_dim, dim_mlp)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_mlp, embed_dim)
+
+ self.norm1 = nn.LayerNorm(embed_dim)
+ self.norm2 = nn.LayerNorm(embed_dim)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward(self, tgt,
+ tgt_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+
+ # self attention
+ tgt2 = self.norm1(tgt)
+ q = k = self.with_pos_embed(tgt2, query_pos)
+ tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout1(tgt2)
+
+ # ffn
+ tgt2 = self.norm2(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+ tgt = tgt + self.dropout2(tgt2)
+ return tgt
+
+class Fuse_sft_block(nn.Module):
+ def __init__(self, in_ch, out_ch):
+ super().__init__()
+ self.encode_enc = ResBlock(2*in_ch, out_ch)
+
+ self.scale = nn.Sequential(
+ nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+ nn.LeakyReLU(0.2, True),
+ nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+
+ self.shift = nn.Sequential(
+ nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+ nn.LeakyReLU(0.2, True),
+ nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+
+ def forward(self, enc_feat, dec_feat, w=1):
+ enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
+ scale = self.scale(enc_feat)
+ shift = self.shift(enc_feat)
+ residual = w * (dec_feat * scale + shift)
+ out = dec_feat + residual
+ return out
+
+
+@ARCH_REGISTRY.register()
+class CodeFormer(VQAutoEncoder):
+ def __init__(self, dim_embd=512, n_head=8, n_layers=9,
+ codebook_size=1024, latent_size=256,
+ connect_list=('32', '64', '128', '256'),
+ fix_modules=('quantize', 'generator')):
+ super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
+
+ if fix_modules is not None:
+ for module in fix_modules:
+ for param in getattr(self, module).parameters():
+ param.requires_grad = False
+
+ self.connect_list = connect_list
+ self.n_layers = n_layers
+ self.dim_embd = dim_embd
+ self.dim_mlp = dim_embd*2
+
+ self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
+ self.feat_emb = nn.Linear(256, self.dim_embd)
+
+ # transformer
+ self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
+ for _ in range(self.n_layers)])
+
+ # logits_predict head
+ self.idx_pred_layer = nn.Sequential(
+ nn.LayerNorm(dim_embd),
+ nn.Linear(dim_embd, codebook_size, bias=False))
+
+ self.channels = {
+ '16': 512,
+ '32': 256,
+ '64': 256,
+ '128': 128,
+ '256': 128,
+ '512': 64,
+ }
+
+ # after second residual block for > 16, before attn layer for ==16
+ self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
+ # after first residual block for > 16, before attn layer for ==16
+ self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
+
+ # fuse_convs_dict
+ self.fuse_convs_dict = nn.ModuleDict()
+ for f_size in self.connect_list:
+ in_ch = self.channels[f_size]
+ self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
+
+ def _init_weights(self, module):
+ if isinstance(module, (nn.Linear, nn.Embedding)):
+ module.weight.data.normal_(mean=0.0, std=0.02)
+ if isinstance(module, nn.Linear) and module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+ def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
+ # ################### Encoder #####################
+ enc_feat_dict = {}
+ out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
+ for i, block in enumerate(self.encoder.blocks):
+ x = block(x)
+ if i in out_list:
+ enc_feat_dict[str(x.shape[-1])] = x.clone()
+
+ lq_feat = x
+ # ################# Transformer ###################
+ # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
+ pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
+ # BCHW -> BC(HW) -> (HW)BC
+ feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
+ query_emb = feat_emb
+ # Transformer encoder
+ for layer in self.ft_layers:
+ query_emb = layer(query_emb, query_pos=pos_emb)
+
+ # output logits
+ logits = self.idx_pred_layer(query_emb) # (hw)bn
+ logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
+
+ if code_only: # for training stage II
+ # logits doesn't need softmax before cross_entropy loss
+ return logits, lq_feat
+
+ # ################# Quantization ###################
+ # if self.training:
+ # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
+ # # b(hw)c -> bc(hw) -> bchw
+ # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
+ # ------------
+ soft_one_hot = F.softmax(logits, dim=2)
+ _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
+ quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
+ # preserve gradients
+ # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
+
+ if detach_16:
+ quant_feat = quant_feat.detach() # for training stage III
+ if adain:
+ quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
+
+ # ################## Generator ####################
+ x = quant_feat
+ fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
+
+ for i, block in enumerate(self.generator.blocks):
+ x = block(x)
+ if i in fuse_list: # fuse after i-th block
+ f_size = str(x.shape[-1])
+ if w>0:
+ x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
+ out = x
+ # logits doesn't need softmax before cross_entropy loss
+ return out, logits, lq_feat
diff --git a/stable-diffusion-webui/modules/codeformer/vqgan_arch.py b/stable-diffusion-webui/modules/codeformer/vqgan_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..09ee6660dc537e41fb9d9c7be7196c94c04aa8c6
--- /dev/null
+++ b/stable-diffusion-webui/modules/codeformer/vqgan_arch.py
@@ -0,0 +1,435 @@
+# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
+
+'''
+VQGAN code, adapted from the original created by the Unleashing Transformers authors:
+https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
+
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import ARCH_REGISTRY
+
+def normalize(in_channels):
+ return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+@torch.jit.script
+def swish(x):
+ return x*torch.sigmoid(x)
+
+
+# Define VQVAE classes
+class VectorQuantizer(nn.Module):
+ def __init__(self, codebook_size, emb_dim, beta):
+ super(VectorQuantizer, self).__init__()
+ self.codebook_size = codebook_size # number of embeddings
+ self.emb_dim = emb_dim # dimension of embedding
+ self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
+ self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
+ self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
+
+ def forward(self, z):
+ # reshape z -> (batch, height, width, channel) and flatten
+ z = z.permute(0, 2, 3, 1).contiguous()
+ z_flattened = z.view(-1, self.emb_dim)
+
+ # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+ d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
+ 2 * torch.matmul(z_flattened, self.embedding.weight.t())
+
+ mean_distance = torch.mean(d)
+ # find closest encodings
+ # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
+ min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
+ # [0-1], higher score, higher confidence
+ min_encoding_scores = torch.exp(-min_encoding_scores/10)
+
+ min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
+ min_encodings.scatter_(1, min_encoding_indices, 1)
+
+ # get quantized latent vectors
+ z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
+ # compute loss for embedding
+ loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
+ # preserve gradients
+ z_q = z + (z_q - z).detach()
+
+ # perplexity
+ e_mean = torch.mean(min_encodings, dim=0)
+ perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
+ # reshape back to match original input shape
+ z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+ return z_q, loss, {
+ "perplexity": perplexity,
+ "min_encodings": min_encodings,
+ "min_encoding_indices": min_encoding_indices,
+ "min_encoding_scores": min_encoding_scores,
+ "mean_distance": mean_distance
+ }
+
+ def get_codebook_feat(self, indices, shape):
+ # input indices: batch*token_num -> (batch*token_num)*1
+ # shape: batch, height, width, channel
+ indices = indices.view(-1,1)
+ min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
+ min_encodings.scatter_(1, indices, 1)
+ # get quantized latent vectors
+ z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
+
+ if shape is not None: # reshape back to match original input shape
+ z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
+
+ return z_q
+
+
+class GumbelQuantizer(nn.Module):
+ def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
+ super().__init__()
+ self.codebook_size = codebook_size # number of embeddings
+ self.emb_dim = emb_dim # dimension of embedding
+ self.straight_through = straight_through
+ self.temperature = temp_init
+ self.kl_weight = kl_weight
+ self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits
+ self.embed = nn.Embedding(codebook_size, emb_dim)
+
+ def forward(self, z):
+ hard = self.straight_through if self.training else True
+
+ logits = self.proj(z)
+
+ soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
+
+ z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
+
+ # + kl divergence to the prior loss
+ qy = F.softmax(logits, dim=1)
+ diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
+ min_encoding_indices = soft_one_hot.argmax(dim=1)
+
+ return z_q, diff, {
+ "min_encoding_indices": min_encoding_indices
+ }
+
+
+class Downsample(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+ def forward(self, x):
+ pad = (0, 1, 0, 1)
+ x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+ x = self.conv(x)
+ return x
+
+
+class Upsample(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+ def forward(self, x):
+ x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+ x = self.conv(x)
+
+ return x
+
+
+class ResBlock(nn.Module):
+ def __init__(self, in_channels, out_channels=None):
+ super(ResBlock, self).__init__()
+ self.in_channels = in_channels
+ self.out_channels = in_channels if out_channels is None else out_channels
+ self.norm1 = normalize(in_channels)
+ self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ self.norm2 = normalize(out_channels)
+ self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+ if self.in_channels != self.out_channels:
+ self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+ def forward(self, x_in):
+ x = x_in
+ x = self.norm1(x)
+ x = swish(x)
+ x = self.conv1(x)
+ x = self.norm2(x)
+ x = swish(x)
+ x = self.conv2(x)
+ if self.in_channels != self.out_channels:
+ x_in = self.conv_out(x_in)
+
+ return x + x_in
+
+
+class AttnBlock(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = normalize(in_channels)
+ self.q = torch.nn.Conv2d(
+ in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0
+ )
+ self.k = torch.nn.Conv2d(
+ in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0
+ )
+ self.v = torch.nn.Conv2d(
+ in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0
+ )
+ self.proj_out = torch.nn.Conv2d(
+ in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0
+ )
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b, c, h, w = q.shape
+ q = q.reshape(b, c, h*w)
+ q = q.permute(0, 2, 1)
+ k = k.reshape(b, c, h*w)
+ w_ = torch.bmm(q, k)
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = F.softmax(w_, dim=2)
+
+ # attend to values
+ v = v.reshape(b, c, h*w)
+ w_ = w_.permute(0, 2, 1)
+ h_ = torch.bmm(v, w_)
+ h_ = h_.reshape(b, c, h, w)
+
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+
+class Encoder(nn.Module):
+ def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
+ super().__init__()
+ self.nf = nf
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.attn_resolutions = attn_resolutions
+
+ curr_res = self.resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+
+ blocks = []
+ # initial convultion
+ blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
+
+ # residual and downsampling blocks, with attention on smaller res (16x16)
+ for i in range(self.num_resolutions):
+ block_in_ch = nf * in_ch_mult[i]
+ block_out_ch = nf * ch_mult[i]
+ for _ in range(self.num_res_blocks):
+ blocks.append(ResBlock(block_in_ch, block_out_ch))
+ block_in_ch = block_out_ch
+ if curr_res in attn_resolutions:
+ blocks.append(AttnBlock(block_in_ch))
+
+ if i != self.num_resolutions - 1:
+ blocks.append(Downsample(block_in_ch))
+ curr_res = curr_res // 2
+
+ # non-local attention block
+ blocks.append(ResBlock(block_in_ch, block_in_ch))
+ blocks.append(AttnBlock(block_in_ch))
+ blocks.append(ResBlock(block_in_ch, block_in_ch))
+
+ # normalise and convert to latent size
+ blocks.append(normalize(block_in_ch))
+ blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
+ self.blocks = nn.ModuleList(blocks)
+
+ def forward(self, x):
+ for block in self.blocks:
+ x = block(x)
+
+ return x
+
+
+class Generator(nn.Module):
+ def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
+ super().__init__()
+ self.nf = nf
+ self.ch_mult = ch_mult
+ self.num_resolutions = len(self.ch_mult)
+ self.num_res_blocks = res_blocks
+ self.resolution = img_size
+ self.attn_resolutions = attn_resolutions
+ self.in_channels = emb_dim
+ self.out_channels = 3
+ block_in_ch = self.nf * self.ch_mult[-1]
+ curr_res = self.resolution // 2 ** (self.num_resolutions-1)
+
+ blocks = []
+ # initial conv
+ blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
+
+ # non-local attention block
+ blocks.append(ResBlock(block_in_ch, block_in_ch))
+ blocks.append(AttnBlock(block_in_ch))
+ blocks.append(ResBlock(block_in_ch, block_in_ch))
+
+ for i in reversed(range(self.num_resolutions)):
+ block_out_ch = self.nf * self.ch_mult[i]
+
+ for _ in range(self.num_res_blocks):
+ blocks.append(ResBlock(block_in_ch, block_out_ch))
+ block_in_ch = block_out_ch
+
+ if curr_res in self.attn_resolutions:
+ blocks.append(AttnBlock(block_in_ch))
+
+ if i != 0:
+ blocks.append(Upsample(block_in_ch))
+ curr_res = curr_res * 2
+
+ blocks.append(normalize(block_in_ch))
+ blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
+
+ self.blocks = nn.ModuleList(blocks)
+
+
+ def forward(self, x):
+ for block in self.blocks:
+ x = block(x)
+
+ return x
+
+
+@ARCH_REGISTRY.register()
+class VQAutoEncoder(nn.Module):
+ def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
+ beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
+ super().__init__()
+ logger = get_root_logger()
+ self.in_channels = 3
+ self.nf = nf
+ self.n_blocks = res_blocks
+ self.codebook_size = codebook_size
+ self.embed_dim = emb_dim
+ self.ch_mult = ch_mult
+ self.resolution = img_size
+ self.attn_resolutions = attn_resolutions or [16]
+ self.quantizer_type = quantizer
+ self.encoder = Encoder(
+ self.in_channels,
+ self.nf,
+ self.embed_dim,
+ self.ch_mult,
+ self.n_blocks,
+ self.resolution,
+ self.attn_resolutions
+ )
+ if self.quantizer_type == "nearest":
+ self.beta = beta #0.25
+ self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
+ elif self.quantizer_type == "gumbel":
+ self.gumbel_num_hiddens = emb_dim
+ self.straight_through = gumbel_straight_through
+ self.kl_weight = gumbel_kl_weight
+ self.quantize = GumbelQuantizer(
+ self.codebook_size,
+ self.embed_dim,
+ self.gumbel_num_hiddens,
+ self.straight_through,
+ self.kl_weight
+ )
+ self.generator = Generator(
+ self.nf,
+ self.embed_dim,
+ self.ch_mult,
+ self.n_blocks,
+ self.resolution,
+ self.attn_resolutions
+ )
+
+ if model_path is not None:
+ chkpt = torch.load(model_path, map_location='cpu')
+ if 'params_ema' in chkpt:
+ self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
+ logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
+ elif 'params' in chkpt:
+ self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
+ logger.info(f'vqgan is loaded from: {model_path} [params]')
+ else:
+ raise ValueError('Wrong params!')
+
+
+ def forward(self, x):
+ x = self.encoder(x)
+ quant, codebook_loss, quant_stats = self.quantize(x)
+ x = self.generator(quant)
+ return x, codebook_loss, quant_stats
+
+
+
+# patch based discriminator
+@ARCH_REGISTRY.register()
+class VQGANDiscriminator(nn.Module):
+ def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
+ super().__init__()
+
+ layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
+ ndf_mult = 1
+ ndf_mult_prev = 1
+ for n in range(1, n_layers): # gradually increase the number of filters
+ ndf_mult_prev = ndf_mult
+ ndf_mult = min(2 ** n, 8)
+ layers += [
+ nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
+ nn.BatchNorm2d(ndf * ndf_mult),
+ nn.LeakyReLU(0.2, True)
+ ]
+
+ ndf_mult_prev = ndf_mult
+ ndf_mult = min(2 ** n_layers, 8)
+
+ layers += [
+ nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
+ nn.BatchNorm2d(ndf * ndf_mult),
+ nn.LeakyReLU(0.2, True)
+ ]
+
+ layers += [
+ nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map
+ self.main = nn.Sequential(*layers)
+
+ if model_path is not None:
+ chkpt = torch.load(model_path, map_location='cpu')
+ if 'params_d' in chkpt:
+ self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
+ elif 'params' in chkpt:
+ self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
+ else:
+ raise ValueError('Wrong params!')
+
+ def forward(self, x):
+ return self.main(x)
diff --git a/stable-diffusion-webui/modules/codeformer_model.py b/stable-diffusion-webui/modules/codeformer_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ad8a9db806d3406610d81534a6d7c85301cceb0
--- /dev/null
+++ b/stable-diffusion-webui/modules/codeformer_model.py
@@ -0,0 +1,132 @@
+import os
+
+import cv2
+import torch
+
+import modules.face_restoration
+import modules.shared
+from modules import shared, devices, modelloader, errors
+from modules.paths import models_path
+
+# codeformer people made a choice to include modified basicsr library to their project which makes
+# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
+# I am making a choice to include some files from codeformer to work around this issue.
+model_dir = "Codeformer"
+model_path = os.path.join(models_path, model_dir)
+model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
+
+codeformer = None
+
+
+def setup_model(dirname):
+ os.makedirs(model_path, exist_ok=True)
+
+ path = modules.paths.paths.get("CodeFormer", None)
+ if path is None:
+ return
+
+ try:
+ from torchvision.transforms.functional import normalize
+ from modules.codeformer.codeformer_arch import CodeFormer
+ from basicsr.utils import img2tensor, tensor2img
+ from facelib.utils.face_restoration_helper import FaceRestoreHelper
+ from facelib.detection.retinaface import retinaface
+
+ net_class = CodeFormer
+
+ class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
+ def name(self):
+ return "CodeFormer"
+
+ def __init__(self, dirname):
+ self.net = None
+ self.face_helper = None
+ self.cmd_dir = dirname
+
+ def create_models(self):
+
+ if self.net is not None and self.face_helper is not None:
+ self.net.to(devices.device_codeformer)
+ return self.net, self.face_helper
+ model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
+ if len(model_paths) != 0:
+ ckpt_path = model_paths[0]
+ else:
+ print("Unable to load codeformer model.")
+ return None, None
+ net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
+ checkpoint = torch.load(ckpt_path)['params_ema']
+ net.load_state_dict(checkpoint)
+ net.eval()
+
+ if hasattr(retinaface, 'device'):
+ retinaface.device = devices.device_codeformer
+ face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
+
+ self.net = net
+ self.face_helper = face_helper
+
+ return net, face_helper
+
+ def send_model_to(self, device):
+ self.net.to(device)
+ self.face_helper.face_det.to(device)
+ self.face_helper.face_parse.to(device)
+
+ def restore(self, np_image, w=None):
+ np_image = np_image[:, :, ::-1]
+
+ original_resolution = np_image.shape[0:2]
+
+ self.create_models()
+ if self.net is None or self.face_helper is None:
+ return np_image
+
+ self.send_model_to(devices.device_codeformer)
+
+ self.face_helper.clean_all()
+ self.face_helper.read_image(np_image)
+ self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+ self.face_helper.align_warp_face()
+
+ for cropped_face in self.face_helper.cropped_faces:
+ cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+ normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+ cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
+
+ try:
+ with torch.no_grad():
+ output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
+ restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+ del output
+ devices.torch_gc()
+ except Exception:
+ errors.report('Failed inference for CodeFormer', exc_info=True)
+ restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+
+ restored_face = restored_face.astype('uint8')
+ self.face_helper.add_restored_face(restored_face)
+
+ self.face_helper.get_inverse_affine(None)
+
+ restored_img = self.face_helper.paste_faces_to_input_image()
+ restored_img = restored_img[:, :, ::-1]
+
+ if original_resolution != restored_img.shape[0:2]:
+ restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
+
+ self.face_helper.clean_all()
+
+ if shared.opts.face_restoration_unload:
+ self.send_model_to(devices.cpu)
+
+ return restored_img
+
+ global codeformer
+ codeformer = FaceRestorerCodeFormer(dirname)
+ shared.face_restorers.append(codeformer)
+
+ except Exception:
+ errors.report("Error setting up CodeFormer", exc_info=True)
+
+ # sys.path = stored_sys_path
diff --git a/stable-diffusion-webui/modules/config_states.py b/stable-diffusion-webui/modules/config_states.py
new file mode 100644
index 0000000000000000000000000000000000000000..b766aef11d87a74ea4cd6fa8a580e12e830e5691
--- /dev/null
+++ b/stable-diffusion-webui/modules/config_states.py
@@ -0,0 +1,199 @@
+"""
+Supports saving and restoring webui and extensions from a known working set of commits
+"""
+
+import os
+import json
+import time
+import tqdm
+
+from datetime import datetime
+import git
+
+from modules import shared, extensions, errors
+from modules.paths_internal import script_path, config_states_dir
+
+all_config_states = {}
+
+
+def list_config_states():
+ global all_config_states
+
+ all_config_states.clear()
+ os.makedirs(config_states_dir, exist_ok=True)
+
+ config_states = []
+ for filename in os.listdir(config_states_dir):
+ if filename.endswith(".json"):
+ path = os.path.join(config_states_dir, filename)
+ try:
+ with open(path, "r", encoding="utf-8") as f:
+ j = json.load(f)
+ assert "created_at" in j, '"created_at" does not exist'
+ j["filepath"] = path
+ config_states.append(j)
+ except Exception as e:
+ print(f'[ERROR]: Config states {path}, {e}')
+
+ config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
+
+ for cs in config_states:
+ timestamp = time.asctime(time.gmtime(cs["created_at"]))
+ name = cs.get("name", "Config")
+ full_name = f"{name}: {timestamp}"
+ all_config_states[full_name] = cs
+
+ return all_config_states
+
+
+def get_webui_config():
+ webui_repo = None
+
+ try:
+ if os.path.exists(os.path.join(script_path, ".git")):
+ webui_repo = git.Repo(script_path)
+ except Exception:
+ errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
+
+ webui_remote = None
+ webui_commit_hash = None
+ webui_commit_date = None
+ webui_branch = None
+ if webui_repo and not webui_repo.bare:
+ try:
+ webui_remote = next(webui_repo.remote().urls, None)
+ head = webui_repo.head.commit
+ webui_commit_date = webui_repo.head.commit.committed_date
+ webui_commit_hash = head.hexsha
+ webui_branch = webui_repo.active_branch.name
+
+ except Exception:
+ webui_remote = None
+
+ return {
+ "remote": webui_remote,
+ "commit_hash": webui_commit_hash,
+ "commit_date": webui_commit_date,
+ "branch": webui_branch,
+ }
+
+
+def get_extension_config():
+ ext_config = {}
+
+ for ext in extensions.extensions:
+ ext.read_info_from_repo()
+
+ entry = {
+ "name": ext.name,
+ "path": ext.path,
+ "enabled": ext.enabled,
+ "is_builtin": ext.is_builtin,
+ "remote": ext.remote,
+ "commit_hash": ext.commit_hash,
+ "commit_date": ext.commit_date,
+ "branch": ext.branch,
+ "have_info_from_repo": ext.have_info_from_repo
+ }
+
+ ext_config[ext.name] = entry
+
+ return ext_config
+
+
+def get_config():
+ creation_time = datetime.now().timestamp()
+ webui_config = get_webui_config()
+ ext_config = get_extension_config()
+
+ return {
+ "created_at": creation_time,
+ "webui": webui_config,
+ "extensions": ext_config
+ }
+
+
+def restore_webui_config(config):
+ print("* Restoring webui state...")
+
+ if "webui" not in config:
+ print("Error: No webui data saved to config")
+ return
+
+ webui_config = config["webui"]
+
+ if "commit_hash" not in webui_config:
+ print("Error: No commit saved to webui config")
+ return
+
+ webui_commit_hash = webui_config.get("commit_hash", None)
+ webui_repo = None
+
+ try:
+ if os.path.exists(os.path.join(script_path, ".git")):
+ webui_repo = git.Repo(script_path)
+ except Exception:
+ errors.report(f"Error reading webui git info from {script_path}", exc_info=True)
+ return
+
+ try:
+ webui_repo.git.fetch(all=True)
+ webui_repo.git.reset(webui_commit_hash, hard=True)
+ print(f"* Restored webui to commit {webui_commit_hash}.")
+ except Exception:
+ errors.report(f"Error restoring webui to commit{webui_commit_hash}")
+
+
+def restore_extension_config(config):
+ print("* Restoring extension state...")
+
+ if "extensions" not in config:
+ print("Error: No extension data saved to config")
+ return
+
+ ext_config = config["extensions"]
+
+ results = []
+ disabled = []
+
+ for ext in tqdm.tqdm(extensions.extensions):
+ if ext.is_builtin:
+ continue
+
+ ext.read_info_from_repo()
+ current_commit = ext.commit_hash
+
+ if ext.name not in ext_config:
+ ext.disabled = True
+ disabled.append(ext.name)
+ results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled"))
+ continue
+
+ entry = ext_config[ext.name]
+
+ if "commit_hash" in entry and entry["commit_hash"]:
+ try:
+ ext.fetch_and_reset_hard(entry["commit_hash"])
+ ext.read_info_from_repo()
+ if current_commit != entry["commit_hash"]:
+ results.append((ext, current_commit[:8], True, entry["commit_hash"][:8]))
+ except Exception as ex:
+ results.append((ext, current_commit[:8], False, ex))
+ else:
+ results.append((ext, current_commit[:8], False, "No commit hash found in config"))
+
+ if not entry.get("enabled", False):
+ ext.disabled = True
+ disabled.append(ext.name)
+ else:
+ ext.disabled = False
+
+ shared.opts.disabled_extensions = disabled
+ shared.opts.save(shared.config_filename)
+
+ print("* Finished restoring extensions. Results:")
+ for ext, prev_commit, success, result in results:
+ if success:
+ print(f" + {ext.name}: {prev_commit} -> {result}")
+ else:
+ print(f" ! {ext.name}: FAILURE ({result})")
diff --git a/stable-diffusion-webui/modules/deepbooru.py b/stable-diffusion-webui/modules/deepbooru.py
new file mode 100644
index 0000000000000000000000000000000000000000..547e1b4c67aeb75a06c9991f957f51b0ef6fdd0f
--- /dev/null
+++ b/stable-diffusion-webui/modules/deepbooru.py
@@ -0,0 +1,98 @@
+import os
+import re
+
+import torch
+import numpy as np
+
+from modules import modelloader, paths, deepbooru_model, devices, images, shared
+
+re_special = re.compile(r'([\\()])')
+
+
+class DeepDanbooru:
+ def __init__(self):
+ self.model = None
+
+ def load(self):
+ if self.model is not None:
+ return
+
+ files = modelloader.load_models(
+ model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
+ model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
+ ext_filter=[".pt"],
+ download_name='model-resnet_custom_v3.pt',
+ )
+
+ self.model = deepbooru_model.DeepDanbooruModel()
+ self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
+
+ self.model.eval()
+ self.model.to(devices.cpu, devices.dtype)
+
+ def start(self):
+ self.load()
+ self.model.to(devices.device)
+
+ def stop(self):
+ if not shared.opts.interrogate_keep_models_in_memory:
+ self.model.to(devices.cpu)
+ devices.torch_gc()
+
+ def tag(self, pil_image):
+ self.start()
+ res = self.tag_multi(pil_image)
+ self.stop()
+
+ return res
+
+ def tag_multi(self, pil_image, force_disable_ranks=False):
+ threshold = shared.opts.interrogate_deepbooru_score_threshold
+ use_spaces = shared.opts.deepbooru_use_spaces
+ use_escape = shared.opts.deepbooru_escape
+ alpha_sort = shared.opts.deepbooru_sort_alpha
+ include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
+
+ pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
+ a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
+
+ with torch.no_grad(), devices.autocast():
+ x = torch.from_numpy(a).to(devices.device)
+ y = self.model(x)[0].detach().cpu().numpy()
+
+ probability_dict = {}
+
+ for tag, probability in zip(self.model.tags, y):
+ if probability < threshold:
+ continue
+
+ if tag.startswith("rating:"):
+ continue
+
+ probability_dict[tag] = probability
+
+ if alpha_sort:
+ tags = sorted(probability_dict)
+ else:
+ tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
+
+ res = []
+
+ filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")}
+
+ for tag in [x for x in tags if x not in filtertags]:
+ probability = probability_dict[tag]
+ tag_outformat = tag
+ if use_spaces:
+ tag_outformat = tag_outformat.replace('_', ' ')
+ if use_escape:
+ tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
+ if include_ranks:
+ tag_outformat = f"({tag_outformat}:{probability:.3f})"
+
+ res.append(tag_outformat)
+
+ return ", ".join(res)
+
+
+model = DeepDanbooru()
diff --git a/stable-diffusion-webui/modules/deepbooru_model.py b/stable-diffusion-webui/modules/deepbooru_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a53884624e96284c35214ce02b8a2891d92c3e8
--- /dev/null
+++ b/stable-diffusion-webui/modules/deepbooru_model.py
@@ -0,0 +1,678 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from modules import devices
+
+# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
+
+
+class DeepDanbooruModel(nn.Module):
+ def __init__(self):
+ super(DeepDanbooruModel, self).__init__()
+
+ self.tags = []
+
+ self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
+ self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
+ self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
+ self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+ self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+ self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+ self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
+ self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
+ self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
+ self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
+ self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
+ self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
+ self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
+ self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
+ self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
+ self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
+ self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
+ self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+ self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
+ self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
+ self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
+ self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
+ self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
+ self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
+ self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
+ self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
+ self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+ self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+ self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+ self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+ self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
+ self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
+ self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
+ self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
+ self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
+ self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
+ self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+ self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+ self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+ self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+ self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
+ self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
+ self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
+ self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
+
+ def forward(self, *inputs):
+ t_358, = inputs
+ t_359 = t_358.permute(*[0, 3, 1, 2])
+ t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
+ t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
+ t_361 = F.relu(t_360)
+ t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
+ t_362 = self.n_MaxPool_0(t_361)
+ t_363 = self.n_Conv_1(t_362)
+ t_364 = self.n_Conv_2(t_362)
+ t_365 = F.relu(t_364)
+ t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
+ t_366 = self.n_Conv_3(t_365_padded)
+ t_367 = F.relu(t_366)
+ t_368 = self.n_Conv_4(t_367)
+ t_369 = torch.add(t_368, t_363)
+ t_370 = F.relu(t_369)
+ t_371 = self.n_Conv_5(t_370)
+ t_372 = F.relu(t_371)
+ t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
+ t_373 = self.n_Conv_6(t_372_padded)
+ t_374 = F.relu(t_373)
+ t_375 = self.n_Conv_7(t_374)
+ t_376 = torch.add(t_375, t_370)
+ t_377 = F.relu(t_376)
+ t_378 = self.n_Conv_8(t_377)
+ t_379 = F.relu(t_378)
+ t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
+ t_380 = self.n_Conv_9(t_379_padded)
+ t_381 = F.relu(t_380)
+ t_382 = self.n_Conv_10(t_381)
+ t_383 = torch.add(t_382, t_377)
+ t_384 = F.relu(t_383)
+ t_385 = self.n_Conv_11(t_384)
+ t_386 = self.n_Conv_12(t_384)
+ t_387 = F.relu(t_386)
+ t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
+ t_388 = self.n_Conv_13(t_387_padded)
+ t_389 = F.relu(t_388)
+ t_390 = self.n_Conv_14(t_389)
+ t_391 = torch.add(t_390, t_385)
+ t_392 = F.relu(t_391)
+ t_393 = self.n_Conv_15(t_392)
+ t_394 = F.relu(t_393)
+ t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
+ t_395 = self.n_Conv_16(t_394_padded)
+ t_396 = F.relu(t_395)
+ t_397 = self.n_Conv_17(t_396)
+ t_398 = torch.add(t_397, t_392)
+ t_399 = F.relu(t_398)
+ t_400 = self.n_Conv_18(t_399)
+ t_401 = F.relu(t_400)
+ t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
+ t_402 = self.n_Conv_19(t_401_padded)
+ t_403 = F.relu(t_402)
+ t_404 = self.n_Conv_20(t_403)
+ t_405 = torch.add(t_404, t_399)
+ t_406 = F.relu(t_405)
+ t_407 = self.n_Conv_21(t_406)
+ t_408 = F.relu(t_407)
+ t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
+ t_409 = self.n_Conv_22(t_408_padded)
+ t_410 = F.relu(t_409)
+ t_411 = self.n_Conv_23(t_410)
+ t_412 = torch.add(t_411, t_406)
+ t_413 = F.relu(t_412)
+ t_414 = self.n_Conv_24(t_413)
+ t_415 = F.relu(t_414)
+ t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
+ t_416 = self.n_Conv_25(t_415_padded)
+ t_417 = F.relu(t_416)
+ t_418 = self.n_Conv_26(t_417)
+ t_419 = torch.add(t_418, t_413)
+ t_420 = F.relu(t_419)
+ t_421 = self.n_Conv_27(t_420)
+ t_422 = F.relu(t_421)
+ t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
+ t_423 = self.n_Conv_28(t_422_padded)
+ t_424 = F.relu(t_423)
+ t_425 = self.n_Conv_29(t_424)
+ t_426 = torch.add(t_425, t_420)
+ t_427 = F.relu(t_426)
+ t_428 = self.n_Conv_30(t_427)
+ t_429 = F.relu(t_428)
+ t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
+ t_430 = self.n_Conv_31(t_429_padded)
+ t_431 = F.relu(t_430)
+ t_432 = self.n_Conv_32(t_431)
+ t_433 = torch.add(t_432, t_427)
+ t_434 = F.relu(t_433)
+ t_435 = self.n_Conv_33(t_434)
+ t_436 = F.relu(t_435)
+ t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
+ t_437 = self.n_Conv_34(t_436_padded)
+ t_438 = F.relu(t_437)
+ t_439 = self.n_Conv_35(t_438)
+ t_440 = torch.add(t_439, t_434)
+ t_441 = F.relu(t_440)
+ t_442 = self.n_Conv_36(t_441)
+ t_443 = self.n_Conv_37(t_441)
+ t_444 = F.relu(t_443)
+ t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
+ t_445 = self.n_Conv_38(t_444_padded)
+ t_446 = F.relu(t_445)
+ t_447 = self.n_Conv_39(t_446)
+ t_448 = torch.add(t_447, t_442)
+ t_449 = F.relu(t_448)
+ t_450 = self.n_Conv_40(t_449)
+ t_451 = F.relu(t_450)
+ t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
+ t_452 = self.n_Conv_41(t_451_padded)
+ t_453 = F.relu(t_452)
+ t_454 = self.n_Conv_42(t_453)
+ t_455 = torch.add(t_454, t_449)
+ t_456 = F.relu(t_455)
+ t_457 = self.n_Conv_43(t_456)
+ t_458 = F.relu(t_457)
+ t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
+ t_459 = self.n_Conv_44(t_458_padded)
+ t_460 = F.relu(t_459)
+ t_461 = self.n_Conv_45(t_460)
+ t_462 = torch.add(t_461, t_456)
+ t_463 = F.relu(t_462)
+ t_464 = self.n_Conv_46(t_463)
+ t_465 = F.relu(t_464)
+ t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
+ t_466 = self.n_Conv_47(t_465_padded)
+ t_467 = F.relu(t_466)
+ t_468 = self.n_Conv_48(t_467)
+ t_469 = torch.add(t_468, t_463)
+ t_470 = F.relu(t_469)
+ t_471 = self.n_Conv_49(t_470)
+ t_472 = F.relu(t_471)
+ t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
+ t_473 = self.n_Conv_50(t_472_padded)
+ t_474 = F.relu(t_473)
+ t_475 = self.n_Conv_51(t_474)
+ t_476 = torch.add(t_475, t_470)
+ t_477 = F.relu(t_476)
+ t_478 = self.n_Conv_52(t_477)
+ t_479 = F.relu(t_478)
+ t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
+ t_480 = self.n_Conv_53(t_479_padded)
+ t_481 = F.relu(t_480)
+ t_482 = self.n_Conv_54(t_481)
+ t_483 = torch.add(t_482, t_477)
+ t_484 = F.relu(t_483)
+ t_485 = self.n_Conv_55(t_484)
+ t_486 = F.relu(t_485)
+ t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
+ t_487 = self.n_Conv_56(t_486_padded)
+ t_488 = F.relu(t_487)
+ t_489 = self.n_Conv_57(t_488)
+ t_490 = torch.add(t_489, t_484)
+ t_491 = F.relu(t_490)
+ t_492 = self.n_Conv_58(t_491)
+ t_493 = F.relu(t_492)
+ t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
+ t_494 = self.n_Conv_59(t_493_padded)
+ t_495 = F.relu(t_494)
+ t_496 = self.n_Conv_60(t_495)
+ t_497 = torch.add(t_496, t_491)
+ t_498 = F.relu(t_497)
+ t_499 = self.n_Conv_61(t_498)
+ t_500 = F.relu(t_499)
+ t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
+ t_501 = self.n_Conv_62(t_500_padded)
+ t_502 = F.relu(t_501)
+ t_503 = self.n_Conv_63(t_502)
+ t_504 = torch.add(t_503, t_498)
+ t_505 = F.relu(t_504)
+ t_506 = self.n_Conv_64(t_505)
+ t_507 = F.relu(t_506)
+ t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
+ t_508 = self.n_Conv_65(t_507_padded)
+ t_509 = F.relu(t_508)
+ t_510 = self.n_Conv_66(t_509)
+ t_511 = torch.add(t_510, t_505)
+ t_512 = F.relu(t_511)
+ t_513 = self.n_Conv_67(t_512)
+ t_514 = F.relu(t_513)
+ t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
+ t_515 = self.n_Conv_68(t_514_padded)
+ t_516 = F.relu(t_515)
+ t_517 = self.n_Conv_69(t_516)
+ t_518 = torch.add(t_517, t_512)
+ t_519 = F.relu(t_518)
+ t_520 = self.n_Conv_70(t_519)
+ t_521 = F.relu(t_520)
+ t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
+ t_522 = self.n_Conv_71(t_521_padded)
+ t_523 = F.relu(t_522)
+ t_524 = self.n_Conv_72(t_523)
+ t_525 = torch.add(t_524, t_519)
+ t_526 = F.relu(t_525)
+ t_527 = self.n_Conv_73(t_526)
+ t_528 = F.relu(t_527)
+ t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
+ t_529 = self.n_Conv_74(t_528_padded)
+ t_530 = F.relu(t_529)
+ t_531 = self.n_Conv_75(t_530)
+ t_532 = torch.add(t_531, t_526)
+ t_533 = F.relu(t_532)
+ t_534 = self.n_Conv_76(t_533)
+ t_535 = F.relu(t_534)
+ t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
+ t_536 = self.n_Conv_77(t_535_padded)
+ t_537 = F.relu(t_536)
+ t_538 = self.n_Conv_78(t_537)
+ t_539 = torch.add(t_538, t_533)
+ t_540 = F.relu(t_539)
+ t_541 = self.n_Conv_79(t_540)
+ t_542 = F.relu(t_541)
+ t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
+ t_543 = self.n_Conv_80(t_542_padded)
+ t_544 = F.relu(t_543)
+ t_545 = self.n_Conv_81(t_544)
+ t_546 = torch.add(t_545, t_540)
+ t_547 = F.relu(t_546)
+ t_548 = self.n_Conv_82(t_547)
+ t_549 = F.relu(t_548)
+ t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
+ t_550 = self.n_Conv_83(t_549_padded)
+ t_551 = F.relu(t_550)
+ t_552 = self.n_Conv_84(t_551)
+ t_553 = torch.add(t_552, t_547)
+ t_554 = F.relu(t_553)
+ t_555 = self.n_Conv_85(t_554)
+ t_556 = F.relu(t_555)
+ t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
+ t_557 = self.n_Conv_86(t_556_padded)
+ t_558 = F.relu(t_557)
+ t_559 = self.n_Conv_87(t_558)
+ t_560 = torch.add(t_559, t_554)
+ t_561 = F.relu(t_560)
+ t_562 = self.n_Conv_88(t_561)
+ t_563 = F.relu(t_562)
+ t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
+ t_564 = self.n_Conv_89(t_563_padded)
+ t_565 = F.relu(t_564)
+ t_566 = self.n_Conv_90(t_565)
+ t_567 = torch.add(t_566, t_561)
+ t_568 = F.relu(t_567)
+ t_569 = self.n_Conv_91(t_568)
+ t_570 = F.relu(t_569)
+ t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
+ t_571 = self.n_Conv_92(t_570_padded)
+ t_572 = F.relu(t_571)
+ t_573 = self.n_Conv_93(t_572)
+ t_574 = torch.add(t_573, t_568)
+ t_575 = F.relu(t_574)
+ t_576 = self.n_Conv_94(t_575)
+ t_577 = F.relu(t_576)
+ t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
+ t_578 = self.n_Conv_95(t_577_padded)
+ t_579 = F.relu(t_578)
+ t_580 = self.n_Conv_96(t_579)
+ t_581 = torch.add(t_580, t_575)
+ t_582 = F.relu(t_581)
+ t_583 = self.n_Conv_97(t_582)
+ t_584 = F.relu(t_583)
+ t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
+ t_585 = self.n_Conv_98(t_584_padded)
+ t_586 = F.relu(t_585)
+ t_587 = self.n_Conv_99(t_586)
+ t_588 = self.n_Conv_100(t_582)
+ t_589 = torch.add(t_587, t_588)
+ t_590 = F.relu(t_589)
+ t_591 = self.n_Conv_101(t_590)
+ t_592 = F.relu(t_591)
+ t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
+ t_593 = self.n_Conv_102(t_592_padded)
+ t_594 = F.relu(t_593)
+ t_595 = self.n_Conv_103(t_594)
+ t_596 = torch.add(t_595, t_590)
+ t_597 = F.relu(t_596)
+ t_598 = self.n_Conv_104(t_597)
+ t_599 = F.relu(t_598)
+ t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
+ t_600 = self.n_Conv_105(t_599_padded)
+ t_601 = F.relu(t_600)
+ t_602 = self.n_Conv_106(t_601)
+ t_603 = torch.add(t_602, t_597)
+ t_604 = F.relu(t_603)
+ t_605 = self.n_Conv_107(t_604)
+ t_606 = F.relu(t_605)
+ t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
+ t_607 = self.n_Conv_108(t_606_padded)
+ t_608 = F.relu(t_607)
+ t_609 = self.n_Conv_109(t_608)
+ t_610 = torch.add(t_609, t_604)
+ t_611 = F.relu(t_610)
+ t_612 = self.n_Conv_110(t_611)
+ t_613 = F.relu(t_612)
+ t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
+ t_614 = self.n_Conv_111(t_613_padded)
+ t_615 = F.relu(t_614)
+ t_616 = self.n_Conv_112(t_615)
+ t_617 = torch.add(t_616, t_611)
+ t_618 = F.relu(t_617)
+ t_619 = self.n_Conv_113(t_618)
+ t_620 = F.relu(t_619)
+ t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
+ t_621 = self.n_Conv_114(t_620_padded)
+ t_622 = F.relu(t_621)
+ t_623 = self.n_Conv_115(t_622)
+ t_624 = torch.add(t_623, t_618)
+ t_625 = F.relu(t_624)
+ t_626 = self.n_Conv_116(t_625)
+ t_627 = F.relu(t_626)
+ t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
+ t_628 = self.n_Conv_117(t_627_padded)
+ t_629 = F.relu(t_628)
+ t_630 = self.n_Conv_118(t_629)
+ t_631 = torch.add(t_630, t_625)
+ t_632 = F.relu(t_631)
+ t_633 = self.n_Conv_119(t_632)
+ t_634 = F.relu(t_633)
+ t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
+ t_635 = self.n_Conv_120(t_634_padded)
+ t_636 = F.relu(t_635)
+ t_637 = self.n_Conv_121(t_636)
+ t_638 = torch.add(t_637, t_632)
+ t_639 = F.relu(t_638)
+ t_640 = self.n_Conv_122(t_639)
+ t_641 = F.relu(t_640)
+ t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
+ t_642 = self.n_Conv_123(t_641_padded)
+ t_643 = F.relu(t_642)
+ t_644 = self.n_Conv_124(t_643)
+ t_645 = torch.add(t_644, t_639)
+ t_646 = F.relu(t_645)
+ t_647 = self.n_Conv_125(t_646)
+ t_648 = F.relu(t_647)
+ t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
+ t_649 = self.n_Conv_126(t_648_padded)
+ t_650 = F.relu(t_649)
+ t_651 = self.n_Conv_127(t_650)
+ t_652 = torch.add(t_651, t_646)
+ t_653 = F.relu(t_652)
+ t_654 = self.n_Conv_128(t_653)
+ t_655 = F.relu(t_654)
+ t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
+ t_656 = self.n_Conv_129(t_655_padded)
+ t_657 = F.relu(t_656)
+ t_658 = self.n_Conv_130(t_657)
+ t_659 = torch.add(t_658, t_653)
+ t_660 = F.relu(t_659)
+ t_661 = self.n_Conv_131(t_660)
+ t_662 = F.relu(t_661)
+ t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
+ t_663 = self.n_Conv_132(t_662_padded)
+ t_664 = F.relu(t_663)
+ t_665 = self.n_Conv_133(t_664)
+ t_666 = torch.add(t_665, t_660)
+ t_667 = F.relu(t_666)
+ t_668 = self.n_Conv_134(t_667)
+ t_669 = F.relu(t_668)
+ t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
+ t_670 = self.n_Conv_135(t_669_padded)
+ t_671 = F.relu(t_670)
+ t_672 = self.n_Conv_136(t_671)
+ t_673 = torch.add(t_672, t_667)
+ t_674 = F.relu(t_673)
+ t_675 = self.n_Conv_137(t_674)
+ t_676 = F.relu(t_675)
+ t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
+ t_677 = self.n_Conv_138(t_676_padded)
+ t_678 = F.relu(t_677)
+ t_679 = self.n_Conv_139(t_678)
+ t_680 = torch.add(t_679, t_674)
+ t_681 = F.relu(t_680)
+ t_682 = self.n_Conv_140(t_681)
+ t_683 = F.relu(t_682)
+ t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
+ t_684 = self.n_Conv_141(t_683_padded)
+ t_685 = F.relu(t_684)
+ t_686 = self.n_Conv_142(t_685)
+ t_687 = torch.add(t_686, t_681)
+ t_688 = F.relu(t_687)
+ t_689 = self.n_Conv_143(t_688)
+ t_690 = F.relu(t_689)
+ t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
+ t_691 = self.n_Conv_144(t_690_padded)
+ t_692 = F.relu(t_691)
+ t_693 = self.n_Conv_145(t_692)
+ t_694 = torch.add(t_693, t_688)
+ t_695 = F.relu(t_694)
+ t_696 = self.n_Conv_146(t_695)
+ t_697 = F.relu(t_696)
+ t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
+ t_698 = self.n_Conv_147(t_697_padded)
+ t_699 = F.relu(t_698)
+ t_700 = self.n_Conv_148(t_699)
+ t_701 = torch.add(t_700, t_695)
+ t_702 = F.relu(t_701)
+ t_703 = self.n_Conv_149(t_702)
+ t_704 = F.relu(t_703)
+ t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
+ t_705 = self.n_Conv_150(t_704_padded)
+ t_706 = F.relu(t_705)
+ t_707 = self.n_Conv_151(t_706)
+ t_708 = torch.add(t_707, t_702)
+ t_709 = F.relu(t_708)
+ t_710 = self.n_Conv_152(t_709)
+ t_711 = F.relu(t_710)
+ t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
+ t_712 = self.n_Conv_153(t_711_padded)
+ t_713 = F.relu(t_712)
+ t_714 = self.n_Conv_154(t_713)
+ t_715 = torch.add(t_714, t_709)
+ t_716 = F.relu(t_715)
+ t_717 = self.n_Conv_155(t_716)
+ t_718 = F.relu(t_717)
+ t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
+ t_719 = self.n_Conv_156(t_718_padded)
+ t_720 = F.relu(t_719)
+ t_721 = self.n_Conv_157(t_720)
+ t_722 = torch.add(t_721, t_716)
+ t_723 = F.relu(t_722)
+ t_724 = self.n_Conv_158(t_723)
+ t_725 = self.n_Conv_159(t_723)
+ t_726 = F.relu(t_725)
+ t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
+ t_727 = self.n_Conv_160(t_726_padded)
+ t_728 = F.relu(t_727)
+ t_729 = self.n_Conv_161(t_728)
+ t_730 = torch.add(t_729, t_724)
+ t_731 = F.relu(t_730)
+ t_732 = self.n_Conv_162(t_731)
+ t_733 = F.relu(t_732)
+ t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
+ t_734 = self.n_Conv_163(t_733_padded)
+ t_735 = F.relu(t_734)
+ t_736 = self.n_Conv_164(t_735)
+ t_737 = torch.add(t_736, t_731)
+ t_738 = F.relu(t_737)
+ t_739 = self.n_Conv_165(t_738)
+ t_740 = F.relu(t_739)
+ t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
+ t_741 = self.n_Conv_166(t_740_padded)
+ t_742 = F.relu(t_741)
+ t_743 = self.n_Conv_167(t_742)
+ t_744 = torch.add(t_743, t_738)
+ t_745 = F.relu(t_744)
+ t_746 = self.n_Conv_168(t_745)
+ t_747 = self.n_Conv_169(t_745)
+ t_748 = F.relu(t_747)
+ t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
+ t_749 = self.n_Conv_170(t_748_padded)
+ t_750 = F.relu(t_749)
+ t_751 = self.n_Conv_171(t_750)
+ t_752 = torch.add(t_751, t_746)
+ t_753 = F.relu(t_752)
+ t_754 = self.n_Conv_172(t_753)
+ t_755 = F.relu(t_754)
+ t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
+ t_756 = self.n_Conv_173(t_755_padded)
+ t_757 = F.relu(t_756)
+ t_758 = self.n_Conv_174(t_757)
+ t_759 = torch.add(t_758, t_753)
+ t_760 = F.relu(t_759)
+ t_761 = self.n_Conv_175(t_760)
+ t_762 = F.relu(t_761)
+ t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
+ t_763 = self.n_Conv_176(t_762_padded)
+ t_764 = F.relu(t_763)
+ t_765 = self.n_Conv_177(t_764)
+ t_766 = torch.add(t_765, t_760)
+ t_767 = F.relu(t_766)
+ t_768 = self.n_Conv_178(t_767)
+ t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
+ t_770 = torch.squeeze(t_769, 3)
+ t_770 = torch.squeeze(t_770, 2)
+ t_771 = torch.sigmoid(t_770)
+ return t_771
+
+ def load_state_dict(self, state_dict, **kwargs):
+ self.tags = state_dict.get('tags', [])
+
+ super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
+
diff --git a/stable-diffusion-webui/modules/devices.py b/stable-diffusion-webui/modules/devices.py
new file mode 100644
index 0000000000000000000000000000000000000000..c01f06024b4cffd4a44f97b6f7699397e27abdb2
--- /dev/null
+++ b/stable-diffusion-webui/modules/devices.py
@@ -0,0 +1,153 @@
+import sys
+import contextlib
+from functools import lru_cache
+
+import torch
+from modules import errors, shared
+
+if sys.platform == "darwin":
+ from modules import mac_specific
+
+
+def has_mps() -> bool:
+ if sys.platform != "darwin":
+ return False
+ else:
+ return mac_specific.has_mps
+
+
+def get_cuda_device_string():
+ if shared.cmd_opts.device_id is not None:
+ return f"cuda:{shared.cmd_opts.device_id}"
+
+ return "cuda"
+
+
+def get_optimal_device_name():
+ if torch.cuda.is_available():
+ return get_cuda_device_string()
+
+ if has_mps():
+ return "mps"
+
+ return "cpu"
+
+
+def get_optimal_device():
+ return torch.device(get_optimal_device_name())
+
+
+def get_device_for(task):
+ if task in shared.cmd_opts.use_cpu:
+ return cpu
+
+ return get_optimal_device()
+
+
+def torch_gc():
+
+ if torch.cuda.is_available():
+ with torch.cuda.device(get_cuda_device_string()):
+ torch.cuda.empty_cache()
+ torch.cuda.ipc_collect()
+
+ if has_mps():
+ mac_specific.torch_mps_gc()
+
+
+def enable_tf32():
+ if torch.cuda.is_available():
+
+ # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
+ # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
+ if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
+ torch.backends.cudnn.benchmark = True
+
+ torch.backends.cuda.matmul.allow_tf32 = True
+ torch.backends.cudnn.allow_tf32 = True
+
+
+errors.run(enable_tf32, "Enabling TF32")
+
+cpu: torch.device = torch.device("cpu")
+device: torch.device = None
+device_interrogate: torch.device = None
+device_gfpgan: torch.device = None
+device_esrgan: torch.device = None
+device_codeformer: torch.device = None
+dtype: torch.dtype = torch.float16
+dtype_vae: torch.dtype = torch.float16
+dtype_unet: torch.dtype = torch.float16
+unet_needs_upcast = False
+
+
+def cond_cast_unet(input):
+ return input.to(dtype_unet) if unet_needs_upcast else input
+
+
+def cond_cast_float(input):
+ return input.float() if unet_needs_upcast else input
+
+
+nv_rng = None
+
+
+def autocast(disable=False):
+ if disable:
+ return contextlib.nullcontext()
+
+ if dtype == torch.float32 or shared.cmd_opts.precision == "full":
+ return contextlib.nullcontext()
+
+ return torch.autocast("cuda")
+
+
+def without_autocast(disable=False):
+ return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()
+
+
+class NansException(Exception):
+ pass
+
+
+def test_for_nans(x, where):
+ if shared.cmd_opts.disable_nan_check:
+ return
+
+ if not torch.all(torch.isnan(x)).item():
+ return
+
+ if where == "unet":
+ message = "A tensor with all NaNs was produced in Unet."
+
+ if not shared.cmd_opts.no_half:
+ message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."
+
+ elif where == "vae":
+ message = "A tensor with all NaNs was produced in VAE."
+
+ if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:
+ message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
+ else:
+ message = "A tensor with all NaNs was produced."
+
+ message += " Use --disable-nan-check commandline argument to disable this check."
+
+ raise NansException(message)
+
+
+@lru_cache
+def first_time_calculation():
+ """
+ just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
+ spends about 2.7 seconds doing that, at least wih NVidia.
+ """
+
+ x = torch.zeros((1, 1)).to(device, dtype)
+ linear = torch.nn.Linear(1, 1).to(device, dtype)
+ linear(x)
+
+ x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
+ conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
+ conv2d(x)
+
diff --git a/stable-diffusion-webui/modules/errors.py b/stable-diffusion-webui/modules/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..973ebc10b212836e2baa203659d88d1440f4ba04
--- /dev/null
+++ b/stable-diffusion-webui/modules/errors.py
@@ -0,0 +1,136 @@
+import sys
+import textwrap
+import traceback
+
+
+exception_records = []
+
+
+def record_exception():
+ _, e, tb = sys.exc_info()
+ if e is None:
+ return
+
+ if exception_records and exception_records[-1] == e:
+ return
+
+ from modules import sysinfo
+ exception_records.append(sysinfo.format_exception(e, tb))
+
+ if len(exception_records) > 5:
+ exception_records.pop(0)
+
+
+def report(message: str, *, exc_info: bool = False) -> None:
+ """
+ Print an error message to stderr, with optional traceback.
+ """
+
+ record_exception()
+
+ for line in message.splitlines():
+ print("***", line, file=sys.stderr)
+ if exc_info:
+ print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr)
+ print("---", file=sys.stderr)
+
+
+def print_error_explanation(message):
+ record_exception()
+
+ lines = message.strip().split("\n")
+ max_len = max([len(x) for x in lines])
+
+ print('=' * max_len, file=sys.stderr)
+ for line in lines:
+ print(line, file=sys.stderr)
+ print('=' * max_len, file=sys.stderr)
+
+
+def display(e: Exception, task, *, full_traceback=False):
+ record_exception()
+
+ print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
+ te = traceback.TracebackException.from_exception(e)
+ if full_traceback:
+ # include frames leading up to the try-catch block
+ te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack)
+ print(*te.format(), sep="", file=sys.stderr)
+
+ message = str(e)
+ if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
+ print_error_explanation("""
+The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its config file.
+See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
+ """)
+
+
+already_displayed = {}
+
+
+def display_once(e: Exception, task):
+ record_exception()
+
+ if task in already_displayed:
+ return
+
+ display(e, task)
+
+ already_displayed[task] = 1
+
+
+def run(code, task):
+ try:
+ code()
+ except Exception as e:
+ display(task, e)
+
+
+def check_versions():
+ from packaging import version
+ from modules import shared
+
+ import torch
+ import gradio
+
+ expected_torch_version = "2.0.0"
+ expected_xformers_version = "0.0.20"
+ expected_gradio_version = "3.41.2"
+
+ if version.parse(torch.__version__) < version.parse(expected_torch_version):
+ print_error_explanation(f"""
+You are running torch {torch.__version__}.
+The program is tested to work with torch {expected_torch_version}.
+To reinstall the desired version, run with commandline flag --reinstall-torch.
+Beware that this will cause a lot of large files to be downloaded, as well as
+there are reports of issues with training tab on the latest version.
+
+Use --skip-version-check commandline argument to disable this check.
+ """.strip())
+
+ if shared.xformers_available:
+ import xformers
+
+ if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
+ print_error_explanation(f"""
+You are running xformers {xformers.__version__}.
+The program is tested to work with xformers {expected_xformers_version}.
+To reinstall the desired version, run with commandline flag --reinstall-xformers.
+
+Use --skip-version-check commandline argument to disable this check.
+ """.strip())
+
+ if gradio.__version__ != expected_gradio_version:
+ print_error_explanation(f"""
+You are running gradio {gradio.__version__}.
+The program is designed to work with gradio {expected_gradio_version}.
+Using a different version of gradio is extremely likely to break the program.
+
+Reasons why you have the mismatched gradio version can be:
+ - you use --skip-install flag.
+ - you use webui.py to start the program instead of launch.py.
+ - an extension installs the incompatible gradio version.
+
+Use --skip-version-check commandline argument to disable this check.
+ """.strip())
+
diff --git a/stable-diffusion-webui/modules/esrgan_model.py b/stable-diffusion-webui/modules/esrgan_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e4260e2c62dbb14387e90e369dc109f435867b0
--- /dev/null
+++ b/stable-diffusion-webui/modules/esrgan_model.py
@@ -0,0 +1,229 @@
+import sys
+
+import numpy as np
+import torch
+from PIL import Image
+
+import modules.esrgan_model_arch as arch
+from modules import modelloader, images, devices
+from modules.shared import opts
+from modules.upscaler import Upscaler, UpscalerData
+
+
+def mod2normal(state_dict):
+ # this code is copied from https://github.com/victorca25/iNNfer
+ if 'conv_first.weight' in state_dict:
+ crt_net = {}
+ items = list(state_dict)
+
+ crt_net['model.0.weight'] = state_dict['conv_first.weight']
+ crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+ for k in items.copy():
+ if 'RDB' in k:
+ ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
+ if '.weight' in k:
+ ori_k = ori_k.replace('.weight', '.0.weight')
+ elif '.bias' in k:
+ ori_k = ori_k.replace('.bias', '.0.bias')
+ crt_net[ori_k] = state_dict[k]
+ items.remove(k)
+
+ crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
+ crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
+ crt_net['model.3.weight'] = state_dict['upconv1.weight']
+ crt_net['model.3.bias'] = state_dict['upconv1.bias']
+ crt_net['model.6.weight'] = state_dict['upconv2.weight']
+ crt_net['model.6.bias'] = state_dict['upconv2.bias']
+ crt_net['model.8.weight'] = state_dict['HRconv.weight']
+ crt_net['model.8.bias'] = state_dict['HRconv.bias']
+ crt_net['model.10.weight'] = state_dict['conv_last.weight']
+ crt_net['model.10.bias'] = state_dict['conv_last.bias']
+ state_dict = crt_net
+ return state_dict
+
+
+def resrgan2normal(state_dict, nb=23):
+ # this code is copied from https://github.com/victorca25/iNNfer
+ if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+ re8x = 0
+ crt_net = {}
+ items = list(state_dict)
+
+ crt_net['model.0.weight'] = state_dict['conv_first.weight']
+ crt_net['model.0.bias'] = state_dict['conv_first.bias']
+
+ for k in items.copy():
+ if "rdb" in k:
+ ori_k = k.replace('body.', 'model.1.sub.')
+ ori_k = ori_k.replace('.rdb', '.RDB')
+ if '.weight' in k:
+ ori_k = ori_k.replace('.weight', '.0.weight')
+ elif '.bias' in k:
+ ori_k = ori_k.replace('.bias', '.0.bias')
+ crt_net[ori_k] = state_dict[k]
+ items.remove(k)
+
+ crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
+ crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
+ crt_net['model.3.weight'] = state_dict['conv_up1.weight']
+ crt_net['model.3.bias'] = state_dict['conv_up1.bias']
+ crt_net['model.6.weight'] = state_dict['conv_up2.weight']
+ crt_net['model.6.bias'] = state_dict['conv_up2.bias']
+
+ if 'conv_up3.weight' in state_dict:
+ # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
+ re8x = 3
+ crt_net['model.9.weight'] = state_dict['conv_up3.weight']
+ crt_net['model.9.bias'] = state_dict['conv_up3.bias']
+
+ crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
+ crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
+ crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
+ crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
+
+ state_dict = crt_net
+ return state_dict
+
+
+def infer_params(state_dict):
+ # this code is copied from https://github.com/victorca25/iNNfer
+ scale2x = 0
+ scalemin = 6
+ n_uplayer = 0
+ plus = False
+
+ for block in list(state_dict):
+ parts = block.split(".")
+ n_parts = len(parts)
+ if n_parts == 5 and parts[2] == "sub":
+ nb = int(parts[3])
+ elif n_parts == 3:
+ part_num = int(parts[1])
+ if (part_num > scalemin
+ and parts[0] == "model"
+ and parts[2] == "weight"):
+ scale2x += 1
+ if part_num > n_uplayer:
+ n_uplayer = part_num
+ out_nc = state_dict[block].shape[0]
+ if not plus and "conv1x1" in block:
+ plus = True
+
+ nf = state_dict["model.0.weight"].shape[0]
+ in_nc = state_dict["model.0.weight"].shape[1]
+ out_nc = out_nc
+ scale = 2 ** scale2x
+
+ return in_nc, out_nc, nf, nb, plus, scale
+
+
+class UpscalerESRGAN(Upscaler):
+ def __init__(self, dirname):
+ self.name = "ESRGAN"
+ self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/ESRGAN.pth"
+ self.model_name = "ESRGAN_4x"
+ self.scalers = []
+ self.user_path = dirname
+ super().__init__()
+ model_paths = self.find_models(ext_filter=[".pt", ".pth"])
+ scalers = []
+ if len(model_paths) == 0:
+ scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
+ scalers.append(scaler_data)
+ for file in model_paths:
+ if file.startswith("http"):
+ name = self.model_name
+ else:
+ name = modelloader.friendly_name(file)
+
+ scaler_data = UpscalerData(name, file, self, 4)
+ self.scalers.append(scaler_data)
+
+ def do_upscale(self, img, selected_model):
+ try:
+ model = self.load_model(selected_model)
+ except Exception as e:
+ print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
+ return img
+ model.to(devices.device_esrgan)
+ img = esrgan_upscale(model, img)
+ return img
+
+ def load_model(self, path: str):
+ if path.startswith("http"):
+ # TODO: this doesn't use `path` at all?
+ filename = modelloader.load_file_from_url(
+ url=self.model_url,
+ model_dir=self.model_download_path,
+ file_name=f"{self.model_name}.pth",
+ )
+ else:
+ filename = path
+
+ state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
+
+ if "params_ema" in state_dict:
+ state_dict = state_dict["params_ema"]
+ elif "params" in state_dict:
+ state_dict = state_dict["params"]
+ num_conv = 16 if "realesr-animevideov3" in filename else 32
+ model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
+ model.load_state_dict(state_dict)
+ model.eval()
+ return model
+
+ if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
+ nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
+ state_dict = resrgan2normal(state_dict, nb)
+ elif "conv_first.weight" in state_dict:
+ state_dict = mod2normal(state_dict)
+ elif "model.0.weight" not in state_dict:
+ raise Exception("The file is not a recognized ESRGAN model.")
+
+ in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
+
+ model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
+ model.load_state_dict(state_dict)
+ model.eval()
+
+ return model
+
+
+def upscale_without_tiling(model, img):
+ img = np.array(img)
+ img = img[:, :, ::-1]
+ img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
+ img = torch.from_numpy(img).float()
+ img = img.unsqueeze(0).to(devices.device_esrgan)
+ with torch.no_grad():
+ output = model(img)
+ output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+ output = 255. * np.moveaxis(output, 0, 2)
+ output = output.astype(np.uint8)
+ output = output[:, :, ::-1]
+ return Image.fromarray(output, 'RGB')
+
+
+def esrgan_upscale(model, img):
+ if opts.ESRGAN_tile == 0:
+ return upscale_without_tiling(model, img)
+
+ grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
+ newtiles = []
+ scale_factor = 1
+
+ for y, h, row in grid.tiles:
+ newrow = []
+ for tiledata in row:
+ x, w, tile = tiledata
+
+ output = upscale_without_tiling(model, tile)
+ scale_factor = output.width // tile.width
+
+ newrow.append([x * scale_factor, w * scale_factor, output])
+ newtiles.append([y * scale_factor, h * scale_factor, newrow])
+
+ newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
+ output = images.combine_grid(newgrid)
+ return output
diff --git a/stable-diffusion-webui/modules/esrgan_model_arch.py b/stable-diffusion-webui/modules/esrgan_model_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..353c70dd867cb894a0ac208f39394280175e4e14
--- /dev/null
+++ b/stable-diffusion-webui/modules/esrgan_model_arch.py
@@ -0,0 +1,465 @@
+# this file is adapted from https://github.com/victorca25/iNNfer
+
+from collections import OrderedDict
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+####################
+# RRDBNet Generator
+####################
+
+class RRDBNet(nn.Module):
+ def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
+ act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
+ finalact=None, gaussian_noise=False, plus=False):
+ super(RRDBNet, self).__init__()
+ n_upscale = int(math.log(upscale, 2))
+ if upscale == 3:
+ n_upscale = 1
+
+ self.resrgan_scale = 0
+ if in_nc % 16 == 0:
+ self.resrgan_scale = 1
+ elif in_nc != 4 and in_nc % 4 == 0:
+ self.resrgan_scale = 2
+
+ fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+ rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+ norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
+ gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
+ LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
+
+ if upsample_mode == 'upconv':
+ upsample_block = upconv_block
+ elif upsample_mode == 'pixelshuffle':
+ upsample_block = pixelshuffle_block
+ else:
+ raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
+ if upscale == 3:
+ upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
+ else:
+ upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
+ HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
+ HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
+
+ outact = act(finalact) if finalact else None
+
+ self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
+ *upsampler, HR_conv0, HR_conv1, outact)
+
+ def forward(self, x, outm=None):
+ if self.resrgan_scale == 1:
+ feat = pixel_unshuffle(x, scale=4)
+ elif self.resrgan_scale == 2:
+ feat = pixel_unshuffle(x, scale=2)
+ else:
+ feat = x
+
+ return self.model(feat)
+
+
+class RRDB(nn.Module):
+ """
+ Residual in Residual Dense Block
+ (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
+ """
+
+ def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+ norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+ spectral_norm=False, gaussian_noise=False, plus=False):
+ super(RRDB, self).__init__()
+ # This is for backwards compatibility with existing models
+ if nr == 3:
+ self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus)
+ self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus)
+ self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus)
+ else:
+ RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
+ norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
+ gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
+ self.RDBs = nn.Sequential(*RDB_list)
+
+ def forward(self, x):
+ if hasattr(self, 'RDB1'):
+ out = self.RDB1(x)
+ out = self.RDB2(out)
+ out = self.RDB3(out)
+ else:
+ out = self.RDBs(x)
+ return out * 0.2 + x
+
+
+class ResidualDenseBlock_5C(nn.Module):
+ """
+ Residual Dense Block
+ The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
+ Modified options that can be used:
+ - "Partial Convolution based Padding" arXiv:1811.11718
+ - "Spectral normalization" arXiv:1802.05957
+ - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
+ {Rakotonirina} and A. {Rasoanaivo}
+ """
+
+ def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
+ norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
+ spectral_norm=False, gaussian_noise=False, plus=False):
+ super(ResidualDenseBlock_5C, self).__init__()
+
+ self.noise = GaussianNoise() if gaussian_noise else None
+ self.conv1x1 = conv1x1(nf, gc) if plus else None
+
+ self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+ if mode == 'CNA':
+ last_act = None
+ else:
+ last_act = act_type
+ self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
+ norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
+ spectral_norm=spectral_norm)
+
+ def forward(self, x):
+ x1 = self.conv1(x)
+ x2 = self.conv2(torch.cat((x, x1), 1))
+ if self.conv1x1:
+ x2 = x2 + self.conv1x1(x)
+ x3 = self.conv3(torch.cat((x, x1, x2), 1))
+ x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
+ if self.conv1x1:
+ x4 = x4 + x2
+ x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
+ if self.noise:
+ return self.noise(x5.mul(0.2) + x)
+ else:
+ return x5 * 0.2 + x
+
+
+####################
+# ESRGANplus
+####################
+
+class GaussianNoise(nn.Module):
+ def __init__(self, sigma=0.1, is_relative_detach=False):
+ super().__init__()
+ self.sigma = sigma
+ self.is_relative_detach = is_relative_detach
+ self.noise = torch.tensor(0, dtype=torch.float)
+
+ def forward(self, x):
+ if self.training and self.sigma != 0:
+ self.noise = self.noise.to(x.device)
+ scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
+ sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
+ x = x + sampled_noise
+ return x
+
+def conv1x1(in_planes, out_planes, stride=1):
+ return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+####################
+# SRVGGNetCompact
+####################
+
+class SRVGGNetCompact(nn.Module):
+ """A compact VGG-style network structure for super-resolution.
+ This class is copied from https://github.com/xinntao/Real-ESRGAN
+ """
+
+ def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
+ super(SRVGGNetCompact, self).__init__()
+ self.num_in_ch = num_in_ch
+ self.num_out_ch = num_out_ch
+ self.num_feat = num_feat
+ self.num_conv = num_conv
+ self.upscale = upscale
+ self.act_type = act_type
+
+ self.body = nn.ModuleList()
+ # the first conv
+ self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
+ # the first activation
+ if act_type == 'relu':
+ activation = nn.ReLU(inplace=True)
+ elif act_type == 'prelu':
+ activation = nn.PReLU(num_parameters=num_feat)
+ elif act_type == 'leakyrelu':
+ activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+ self.body.append(activation)
+
+ # the body structure
+ for _ in range(num_conv):
+ self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
+ # activation
+ if act_type == 'relu':
+ activation = nn.ReLU(inplace=True)
+ elif act_type == 'prelu':
+ activation = nn.PReLU(num_parameters=num_feat)
+ elif act_type == 'leakyrelu':
+ activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+ self.body.append(activation)
+
+ # the last conv
+ self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
+ # upsample
+ self.upsampler = nn.PixelShuffle(upscale)
+
+ def forward(self, x):
+ out = x
+ for i in range(0, len(self.body)):
+ out = self.body[i](out)
+
+ out = self.upsampler(out)
+ # add the nearest upsampled image, so that the network learns the residual
+ base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
+ out += base
+ return out
+
+
+####################
+# Upsampler
+####################
+
+class Upsample(nn.Module):
+ r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
+ The input data is assumed to be of the form
+ `minibatch x channels x [optional depth] x [optional height] x width`.
+ """
+
+ def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
+ super(Upsample, self).__init__()
+ if isinstance(scale_factor, tuple):
+ self.scale_factor = tuple(float(factor) for factor in scale_factor)
+ else:
+ self.scale_factor = float(scale_factor) if scale_factor else None
+ self.mode = mode
+ self.size = size
+ self.align_corners = align_corners
+
+ def forward(self, x):
+ return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
+
+ def extra_repr(self):
+ if self.scale_factor is not None:
+ info = f'scale_factor={self.scale_factor}'
+ else:
+ info = f'size={self.size}'
+ info += f', mode={self.mode}'
+ return info
+
+
+def pixel_unshuffle(x, scale):
+ """ Pixel unshuffle.
+ Args:
+ x (Tensor): Input feature with shape (b, c, hh, hw).
+ scale (int): Downsample ratio.
+ Returns:
+ Tensor: the pixel unshuffled feature.
+ """
+ b, c, hh, hw = x.size()
+ out_channel = c * (scale**2)
+ assert hh % scale == 0 and hw % scale == 0
+ h = hh // scale
+ w = hw // scale
+ x_view = x.view(b, c, h, scale, w, scale)
+ return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
+
+
+def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+ pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
+ """
+ Pixel shuffle layer
+ (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
+ Neural Network, CVPR17)
+ """
+ conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
+ pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
+ pixel_shuffle = nn.PixelShuffle(upscale_factor)
+
+ n = norm(norm_type, out_nc) if norm_type else None
+ a = act(act_type) if act_type else None
+ return sequential(conv, pixel_shuffle, n, a)
+
+
+def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
+ pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
+ """ Upconv layer """
+ upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
+ upsample = Upsample(scale_factor=upscale_factor, mode=mode)
+ conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
+ pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
+ return sequential(upsample, conv)
+
+
+
+
+
+
+
+
+####################
+# Basic blocks
+####################
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+ """Make layers by stacking the same blocks.
+ Args:
+ basic_block (nn.module): nn.module class for basic block. (block)
+ num_basic_block (int): number of blocks. (n_layers)
+ Returns:
+ nn.Sequential: Stacked blocks in nn.Sequential.
+ """
+ layers = []
+ for _ in range(num_basic_block):
+ layers.append(basic_block(**kwarg))
+ return nn.Sequential(*layers)
+
+
+def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
+ """ activation helper """
+ act_type = act_type.lower()
+ if act_type == 'relu':
+ layer = nn.ReLU(inplace)
+ elif act_type in ('leakyrelu', 'lrelu'):
+ layer = nn.LeakyReLU(neg_slope, inplace)
+ elif act_type == 'prelu':
+ layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
+ elif act_type == 'tanh': # [-1, 1] range output
+ layer = nn.Tanh()
+ elif act_type == 'sigmoid': # [0, 1] range output
+ layer = nn.Sigmoid()
+ else:
+ raise NotImplementedError(f'activation layer [{act_type}] is not found')
+ return layer
+
+
+class Identity(nn.Module):
+ def __init__(self, *kwargs):
+ super(Identity, self).__init__()
+
+ def forward(self, x, *kwargs):
+ return x
+
+
+def norm(norm_type, nc):
+ """ Return a normalization layer """
+ norm_type = norm_type.lower()
+ if norm_type == 'batch':
+ layer = nn.BatchNorm2d(nc, affine=True)
+ elif norm_type == 'instance':
+ layer = nn.InstanceNorm2d(nc, affine=False)
+ elif norm_type == 'none':
+ def norm_layer(x): return Identity()
+ else:
+ raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
+ return layer
+
+
+def pad(pad_type, padding):
+ """ padding layer helper """
+ pad_type = pad_type.lower()
+ if padding == 0:
+ return None
+ if pad_type == 'reflect':
+ layer = nn.ReflectionPad2d(padding)
+ elif pad_type == 'replicate':
+ layer = nn.ReplicationPad2d(padding)
+ elif pad_type == 'zero':
+ layer = nn.ZeroPad2d(padding)
+ else:
+ raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
+ return layer
+
+
+def get_valid_padding(kernel_size, dilation):
+ kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
+ padding = (kernel_size - 1) // 2
+ return padding
+
+
+class ShortcutBlock(nn.Module):
+ """ Elementwise sum the output of a submodule to its input """
+ def __init__(self, submodule):
+ super(ShortcutBlock, self).__init__()
+ self.sub = submodule
+
+ def forward(self, x):
+ output = x + self.sub(x)
+ return output
+
+ def __repr__(self):
+ return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
+
+
+def sequential(*args):
+ """ Flatten Sequential. It unwraps nn.Sequential. """
+ if len(args) == 1:
+ if isinstance(args[0], OrderedDict):
+ raise NotImplementedError('sequential does not support OrderedDict input.')
+ return args[0] # No sequential is needed.
+ modules = []
+ for module in args:
+ if isinstance(module, nn.Sequential):
+ for submodule in module.children():
+ modules.append(submodule)
+ elif isinstance(module, nn.Module):
+ modules.append(module)
+ return nn.Sequential(*modules)
+
+
+def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
+ pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
+ spectral_norm=False):
+ """ Conv layer with padding, normalization, activation """
+ assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
+ padding = get_valid_padding(kernel_size, dilation)
+ p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
+ padding = padding if pad_type == 'zero' else 0
+
+ if convtype=='PartialConv2D':
+ from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
+ c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+ elif convtype=='DeformConv2D':
+ from torchvision.ops import DeformConv2d # not tested
+ c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+ elif convtype=='Conv3D':
+ c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+ else:
+ c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
+ dilation=dilation, bias=bias, groups=groups)
+
+ if spectral_norm:
+ c = nn.utils.spectral_norm(c)
+
+ a = act(act_type) if act_type else None
+ if 'CNA' in mode:
+ n = norm(norm_type, out_nc) if norm_type else None
+ return sequential(p, c, n, a)
+ elif mode == 'NAC':
+ if norm_type is None and act_type is not None:
+ a = act(act_type, inplace=False)
+ n = norm(norm_type, in_nc) if norm_type else None
+ return sequential(n, a, p, c)
diff --git a/stable-diffusion-webui/modules/extensions.py b/stable-diffusion-webui/modules/extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6418623118f3213c94b9e9ed1aff5d3d729c2ab8
--- /dev/null
+++ b/stable-diffusion-webui/modules/extensions.py
@@ -0,0 +1,165 @@
+import os
+import threading
+
+from modules import shared, errors, cache, scripts
+from modules.gitpython_hack import Repo
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
+
+extensions = []
+
+os.makedirs(extensions_dir, exist_ok=True)
+
+
+def active():
+ if shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
+ return []
+ elif shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions == "extra":
+ return [x for x in extensions if x.enabled and x.is_builtin]
+ else:
+ return [x for x in extensions if x.enabled]
+
+
+class Extension:
+ lock = threading.Lock()
+ cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version']
+
+ def __init__(self, name, path, enabled=True, is_builtin=False):
+ self.name = name
+ self.path = path
+ self.enabled = enabled
+ self.status = ''
+ self.can_update = False
+ self.is_builtin = is_builtin
+ self.commit_hash = ''
+ self.commit_date = None
+ self.version = ''
+ self.branch = None
+ self.remote = None
+ self.have_info_from_repo = False
+
+ def to_dict(self):
+ return {x: getattr(self, x) for x in self.cached_fields}
+
+ def from_dict(self, d):
+ for field in self.cached_fields:
+ setattr(self, field, d[field])
+
+ def read_info_from_repo(self):
+ if self.is_builtin or self.have_info_from_repo:
+ return
+
+ def read_from_repo():
+ with self.lock:
+ if self.have_info_from_repo:
+ return
+
+ self.do_read_info_from_repo()
+
+ return self.to_dict()
+ try:
+ d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
+ self.from_dict(d)
+ except FileNotFoundError:
+ pass
+ self.status = 'unknown' if self.status == '' else self.status
+
+ def do_read_info_from_repo(self):
+ repo = None
+ try:
+ if os.path.exists(os.path.join(self.path, ".git")):
+ repo = Repo(self.path)
+ except Exception:
+ errors.report(f"Error reading github repository info from {self.path}", exc_info=True)
+
+ if repo is None or repo.bare:
+ self.remote = None
+ else:
+ try:
+ self.remote = next(repo.remote().urls, None)
+ commit = repo.head.commit
+ self.commit_date = commit.committed_date
+ if repo.active_branch:
+ self.branch = repo.active_branch.name
+ self.commit_hash = commit.hexsha
+ self.version = self.commit_hash[:8]
+
+ except Exception:
+ errors.report(f"Failed reading extension data from Git repository ({self.name})", exc_info=True)
+ self.remote = None
+
+ self.have_info_from_repo = True
+
+ def list_files(self, subdir, extension):
+ dirpath = os.path.join(self.path, subdir)
+ if not os.path.isdir(dirpath):
+ return []
+
+ res = []
+ for filename in sorted(os.listdir(dirpath)):
+ res.append(scripts.ScriptFile(self.path, filename, os.path.join(dirpath, filename)))
+
+ res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
+
+ return res
+
+ def check_updates(self):
+ repo = Repo(self.path)
+ for fetch in repo.remote().fetch(dry_run=True):
+ if fetch.flags != fetch.HEAD_UPTODATE:
+ self.can_update = True
+ self.status = "new commits"
+ return
+
+ try:
+ origin = repo.rev_parse('origin')
+ if repo.head.commit != origin:
+ self.can_update = True
+ self.status = "behind HEAD"
+ return
+ except Exception:
+ self.can_update = False
+ self.status = "unknown (remote error)"
+ return
+
+ self.can_update = False
+ self.status = "latest"
+
+ def fetch_and_reset_hard(self, commit='origin'):
+ repo = Repo(self.path)
+ # Fix: `error: Your local changes to the following files would be overwritten by merge`,
+ # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
+ repo.git.fetch(all=True)
+ repo.git.reset(commit, hard=True)
+ self.have_info_from_repo = False
+
+
+def list_extensions():
+ extensions.clear()
+
+ if not os.path.isdir(extensions_dir):
+ return
+
+ if shared.cmd_opts.disable_all_extensions:
+ print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
+ elif shared.opts.disable_all_extensions == "all":
+ print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+ elif shared.cmd_opts.disable_extra_extensions:
+ print("*** \"--disable-extra-extensions\" arg was used, will only load built-in extensions ***")
+ elif shared.opts.disable_all_extensions == "extra":
+ print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
+
+ extension_paths = []
+ for dirname in [extensions_dir, extensions_builtin_dir]:
+ if not os.path.isdir(dirname):
+ return
+
+ for extension_dirname in sorted(os.listdir(dirname)):
+ path = os.path.join(dirname, extension_dirname)
+ if not os.path.isdir(path):
+ continue
+
+ extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+
+ for dirname, path, is_builtin in extension_paths:
+ extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
+ extensions.append(extension)
diff --git a/stable-diffusion-webui/modules/extra_networks.py b/stable-diffusion-webui/modules/extra_networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ebe72260488f31b30532b94d0f6fff1ea2e1660
--- /dev/null
+++ b/stable-diffusion-webui/modules/extra_networks.py
@@ -0,0 +1,224 @@
+import json
+import os
+import re
+import logging
+from collections import defaultdict
+
+from modules import errors
+
+extra_network_registry = {}
+extra_network_aliases = {}
+
+
+def initialize():
+ extra_network_registry.clear()
+ extra_network_aliases.clear()
+
+
+def register_extra_network(extra_network):
+ extra_network_registry[extra_network.name] = extra_network
+
+
+def register_extra_network_alias(extra_network, alias):
+ extra_network_aliases[alias] = extra_network
+
+
+def register_default_extra_networks():
+ from modules.extra_networks_hypernet import ExtraNetworkHypernet
+ register_extra_network(ExtraNetworkHypernet())
+
+
+class ExtraNetworkParams:
+ def __init__(self, items=None):
+ self.items = items or []
+ self.positional = []
+ self.named = {}
+
+ for item in self.items:
+ parts = item.split('=', 2) if isinstance(item, str) else [item]
+ if len(parts) == 2:
+ self.named[parts[0]] = parts[1]
+ else:
+ self.positional.append(item)
+
+ def __eq__(self, other):
+ return self.items == other.items
+
+
+class ExtraNetwork:
+ def __init__(self, name):
+ self.name = name
+
+ def activate(self, p, params_list):
+ """
+ Called by processing on every run. Whatever the extra network is meant to do should be activated here.
+ Passes arguments related to this extra network in params_list.
+ User passes arguments by specifying this in his prompt:
+
+ <name:arg1:arg2:arg3>
+
+ Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments
+ separated by colon.
+
+ Even if the user does not mention this ExtraNetwork in his prompt, the call will stil be made, with empty params_list -
+ in this case, all effects of this extra networks should be disabled.
+
+ Can be called multiple times before deactivate() - each new call should override the previous call completely.
+
+ For example, if this ExtraNetwork's name is 'hypernet' and user's prompt is:
+
+ > "1girl, <hypernet:agm:1.1> <extrasupernet:master:12:13:14> <hypernet:ray>"
+
+ params_list will be:
+
+ [
+ ExtraNetworkParams(items=["agm", "1.1"]),
+ ExtraNetworkParams(items=["ray"])
+ ]
+
+ """
+ raise NotImplementedError
+
+ def deactivate(self, p):
+ """
+ Called at the end of processing for housekeeping. No need to do anything here.
+ """
+
+ raise NotImplementedError
+
+
+def lookup_extra_networks(extra_network_data):
+ """returns a dict mapping ExtraNetwork objects to lists of arguments for those extra networks.
+
+ Example input:
+ {
+ 'lora': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58310>],
+ 'lyco': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58F70>],
+ 'hypernet': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D5A800>]
+ }
+
+ Example output:
+
+ {
+ <extra_networks_lora.ExtraNetworkLora object at 0x0000020581BEECE0>: [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58310>, <modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58F70>],
+ <modules.extra_networks_hypernet.ExtraNetworkHypernet object at 0x0000020581BEEE60>: [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D5A800>]
+ }
+ """
+
+ res = {}
+
+ for extra_network_name, extra_network_args in list(extra_network_data.items()):
+ extra_network = extra_network_registry.get(extra_network_name, None)
+ alias = extra_network_aliases.get(extra_network_name, None)
+
+ if alias is not None and extra_network is None:
+ extra_network = alias
+
+ if extra_network is None:
+ logging.info(f"Skipping unknown extra network: {extra_network_name}")
+ continue
+
+ res.setdefault(extra_network, []).extend(extra_network_args)
+
+ return res
+
+
+def activate(p, extra_network_data):
+ """call activate for extra networks in extra_network_data in specified order, then call
+ activate for all remaining registered networks with an empty argument list"""
+
+ activated = []
+
+ for extra_network, extra_network_args in lookup_extra_networks(extra_network_data).items():
+
+ try:
+ extra_network.activate(p, extra_network_args)
+ activated.append(extra_network)
+ except Exception as e:
+ errors.display(e, f"activating extra network {extra_network.name} with arguments {extra_network_args}")
+
+ for extra_network_name, extra_network in extra_network_registry.items():
+ if extra_network in activated:
+ continue
+
+ try:
+ extra_network.activate(p, [])
+ except Exception as e:
+ errors.display(e, f"activating extra network {extra_network_name}")
+
+ if p.scripts is not None:
+ p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data)
+
+
+def deactivate(p, extra_network_data):
+ """call deactivate for extra networks in extra_network_data in specified order, then call
+ deactivate for all remaining registered networks"""
+
+ data = lookup_extra_networks(extra_network_data)
+
+ for extra_network in data:
+ try:
+ extra_network.deactivate(p)
+ except Exception as e:
+ errors.display(e, f"deactivating extra network {extra_network.name}")
+
+ for extra_network_name, extra_network in extra_network_registry.items():
+ if extra_network in data:
+ continue
+
+ try:
+ extra_network.deactivate(p)
+ except Exception as e:
+ errors.display(e, f"deactivating unmentioned extra network {extra_network_name}")
+
+
+re_extra_net = re.compile(r"<(\w+):([^>]+)>")
+
+
+def parse_prompt(prompt):
+ res = defaultdict(list)
+
+ def found(m):
+ name = m.group(1)
+ args = m.group(2)
+
+ res[name].append(ExtraNetworkParams(items=args.split(":")))
+
+ return ""
+
+ prompt = re.sub(re_extra_net, found, prompt)
+
+ return prompt, res
+
+
+def parse_prompts(prompts):
+ res = []
+ extra_data = None
+
+ for prompt in prompts:
+ updated_prompt, parsed_extra_data = parse_prompt(prompt)
+
+ if extra_data is None:
+ extra_data = parsed_extra_data
+
+ res.append(updated_prompt)
+
+ return res, extra_data
+
+
+def get_user_metadata(filename):
+ if filename is None:
+ return {}
+
+ basename, ext = os.path.splitext(filename)
+ metadata_filename = basename + '.json'
+
+ metadata = {}
+ try:
+ if os.path.isfile(metadata_filename):
+ with open(metadata_filename, "r", encoding="utf8") as file:
+ metadata = json.load(file)
+ except Exception as e:
+ errors.display(e, f"reading extra network user metadata from {metadata_filename}")
+
+ return metadata
diff --git a/stable-diffusion-webui/modules/extra_networks_hypernet.py b/stable-diffusion-webui/modules/extra_networks_hypernet.py
new file mode 100644
index 0000000000000000000000000000000000000000..192f11b9cbd88447a0f80dbd2f0ace26d74f18b2
--- /dev/null
+++ b/stable-diffusion-webui/modules/extra_networks_hypernet.py
@@ -0,0 +1,28 @@
+from modules import extra_networks, shared
+from modules.hypernetworks import hypernetwork
+
+
+class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
+ def __init__(self):
+ super().__init__('hypernet')
+
+ def activate(self, p, params_list):
+ additional = shared.opts.sd_hypernetwork
+
+ if additional != "None" and additional in shared.hypernetworks and not any(x for x in params_list if x.items[0] == additional):
+ hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
+ p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
+ names = []
+ multipliers = []
+ for params in params_list:
+ assert params.items
+
+ names.append(params.items[0])
+ multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
+
+ hypernetwork.load_hypernetworks(names, multipliers)
+
+ def deactivate(self, p):
+ pass
diff --git a/stable-diffusion-webui/modules/extras.py b/stable-diffusion-webui/modules/extras.py
new file mode 100644
index 0000000000000000000000000000000000000000..4653c3f6edacc067eb935c81d7e71c71790ce449
--- /dev/null
+++ b/stable-diffusion-webui/modules/extras.py
@@ -0,0 +1,330 @@
+import os
+import re
+import shutil
+import json
+
+
+import torch
+import tqdm
+
+from modules import shared, images, sd_models, sd_vae, sd_models_config, errors
+from modules.ui_common import plaintext_to_html
+import gradio as gr
+import safetensors.torch
+
+
+def run_pnginfo(image):
+ if image is None:
+ return '', '', ''
+
+ geninfo, items = images.read_info_from_image(image)
+ items = {**{'parameters': geninfo}, **items}
+
+ info = ''
+ for key, text in items.items():
+ info += f"""
+<div>
+<p><b>{plaintext_to_html(str(key))}</b></p>
+<p>{plaintext_to_html(str(text))}</p>
+</div>
+""".strip()+"\n"
+
+ if len(info) == 0:
+ message = "Nothing found in the image."
+ info = f"<div><p>{message}<p></div>"
+
+ return '', geninfo, info
+
+
+def create_config(ckpt_result, config_source, a, b, c):
+ def config(x):
+ res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None
+ return res if res != shared.sd_default_config else None
+
+ if config_source == 0:
+ cfg = config(a) or config(b) or config(c)
+ elif config_source == 1:
+ cfg = config(b)
+ elif config_source == 2:
+ cfg = config(c)
+ else:
+ cfg = None
+
+ if cfg is None:
+ return
+
+ filename, _ = os.path.splitext(ckpt_result)
+ checkpoint_filename = filename + ".yaml"
+
+ print("Copying config:")
+ print(" from:", cfg)
+ print(" to:", checkpoint_filename)
+ shutil.copyfile(cfg, checkpoint_filename)
+
+
+checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]
+
+
+def to_half(tensor, enable):
+ if enable and tensor.dtype == torch.float:
+ return tensor.half()
+
+ return tensor
+
+
+def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name):
+ metadata = {}
+
+ for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]:
+ checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None)
+ if checkpoint_info is None:
+ continue
+
+ metadata.update(checkpoint_info.metadata)
+
+ return json.dumps(metadata, indent=4, ensure_ascii=False)
+
+
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json):
+ shared.state.begin(job="model-merge")
+
+ def fail(message):
+ shared.state.textinfo = message
+ shared.state.end()
+ return [*[gr.update() for _ in range(4)], message]
+
+ def weighted_sum(theta0, theta1, alpha):
+ return ((1 - alpha) * theta0) + (alpha * theta1)
+
+ def get_difference(theta1, theta2):
+ return theta1 - theta2
+
+ def add_difference(theta0, theta1_2_diff, alpha):
+ return theta0 + (alpha * theta1_2_diff)
+
+ def filename_weighted_sum():
+ a = primary_model_info.model_name
+ b = secondary_model_info.model_name
+ Ma = round(1 - multiplier, 2)
+ Mb = round(multiplier, 2)
+
+ return f"{Ma}({a}) + {Mb}({b})"
+
+ def filename_add_difference():
+ a = primary_model_info.model_name
+ b = secondary_model_info.model_name
+ c = tertiary_model_info.model_name
+ M = round(multiplier, 2)
+
+ return f"{a} + {M}({b} - {c})"
+
+ def filename_nothing():
+ return primary_model_info.model_name
+
+ theta_funcs = {
+ "Weighted sum": (filename_weighted_sum, None, weighted_sum),
+ "Add difference": (filename_add_difference, get_difference, add_difference),
+ "No interpolation": (filename_nothing, None, None),
+ }
+ filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method]
+ shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0)
+
+ if not primary_model_name:
+ return fail("Failed: Merging requires a primary model.")
+
+ primary_model_info = sd_models.checkpoints_list[primary_model_name]
+
+ if theta_func2 and not secondary_model_name:
+ return fail("Failed: Merging requires a secondary model.")
+
+ secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None
+
+ if theta_func1 and not tertiary_model_name:
+ return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.")
+
+ tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None
+
+ result_is_inpainting_model = False
+ result_is_instruct_pix2pix_model = False
+
+ if theta_func2:
+ shared.state.textinfo = "Loading B"
+ print(f"Loading {secondary_model_info.filename}...")
+ theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
+ else:
+ theta_1 = None
+
+ if theta_func1:
+ shared.state.textinfo = "Loading C"
+ print(f"Loading {tertiary_model_info.filename}...")
+ theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
+
+ shared.state.textinfo = 'Merging B and C'
+ shared.state.sampling_steps = len(theta_1.keys())
+ for key in tqdm.tqdm(theta_1.keys()):
+ if key in checkpoint_dict_skip_on_merge:
+ continue
+
+ if 'model' in key:
+ if key in theta_2:
+ t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
+ theta_1[key] = theta_func1(theta_1[key], t2)
+ else:
+ theta_1[key] = torch.zeros_like(theta_1[key])
+
+ shared.state.sampling_step += 1
+ del theta_2
+
+ shared.state.nextjob()
+
+ shared.state.textinfo = f"Loading {primary_model_info.filename}..."
+ print(f"Loading {primary_model_info.filename}...")
+ theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
+
+ print("Merging...")
+ shared.state.textinfo = 'Merging A and B'
+ shared.state.sampling_steps = len(theta_0.keys())
+ for key in tqdm.tqdm(theta_0.keys()):
+ if theta_1 and 'model' in key and key in theta_1:
+
+ if key in checkpoint_dict_skip_on_merge:
+ continue
+
+ a = theta_0[key]
+ b = theta_1[key]
+
+ # this enables merging an inpainting model (A) with another one (B);
+ # where normal model would have 4 channels, for latenst space, inpainting model would
+ # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
+ if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
+ if a.shape[1] == 4 and b.shape[1] == 9:
+ raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
+ if a.shape[1] == 4 and b.shape[1] == 8:
+ raise RuntimeError("When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model.")
+
+ if a.shape[1] == 8 and b.shape[1] == 4:#If we have an Instruct-Pix2Pix model...
+ theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common. Otherwise we get an error due to dimension mismatch.
+ result_is_instruct_pix2pix_model = True
+ else:
+ assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
+ theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
+ result_is_inpainting_model = True
+ else:
+ theta_0[key] = theta_func2(a, b, multiplier)
+
+ theta_0[key] = to_half(theta_0[key], save_as_half)
+
+ shared.state.sampling_step += 1
+
+ del theta_1
+
+ bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None)
+ if bake_in_vae_filename is not None:
+ print(f"Baking in VAE from {bake_in_vae_filename}")
+ shared.state.textinfo = 'Baking in VAE'
+ vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename, map_location='cpu')
+
+ for key in vae_dict.keys():
+ theta_0_key = 'first_stage_model.' + key
+ if theta_0_key in theta_0:
+ theta_0[theta_0_key] = to_half(vae_dict[key], save_as_half)
+
+ del vae_dict
+
+ if save_as_half and not theta_func2:
+ for key in theta_0.keys():
+ theta_0[key] = to_half(theta_0[key], save_as_half)
+
+ if discard_weights:
+ regex = re.compile(discard_weights)
+ for key in list(theta_0):
+ if re.search(regex, key):
+ theta_0.pop(key, None)
+
+ ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
+
+ filename = filename_generator() if custom_name == '' else custom_name
+ filename += ".inpainting" if result_is_inpainting_model else ""
+ filename += ".instruct-pix2pix" if result_is_instruct_pix2pix_model else ""
+ filename += "." + checkpoint_format
+
+ output_modelname = os.path.join(ckpt_dir, filename)
+
+ shared.state.nextjob()
+ shared.state.textinfo = "Saving"
+ print(f"Saving to {output_modelname}...")
+
+ metadata = {}
+
+ if save_metadata and copy_metadata_fields:
+ if primary_model_info:
+ metadata.update(primary_model_info.metadata)
+ if secondary_model_info:
+ metadata.update(secondary_model_info.metadata)
+ if tertiary_model_info:
+ metadata.update(tertiary_model_info.metadata)
+
+ if save_metadata:
+ try:
+ metadata.update(json.loads(metadata_json))
+ except Exception as e:
+ errors.display(e, "readin metadata from json")
+
+ metadata["format"] = "pt"
+
+ if save_metadata and add_merge_recipe:
+ merge_recipe = {
+ "type": "webui", # indicate this model was merged with webui's built-in merger
+ "primary_model_hash": primary_model_info.sha256,
+ "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
+ "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
+ "interp_method": interp_method,
+ "multiplier": multiplier,
+ "save_as_half": save_as_half,
+ "custom_name": custom_name,
+ "config_source": config_source,
+ "bake_in_vae": bake_in_vae,
+ "discard_weights": discard_weights,
+ "is_inpainting": result_is_inpainting_model,
+ "is_instruct_pix2pix": result_is_instruct_pix2pix_model
+ }
+
+ sd_merge_models = {}
+
+ def add_model_metadata(checkpoint_info):
+ checkpoint_info.calculate_shorthash()
+ sd_merge_models[checkpoint_info.sha256] = {
+ "name": checkpoint_info.name,
+ "legacy_hash": checkpoint_info.hash,
+ "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
+ }
+
+ sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {}))
+
+ add_model_metadata(primary_model_info)
+ if secondary_model_info:
+ add_model_metadata(secondary_model_info)
+ if tertiary_model_info:
+ add_model_metadata(tertiary_model_info)
+
+ metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
+ metadata["sd_merge_models"] = json.dumps(sd_merge_models)
+
+ _, extension = os.path.splitext(output_modelname)
+ if extension.lower() == ".safetensors":
+ safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None)
+ else:
+ torch.save(theta_0, output_modelname)
+
+ sd_models.list_models()
+ created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
+ if created_model:
+ created_model.calculate_shorthash()
+
+ create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
+
+ print(f"Checkpoint saved to {output_modelname}.")
+ shared.state.textinfo = "Checkpoint saved"
+ shared.state.end()
+
+ return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname]
diff --git a/stable-diffusion-webui/modules/face_restoration.py b/stable-diffusion-webui/modules/face_restoration.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c86c6ccce338a1411f4367a0bc6e4046ad67cae
--- /dev/null
+++ b/stable-diffusion-webui/modules/face_restoration.py
@@ -0,0 +1,19 @@
+from modules import shared
+
+
+class FaceRestoration:
+ def name(self):
+ return "None"
+
+ def restore(self, np_image):
+ return np_image
+
+
+def restore_faces(np_image):
+ face_restorers = [x for x in shared.face_restorers if x.name() == shared.opts.face_restoration_model or shared.opts.face_restoration_model is None]
+ if len(face_restorers) == 0:
+ return np_image
+
+ face_restorer = face_restorers[0]
+
+ return face_restorer.restore(np_image)
diff --git a/stable-diffusion-webui/modules/fifo_lock.py b/stable-diffusion-webui/modules/fifo_lock.py
new file mode 100644
index 0000000000000000000000000000000000000000..c35b3ae25a3cf383c8beae04db3e0a3d66785135
--- /dev/null
+++ b/stable-diffusion-webui/modules/fifo_lock.py
@@ -0,0 +1,37 @@
+import threading
+import collections
+
+
+# reference: https://gist.github.com/vitaliyp/6d54dd76ca2c3cdfc1149d33007dc34a
+class FIFOLock(object):
+ def __init__(self):
+ self._lock = threading.Lock()
+ self._inner_lock = threading.Lock()
+ self._pending_threads = collections.deque()
+
+ def acquire(self, blocking=True):
+ with self._inner_lock:
+ lock_acquired = self._lock.acquire(False)
+ if lock_acquired:
+ return True
+ elif not blocking:
+ return False
+
+ release_event = threading.Event()
+ self._pending_threads.append(release_event)
+
+ release_event.wait()
+ return self._lock.acquire()
+
+ def release(self):
+ with self._inner_lock:
+ if self._pending_threads:
+ release_event = self._pending_threads.popleft()
+ release_event.set()
+
+ self._lock.release()
+
+ __enter__ = acquire
+
+ def __exit__(self, t, v, tb):
+ self.release()
diff --git a/stable-diffusion-webui/modules/generation_parameters_copypaste.py b/stable-diffusion-webui/modules/generation_parameters_copypaste.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ef9de05a7fa7a57ea6adf955dfb43a20f5e8b58
--- /dev/null
+++ b/stable-diffusion-webui/modules/generation_parameters_copypaste.py
@@ -0,0 +1,445 @@
+import base64
+import io
+import json
+import os
+import re
+
+import gradio as gr
+from modules.paths import data_path
+from modules import shared, ui_tempdir, script_callbacks, processing
+from PIL import Image
+
+re_param_code = r'\s*([\w ]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
+re_param = re.compile(re_param_code)
+re_imagesize = re.compile(r"^(\d+)x(\d+)$")
+re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
+type_of_gr_update = type(gr.update())
+
+paste_fields = {}
+registered_param_bindings = []
+
+
+class ParamBinding:
+ def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
+ self.paste_button = paste_button
+ self.tabname = tabname
+ self.source_text_component = source_text_component
+ self.source_image_component = source_image_component
+ self.source_tabname = source_tabname
+ self.override_settings_component = override_settings_component
+ self.paste_field_names = paste_field_names or []
+
+
+def reset():
+ paste_fields.clear()
+ registered_param_bindings.clear()
+
+
+def quote(text):
+ if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
+ return text
+
+ return json.dumps(text, ensure_ascii=False)
+
+
+def unquote(text):
+ if len(text) == 0 or text[0] != '"' or text[-1] != '"':
+ return text
+
+ try:
+ return json.loads(text)
+ except Exception:
+ return text
+
+
+def image_from_url_text(filedata):
+ if filedata is None:
+ return None
+
+ if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False):
+ filedata = filedata[0]
+
+ if type(filedata) == dict and filedata.get("is_file", False):
+ filename = filedata["name"]
+ is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
+ assert is_in_right_dir, 'trying to open image file outside of allowed directories'
+
+ filename = filename.rsplit('?', 1)[0]
+ return Image.open(filename)
+
+ if type(filedata) == list:
+ if len(filedata) == 0:
+ return None
+
+ filedata = filedata[0]
+
+ if filedata.startswith("data:image/png;base64,"):
+ filedata = filedata[len("data:image/png;base64,"):]
+
+ filedata = base64.decodebytes(filedata.encode('utf-8'))
+ image = Image.open(io.BytesIO(filedata))
+ return image
+
+
+def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
+ paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
+
+ # backwards compatibility for existing extensions
+ import modules.ui
+ if tabname == 'txt2img':
+ modules.ui.txt2img_paste_fields = fields
+ elif tabname == 'img2img':
+ modules.ui.img2img_paste_fields = fields
+
+
+def create_buttons(tabs_list):
+ buttons = {}
+ for tab in tabs_list:
+ buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
+ return buttons
+
+
+def bind_buttons(buttons, send_image, send_generate_info):
+ """old function for backwards compatibility; do not use this, use register_paste_params_button"""
+ for tabname, button in buttons.items():
+ source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
+ source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
+
+ register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname))
+
+
+def register_paste_params_button(binding: ParamBinding):
+ registered_param_bindings.append(binding)
+
+
+def connect_paste_params_buttons():
+ binding: ParamBinding
+ for binding in registered_param_bindings:
+ destination_image_component = paste_fields[binding.tabname]["init_img"]
+ fields = paste_fields[binding.tabname]["fields"]
+ override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
+
+ destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+ destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+ if binding.source_image_component and destination_image_component:
+ if isinstance(binding.source_image_component, gr.Gallery):
+ func = send_image_and_dimensions if destination_width_component else image_from_url_text
+ jsfunc = "extract_image_from_gallery"
+ else:
+ func = send_image_and_dimensions if destination_width_component else lambda x: x
+ jsfunc = None
+
+ binding.paste_button.click(
+ fn=func,
+ _js=jsfunc,
+ inputs=[binding.source_image_component],
+ outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+ show_progress=False,
+ )
+
+ if binding.source_text_component is not None and fields is not None:
+ connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
+
+ if binding.source_tabname is not None and fields is not None:
+ paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) + binding.paste_field_names
+ binding.paste_button.click(
+ fn=lambda *x: x,
+ inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
+ outputs=[field for field, name in fields if name in paste_field_names],
+ show_progress=False,
+ )
+
+ binding.paste_button.click(
+ fn=None,
+ _js=f"switch_to_{binding.tabname}",
+ inputs=None,
+ outputs=None,
+ show_progress=False,
+ )
+
+
+def send_image_and_dimensions(x):
+ if isinstance(x, Image.Image):
+ img = x
+ else:
+ img = image_from_url_text(x)
+
+ if shared.opts.send_size and isinstance(img, Image.Image):
+ w = img.width
+ h = img.height
+ else:
+ w = gr.update()
+ h = gr.update()
+
+ return img, w, h
+
+
+def restore_old_hires_fix_params(res):
+ """for infotexts that specify old First pass size parameter, convert it into
+ width, height, and hr scale"""
+
+ firstpass_width = res.get('First pass size-1', None)
+ firstpass_height = res.get('First pass size-2', None)
+
+ if shared.opts.use_old_hires_fix_width_height:
+ hires_width = int(res.get("Hires resize-1", 0))
+ hires_height = int(res.get("Hires resize-2", 0))
+
+ if hires_width and hires_height:
+ res['Size-1'] = hires_width
+ res['Size-2'] = hires_height
+ return
+
+ if firstpass_width is None or firstpass_height is None:
+ return
+
+ firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
+ width = int(res.get("Size-1", 512))
+ height = int(res.get("Size-2", 512))
+
+ if firstpass_width == 0 or firstpass_height == 0:
+ firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)
+
+ res['Size-1'] = firstpass_width
+ res['Size-2'] = firstpass_height
+ res['Hires resize-1'] = width
+ res['Hires resize-2'] = height
+
+
+def parse_generation_parameters(x: str):
+ """parses generation parameters string, the one you see in text field under the picture in UI:
+```
+girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate
+Negative prompt: ugly, fat, obese, chubby, (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing
+Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hash: 45dee52b
+```
+
+ returns a dict with field values
+ """
+
+ res = {}
+
+ prompt = ""
+ negative_prompt = ""
+
+ done_with_prompt = False
+
+ *lines, lastline = x.strip().split("\n")
+ if len(re_param.findall(lastline)) < 3:
+ lines.append(lastline)
+ lastline = ''
+
+ for line in lines:
+ line = line.strip()
+ if line.startswith("Negative prompt:"):
+ done_with_prompt = True
+ line = line[16:].strip()
+ if done_with_prompt:
+ negative_prompt += ("" if negative_prompt == "" else "\n") + line
+ else:
+ prompt += ("" if prompt == "" else "\n") + line
+
+ if shared.opts.infotext_styles != "Ignore":
+ found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt)
+
+ if shared.opts.infotext_styles == "Apply":
+ res["Styles array"] = found_styles
+ elif shared.opts.infotext_styles == "Apply if any" and found_styles:
+ res["Styles array"] = found_styles
+
+ res["Prompt"] = prompt
+ res["Negative prompt"] = negative_prompt
+
+ for k, v in re_param.findall(lastline):
+ try:
+ if v[0] == '"' and v[-1] == '"':
+ v = unquote(v)
+
+ m = re_imagesize.match(v)
+ if m is not None:
+ res[f"{k}-1"] = m.group(1)
+ res[f"{k}-2"] = m.group(2)
+ else:
+ res[k] = v
+ except Exception:
+ print(f"Error parsing \"{k}: {v}\"")
+
+ # Missing CLIP skip means it was set to 1 (the default)
+ if "Clip skip" not in res:
+ res["Clip skip"] = "1"
+
+ hypernet = res.get("Hypernet", None)
+ if hypernet is not None:
+ res["Prompt"] += f"""<hypernet:{hypernet}:{res.get("Hypernet strength", "1.0")}>"""
+
+ if "Hires resize-1" not in res:
+ res["Hires resize-1"] = 0
+ res["Hires resize-2"] = 0
+
+ if "Hires sampler" not in res:
+ res["Hires sampler"] = "Use same sampler"
+
+ if "Hires checkpoint" not in res:
+ res["Hires checkpoint"] = "Use same checkpoint"
+
+ if "Hires prompt" not in res:
+ res["Hires prompt"] = ""
+
+ if "Hires negative prompt" not in res:
+ res["Hires negative prompt"] = ""
+
+ restore_old_hires_fix_params(res)
+
+ # Missing RNG means the default was set, which is GPU RNG
+ if "RNG" not in res:
+ res["RNG"] = "GPU"
+
+ if "Schedule type" not in res:
+ res["Schedule type"] = "Automatic"
+
+ if "Schedule max sigma" not in res:
+ res["Schedule max sigma"] = 0
+
+ if "Schedule min sigma" not in res:
+ res["Schedule min sigma"] = 0
+
+ if "Schedule rho" not in res:
+ res["Schedule rho"] = 0
+
+ if "VAE Encoder" not in res:
+ res["VAE Encoder"] = "Full"
+
+ if "VAE Decoder" not in res:
+ res["VAE Decoder"] = "Full"
+
+ return res
+
+
+infotext_to_setting_name_mapping = [
+
+]
+"""Mapping of infotext labels to setting names. Only left for backwards compatibility - use OptionInfo(..., infotext='...') instead.
+Example content:
+
+infotext_to_setting_name_mapping = [
+ ('Conditional mask weight', 'inpainting_mask_weight'),
+ ('Model hash', 'sd_model_checkpoint'),
+ ('ENSD', 'eta_noise_seed_delta'),
+ ('Schedule type', 'k_sched_type'),
+]
+"""
+
+
+def create_override_settings_dict(text_pairs):
+ """creates processing's override_settings parameters from gradio's multiselect
+
+ Example input:
+ ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
+
+ Example output:
+ {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
+ """
+
+ res = {}
+
+ params = {}
+ for pair in text_pairs:
+ k, v = pair.split(":", maxsplit=1)
+
+ params[k] = v.strip()
+
+ mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+ for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
+ value = params.get(param_name, None)
+
+ if value is None:
+ continue
+
+ res[setting_name] = shared.opts.cast_value(setting_name, value)
+
+ return res
+
+
+def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
+ def paste_func(prompt):
+ if not prompt and not shared.cmd_opts.hide_ui_dir_config:
+ filename = os.path.join(data_path, "params.txt")
+ if os.path.exists(filename):
+ with open(filename, "r", encoding="utf8") as file:
+ prompt = file.read()
+
+ params = parse_generation_parameters(prompt)
+ script_callbacks.infotext_pasted_callback(prompt, params)
+ res = []
+
+ for output, key in paste_fields:
+ if callable(key):
+ v = key(params)
+ else:
+ v = params.get(key, None)
+
+ if v is None:
+ res.append(gr.update())
+ elif isinstance(v, type_of_gr_update):
+ res.append(v)
+ else:
+ try:
+ valtype = type(output.value)
+
+ if valtype == bool and v == "False":
+ val = False
+ else:
+ val = valtype(v)
+
+ res.append(gr.update(value=val))
+ except Exception:
+ res.append(gr.update())
+
+ return res
+
+ if override_settings_component is not None:
+ already_handled_fields = {key: 1 for _, key in paste_fields}
+
+ def paste_settings(params):
+ vals = {}
+
+ mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+ for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
+ if param_name in already_handled_fields:
+ continue
+
+ v = params.get(param_name, None)
+ if v is None:
+ continue
+
+ if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
+ continue
+
+ v = shared.opts.cast_value(setting_name, v)
+ current_value = getattr(shared.opts, setting_name, None)
+
+ if v == current_value:
+ continue
+
+ vals[param_name] = v
+
+ vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
+
+ return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
+
+ paste_fields = paste_fields + [(override_settings_component, paste_settings)]
+
+ button.click(
+ fn=paste_func,
+ inputs=[input_comp],
+ outputs=[x[0] for x in paste_fields],
+ show_progress=False,
+ )
+ button.click(
+ fn=None,
+ _js=f"recalculate_prompts_{tabname}",
+ inputs=[],
+ outputs=[],
+ show_progress=False,
+ )
diff --git a/stable-diffusion-webui/modules/gfpgan_model.py b/stable-diffusion-webui/modules/gfpgan_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2b58f0b4a864977b602d513423120ad9b29d65d
--- /dev/null
+++ b/stable-diffusion-webui/modules/gfpgan_model.py
@@ -0,0 +1,110 @@
+import os
+
+import facexlib
+import gfpgan
+
+import modules.face_restoration
+from modules import paths, shared, devices, modelloader, errors
+
+model_dir = "GFPGAN"
+user_path = None
+model_path = os.path.join(paths.models_path, model_dir)
+model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
+have_gfpgan = False
+loaded_gfpgan_model = None
+
+
+def gfpgann():
+ global loaded_gfpgan_model
+ global model_path
+ if loaded_gfpgan_model is not None:
+ loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
+ return loaded_gfpgan_model
+
+ if gfpgan_constructor is None:
+ return None
+
+ models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
+ if len(models) == 1 and models[0].startswith("http"):
+ model_file = models[0]
+ elif len(models) != 0:
+ latest_file = max(models, key=os.path.getctime)
+ model_file = latest_file
+ else:
+ print("Unable to load gfpgan model!")
+ return None
+ if hasattr(facexlib.detection.retinaface, 'device'):
+ facexlib.detection.retinaface.device = devices.device_gfpgan
+ model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
+ loaded_gfpgan_model = model
+
+ return model
+
+
+def send_model_to(model, device):
+ model.gfpgan.to(device)
+ model.face_helper.face_det.to(device)
+ model.face_helper.face_parse.to(device)
+
+
+def gfpgan_fix_faces(np_image):
+ model = gfpgann()
+ if model is None:
+ return np_image
+
+ send_model_to(model, devices.device_gfpgan)
+
+ np_image_bgr = np_image[:, :, ::-1]
+ cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+ np_image = gfpgan_output_bgr[:, :, ::-1]
+
+ model.face_helper.clean_all()
+
+ if shared.opts.face_restoration_unload:
+ send_model_to(model, devices.cpu)
+
+ return np_image
+
+
+gfpgan_constructor = None
+
+
+def setup_model(dirname):
+ try:
+ os.makedirs(model_path, exist_ok=True)
+ from gfpgan import GFPGANer
+ from facexlib import detection, parsing # noqa: F401
+ global user_path
+ global have_gfpgan
+ global gfpgan_constructor
+
+ load_file_from_url_orig = gfpgan.utils.load_file_from_url
+ facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
+ facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
+
+ def my_load_file_from_url(**kwargs):
+ return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
+
+ def facex_load_file_from_url(**kwargs):
+ return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+ def facex_load_file_from_url2(**kwargs):
+ return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
+
+ gfpgan.utils.load_file_from_url = my_load_file_from_url
+ facexlib.detection.load_file_from_url = facex_load_file_from_url
+ facexlib.parsing.load_file_from_url = facex_load_file_from_url2
+ user_path = dirname
+ have_gfpgan = True
+ gfpgan_constructor = GFPGANer
+
+ class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
+ def name(self):
+ return "GFPGAN"
+
+ def restore(self, np_image):
+ return gfpgan_fix_faces(np_image)
+
+ shared.face_restorers.append(FaceRestorerGFPGAN())
+ except Exception:
+ errors.report("Error setting up GFPGAN", exc_info=True)
diff --git a/stable-diffusion-webui/modules/gitpython_hack.py b/stable-diffusion-webui/modules/gitpython_hack.py
new file mode 100644
index 0000000000000000000000000000000000000000..e537c1df93e15679d90e9eea3337035a8d50da89
--- /dev/null
+++ b/stable-diffusion-webui/modules/gitpython_hack.py
@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+import io
+import subprocess
+
+import git
+
+
+class Git(git.Git):
+ """
+ Git subclassed to never use persistent processes.
+ """
+
+ def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs):
+ raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})")
+
+ def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]:
+ ret = subprocess.check_output(
+ [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"],
+ input=self._prepare_ref(ref),
+ cwd=self._working_dir,
+ timeout=2,
+ )
+ return self._parse_object_header(ret)
+
+ def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
+ # Not really streaming, per se; this buffers the entire object in memory.
+ # Shouldn't be a problem for our use case, since we're only using this for
+ # object headers (commit objects).
+ ret = subprocess.check_output(
+ [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"],
+ input=self._prepare_ref(ref),
+ cwd=self._working_dir,
+ timeout=30,
+ )
+ bio = io.BytesIO(ret)
+ hexsha, typename, size = self._parse_object_header(bio.readline())
+ return (hexsha, typename, size, self.CatFileContentStream(size, bio))
+
+
+class Repo(git.Repo):
+ GitCommandWrapperType = Git
diff --git a/stable-diffusion-webui/modules/gradio_extensons.py b/stable-diffusion-webui/modules/gradio_extensons.py
new file mode 100644
index 0000000000000000000000000000000000000000..aac742ef0c17cdde7e470d7a068cdbbcec09b57e
--- /dev/null
+++ b/stable-diffusion-webui/modules/gradio_extensons.py
@@ -0,0 +1,73 @@
+import gradio as gr
+
+from modules import scripts, ui_tempdir, patches
+
+
+def add_classes_to_gradio_component(comp):
+ """
+ this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+ """
+
+ comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
+
+ if getattr(comp, 'multiselect', False):
+ comp.elem_classes.append('multiselect')
+
+
+def IOComponent_init(self, *args, **kwargs):
+ self.webui_tooltip = kwargs.pop('tooltip', None)
+
+ if scripts.scripts_current is not None:
+ scripts.scripts_current.before_component(self, **kwargs)
+
+ scripts.script_callbacks.before_component_callback(self, **kwargs)
+
+ res = original_IOComponent_init(self, *args, **kwargs)
+
+ add_classes_to_gradio_component(self)
+
+ scripts.script_callbacks.after_component_callback(self, **kwargs)
+
+ if scripts.scripts_current is not None:
+ scripts.scripts_current.after_component(self, **kwargs)
+
+ return res
+
+
+def Block_get_config(self):
+ config = original_Block_get_config(self)
+
+ webui_tooltip = getattr(self, 'webui_tooltip', None)
+ if webui_tooltip:
+ config["webui_tooltip"] = webui_tooltip
+
+ config.pop('example_inputs', None)
+
+ return config
+
+
+def BlockContext_init(self, *args, **kwargs):
+ res = original_BlockContext_init(self, *args, **kwargs)
+
+ add_classes_to_gradio_component(self)
+
+ return res
+
+
+def Blocks_get_config_file(self, *args, **kwargs):
+ config = original_Blocks_get_config_file(self, *args, **kwargs)
+
+ for comp_config in config["components"]:
+ if "example_inputs" in comp_config:
+ comp_config["example_inputs"] = {"serialized": []}
+
+ return config
+
+
+original_IOComponent_init = patches.patch(__name__, obj=gr.components.IOComponent, field="__init__", replacement=IOComponent_init)
+original_Block_get_config = patches.patch(__name__, obj=gr.blocks.Block, field="get_config", replacement=Block_get_config)
+original_BlockContext_init = patches.patch(__name__, obj=gr.blocks.BlockContext, field="__init__", replacement=BlockContext_init)
+original_Blocks_get_config_file = patches.patch(__name__, obj=gr.blocks.Blocks, field="get_config_file", replacement=Blocks_get_config_file)
+
+
+ui_tempdir.install_ui_tempdir_override()
diff --git a/stable-diffusion-webui/modules/hashes.py b/stable-diffusion-webui/modules/hashes.py
new file mode 100644
index 0000000000000000000000000000000000000000..59a81eaabc91567a1a3a3caa12f1f9944f487806
--- /dev/null
+++ b/stable-diffusion-webui/modules/hashes.py
@@ -0,0 +1,81 @@
+import hashlib
+import os.path
+
+from modules import shared
+import modules.cache
+
+dump_cache = modules.cache.dump_cache
+cache = modules.cache.cache
+
+
+def calculate_sha256(filename):
+ hash_sha256 = hashlib.sha256()
+ blksize = 1024 * 1024
+
+ with open(filename, "rb") as f:
+ for chunk in iter(lambda: f.read(blksize), b""):
+ hash_sha256.update(chunk)
+
+ return hash_sha256.hexdigest()
+
+
+def sha256_from_cache(filename, title, use_addnet_hash=False):
+ hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
+ ondisk_mtime = os.path.getmtime(filename)
+
+ if title not in hashes:
+ return None
+
+ cached_sha256 = hashes[title].get("sha256", None)
+ cached_mtime = hashes[title].get("mtime", 0)
+
+ if ondisk_mtime > cached_mtime or cached_sha256 is None:
+ return None
+
+ return cached_sha256
+
+
+def sha256(filename, title, use_addnet_hash=False):
+ hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
+
+ sha256_value = sha256_from_cache(filename, title, use_addnet_hash)
+ if sha256_value is not None:
+ return sha256_value
+
+ if shared.cmd_opts.no_hashing:
+ return None
+
+ print(f"Calculating sha256 for {filename}: ", end='')
+ if use_addnet_hash:
+ with open(filename, "rb") as file:
+ sha256_value = addnet_hash_safetensors(file)
+ else:
+ sha256_value = calculate_sha256(filename)
+ print(f"{sha256_value}")
+
+ hashes[title] = {
+ "mtime": os.path.getmtime(filename),
+ "sha256": sha256_value,
+ }
+
+ dump_cache()
+
+ return sha256_value
+
+
+def addnet_hash_safetensors(b):
+ """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
+ hash_sha256 = hashlib.sha256()
+ blksize = 1024 * 1024
+
+ b.seek(0)
+ header = b.read(8)
+ n = int.from_bytes(header, "little")
+
+ offset = n + 8
+ b.seek(offset)
+ for chunk in iter(lambda: b.read(blksize), b""):
+ hash_sha256.update(chunk)
+
+ return hash_sha256.hexdigest()
+
diff --git a/stable-diffusion-webui/modules/hypernetworks/hypernetwork.py b/stable-diffusion-webui/modules/hypernetworks/hypernetwork.py
new file mode 100644
index 0000000000000000000000000000000000000000..48e1fb91c399beb2712f49dca9e9e0380c00ef79
--- /dev/null
+++ b/stable-diffusion-webui/modules/hypernetworks/hypernetwork.py
@@ -0,0 +1,782 @@
+import datetime
+import glob
+import html
+import os
+import inspect
+from contextlib import closing
+
+import modules.textual_inversion.dataset
+import torch
+import tqdm
+from einops import rearrange, repeat
+from ldm.util import default
+from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
+from modules.textual_inversion import textual_inversion, logging
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
+from torch import einsum
+from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
+
+from collections import deque
+from statistics import stdev, mean
+
+
+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
+
+class HypernetworkModule(torch.nn.Module):
+ activation_dict = {
+ "linear": torch.nn.Identity,
+ "relu": torch.nn.ReLU,
+ "leakyrelu": torch.nn.LeakyReLU,
+ "elu": torch.nn.ELU,
+ "swish": torch.nn.Hardswish,
+ "tanh": torch.nn.Tanh,
+ "sigmoid": torch.nn.Sigmoid,
+ }
+ activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
+
+ def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+ add_layer_norm=False, activate_output=False, dropout_structure=None):
+ super().__init__()
+
+ self.multiplier = 1.0
+
+ assert layer_structure is not None, "layer_structure must not be None"
+ assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
+ assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
+
+ linears = []
+ for i in range(len(layer_structure) - 1):
+
+ # Add a fully-connected layer
+ linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+
+ # Add an activation func except last layer
+ if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
+ pass
+ elif activation_func in self.activation_dict:
+ linears.append(self.activation_dict[activation_func]())
+ else:
+ raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
+
+ # Add layer normalization
+ if add_layer_norm:
+ linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
+
+ # Everything should be now parsed into dropout structure, and applied here.
+ # Since we only have dropouts after layers, dropout structure should start with 0 and end with 0.
+ if dropout_structure is not None and dropout_structure[i+1] > 0:
+ assert 0 < dropout_structure[i+1] < 1, "Dropout probability should be 0 or float between 0 and 1!"
+ linears.append(torch.nn.Dropout(p=dropout_structure[i+1]))
+ # Code explanation : [1, 2, 1] -> dropout is missing when last_layer_dropout is false. [1, 2, 2, 1] -> [0, 0.3, 0, 0], when its True, [0, 0.3, 0.3, 0].
+
+ self.linear = torch.nn.Sequential(*linears)
+
+ if state_dict is not None:
+ self.fix_old_state_dict(state_dict)
+ self.load_state_dict(state_dict)
+ else:
+ for layer in self.linear:
+ if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
+ w, b = layer.weight.data, layer.bias.data
+ if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
+ normal_(w, mean=0.0, std=0.01)
+ normal_(b, mean=0.0, std=0)
+ elif weight_init == 'XavierUniform':
+ xavier_uniform_(w)
+ zeros_(b)
+ elif weight_init == 'XavierNormal':
+ xavier_normal_(w)
+ zeros_(b)
+ elif weight_init == 'KaimingUniform':
+ kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
+ zeros_(b)
+ elif weight_init == 'KaimingNormal':
+ kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
+ zeros_(b)
+ else:
+ raise KeyError(f"Key {weight_init} is not defined as initialization!")
+ self.to(devices.device)
+
+ def fix_old_state_dict(self, state_dict):
+ changes = {
+ 'linear1.bias': 'linear.0.bias',
+ 'linear1.weight': 'linear.0.weight',
+ 'linear2.bias': 'linear.1.bias',
+ 'linear2.weight': 'linear.1.weight',
+ }
+
+ for fr, to in changes.items():
+ x = state_dict.get(fr, None)
+ if x is None:
+ continue
+
+ del state_dict[fr]
+ state_dict[to] = x
+
+ def forward(self, x):
+ return x + self.linear(x) * (self.multiplier if not self.training else 1)
+
+ def trainables(self):
+ layer_structure = []
+ for layer in self.linear:
+ if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
+ layer_structure += [layer.weight, layer.bias]
+ return layer_structure
+
+
+#param layer_structure : sequence used for length, use_dropout : controlling boolean, last_layer_dropout : for compatibility check.
+def parse_dropout_structure(layer_structure, use_dropout, last_layer_dropout):
+ if layer_structure is None:
+ layer_structure = [1, 2, 1]
+ if not use_dropout:
+ return [0] * len(layer_structure)
+ dropout_values = [0]
+ dropout_values.extend([0.3] * (len(layer_structure) - 3))
+ if last_layer_dropout:
+ dropout_values.append(0.3)
+ else:
+ dropout_values.append(0)
+ dropout_values.append(0)
+ return dropout_values
+
+
+class Hypernetwork:
+ filename = None
+ name = None
+
+ def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
+ self.filename = None
+ self.name = name
+ self.layers = {}
+ self.step = 0
+ self.sd_checkpoint = None
+ self.sd_checkpoint_name = None
+ self.layer_structure = layer_structure
+ self.activation_func = activation_func
+ self.weight_init = weight_init
+ self.add_layer_norm = add_layer_norm
+ self.use_dropout = use_dropout
+ self.activate_output = activate_output
+ self.last_layer_dropout = kwargs.get('last_layer_dropout', True)
+ self.dropout_structure = kwargs.get('dropout_structure', None)
+ if self.dropout_structure is None:
+ self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
+ self.optimizer_name = None
+ self.optimizer_state_dict = None
+ self.optional_info = None
+
+ for size in enable_sizes or []:
+ self.layers[size] = (
+ HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure),
+ HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure),
+ )
+ self.eval()
+
+ def weights(self):
+ res = []
+ for layers in self.layers.values():
+ for layer in layers:
+ res += layer.parameters()
+ return res
+
+ def train(self, mode=True):
+ for layers in self.layers.values():
+ for layer in layers:
+ layer.train(mode=mode)
+ for param in layer.parameters():
+ param.requires_grad = mode
+
+ def to(self, device):
+ for layers in self.layers.values():
+ for layer in layers:
+ layer.to(device)
+
+ return self
+
+ def set_multiplier(self, multiplier):
+ for layers in self.layers.values():
+ for layer in layers:
+ layer.multiplier = multiplier
+
+ return self
+
+ def eval(self):
+ for layers in self.layers.values():
+ for layer in layers:
+ layer.eval()
+ for param in layer.parameters():
+ param.requires_grad = False
+
+ def save(self, filename):
+ state_dict = {}
+ optimizer_saved_dict = {}
+
+ for k, v in self.layers.items():
+ state_dict[k] = (v[0].state_dict(), v[1].state_dict())
+
+ state_dict['step'] = self.step
+ state_dict['name'] = self.name
+ state_dict['layer_structure'] = self.layer_structure
+ state_dict['activation_func'] = self.activation_func
+ state_dict['is_layer_norm'] = self.add_layer_norm
+ state_dict['weight_initialization'] = self.weight_init
+ state_dict['sd_checkpoint'] = self.sd_checkpoint
+ state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+ state_dict['activate_output'] = self.activate_output
+ state_dict['use_dropout'] = self.use_dropout
+ state_dict['dropout_structure'] = self.dropout_structure
+ state_dict['last_layer_dropout'] = (self.dropout_structure[-2] != 0) if self.dropout_structure is not None else self.last_layer_dropout
+ state_dict['optional_info'] = self.optional_info if self.optional_info else None
+
+ if self.optimizer_name is not None:
+ optimizer_saved_dict['optimizer_name'] = self.optimizer_name
+
+ torch.save(state_dict, filename)
+ if shared.opts.save_optimizer_state and self.optimizer_state_dict:
+ optimizer_saved_dict['hash'] = self.shorthash()
+ optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
+ torch.save(optimizer_saved_dict, filename + '.optim')
+
+ def load(self, filename):
+ self.filename = filename
+ if self.name is None:
+ self.name = os.path.splitext(os.path.basename(filename))[0]
+
+ state_dict = torch.load(filename, map_location='cpu')
+
+ self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
+ self.optional_info = state_dict.get('optional_info', None)
+ self.activation_func = state_dict.get('activation_func', None)
+ self.weight_init = state_dict.get('weight_initialization', 'Normal')
+ self.add_layer_norm = state_dict.get('is_layer_norm', False)
+ self.dropout_structure = state_dict.get('dropout_structure', None)
+ self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False)
+ self.activate_output = state_dict.get('activate_output', True)
+ self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
+ # Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0.
+ if self.dropout_structure is None:
+ self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout)
+
+ if shared.opts.print_hypernet_extra:
+ if self.optional_info is not None:
+ print(f" INFO:\n {self.optional_info}\n")
+
+ print(f" Layer structure: {self.layer_structure}")
+ print(f" Activation function: {self.activation_func}")
+ print(f" Weight initialization: {self.weight_init}")
+ print(f" Layer norm: {self.add_layer_norm}")
+ print(f" Dropout usage: {self.use_dropout}" )
+ print(f" Activate last layer: {self.activate_output}")
+ print(f" Dropout structure: {self.dropout_structure}")
+
+ optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {}
+
+ if self.shorthash() == optimizer_saved_dict.get('hash', None):
+ self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+ else:
+ self.optimizer_state_dict = None
+ if self.optimizer_state_dict:
+ self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
+ if shared.opts.print_hypernet_extra:
+ print("Loaded existing optimizer from checkpoint")
+ print(f"Optimizer name is {self.optimizer_name}")
+ else:
+ self.optimizer_name = "AdamW"
+ if shared.opts.print_hypernet_extra:
+ print("No saved optimizer exists in checkpoint")
+
+ for size, sd in state_dict.items():
+ if type(size) == int:
+ self.layers[size] = (
+ HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.activate_output, self.dropout_structure),
+ HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.activate_output, self.dropout_structure),
+ )
+
+ self.name = state_dict.get('name', self.name)
+ self.step = state_dict.get('step', 0)
+ self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
+ self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
+ self.eval()
+
+ def shorthash(self):
+ sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')
+
+ return sha256[0:10] if sha256 else None
+
+
+def list_hypernetworks(path):
+ res = {}
+ for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
+ name = os.path.splitext(os.path.basename(filename))[0]
+ # Prevent a hypothetical "None.pt" from being listed.
+ if name != "None":
+ res[name] = filename
+ return res
+
+
+def load_hypernetwork(name):
+ path = shared.hypernetworks.get(name, None)
+
+ if path is None:
+ return None
+
+ try:
+ hypernetwork = Hypernetwork()
+ hypernetwork.load(path)
+ return hypernetwork
+ except Exception:
+ errors.report(f"Error loading hypernetwork {path}", exc_info=True)
+ return None
+
+
+def load_hypernetworks(names, multipliers=None):
+ already_loaded = {}
+
+ for hypernetwork in shared.loaded_hypernetworks:
+ if hypernetwork.name in names:
+ already_loaded[hypernetwork.name] = hypernetwork
+
+ shared.loaded_hypernetworks.clear()
+
+ for i, name in enumerate(names):
+ hypernetwork = already_loaded.get(name, None)
+ if hypernetwork is None:
+ hypernetwork = load_hypernetwork(name)
+
+ if hypernetwork is None:
+ continue
+
+ hypernetwork.set_multiplier(multipliers[i] if multipliers else 1.0)
+ shared.loaded_hypernetworks.append(hypernetwork)
+
+
+def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
+ hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None)
+
+ if hypernetwork_layers is None:
+ return context_k, context_v
+
+ if layer is not None:
+ layer.hyper_k = hypernetwork_layers[0]
+ layer.hyper_v = hypernetwork_layers[1]
+
+ context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k)))
+ context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v)))
+ return context_k, context_v
+
+
+def apply_hypernetworks(hypernetworks, context, layer=None):
+ context_k = context
+ context_v = context
+ for hypernetwork in hypernetworks:
+ context_k, context_v = apply_single_hypernetwork(hypernetwork, context_k, context_v, layer)
+
+ return context_k, context_v
+
+
+def attention_CrossAttention_forward(self, x, context=None, mask=None, **kwargs):
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = apply_hypernetworks(shared.loaded_hypernetworks, context, self)
+ k = self.to_k(context_k)
+ v = self.to_v(context_v)
+
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
+
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+ if mask is not None:
+ mask = rearrange(mask, 'b ... -> b (...)')
+ max_neg_value = -torch.finfo(sim.dtype).max
+ mask = repeat(mask, 'b j -> (b h) () j', h=h)
+ sim.masked_fill_(~mask, max_neg_value)
+
+ # attention, what we cannot get enough of
+ attn = sim.softmax(dim=-1)
+
+ out = einsum('b i j, b j d -> b i d', attn, v)
+ out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+ return self.to_out(out)
+
+
+def stack_conds(conds):
+ if len(conds) == 1:
+ return torch.stack(conds)
+
+ # same as in reconstruct_multicond_batch
+ token_count = max([x.shape[0] for x in conds])
+ for i in range(len(conds)):
+ if conds[i].shape[0] != token_count:
+ last_vector = conds[i][-1:]
+ last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
+ conds[i] = torch.vstack([conds[i], last_vector_repeated])
+
+ return torch.stack(conds)
+
+
+def statistics(data):
+ if len(data) < 2:
+ std = 0
+ else:
+ std = stdev(data)
+ total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
+ recent_data = data[-32:]
+ if len(recent_data) < 2:
+ std = 0
+ else:
+ std = stdev(recent_data)
+ recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"
+ return total_information, recent_information
+
+
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
+ # Remove illegal characters from name.
+ name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+ assert name, "Name cannot be empty!"
+
+ fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
+ if not overwrite_old:
+ assert not os.path.exists(fn), f"file {fn} already exists"
+
+ if type(layer_structure) == str:
+ layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
+
+ if use_dropout and dropout_structure and type(dropout_structure) == str:
+ dropout_structure = [float(x.strip()) for x in dropout_structure.split(",")]
+ else:
+ dropout_structure = [0] * len(layer_structure)
+
+ hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(
+ name=name,
+ enable_sizes=[int(x) for x in enable_sizes],
+ layer_structure=layer_structure,
+ activation_func=activation_func,
+ weight_init=weight_init,
+ add_layer_norm=add_layer_norm,
+ use_dropout=use_dropout,
+ dropout_structure=dropout_structure
+ )
+ hypernet.save(fn)
+
+ shared.reload_hypernetworks()
+
+
+def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+ from modules import images, processing
+
+ save_hypernetwork_every = save_hypernetwork_every or 0
+ create_image_every = create_image_every or 0
+ template_file = textual_inversion.textual_inversion_templates.get(template_filename, None)
+ textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork")
+ template_file = template_file.path
+
+ path = shared.hypernetworks.get(hypernetwork_name, None)
+ hypernetwork = Hypernetwork()
+ hypernetwork.load(path)
+ shared.loaded_hypernetworks = [hypernetwork]
+
+ shared.state.job = "train-hypernetwork"
+ shared.state.textinfo = "Initializing hypernetwork training..."
+ shared.state.job_count = steps
+
+ hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
+ filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+
+ log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+ unload = shared.opts.unload_models_when_training
+
+ if save_hypernetwork_every > 0:
+ hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
+ os.makedirs(hypernetwork_dir, exist_ok=True)
+ else:
+ hypernetwork_dir = None
+
+ if create_image_every > 0:
+ images_dir = os.path.join(log_directory, "images")
+ os.makedirs(images_dir, exist_ok=True)
+ else:
+ images_dir = None
+
+ checkpoint = sd_models.select_checkpoint()
+
+ initial_step = hypernetwork.step or 0
+ if initial_step >= steps:
+ shared.state.textinfo = "Model has already been trained beyond specified max steps"
+ return hypernetwork, filename
+
+ scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
+
+ clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
+ if clip_grad:
+ clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
+
+ if shared.opts.training_enable_tensorboard:
+ tensorboard_writer = textual_inversion.tensorboard_setup(log_directory)
+
+ # dataset loading may take a while, so input validations and early returns should be done before this
+ shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+
+ pin_memory = shared.opts.pin_memory
+
+ ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
+
+ if shared.opts.save_training_settings_to_txt:
+ saved_params = dict(
+ model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds),
+ **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]}
+ )
+ logging.save_settings_to_file(log_directory, {**saved_params, **locals()})
+
+ latent_sampling_method = ds.latent_sampling_method
+
+ dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
+
+ old_parallel_processing_allowed = shared.parallel_processing_allowed
+
+ if unload:
+ shared.parallel_processing_allowed = False
+ shared.sd_model.cond_stage_model.to(devices.cpu)
+ shared.sd_model.first_stage_model.to(devices.cpu)
+
+ weights = hypernetwork.weights()
+ hypernetwork.train()
+
+ # Here we use optimizer from saved HN, or we can specify as UI option.
+ if hypernetwork.optimizer_name in optimizer_dict:
+ optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+ optimizer_name = hypernetwork.optimizer_name
+ else:
+ print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
+ optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+ optimizer_name = 'AdamW'
+
+ if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
+ try:
+ optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
+ except RuntimeError as e:
+ print("Cannot resume from saved optimizer!")
+ print(e)
+
+ scaler = torch.cuda.amp.GradScaler()
+
+ batch_size = ds.batch_size
+ gradient_step = ds.gradient_step
+ # n steps = batch_size * gradient_step * n image processed
+ steps_per_epoch = len(ds) // batch_size // gradient_step
+ max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
+ loss_step = 0
+ _loss_step = 0 #internal
+ # size = len(ds.indexes)
+ # loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+ loss_logging = deque(maxlen=len(ds) * 3) # this should be configurable parameter, this is 3 * epoch(dataset size)
+ # losses = torch.zeros((size,))
+ # previous_mean_losses = [0]
+ # previous_mean_loss = 0
+ # print("Mean loss of {} elements".format(size))
+
+ steps_without_grad = 0
+
+ last_saved_file = "<none>"
+ last_saved_image = "<none>"
+ forced_filename = "<none>"
+
+ pbar = tqdm.tqdm(total=steps - initial_step)
+ try:
+ sd_hijack_checkpoint.add()
+
+ for _ in range((steps-initial_step) * gradient_step):
+ if scheduler.finished:
+ break
+ if shared.state.interrupted:
+ break
+ for j, batch in enumerate(dl):
+ # works as a drop_last=True for gradient accumulation
+ if j == max_steps_per_epoch:
+ break
+ scheduler.apply(optimizer, hypernetwork.step)
+ if scheduler.finished:
+ break
+ if shared.state.interrupted:
+ break
+
+ if clip_grad:
+ clip_grad_sched.step(hypernetwork.step)
+
+ with devices.autocast():
+ x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+ if use_weight:
+ w = batch.weight.to(devices.device, non_blocking=pin_memory)
+ if tag_drop_out != 0 or shuffle_tags:
+ shared.sd_model.cond_stage_model.to(devices.device)
+ c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
+ shared.sd_model.cond_stage_model.to(devices.cpu)
+ else:
+ c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
+ if use_weight:
+ loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
+ del w
+ else:
+ loss = shared.sd_model.forward(x, c)[0] / gradient_step
+ del x
+ del c
+
+ _loss_step += loss.item()
+ scaler.scale(loss).backward()
+
+ # go back until we reach gradient accumulation steps
+ if (j + 1) % gradient_step != 0:
+ continue
+ loss_logging.append(_loss_step)
+ if clip_grad:
+ clip_grad(weights, clip_grad_sched.learn_rate)
+
+ scaler.step(optimizer)
+ scaler.update()
+ hypernetwork.step += 1
+ pbar.update()
+ optimizer.zero_grad(set_to_none=True)
+ loss_step = _loss_step
+ _loss_step = 0
+
+ steps_done = hypernetwork.step + 1
+
+ epoch_num = hypernetwork.step // steps_per_epoch
+ epoch_step = hypernetwork.step % steps_per_epoch
+
+ description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+ pbar.set_description(description)
+ if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
+ # Before saving, change name to match current checkpoint.
+ hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
+ last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+ hypernetwork.optimizer_name = optimizer_name
+ if shared.opts.save_optimizer_state:
+ hypernetwork.optimizer_state_dict = optimizer.state_dict()
+ save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
+ hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
+
+
+
+ if shared.opts.training_enable_tensorboard:
+ epoch_num = hypernetwork.step // len(ds)
+ epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
+ mean_loss = sum(loss_logging) / len(loss_logging)
+ textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
+
+ textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {
+ "loss": f"{loss_step:.7f}",
+ "learn_rate": scheduler.learn_rate
+ })
+
+ if images_dir is not None and steps_done % create_image_every == 0:
+ forced_filename = f'{hypernetwork_name}-{steps_done}'
+ last_saved_image = os.path.join(images_dir, forced_filename)
+ hypernetwork.eval()
+ rng_state = torch.get_rng_state()
+ cuda_rng_state = None
+ if torch.cuda.is_available():
+ cuda_rng_state = torch.cuda.get_rng_state_all()
+ shared.sd_model.cond_stage_model.to(devices.device)
+ shared.sd_model.first_stage_model.to(devices.device)
+
+ p = processing.StableDiffusionProcessingTxt2Img(
+ sd_model=shared.sd_model,
+ do_not_save_grid=True,
+ do_not_save_samples=True,
+ )
+
+ p.disable_extra_networks = True
+
+ if preview_from_txt2img:
+ p.prompt = preview_prompt
+ p.negative_prompt = preview_negative_prompt
+ p.steps = preview_steps
+ p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+ p.cfg_scale = preview_cfg_scale
+ p.seed = preview_seed
+ p.width = preview_width
+ p.height = preview_height
+ else:
+ p.prompt = batch.cond_text[0]
+ p.steps = 20
+ p.width = training_width
+ p.height = training_height
+
+ preview_text = p.prompt
+
+ with closing(p):
+ processed = processing.process_images(p)
+ image = processed.images[0] if len(processed.images) > 0 else None
+
+ if unload:
+ shared.sd_model.cond_stage_model.to(devices.cpu)
+ shared.sd_model.first_stage_model.to(devices.cpu)
+ torch.set_rng_state(rng_state)
+ if torch.cuda.is_available():
+ torch.cuda.set_rng_state_all(cuda_rng_state)
+ hypernetwork.train()
+ if image is not None:
+ shared.state.assign_current_image(image)
+ if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+ textual_inversion.tensorboard_add_image(tensorboard_writer,
+ f"Validation at epoch {epoch_num}", image,
+ hypernetwork.step)
+ last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+ last_saved_image += f", prompt: {preview_text}"
+
+ shared.state.job_no = hypernetwork.step
+
+ shared.state.textinfo = f"""
+<p>
+Loss: {loss_step:.7f}<br/>
+Step: {steps_done}<br/>
+Last prompt: {html.escape(batch.cond_text[0])}<br/>
+Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+ except Exception:
+ errors.report("Exception in training hypernetwork", exc_info=True)
+ finally:
+ pbar.leave = False
+ pbar.close()
+ hypernetwork.eval()
+ sd_hijack_checkpoint.remove()
+
+
+
+ filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+ hypernetwork.optimizer_name = optimizer_name
+ if shared.opts.save_optimizer_state:
+ hypernetwork.optimizer_state_dict = optimizer.state_dict()
+ save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
+
+ del optimizer
+ hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
+ shared.sd_model.cond_stage_model.to(devices.device)
+ shared.sd_model.first_stage_model.to(devices.device)
+ shared.parallel_processing_allowed = old_parallel_processing_allowed
+
+ return hypernetwork, filename
+
+def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
+ old_hypernetwork_name = hypernetwork.name
+ old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None
+ old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None
+ try:
+ hypernetwork.sd_checkpoint = checkpoint.shorthash
+ hypernetwork.sd_checkpoint_name = checkpoint.model_name
+ hypernetwork.name = hypernetwork_name
+ hypernetwork.save(filename)
+ except:
+ hypernetwork.sd_checkpoint = old_sd_checkpoint
+ hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name
+ hypernetwork.name = old_hypernetwork_name
+ raise
diff --git a/stable-diffusion-webui/modules/hypernetworks/ui.py b/stable-diffusion-webui/modules/hypernetworks/ui.py
new file mode 100644
index 0000000000000000000000000000000000000000..351910461dadbf3bfe027e542e0fddf896352d17
--- /dev/null
+++ b/stable-diffusion-webui/modules/hypernetworks/ui.py
@@ -0,0 +1,38 @@
+import html
+
+import gradio as gr
+import modules.hypernetworks.hypernetwork
+from modules import devices, sd_hijack, shared
+
+not_available = ["hardswish", "multiheadattention"]
+keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available]
+
+
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
+ filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
+
+ return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", ""
+
+
+def train_hypernetwork(*args):
+ shared.loaded_hypernetworks = []
+
+ assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'
+
+ try:
+ sd_hijack.undo_optimizations()
+
+ hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
+
+ res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
+Hypernetwork saved to {html.escape(filename)}
+"""
+ return res, ""
+ except Exception:
+ raise
+ finally:
+ shared.sd_model.cond_stage_model.to(devices.device)
+ shared.sd_model.first_stage_model.to(devices.device)
+ sd_hijack.apply_optimizations()
+
diff --git a/stable-diffusion-webui/modules/images.py b/stable-diffusion-webui/modules/images.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b37cc3daee6591e6c59eb33546cc7944e5c8d41
--- /dev/null
+++ b/stable-diffusion-webui/modules/images.py
@@ -0,0 +1,778 @@
+from __future__ import annotations
+
+import datetime
+
+import pytz
+import io
+import math
+import os
+from collections import namedtuple
+import re
+
+import numpy as np
+import piexif
+import piexif.helper
+from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin
+import string
+import json
+import hashlib
+
+from modules import sd_samplers, shared, script_callbacks, errors
+from modules.paths_internal import roboto_ttf_file
+from modules.shared import opts
+
+LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+
+
+def get_font(fontsize: int):
+ try:
+ return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize)
+ except Exception:
+ return ImageFont.truetype(roboto_ttf_file, fontsize)
+
+
+def image_grid(imgs, batch_size=1, rows=None):
+ if rows is None:
+ if opts.n_rows > 0:
+ rows = opts.n_rows
+ elif opts.n_rows == 0:
+ rows = batch_size
+ elif opts.grid_prevent_empty_spots:
+ rows = math.floor(math.sqrt(len(imgs)))
+ while len(imgs) % rows != 0:
+ rows -= 1
+ else:
+ rows = math.sqrt(len(imgs))
+ rows = round(rows)
+ if rows > len(imgs):
+ rows = len(imgs)
+
+ cols = math.ceil(len(imgs) / rows)
+
+ params = script_callbacks.ImageGridLoopParams(imgs, cols, rows)
+ script_callbacks.image_grid_callback(params)
+
+ w, h = imgs[0].size
+ grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black')
+
+ for i, img in enumerate(params.imgs):
+ grid.paste(img, box=(i % params.cols * w, i // params.cols * h))
+
+ return grid
+
+
+Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
+
+
+def split_grid(image, tile_w=512, tile_h=512, overlap=64):
+ w = image.width
+ h = image.height
+
+ non_overlap_width = tile_w - overlap
+ non_overlap_height = tile_h - overlap
+
+ cols = math.ceil((w - overlap) / non_overlap_width)
+ rows = math.ceil((h - overlap) / non_overlap_height)
+
+ dx = (w - tile_w) / (cols - 1) if cols > 1 else 0
+ dy = (h - tile_h) / (rows - 1) if rows > 1 else 0
+
+ grid = Grid([], tile_w, tile_h, w, h, overlap)
+ for row in range(rows):
+ row_images = []
+
+ y = int(row * dy)
+
+ if y + tile_h >= h:
+ y = h - tile_h
+
+ for col in range(cols):
+ x = int(col * dx)
+
+ if x + tile_w >= w:
+ x = w - tile_w
+
+ tile = image.crop((x, y, x + tile_w, y + tile_h))
+
+ row_images.append([x, tile_w, tile])
+
+ grid.tiles.append([y, tile_h, row_images])
+
+ return grid
+
+
+def combine_grid(grid):
+ def make_mask_image(r):
+ r = r * 255 / grid.overlap
+ r = r.astype(np.uint8)
+ return Image.fromarray(r, 'L')
+
+ mask_w = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((1, grid.overlap)).repeat(grid.tile_h, axis=0))
+ mask_h = make_mask_image(np.arange(grid.overlap, dtype=np.float32).reshape((grid.overlap, 1)).repeat(grid.image_w, axis=1))
+
+ combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
+ for y, h, row in grid.tiles:
+ combined_row = Image.new("RGB", (grid.image_w, h))
+ for x, w, tile in row:
+ if x == 0:
+ combined_row.paste(tile, (0, 0))
+ continue
+
+ combined_row.paste(tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w)
+ combined_row.paste(tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0))
+
+ if y == 0:
+ combined_image.paste(combined_row, (0, 0))
+ continue
+
+ combined_image.paste(combined_row.crop((0, 0, combined_row.width, grid.overlap)), (0, y), mask=mask_h)
+ combined_image.paste(combined_row.crop((0, grid.overlap, combined_row.width, h)), (0, y + grid.overlap))
+
+ return combined_image
+
+
+class GridAnnotation:
+ def __init__(self, text='', is_active=True):
+ self.text = text
+ self.is_active = is_active
+ self.size = None
+
+
+def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
+
+ color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB')
+ color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB')
+ color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB')
+
+ def wrap(drawing, text, font, line_length):
+ lines = ['']
+ for word in text.split():
+ line = f'{lines[-1]} {word}'.strip()
+ if drawing.textlength(line, font=font) <= line_length:
+ lines[-1] = line
+ else:
+ lines.append(word)
+ return lines
+
+ def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
+ for line in lines:
+ fnt = initial_fnt
+ fontsize = initial_fontsize
+ while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
+ fontsize -= 1
+ fnt = get_font(fontsize)
+ drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center")
+
+ if not line.is_active:
+ drawing.line((draw_x - line.size[0] // 2, draw_y + line.size[1] // 2, draw_x + line.size[0] // 2, draw_y + line.size[1] // 2), fill=color_inactive, width=4)
+
+ draw_y += line.size[1] + line_spacing
+
+ fontsize = (width + height) // 25
+ line_spacing = fontsize // 2
+
+ fnt = get_font(fontsize)
+
+ pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4
+
+ cols = im.width // width
+ rows = im.height // height
+
+ assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}'
+ assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}'
+
+ calc_img = Image.new("RGB", (1, 1), color_background)
+ calc_d = ImageDraw.Draw(calc_img)
+
+ for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)):
+ items = [] + texts
+ texts.clear()
+
+ for line in items:
+ wrapped = wrap(calc_d, line.text, fnt, allowed_width)
+ texts += [GridAnnotation(x, line.is_active) for x in wrapped]
+
+ for line in texts:
+ bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt)
+ line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1])
+ line.allowed_width = allowed_width
+
+ hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
+ ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]
+
+ pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2
+
+ result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background)
+
+ for row in range(rows):
+ for col in range(cols):
+ cell = im.crop((width * col, height * row, width * (col+1), height * (row+1)))
+ result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row))
+
+ d = ImageDraw.Draw(result)
+
+ for col in range(cols):
+ x = pad_left + (width + margin) * col + width / 2
+ y = pad_top / 2 - hor_text_heights[col] / 2
+
+ draw_texts(d, x, y, hor_texts[col], fnt, fontsize)
+
+ for row in range(rows):
+ x = pad_left / 2
+ y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2
+
+ draw_texts(d, x, y, ver_texts[row], fnt, fontsize)
+
+ return result
+
+
+def draw_prompt_matrix(im, width, height, all_prompts, margin=0):
+ prompts = all_prompts[1:]
+ boundary = math.ceil(len(prompts) / 2)
+
+ prompts_horiz = prompts[:boundary]
+ prompts_vert = prompts[boundary:]
+
+ hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
+ ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]
+
+ return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin)
+
+
+def resize_image(resize_mode, im, width, height, upscaler_name=None):
+ """
+ Resizes an image with the specified resize_mode, width, and height.
+
+ Args:
+ resize_mode: The mode to use when resizing the image.
+ 0: Resize the image to the specified width and height.
+ 1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
+ 2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
+ im: The image to resize.
+ width: The width to resize the image to.
+ height: The height to resize the image to.
+ upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img.
+ """
+
+ upscaler_name = upscaler_name or opts.upscaler_for_img2img
+
+ def resize(im, w, h):
+ if upscaler_name is None or upscaler_name == "None" or im.mode == 'L':
+ return im.resize((w, h), resample=LANCZOS)
+
+ scale = max(w / im.width, h / im.height)
+
+ if scale > 1.0:
+ upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
+ if len(upscalers) == 0:
+ upscaler = shared.sd_upscalers[0]
+ print(f"could not find upscaler named {upscaler_name or '<empty string>'}, using {upscaler.name} as a fallback")
+ else:
+ upscaler = upscalers[0]
+
+ im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
+
+ if im.width != w or im.height != h:
+ im = im.resize((w, h), resample=LANCZOS)
+
+ return im
+
+ if resize_mode == 0:
+ res = resize(im, width, height)
+
+ elif resize_mode == 1:
+ ratio = width / height
+ src_ratio = im.width / im.height
+
+ src_w = width if ratio > src_ratio else im.width * height // im.height
+ src_h = height if ratio <= src_ratio else im.height * width // im.width
+
+ resized = resize(im, src_w, src_h)
+ res = Image.new("RGB", (width, height))
+ res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+
+ else:
+ ratio = width / height
+ src_ratio = im.width / im.height
+
+ src_w = width if ratio < src_ratio else im.width * height // im.height
+ src_h = height if ratio >= src_ratio else im.height * width // im.width
+
+ resized = resize(im, src_w, src_h)
+ res = Image.new("RGB", (width, height))
+ res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+
+ if ratio < src_ratio:
+ fill_height = height // 2 - src_h // 2
+ if fill_height > 0:
+ res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0))
+ res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h))
+ elif ratio > src_ratio:
+ fill_width = width // 2 - src_w // 2
+ if fill_width > 0:
+ res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0))
+ res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0))
+
+ return res
+
+
+invalid_filename_chars = '<>:"/\\|?*\n\r\t'
+invalid_filename_prefix = ' '
+invalid_filename_postfix = ' .'
+re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
+re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
+re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
+max_filename_part_length = 128
+NOTHING_AND_SKIP_PREVIOUS_TEXT = object()
+
+
+def sanitize_filename_part(text, replace_spaces=True):
+ if text is None:
+ return None
+
+ if replace_spaces:
+ text = text.replace(' ', '_')
+
+ text = text.translate({ord(x): '_' for x in invalid_filename_chars})
+ text = text.lstrip(invalid_filename_prefix)[:max_filename_part_length]
+ text = text.rstrip(invalid_filename_postfix)
+ return text
+
+
+class FilenameGenerator:
+ replacements = {
+ 'seed': lambda self: self.seed if self.seed is not None else '',
+ 'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
+ 'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
+ 'steps': lambda self: self.p and self.p.steps,
+ 'cfg': lambda self: self.p and self.p.cfg_scale,
+ 'width': lambda self: self.image.width,
+ 'height': lambda self: self.image.height,
+ 'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
+ 'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
+ 'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
+ 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
+ 'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
+ 'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
+ 'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
+ 'prompt_hash': lambda self, *args: self.string_hash(self.prompt, *args),
+ 'negative_prompt_hash': lambda self, *args: self.string_hash(self.p.negative_prompt, *args),
+ 'full_prompt_hash': lambda self, *args: self.string_hash(f"{self.p.prompt} {self.p.negative_prompt}", *args), # a space in between to create a unique string
+ 'prompt': lambda self: sanitize_filename_part(self.prompt),
+ 'prompt_no_styles': lambda self: self.prompt_no_style(),
+ 'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
+ 'prompt_words': lambda self: self.prompt_words(),
+ 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 or self.zip else self.p.batch_index + 1,
+ 'batch_size': lambda self: self.p.batch_size,
+ 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+ 'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
+ 'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
+ 'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
+ 'user': lambda self: self.p.user,
+ 'vae_filename': lambda self: self.get_vae_filename(),
+ 'none': lambda self: '', # Overrides the default, so you can get just the sequence number
+ 'image_hash': lambda self, *args: self.image_hash(*args) # accepts formats: [image_hash<length>] default full hash
+ }
+ default_time_format = '%Y%m%d%H%M%S'
+
+ def __init__(self, p, seed, prompt, image, zip=False):
+ self.p = p
+ self.seed = seed
+ self.prompt = prompt
+ self.image = image
+ self.zip = zip
+
+ def get_vae_filename(self):
+ """Get the name of the VAE file."""
+
+ import modules.sd_vae as sd_vae
+
+ if sd_vae.loaded_vae_file is None:
+ return "NoneType"
+
+ file_name = os.path.basename(sd_vae.loaded_vae_file)
+ split_file_name = file_name.split('.')
+ if len(split_file_name) > 1 and split_file_name[0] == '':
+ return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
+ else:
+ return split_file_name[0]
+
+
+ def hasprompt(self, *args):
+ lower = self.prompt.lower()
+ if self.p is None or self.prompt is None:
+ return None
+ outres = ""
+ for arg in args:
+ if arg != "":
+ division = arg.split("|")
+ expected = division[0].lower()
+ default = division[1] if len(division) > 1 else ""
+ if lower.find(expected) >= 0:
+ outres = f'{outres}{expected}'
+ else:
+ outres = outres if default == "" else f'{outres}{default}'
+ return sanitize_filename_part(outres)
+
+ def prompt_no_style(self):
+ if self.p is None or self.prompt is None:
+ return None
+
+ prompt_no_style = self.prompt
+ for style in shared.prompt_styles.get_style_prompts(self.p.styles):
+ if style:
+ for part in style.split("{prompt}"):
+ prompt_no_style = prompt_no_style.replace(part, "").replace(", ,", ",").strip().strip(',')
+
+ prompt_no_style = prompt_no_style.replace(style, "").strip().strip(',').strip()
+
+ return sanitize_filename_part(prompt_no_style, replace_spaces=False)
+
+ def prompt_words(self):
+ words = [x for x in re_nonletters.split(self.prompt or "") if x]
+ if len(words) == 0:
+ words = ["empty"]
+ return sanitize_filename_part(" ".join(words[0:opts.directories_max_prompt_words]), replace_spaces=False)
+
+ def datetime(self, *args):
+ time_datetime = datetime.datetime.now()
+
+ time_format = args[0] if (args and args[0] != "") else self.default_time_format
+ try:
+ time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
+ except pytz.exceptions.UnknownTimeZoneError:
+ time_zone = None
+
+ time_zone_time = time_datetime.astimezone(time_zone)
+ try:
+ formatted_time = time_zone_time.strftime(time_format)
+ except (ValueError, TypeError):
+ formatted_time = time_zone_time.strftime(self.default_time_format)
+
+ return sanitize_filename_part(formatted_time, replace_spaces=False)
+
+ def image_hash(self, *args):
+ length = int(args[0]) if (args and args[0] != "") else None
+ return hashlib.sha256(self.image.tobytes()).hexdigest()[0:length]
+
+ def string_hash(self, text, *args):
+ length = int(args[0]) if (args and args[0] != "") else 8
+ return hashlib.sha256(text.encode()).hexdigest()[0:length]
+
+ def apply(self, x):
+ res = ''
+
+ for m in re_pattern.finditer(x):
+ text, pattern = m.groups()
+
+ if pattern is None:
+ res += text
+ continue
+
+ pattern_args = []
+ while True:
+ m = re_pattern_arg.match(pattern)
+ if m is None:
+ break
+
+ pattern, arg = m.groups()
+ pattern_args.insert(0, arg)
+
+ fun = self.replacements.get(pattern.lower())
+ if fun is not None:
+ try:
+ replacement = fun(self, *pattern_args)
+ except Exception:
+ replacement = None
+ errors.report(f"Error adding [{pattern}] to filename", exc_info=True)
+
+ if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
+ continue
+ elif replacement is not None:
+ res += text + str(replacement)
+ continue
+
+ res += f'{text}[{pattern}]'
+
+ return res
+
+
+def get_next_sequence_number(path, basename):
+ """
+ Determines and returns the next sequence number to use when saving an image in the specified directory.
+
+ The sequence starts at 0.
+ """
+ result = -1
+ if basename != '':
+ basename = f"{basename}-"
+
+ prefix_length = len(basename)
+ for p in os.listdir(path):
+ if p.startswith(basename):
+ parts = os.path.splitext(p[prefix_length:])[0].split('-') # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+ try:
+ result = max(int(parts[0]), result)
+ except ValueError:
+ pass
+
+ return result + 1
+
+
+def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None, pnginfo_section_name='parameters'):
+ """
+ Saves image to filename, including geninfo as text information for generation info.
+ For PNG images, geninfo is added to existing pnginfo dictionary using the pnginfo_section_name argument as key.
+ For JPG images, there's no dictionary and geninfo just replaces the EXIF description.
+ """
+
+ if extension is None:
+ extension = os.path.splitext(filename)[1]
+
+ image_format = Image.registered_extensions()[extension]
+
+ if extension.lower() == '.png':
+ existing_pnginfo = existing_pnginfo or {}
+ if opts.enable_pnginfo:
+ existing_pnginfo[pnginfo_section_name] = geninfo
+
+ if opts.enable_pnginfo:
+ pnginfo_data = PngImagePlugin.PngInfo()
+ for k, v in (existing_pnginfo or {}).items():
+ pnginfo_data.add_text(k, str(v))
+ else:
+ pnginfo_data = None
+
+ image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+
+ elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+ if image.mode == 'RGBA':
+ image = image.convert("RGB")
+ elif image.mode == 'I;16':
+ image = image.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
+
+ image.save(filename, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
+
+ if opts.enable_pnginfo and geninfo is not None:
+ exif_bytes = piexif.dump({
+ "Exif": {
+ piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
+ },
+ })
+
+ piexif.insert(exif_bytes, filename)
+ else:
+ image.save(filename, format=image_format, quality=opts.jpeg_quality)
+
+
+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
+ """Save an image.
+
+ Args:
+ image (`PIL.Image`):
+ The image to be saved.
+ path (`str`):
+ The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
+ basename (`str`):
+ The base filename which will be applied to `filename pattern`.
+ seed, prompt, short_filename,
+ extension (`str`):
+ Image file extension, default is `png`.
+ pngsectionname (`str`):
+ Specify the name of the section which `info` will be saved in.
+ info (`str` or `PngImagePlugin.iTXt`):
+ PNG info chunks.
+ existing_info (`dict`):
+ Additional PNG info. `existing_info == {pngsectionname: info, ...}`
+ no_prompt:
+ TODO I don't know its meaning.
+ p (`StableDiffusionProcessing`)
+ forced_filename (`str`):
+ If specified, `basename` and filename pattern will be ignored.
+ save_to_dirs (bool):
+ If true, the image will be saved into a subdirectory of `path`.
+
+ Returns: (fullfn, txt_fullfn)
+ fullfn (`str`):
+ The full path of the saved imaged.
+ txt_fullfn (`str` or None):
+ If a text file is saved for this image, this will be its full path. Otherwise None.
+ """
+ namegen = FilenameGenerator(p, seed, prompt, image)
+
+ # WebP and JPG formats have maximum dimension limits of 16383 and 65535 respectively. switch to PNG which has a much higher limit
+ if (image.height > 65535 or image.width > 65535) and extension.lower() in ("jpg", "jpeg") or (image.height > 16383 or image.width > 16383) and extension.lower() == "webp":
+ print('Image dimensions too large; saving as PNG')
+ extension = ".png"
+
+ if save_to_dirs is None:
+ save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
+
+ if save_to_dirs:
+ dirname = namegen.apply(opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /')
+ path = os.path.join(path, dirname)
+
+ os.makedirs(path, exist_ok=True)
+
+ if forced_filename is None:
+ if short_filename or seed is None:
+ file_decoration = ""
+ elif opts.save_to_dirs:
+ file_decoration = opts.samples_filename_pattern or "[seed]"
+ else:
+ file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
+
+ file_decoration = namegen.apply(file_decoration) + suffix
+
+ add_number = opts.save_images_add_number or file_decoration == ''
+
+ if file_decoration != "" and add_number:
+ file_decoration = f"-{file_decoration}"
+
+ if add_number:
+ basecount = get_next_sequence_number(path, basename)
+ fullfn = None
+ for i in range(500):
+ fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}"
+ fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}")
+ if not os.path.exists(fullfn):
+ break
+ else:
+ fullfn = os.path.join(path, f"{file_decoration}.{extension}")
+ else:
+ fullfn = os.path.join(path, f"{forced_filename}.{extension}")
+
+ pnginfo = existing_info or {}
+ if info is not None:
+ pnginfo[pnginfo_section_name] = info
+
+ params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo)
+ script_callbacks.before_image_saved_callback(params)
+
+ image = params.image
+ fullfn = params.filename
+ info = params.pnginfo.get(pnginfo_section_name, None)
+
+ def _atomically_save_image(image_to_save, filename_without_extension, extension):
+ """
+ save image with .tmp extension to avoid race condition when another process detects new image in the directory
+ """
+ temp_file_path = f"{filename_without_extension}.tmp"
+
+ save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
+
+ os.replace(temp_file_path, filename_without_extension + extension)
+
+ fullfn_without_extension, extension = os.path.splitext(params.filename)
+ if hasattr(os, 'statvfs'):
+ max_name_len = os.statvfs(path).f_namemax
+ fullfn_without_extension = fullfn_without_extension[:max_name_len - max(4, len(extension))]
+ params.filename = fullfn_without_extension + extension
+ fullfn = params.filename
+ _atomically_save_image(image, fullfn_without_extension, extension)
+
+ image.already_saved_as = fullfn
+
+ oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
+ if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
+ ratio = image.width / image.height
+ resize_to = None
+ if oversize and ratio > 1:
+ resize_to = round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)
+ elif oversize:
+ resize_to = round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)
+
+ if resize_to is not None:
+ try:
+ # Resizing image with LANCZOS could throw an exception if e.g. image mode is I;16
+ image = image.resize(resize_to, LANCZOS)
+ except Exception:
+ image = image.resize(resize_to)
+ try:
+ _atomically_save_image(image, fullfn_without_extension, ".jpg")
+ except Exception as e:
+ errors.display(e, "saving image as downscaled JPG")
+
+ if opts.save_txt and info is not None:
+ txt_fullfn = f"{fullfn_without_extension}.txt"
+ with open(txt_fullfn, "w", encoding="utf8") as file:
+ file.write(f"{info}\n")
+ else:
+ txt_fullfn = None
+
+ script_callbacks.image_saved_callback(params)
+
+ return fullfn, txt_fullfn
+
+
+IGNORED_INFO_KEYS = {
+ 'jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
+ 'loop', 'background', 'timestamp', 'duration', 'progressive', 'progression',
+ 'icc_profile', 'chromaticity', 'photoshop',
+}
+
+
+def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
+ items = (image.info or {}).copy()
+
+ geninfo = items.pop('parameters', None)
+
+ if "exif" in items:
+ exif = piexif.load(items["exif"])
+ exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
+ try:
+ exif_comment = piexif.helper.UserComment.load(exif_comment)
+ except ValueError:
+ exif_comment = exif_comment.decode('utf8', errors="ignore")
+
+ if exif_comment:
+ items['exif comment'] = exif_comment
+ geninfo = exif_comment
+
+ for field in IGNORED_INFO_KEYS:
+ items.pop(field, None)
+
+ if items.get("Software", None) == "NovelAI":
+ try:
+ json_info = json.loads(items["Comment"])
+ sampler = sd_samplers.samplers_map.get(json_info["sampler"], "Euler a")
+
+ geninfo = f"""{items["Description"]}
+Negative prompt: {json_info["uc"]}
+Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
+ except Exception:
+ errors.report("Error parsing NovelAI image generation parameters", exc_info=True)
+
+ return geninfo, items
+
+
+def image_data(data):
+ import gradio as gr
+
+ try:
+ image = Image.open(io.BytesIO(data))
+ textinfo, _ = read_info_from_image(image)
+ return textinfo, None
+ except Exception:
+ pass
+
+ try:
+ text = data.decode('utf8')
+ assert len(text) < 10000
+ return text, None
+
+ except Exception:
+ pass
+
+ return gr.update(), None
+
+
+def flatten(img, bgcolor):
+ """replaces transparency with bgcolor (example: "#ffffff"), returning an RGB mode image with no transparency"""
+
+ if img.mode == "RGBA":
+ background = Image.new('RGBA', img.size, bgcolor)
+ background.paste(img, mask=img)
+ img = background
+
+ return img.convert('RGB')
diff --git a/stable-diffusion-webui/modules/img2img.py b/stable-diffusion-webui/modules/img2img.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f300606b5161f3d8e7c58a9df3e889c91f38eca
--- /dev/null
+++ b/stable-diffusion-webui/modules/img2img.py
@@ -0,0 +1,219 @@
+import os
+from contextlib import closing
+from pathlib import Path
+
+import numpy as np
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
+import gradio as gr
+
+from modules import images as imgutil
+from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
+from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
+from modules.shared import opts, state
+import modules.shared as shared
+import modules.processing as processing
+from modules.ui import plaintext_to_html
+import modules.scripts
+
+
+def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
+ output_dir = output_dir.strip()
+ processing.fix_seed(p)
+
+ images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
+
+ is_inpaint_batch = False
+ if inpaint_mask_dir:
+ inpaint_masks = shared.listfiles(inpaint_mask_dir)
+ is_inpaint_batch = bool(inpaint_masks)
+
+ if is_inpaint_batch:
+ print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
+
+ print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
+
+ state.job_count = len(images) * p.n_iter
+
+ # extract "default" params to use in case getting png info fails
+ prompt = p.prompt
+ negative_prompt = p.negative_prompt
+ seed = p.seed
+ cfg_scale = p.cfg_scale
+ sampler_name = p.sampler_name
+ steps = p.steps
+
+ for i, image in enumerate(images):
+ state.job = f"{i+1} out of {len(images)}"
+ if state.skipped:
+ state.skipped = False
+
+ if state.interrupted:
+ break
+
+ try:
+ img = Image.open(image)
+ except UnidentifiedImageError as e:
+ print(e)
+ continue
+ # Use the EXIF orientation of photos taken by smartphones.
+ img = ImageOps.exif_transpose(img)
+
+ if to_scale:
+ p.width = int(img.width * scale_by)
+ p.height = int(img.height * scale_by)
+
+ p.init_images = [img] * p.batch_size
+
+ image_path = Path(image)
+ if is_inpaint_batch:
+ # try to find corresponding mask for an image using simple filename matching
+ if len(inpaint_masks) == 1:
+ mask_image_path = inpaint_masks[0]
+ else:
+ # try to find corresponding mask for an image using simple filename matching
+ mask_image_dir = Path(inpaint_mask_dir)
+ masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*"))
+
+ if len(masks_found) == 0:
+ print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.")
+ continue
+
+ # it should contain only 1 matching mask
+ # otherwise user has many masks with the same name but different extensions
+ mask_image_path = masks_found[0]
+
+ mask_image = Image.open(mask_image_path)
+ p.image_mask = mask_image
+
+ if use_png_info:
+ try:
+ info_img = img
+ if png_info_dir:
+ info_img_path = os.path.join(png_info_dir, os.path.basename(image))
+ info_img = Image.open(info_img_path)
+ geninfo, _ = imgutil.read_info_from_image(info_img)
+ parsed_parameters = parse_generation_parameters(geninfo)
+ parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})}
+ except Exception:
+ parsed_parameters = {}
+
+ p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "")
+ p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "")
+ p.seed = int(parsed_parameters.get("Seed", seed))
+ p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale))
+ p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
+ p.steps = int(parsed_parameters.get("Steps", steps))
+
+ proc = modules.scripts.scripts_img2img.run(p, *args)
+ if proc is None:
+ if output_dir:
+ p.outpath_samples = output_dir
+ p.override_settings['save_to_dirs'] = False
+ if p.n_iter > 1 or p.batch_size > 1:
+ p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+ else:
+ p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+ process_images(p)
+
+
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
+ override_settings = create_override_settings_dict(override_settings_texts)
+
+ is_batch = mode == 5
+
+ if mode == 0: # img2img
+ image = init_img
+ mask = None
+ elif mode == 1: # img2img sketch
+ image = sketch
+ mask = None
+ elif mode == 2: # inpaint
+ image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
+ mask = processing.create_binary_mask(mask)
+ elif mode == 3: # inpaint sketch
+ image = inpaint_color_sketch
+ orig = inpaint_color_sketch_orig or inpaint_color_sketch
+ pred = np.any(np.array(image) != np.array(orig), axis=-1)
+ mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
+ mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
+ blur = ImageFilter.GaussianBlur(mask_blur)
+ image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+ elif mode == 4: # inpaint upload mask
+ image = init_img_inpaint
+ mask = init_mask_inpaint
+ else:
+ image = None
+ mask = None
+
+ # Use the EXIF orientation of photos taken by smartphones.
+ if image is not None:
+ image = ImageOps.exif_transpose(image)
+
+ if selected_scale_tab == 1 and not is_batch:
+ assert image, "Can't scale by because no image is selected"
+
+ width = int(image.width * scale_by)
+ height = int(image.height * scale_by)
+
+ assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
+
+ p = StableDiffusionProcessingImg2Img(
+ sd_model=shared.sd_model,
+ outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
+ outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ styles=prompt_styles,
+ sampler_name=sampler_name,
+ batch_size=batch_size,
+ n_iter=n_iter,
+ steps=steps,
+ cfg_scale=cfg_scale,
+ width=width,
+ height=height,
+ init_images=[image],
+ mask=mask,
+ mask_blur=mask_blur,
+ inpainting_fill=inpainting_fill,
+ resize_mode=resize_mode,
+ denoising_strength=denoising_strength,
+ image_cfg_scale=image_cfg_scale,
+ inpaint_full_res=inpaint_full_res,
+ inpaint_full_res_padding=inpaint_full_res_padding,
+ inpainting_mask_invert=inpainting_mask_invert,
+ override_settings=override_settings,
+ )
+
+ p.scripts = modules.scripts.scripts_img2img
+ p.script_args = args
+
+ p.user = request.username
+
+ if shared.cmd_opts.enable_console_prompts:
+ print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
+
+ if mask:
+ p.extra_generation_params["Mask blur"] = mask_blur
+
+ with closing(p):
+ if is_batch:
+ assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+
+ process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
+
+ processed = Processed(p, [], p.seed, "")
+ else:
+ processed = modules.scripts.scripts_img2img.run(p, *args)
+ if processed is None:
+ processed = process_images(p)
+
+ shared.total_tqdm.clear()
+
+ generation_info_js = processed.js()
+ if opts.samples_log_stdout:
+ print(generation_info_js)
+
+ if opts.do_not_show_images:
+ processed.images = []
+
+ return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
diff --git a/stable-diffusion-webui/modules/import_hook.py b/stable-diffusion-webui/modules/import_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..28c67dfa897abec5eeb4cfac3da79458d6fee278
--- /dev/null
+++ b/stable-diffusion-webui/modules/import_hook.py
@@ -0,0 +1,5 @@
+import sys
+
+# this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
+if "--xformers" not in "".join(sys.argv):
+ sys.modules["xformers"] = None
diff --git a/stable-diffusion-webui/modules/initialize.py b/stable-diffusion-webui/modules/initialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c163c758468e23be44a9d8bb24588b4ad356246
--- /dev/null
+++ b/stable-diffusion-webui/modules/initialize.py
@@ -0,0 +1,168 @@
+import importlib
+import logging
+import sys
+import warnings
+from threading import Thread
+
+from modules.timer import startup_timer
+
+
+def imports():
+ logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) # sshh...
+ logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
+
+ import torch # noqa: F401
+ startup_timer.record("import torch")
+ import pytorch_lightning # noqa: F401
+ startup_timer.record("import torch")
+ warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
+ warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+
+ import gradio # noqa: F401
+ startup_timer.record("import gradio")
+
+ from modules import paths, timer, import_hook, errors # noqa: F401
+ startup_timer.record("setup paths")
+
+ import ldm.modules.encoders.modules # noqa: F401
+ startup_timer.record("import ldm")
+
+ import sgm.modules.encoders.modules # noqa: F401
+ startup_timer.record("import sgm")
+
+ from modules import shared_init
+ shared_init.initialize()
+ startup_timer.record("initialize shared")
+
+ from modules import processing, gradio_extensons, ui # noqa: F401
+ startup_timer.record("other imports")
+
+
+def check_versions():
+ from modules.shared_cmd_options import cmd_opts
+
+ if not cmd_opts.skip_version_check:
+ from modules import errors
+ errors.check_versions()
+
+
+def initialize():
+ from modules import initialize_util
+ initialize_util.fix_torch_version()
+ initialize_util.fix_asyncio_event_loop_policy()
+ initialize_util.validate_tls_options()
+ initialize_util.configure_sigint_handler()
+ initialize_util.configure_opts_onchange()
+
+ from modules import modelloader
+ modelloader.cleanup_models()
+
+ from modules import sd_models
+ sd_models.setup_model()
+ startup_timer.record("setup SD model")
+
+ from modules.shared_cmd_options import cmd_opts
+
+ from modules import codeformer_model
+ warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision.transforms.functional_tensor")
+ codeformer_model.setup_model(cmd_opts.codeformer_models_path)
+ startup_timer.record("setup codeformer")
+
+ from modules import gfpgan_model
+ gfpgan_model.setup_model(cmd_opts.gfpgan_models_path)
+ startup_timer.record("setup gfpgan")
+
+ initialize_rest(reload_script_modules=False)
+
+
+def initialize_rest(*, reload_script_modules=False):
+ """
+ Called both from initialize() and when reloading the webui.
+ """
+ from modules.shared_cmd_options import cmd_opts
+
+ from modules import sd_samplers
+ sd_samplers.set_samplers()
+ startup_timer.record("set samplers")
+
+ from modules import extensions
+ extensions.list_extensions()
+ startup_timer.record("list extensions")
+
+ from modules import initialize_util
+ initialize_util.restore_config_state_file()
+ startup_timer.record("restore config state file")
+
+ from modules import shared, upscaler, scripts
+ if cmd_opts.ui_debug_mode:
+ shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
+ scripts.load_scripts()
+ return
+
+ from modules import sd_models
+ sd_models.list_models()
+ startup_timer.record("list SD models")
+
+ from modules import localization
+ localization.list_localizations(cmd_opts.localizations_dir)
+ startup_timer.record("list localizations")
+
+ with startup_timer.subcategory("load scripts"):
+ scripts.load_scripts()
+
+ if reload_script_modules:
+ for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]:
+ importlib.reload(module)
+ startup_timer.record("reload script modules")
+
+ from modules import modelloader
+ modelloader.load_upscalers()
+ startup_timer.record("load upscalers")
+
+ from modules import sd_vae
+ sd_vae.refresh_vae_list()
+ startup_timer.record("refresh VAE")
+
+ from modules import textual_inversion
+ textual_inversion.textual_inversion.list_textual_inversion_templates()
+ startup_timer.record("refresh textual inversion templates")
+
+ from modules import script_callbacks, sd_hijack_optimizations, sd_hijack
+ script_callbacks.on_list_optimizers(sd_hijack_optimizations.list_optimizers)
+ sd_hijack.list_optimizers()
+ startup_timer.record("scripts list_optimizers")
+
+ from modules import sd_unet
+ sd_unet.list_unets()
+ startup_timer.record("scripts list_unets")
+
+ def load_model():
+ """
+ Accesses shared.sd_model property to load model.
+ After it's available, if it has been loaded before this access by some extension,
+ its optimization may be None because the list of optimizaers has neet been filled
+ by that time, so we apply optimization again.
+ """
+
+ shared.sd_model # noqa: B018
+
+ if sd_hijack.current_optimizer is None:
+ sd_hijack.apply_optimizations()
+
+ from modules import devices
+ devices.first_time_calculation()
+
+ Thread(target=load_model).start()
+
+ from modules import shared_items
+ shared_items.reload_hypernetworks()
+ startup_timer.record("reload hypernetworks")
+
+ from modules import ui_extra_networks
+ ui_extra_networks.initialize()
+ ui_extra_networks.register_default_pages()
+
+ from modules import extra_networks
+ extra_networks.initialize()
+ extra_networks.register_default_extra_networks()
+ startup_timer.record("initialize extra networks")
diff --git a/stable-diffusion-webui/modules/initialize_util.py b/stable-diffusion-webui/modules/initialize_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1803487125730ca4a84dd2316ccb60dc612dcf6
--- /dev/null
+++ b/stable-diffusion-webui/modules/initialize_util.py
@@ -0,0 +1,202 @@
+import json
+import os
+import signal
+import sys
+import re
+
+from modules.timer import startup_timer
+
+
+def gradio_server_name():
+ from modules.shared_cmd_options import cmd_opts
+
+ if cmd_opts.server_name:
+ return cmd_opts.server_name
+ else:
+ return "0.0.0.0" if cmd_opts.listen else None
+
+
+def fix_torch_version():
+ import torch
+
+ # Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
+ if ".dev" in torch.__version__ or "+git" in torch.__version__:
+ torch.__long_version__ = torch.__version__
+ torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
+
+
+def fix_asyncio_event_loop_policy():
+ """
+ The default `asyncio` event loop policy only automatically creates
+ event loops in the main threads. Other threads must create event
+ loops explicitly or `asyncio.get_event_loop` (and therefore
+ `.IOLoop.current`) will fail. Installing this policy allows event
+ loops to be created automatically on any thread, matching the
+ behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
+ """
+
+ import asyncio
+
+ if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
+ # "Any thread" and "selector" should be orthogonal, but there's not a clean
+ # interface for composing policies so pick the right base.
+ _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy # type: ignore
+ else:
+ _BasePolicy = asyncio.DefaultEventLoopPolicy
+
+ class AnyThreadEventLoopPolicy(_BasePolicy): # type: ignore
+ """Event loop policy that allows loop creation on any thread.
+ Usage::
+
+ asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+ """
+
+ def get_event_loop(self) -> asyncio.AbstractEventLoop:
+ try:
+ return super().get_event_loop()
+ except (RuntimeError, AssertionError):
+ # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
+ # and changed to a RuntimeError in 3.4.3.
+ # "There is no current event loop in thread %r"
+ loop = self.new_event_loop()
+ self.set_event_loop(loop)
+ return loop
+
+ asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+
+
+def restore_config_state_file():
+ from modules import shared, config_states
+
+ config_state_file = shared.opts.restore_config_state_file
+ if config_state_file == "":
+ return
+
+ shared.opts.restore_config_state_file = ""
+ shared.opts.save(shared.config_filename)
+
+ if os.path.isfile(config_state_file):
+ print(f"*** About to restore extension state from file: {config_state_file}")
+ with open(config_state_file, "r", encoding="utf-8") as f:
+ config_state = json.load(f)
+ config_states.restore_extension_config(config_state)
+ startup_timer.record("restore extension config")
+ elif config_state_file:
+ print(f"!!! Config state backup not found: {config_state_file}")
+
+
+def validate_tls_options():
+ from modules.shared_cmd_options import cmd_opts
+
+ if not (cmd_opts.tls_keyfile and cmd_opts.tls_certfile):
+ return
+
+ try:
+ if not os.path.exists(cmd_opts.tls_keyfile):
+ print("Invalid path to TLS keyfile given")
+ if not os.path.exists(cmd_opts.tls_certfile):
+ print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
+ except TypeError:
+ cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
+ print("TLS setup invalid, running webui without TLS")
+ else:
+ print("Running with TLS")
+ startup_timer.record("TLS")
+
+
+def get_gradio_auth_creds():
+ """
+ Convert the gradio_auth and gradio_auth_path commandline arguments into
+ an iterable of (username, password) tuples.
+ """
+ from modules.shared_cmd_options import cmd_opts
+
+ def process_credential_line(s):
+ s = s.strip()
+ if not s:
+ return None
+ return tuple(s.split(':', 1))
+
+ if cmd_opts.gradio_auth:
+ for cred in cmd_opts.gradio_auth.split(','):
+ cred = process_credential_line(cred)
+ if cred:
+ yield cred
+
+ if cmd_opts.gradio_auth_path:
+ with open(cmd_opts.gradio_auth_path, 'r', encoding="utf8") as file:
+ for line in file.readlines():
+ for cred in line.strip().split(','):
+ cred = process_credential_line(cred)
+ if cred:
+ yield cred
+
+
+def dumpstacks():
+ import threading
+ import traceback
+
+ id2name = {th.ident: th.name for th in threading.enumerate()}
+ code = []
+ for threadId, stack in sys._current_frames().items():
+ code.append(f"\n# Thread: {id2name.get(threadId, '')}({threadId})")
+ for filename, lineno, name, line in traceback.extract_stack(stack):
+ code.append(f"""File: "{filename}", line {lineno}, in {name}""")
+ if line:
+ code.append(" " + line.strip())
+
+ print("\n".join(code))
+
+
+def configure_sigint_handler():
+ # make the program just exit at ctrl+c without waiting for anything
+ def sigint_handler(sig, frame):
+ print(f'Interrupted with signal {sig} in {frame}')
+
+ dumpstacks()
+
+ os._exit(0)
+
+ if not os.environ.get("COVERAGE_RUN"):
+ # Don't install the immediate-quit handler when running under coverage,
+ # as then the coverage report won't be generated.
+ signal.signal(signal.SIGINT, sigint_handler)
+
+
+def configure_opts_onchange():
+ from modules import shared, sd_models, sd_vae, ui_tempdir, sd_hijack
+ from modules.call_queue import wrap_queued_call
+
+ shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
+ shared.opts.onchange("sd_vae", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
+ shared.opts.onchange("sd_vae_overrides_per_model_preferences", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
+ shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
+ shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
+ shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)
+ startup_timer.record("opts onchange")
+
+
+def setup_middleware(app):
+ from starlette.middleware.gzip import GZipMiddleware
+
+ app.middleware_stack = None # reset current middleware to allow modifying user provided list
+ app.add_middleware(GZipMiddleware, minimum_size=1000)
+ configure_cors_middleware(app)
+ app.build_middleware_stack() # rebuild middleware stack on-the-fly
+
+
+def configure_cors_middleware(app):
+ from starlette.middleware.cors import CORSMiddleware
+ from modules.shared_cmd_options import cmd_opts
+
+ cors_options = {
+ "allow_methods": ["*"],
+ "allow_headers": ["*"],
+ "allow_credentials": True,
+ }
+ if cmd_opts.cors_allow_origins:
+ cors_options["allow_origins"] = cmd_opts.cors_allow_origins.split(',')
+ if cmd_opts.cors_allow_origins_regex:
+ cors_options["allow_origin_regex"] = cmd_opts.cors_allow_origins_regex
+ app.add_middleware(CORSMiddleware, **cors_options)
+
diff --git a/stable-diffusion-webui/modules/interrogate.py b/stable-diffusion-webui/modules/interrogate.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd4c291e361ae36b9ffa9f3ef3830dd56dff19d5
--- /dev/null
+++ b/stable-diffusion-webui/modules/interrogate.py
@@ -0,0 +1,222 @@
+import os
+import sys
+from collections import namedtuple
+from pathlib import Path
+import re
+
+import torch
+import torch.hub
+
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+from modules import devices, paths, shared, lowvram, modelloader, errors
+
+blip_image_eval_size = 384
+clip_model_name = 'ViT-L/14'
+
+Category = namedtuple("Category", ["name", "topn", "items"])
+
+re_topn = re.compile(r"\.top(\d+)\.")
+
+def category_types():
+ return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
+
+
+def download_default_clip_interrogate_categories(content_dir):
+ print("Downloading CLIP categories...")
+
+ tmpdir = f"{content_dir}_tmp"
+ category_types = ["artists", "flavors", "mediums", "movements"]
+
+ try:
+ os.makedirs(tmpdir, exist_ok=True)
+ for category_type in category_types:
+ torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
+ os.rename(tmpdir, content_dir)
+
+ except Exception as e:
+ errors.display(e, "downloading default CLIP interrogate categories")
+ finally:
+ if os.path.exists(tmpdir):
+ os.removedirs(tmpdir)
+
+
+class InterrogateModels:
+ blip_model = None
+ clip_model = None
+ clip_preprocess = None
+ dtype = None
+ running_on_cpu = None
+
+ def __init__(self, content_dir):
+ self.loaded_categories = None
+ self.skip_categories = []
+ self.content_dir = content_dir
+ self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
+
+ def categories(self):
+ if not os.path.exists(self.content_dir):
+ download_default_clip_interrogate_categories(self.content_dir)
+
+ if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:
+ return self.loaded_categories
+
+ self.loaded_categories = []
+
+ if os.path.exists(self.content_dir):
+ self.skip_categories = shared.opts.interrogate_clip_skip_categories
+ category_types = []
+ for filename in Path(self.content_dir).glob('*.txt'):
+ category_types.append(filename.stem)
+ if filename.stem in self.skip_categories:
+ continue
+ m = re_topn.search(filename.stem)
+ topn = 1 if m is None else int(m.group(1))
+ with open(filename, "r", encoding="utf8") as file:
+ lines = [x.strip() for x in file.readlines()]
+
+ self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))
+
+ return self.loaded_categories
+
+ def create_fake_fairscale(self):
+ class FakeFairscale:
+ def checkpoint_wrapper(self):
+ pass
+
+ sys.modules["fairscale.nn.checkpoint.checkpoint_activations"] = FakeFairscale
+
+ def load_blip_model(self):
+ self.create_fake_fairscale()
+ import models.blip
+
+ files = modelloader.load_models(
+ model_path=os.path.join(paths.models_path, "BLIP"),
+ model_url='https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth',
+ ext_filter=[".pth"],
+ download_name='model_base_caption_capfilt_large.pth',
+ )
+
+ blip_model = models.blip.blip_decoder(pretrained=files[0], image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+ blip_model.eval()
+
+ return blip_model
+
+ def load_clip_model(self):
+ import clip
+
+ if self.running_on_cpu:
+ model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
+ else:
+ model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
+
+ model.eval()
+ model = model.to(devices.device_interrogate)
+
+ return model, preprocess
+
+ def load(self):
+ if self.blip_model is None:
+ self.blip_model = self.load_blip_model()
+ if not shared.cmd_opts.no_half and not self.running_on_cpu:
+ self.blip_model = self.blip_model.half()
+
+ self.blip_model = self.blip_model.to(devices.device_interrogate)
+
+ if self.clip_model is None:
+ self.clip_model, self.clip_preprocess = self.load_clip_model()
+ if not shared.cmd_opts.no_half and not self.running_on_cpu:
+ self.clip_model = self.clip_model.half()
+
+ self.clip_model = self.clip_model.to(devices.device_interrogate)
+
+ self.dtype = next(self.clip_model.parameters()).dtype
+
+ def send_clip_to_ram(self):
+ if not shared.opts.interrogate_keep_models_in_memory:
+ if self.clip_model is not None:
+ self.clip_model = self.clip_model.to(devices.cpu)
+
+ def send_blip_to_ram(self):
+ if not shared.opts.interrogate_keep_models_in_memory:
+ if self.blip_model is not None:
+ self.blip_model = self.blip_model.to(devices.cpu)
+
+ def unload(self):
+ self.send_clip_to_ram()
+ self.send_blip_to_ram()
+
+ devices.torch_gc()
+
+ def rank(self, image_features, text_array, top_count=1):
+ import clip
+
+ devices.torch_gc()
+
+ if shared.opts.interrogate_clip_dict_limit != 0:
+ text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
+
+ top_count = min(top_count, len(text_array))
+ text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device_interrogate)
+ text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
+ text_features /= text_features.norm(dim=-1, keepdim=True)
+
+ similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
+ for i in range(image_features.shape[0]):
+ similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
+ similarity /= image_features.shape[0]
+
+ top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
+ return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
+
+ def generate_caption(self, pil_image):
+ gpu_image = transforms.Compose([
+ transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
+ transforms.ToTensor(),
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+ ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
+
+ with torch.no_grad():
+ caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
+
+ return caption[0]
+
+ def interrogate(self, pil_image):
+ res = ""
+ shared.state.begin(job="interrogate")
+ try:
+ lowvram.send_everything_to_cpu()
+ devices.torch_gc()
+
+ self.load()
+
+ caption = self.generate_caption(pil_image)
+ self.send_blip_to_ram()
+ devices.torch_gc()
+
+ res = caption
+
+ clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
+
+ with torch.no_grad(), devices.autocast():
+ image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
+
+ image_features /= image_features.norm(dim=-1, keepdim=True)
+
+ for cat in self.categories():
+ matches = self.rank(image_features, cat.items, top_count=cat.topn)
+ for match, score in matches:
+ if shared.opts.interrogate_return_ranks:
+ res += f", ({match}:{score/100:.3f})"
+ else:
+ res += f", {match}"
+
+ except Exception:
+ errors.report("Error interrogating", exc_info=True)
+ res += "<error>"
+
+ self.unload()
+ shared.state.end()
+
+ return res
diff --git a/stable-diffusion-webui/modules/launch_utils.py b/stable-diffusion-webui/modules/launch_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e8a80af022424fa2ae65241522bec3b7c298095
--- /dev/null
+++ b/stable-diffusion-webui/modules/launch_utils.py
@@ -0,0 +1,449 @@
+# this scripts installs necessary requirements and launches main program in webui.py
+import logging
+import re
+import subprocess
+import os
+import shutil
+import sys
+import importlib.util
+import platform
+import json
+from functools import lru_cache
+
+from modules import cmd_args, errors
+from modules.paths_internal import script_path, extensions_dir
+from modules.timer import startup_timer
+from modules import logging_config
+
+args, _ = cmd_args.parser.parse_known_args()
+logging_config.setup_logging(args.loglevel)
+
+python = sys.executable
+git = os.environ.get('GIT', "git")
+index_url = os.environ.get('INDEX_URL', "")
+dir_repos = "repositories"
+
+# Whether to default to printing command output
+default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
+
+if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
+ os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+
+
+def check_python_version():
+ is_windows = platform.system() == "Windows"
+ major = sys.version_info.major
+ minor = sys.version_info.minor
+ micro = sys.version_info.micro
+
+ if is_windows:
+ supported_minors = [10]
+ else:
+ supported_minors = [7, 8, 9, 10, 11]
+
+ if not (major == 3 and minor in supported_minors):
+ import modules.errors
+
+ modules.errors.print_error_explanation(f"""
+INCOMPATIBLE PYTHON VERSION
+
+This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
+If you encounter an error with "RuntimeError: Couldn't install torch." message,
+or any other error regarding unsuccessful package (library) installation,
+please downgrade (or upgrade) to the latest version of 3.10 Python
+and delete current Python and "venv" folder in WebUI's directory.
+
+You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
+
+{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
+
+Use --skip-python-version-check to suppress this warning.
+""")
+
+
+@lru_cache()
+def commit_hash():
+ try:
+ return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
+ except Exception:
+ return "<none>"
+
+
+@lru_cache()
+def git_tag():
+ try:
+ return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
+ except Exception:
+ try:
+
+ changelog_md = os.path.join(os.path.dirname(os.path.dirname(__file__)), "CHANGELOG.md")
+ with open(changelog_md, "r", encoding="utf-8") as file:
+ line = next((line.strip() for line in file if line.strip()), "<none>")
+ line = line.replace("## ", "")
+ return line
+ except Exception:
+ return "<none>"
+
+
+def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
+ if desc is not None:
+ print(desc)
+
+ run_kwargs = {
+ "args": command,
+ "shell": True,
+ "env": os.environ if custom_env is None else custom_env,
+ "encoding": 'utf8',
+ "errors": 'ignore',
+ }
+
+ if not live:
+ run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE
+
+ result = subprocess.run(**run_kwargs)
+
+ if result.returncode != 0:
+ error_bits = [
+ f"{errdesc or 'Error running command'}.",
+ f"Command: {command}",
+ f"Error code: {result.returncode}",
+ ]
+ if result.stdout:
+ error_bits.append(f"stdout: {result.stdout}")
+ if result.stderr:
+ error_bits.append(f"stderr: {result.stderr}")
+ raise RuntimeError("\n".join(error_bits))
+
+ return (result.stdout or "")
+
+
+def is_installed(package):
+ try:
+ spec = importlib.util.find_spec(package)
+ except ModuleNotFoundError:
+ return False
+
+ return spec is not None
+
+
+def repo_dir(name):
+ return os.path.join(script_path, dir_repos, name)
+
+
+def run_pip(command, desc=None, live=default_command_live):
+ if args.skip_install:
+ return
+
+ index_url_line = f' --index-url {index_url}' if index_url != '' else ''
+ return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
+
+
+def check_run_python(code: str) -> bool:
+ result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
+ return result.returncode == 0
+
+
+def git_fix_workspace(dir, name):
+ run(f'"{git}" -C "{dir}" fetch --refetch --no-auto-gc', f"Fetching all contents for {name}", f"Couldn't fetch {name}", live=True)
+ run(f'"{git}" -C "{dir}" gc --aggressive --prune=now', f"Pruning {name}", f"Couldn't prune {name}", live=True)
+ return
+
+
+def run_git(dir, name, command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live, autofix=True):
+ try:
+ return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
+ except RuntimeError:
+ if not autofix:
+ raise
+
+ print(f"{errdesc}, attempting autofix...")
+ git_fix_workspace(dir, name)
+
+ return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
+
+
+def git_clone(url, dir, name, commithash=None):
+ # TODO clone into temporary dir and move if successful
+
+ if os.path.exists(dir):
+ if commithash is None:
+ return
+
+ current_hash = run_git(dir, name, 'rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
+ if current_hash == commithash:
+ return
+
+ if run_git(dir, name, 'config --get remote.origin.url', None, f"Couldn't determine {name}'s origin URL", live=False).strip() != url:
+ run_git(dir, name, f'remote set-url origin "{url}"', None, f"Failed to set {name}'s origin URL", live=False)
+
+ run_git(dir, name, 'fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
+
+ run_git(dir, name, f'checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+
+ return
+
+ try:
+ run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
+ except RuntimeError:
+ shutil.rmtree(dir, ignore_errors=True)
+ raise
+
+ if commithash is not None:
+ run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
+
+
+def git_pull_recursive(dir):
+ for subdir, _, _ in os.walk(dir):
+ if os.path.exists(os.path.join(subdir, '.git')):
+ try:
+ output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
+ print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
+ except subprocess.CalledProcessError as e:
+ print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
+
+
+def version_check(commit):
+ try:
+ import requests
+ commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
+ if commit != "<none>" and commits['commit']['sha'] != commit:
+ print("--------------------------------------------------------")
+ print("| You are not up to date with the most recent release. |")
+ print("| Consider running `git pull` to update. |")
+ print("--------------------------------------------------------")
+ elif commits['commit']['sha'] == commit:
+ print("You are up to date with the most recent release.")
+ else:
+ print("Not a git clone, can't perform version check.")
+ except Exception as e:
+ print("version check failed", e)
+
+
+def run_extension_installer(extension_dir):
+ path_installer = os.path.join(extension_dir, "install.py")
+ if not os.path.isfile(path_installer):
+ return
+
+ try:
+ env = os.environ.copy()
+ env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
+
+ stdout = run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env).strip()
+ if stdout:
+ print(stdout)
+ except Exception as e:
+ errors.report(str(e))
+
+
+def list_extensions(settings_file):
+ settings = {}
+
+ try:
+ if os.path.isfile(settings_file):
+ with open(settings_file, "r", encoding="utf8") as file:
+ settings = json.load(file)
+ except Exception:
+ errors.report("Could not load settings", exc_info=True)
+
+ disabled_extensions = set(settings.get('disabled_extensions', []))
+ disable_all_extensions = settings.get('disable_all_extensions', 'none')
+
+ if disable_all_extensions != 'none' or args.disable_extra_extensions or args.disable_all_extensions or not os.path.isdir(extensions_dir):
+ return []
+
+ return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
+
+
+def run_extensions_installers(settings_file):
+ if not os.path.isdir(extensions_dir):
+ return
+
+ with startup_timer.subcategory("run extensions installers"):
+ for dirname_extension in list_extensions(settings_file):
+ logging.debug(f"Installing {dirname_extension}")
+
+ path = os.path.join(extensions_dir, dirname_extension)
+
+ if os.path.isdir(path):
+ run_extension_installer(path)
+ startup_timer.record(dirname_extension)
+
+
+re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
+
+
+def requirements_met(requirements_file):
+ """
+ Does a simple parse of a requirements.txt file to determine if all rerqirements in it
+ are already installed. Returns True if so, False if not installed or parsing fails.
+ """
+
+ import importlib.metadata
+ import packaging.version
+
+ with open(requirements_file, "r", encoding="utf8") as file:
+ for line in file:
+ if line.strip() == "":
+ continue
+
+ m = re.match(re_requirement, line)
+ if m is None:
+ return False
+
+ package = m.group(1).strip()
+ version_required = (m.group(2) or "").strip()
+
+ if version_required == "":
+ continue
+
+ try:
+ version_installed = importlib.metadata.version(package)
+ except Exception:
+ return False
+
+ if packaging.version.parse(version_required) != packaging.version.parse(version_installed):
+ return False
+
+ return True
+
+
+def prepare_environment():
+ torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
+ torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
+ requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+
+ xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+ clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
+ openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
+
+ stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
+ stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
+ k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
+ codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
+ blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
+
+ stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
+ stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
+ k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
+ codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
+ blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
+
+ try:
+ # the existence of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
+ os.remove(os.path.join(script_path, "tmp", "restart"))
+ os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
+ except OSError:
+ pass
+
+ if not args.skip_python_version_check:
+ check_python_version()
+
+ startup_timer.record("checks")
+
+ commit = commit_hash()
+ tag = git_tag()
+ startup_timer.record("git version info")
+
+ print(f"Python {sys.version}")
+ print(f"Version: {tag}")
+ print(f"Commit hash: {commit}")
+
+ if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
+ run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
+ startup_timer.record("install torch")
+
+ if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
+ raise RuntimeError(
+ 'Torch is not able to use GPU; '
+ 'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
+ )
+ startup_timer.record("torch GPU test")
+
+ if not is_installed("clip"):
+ run_pip(f"install {clip_package}", "clip")
+ startup_timer.record("install clip")
+
+ if not is_installed("open_clip"):
+ run_pip(f"install {openclip_package}", "open_clip")
+ startup_timer.record("install open_clip")
+
+ if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
+ run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+ startup_timer.record("install xformers")
+
+ if not is_installed("ngrok") and args.ngrok:
+ run_pip("install ngrok", "ngrok")
+ startup_timer.record("install ngrok")
+
+ os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
+
+ git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
+ git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
+ git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+ git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+ git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
+
+ startup_timer.record("clone repositores")
+
+ if not is_installed("lpips"):
+ run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+ startup_timer.record("install CodeFormer requirements")
+
+ if not os.path.isfile(requirements_file):
+ requirements_file = os.path.join(script_path, requirements_file)
+
+ if not requirements_met(requirements_file):
+ run_pip(f"install -r \"{requirements_file}\"", "requirements")
+ startup_timer.record("install requirements")
+
+ if not args.skip_install:
+ run_extensions_installers(settings_file=args.ui_settings_file)
+
+ if args.update_check:
+ version_check(commit)
+ startup_timer.record("check version")
+
+ if args.update_all_extensions:
+ git_pull_recursive(extensions_dir)
+ startup_timer.record("update extensions")
+
+ if "--exit" in sys.argv:
+ print("Exiting because of --exit argument")
+ exit(0)
+
+
+
+def configure_for_tests():
+ if "--api" not in sys.argv:
+ sys.argv.append("--api")
+ if "--ckpt" not in sys.argv:
+ sys.argv.append("--ckpt")
+ sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
+ if "--skip-torch-cuda-test" not in sys.argv:
+ sys.argv.append("--skip-torch-cuda-test")
+ if "--disable-nan-check" not in sys.argv:
+ sys.argv.append("--disable-nan-check")
+
+ os.environ['COMMANDLINE_ARGS'] = ""
+
+
+def start():
+ print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
+ import webui
+ if '--nowebui' in sys.argv:
+ webui.api_only()
+ else:
+ webui.webui()
+
+
+def dump_sysinfo():
+ from modules import sysinfo
+ import datetime
+
+ text = sysinfo.get()
+ filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+ with open(filename, "w", encoding="utf8") as file:
+ file.write(text)
+
+ return filename
diff --git a/stable-diffusion-webui/modules/localization.py b/stable-diffusion-webui/modules/localization.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f4db67b69c7951fe47d36e4224f3c8408a21646
--- /dev/null
+++ b/stable-diffusion-webui/modules/localization.py
@@ -0,0 +1,34 @@
+import json
+import os
+
+from modules import errors, scripts
+
+localizations = {}
+
+
+def list_localizations(dirname):
+ localizations.clear()
+
+ for file in os.listdir(dirname):
+ fn, ext = os.path.splitext(file)
+ if ext.lower() != ".json":
+ continue
+
+ localizations[fn] = os.path.join(dirname, file)
+
+ for file in scripts.list_scripts("localizations", ".json"):
+ fn, ext = os.path.splitext(file.filename)
+ localizations[fn] = file.path
+
+
+def localization_js(current_localization_name: str) -> str:
+ fn = localizations.get(current_localization_name, None)
+ data = {}
+ if fn is not None:
+ try:
+ with open(fn, "r", encoding="utf8") as file:
+ data = json.load(file)
+ except Exception:
+ errors.report(f"Error loading localization from {fn}", exc_info=True)
+
+ return f"window.localization = {json.dumps(data)}"
diff --git a/stable-diffusion-webui/modules/logging_config.py b/stable-diffusion-webui/modules/logging_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad55898885b4c09e998386f0a6f0d829e83fc0b8
--- /dev/null
+++ b/stable-diffusion-webui/modules/logging_config.py
@@ -0,0 +1,16 @@
+import os
+import logging
+
+
+def setup_logging(loglevel):
+ if loglevel is None:
+ loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
+
+ if loglevel:
+ log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
+ logging.basicConfig(
+ level=log_level,
+ format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
+ datefmt='%Y-%m-%d %H:%M:%S',
+ )
+
diff --git a/stable-diffusion-webui/modules/lowvram.py b/stable-diffusion-webui/modules/lowvram.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7b85e4ae17f7fb811e608fbd1c22ae2f52d3e3c
--- /dev/null
+++ b/stable-diffusion-webui/modules/lowvram.py
@@ -0,0 +1,147 @@
+import torch
+from modules import devices, shared
+
+module_in_gpu = None
+cpu = torch.device("cpu")
+
+
+def send_everything_to_cpu():
+ global module_in_gpu
+
+ if module_in_gpu is not None:
+ module_in_gpu.to(cpu)
+
+ module_in_gpu = None
+
+
+def is_needed(sd_model):
+ return shared.cmd_opts.lowvram or shared.cmd_opts.medvram or shared.cmd_opts.medvram_sdxl and hasattr(sd_model, 'conditioner')
+
+
+def apply(sd_model):
+ enable = is_needed(sd_model)
+ shared.parallel_processing_allowed = not enable
+
+ if enable:
+ setup_for_low_vram(sd_model, not shared.cmd_opts.lowvram)
+ else:
+ sd_model.lowvram = False
+
+
+def setup_for_low_vram(sd_model, use_medvram):
+ if getattr(sd_model, 'lowvram', False):
+ return
+
+ sd_model.lowvram = True
+
+ parents = {}
+
+ def send_me_to_gpu(module, _):
+ """send this module to GPU; send whatever tracked module was previous in GPU to CPU;
+ we add this as forward_pre_hook to a lot of modules and this way all but one of them will
+ be in CPU
+ """
+ global module_in_gpu
+
+ module = parents.get(module, module)
+
+ if module_in_gpu == module:
+ return
+
+ if module_in_gpu is not None:
+ module_in_gpu.to(cpu)
+
+ module.to(devices.device)
+ module_in_gpu = module
+
+ # see below for register_forward_pre_hook;
+ # first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
+ # useless here, and we just replace those methods
+
+ first_stage_model = sd_model.first_stage_model
+ first_stage_model_encode = sd_model.first_stage_model.encode
+ first_stage_model_decode = sd_model.first_stage_model.decode
+
+ def first_stage_model_encode_wrap(x):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_encode(x)
+
+ def first_stage_model_decode_wrap(z):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_decode(z)
+
+ to_remain_in_cpu = [
+ (sd_model, 'first_stage_model'),
+ (sd_model, 'depth_model'),
+ (sd_model, 'embedder'),
+ (sd_model, 'model'),
+ (sd_model, 'embedder'),
+ ]
+
+ is_sdxl = hasattr(sd_model, 'conditioner')
+ is_sd2 = not is_sdxl and hasattr(sd_model.cond_stage_model, 'model')
+
+ if is_sdxl:
+ to_remain_in_cpu.append((sd_model, 'conditioner'))
+ elif is_sd2:
+ to_remain_in_cpu.append((sd_model.cond_stage_model, 'model'))
+ else:
+ to_remain_in_cpu.append((sd_model.cond_stage_model, 'transformer'))
+
+ # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model
+ stored = []
+ for obj, field in to_remain_in_cpu:
+ module = getattr(obj, field, None)
+ stored.append(module)
+ setattr(obj, field, None)
+
+ # send the model to GPU.
+ sd_model.to(devices.device)
+
+ # put modules back. the modules will be in CPU.
+ for (obj, field), module in zip(to_remain_in_cpu, stored):
+ setattr(obj, field, module)
+
+ # register hooks for those the first three models
+ if is_sdxl:
+ sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
+ elif is_sd2:
+ sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
+ sd_model.cond_stage_model.model.token_embedding.register_forward_pre_hook(send_me_to_gpu)
+ parents[sd_model.cond_stage_model.model] = sd_model.cond_stage_model
+ parents[sd_model.cond_stage_model.model.token_embedding] = sd_model.cond_stage_model
+ else:
+ sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+ parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
+
+ sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
+ sd_model.first_stage_model.encode = first_stage_model_encode_wrap
+ sd_model.first_stage_model.decode = first_stage_model_decode_wrap
+ if sd_model.depth_model:
+ sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
+ if sd_model.embedder:
+ sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
+
+ if use_medvram:
+ sd_model.model.register_forward_pre_hook(send_me_to_gpu)
+ else:
+ diff_model = sd_model.model.diffusion_model
+
+ # the third remaining model is still too big for 4 GB, so we also do the same for its submodules
+ # so that only one of them is in GPU at a time
+ stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed
+ diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None
+ sd_model.model.to(devices.device)
+ diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored
+
+ # install hooks for bits of third model
+ diff_model.time_embed.register_forward_pre_hook(send_me_to_gpu)
+ for block in diff_model.input_blocks:
+ block.register_forward_pre_hook(send_me_to_gpu)
+ diff_model.middle_block.register_forward_pre_hook(send_me_to_gpu)
+ for block in diff_model.output_blocks:
+ block.register_forward_pre_hook(send_me_to_gpu)
+
+
+def is_enabled(sd_model):
+ return sd_model.lowvram
diff --git a/stable-diffusion-webui/modules/mac_specific.py b/stable-diffusion-webui/modules/mac_specific.py
new file mode 100644
index 0000000000000000000000000000000000000000..89256c5b06073c38a903d71a10d3be31079085df
--- /dev/null
+++ b/stable-diffusion-webui/modules/mac_specific.py
@@ -0,0 +1,83 @@
+import logging
+
+import torch
+import platform
+from modules.sd_hijack_utils import CondFunc
+from packaging import version
+from modules import shared
+
+log = logging.getLogger(__name__)
+
+
+# before torch version 1.13, has_mps is only available in nightly pytorch and macOS 12.3+,
+# use check `getattr` and try it for compatibility.
+# in torch version 1.13, backends.mps.is_available() and backends.mps.is_built() are introduced in to check mps availabilty,
+# since torch 2.0.1+ nightly build, getattr(torch, 'has_mps', False) was deprecated, see https://github.com/pytorch/pytorch/pull/103279
+def check_for_mps() -> bool:
+ if version.parse(torch.__version__) <= version.parse("2.0.1"):
+ if not getattr(torch, 'has_mps', False):
+ return False
+ try:
+ torch.zeros(1).to(torch.device("mps"))
+ return True
+ except Exception:
+ return False
+ else:
+ return torch.backends.mps.is_available() and torch.backends.mps.is_built()
+
+
+has_mps = check_for_mps()
+
+
+def torch_mps_gc() -> None:
+ try:
+ if shared.state.current_latent is not None:
+ log.debug("`current_latent` is set, skipping MPS garbage collection")
+ return
+ from torch.mps import empty_cache
+ empty_cache()
+ except Exception:
+ log.warning("MPS garbage collection failed", exc_info=True)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
+def cumsum_fix(input, cumsum_func, *args, **kwargs):
+ if input.device.type == 'mps':
+ output_dtype = kwargs.get('dtype', input.dtype)
+ if output_dtype == torch.int64:
+ return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
+ elif output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
+ return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
+ return cumsum_func(input, *args, **kwargs)
+
+
+if has_mps:
+ if platform.mac_ver()[0].startswith("13.2."):
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
+ CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
+
+ if version.parse(torch.__version__) < version.parse("1.13"):
+ # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
+
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+ CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
+ lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/80800
+ CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
+ lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+ CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
+ elif version.parse(torch.__version__) > version.parse("1.13.1"):
+ cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
+ cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
+ CondFunc('torch.cumsum', cumsum_fix_func, None)
+ CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
+ CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
+
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
+ CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
+
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/92311
+ if platform.processor() == 'i386':
+ for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
+ CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')
diff --git a/stable-diffusion-webui/modules/masking.py b/stable-diffusion-webui/modules/masking.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc6113a238d57e8148930747eeffa8b4de97b822
--- /dev/null
+++ b/stable-diffusion-webui/modules/masking.py
@@ -0,0 +1,99 @@
+from PIL import Image, ImageFilter, ImageOps
+
+
+def get_crop_region(mask, pad=0):
+ """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
+ For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
+
+ h, w = mask.shape
+
+ crop_left = 0
+ for i in range(w):
+ if not (mask[:, i] == 0).all():
+ break
+ crop_left += 1
+
+ crop_right = 0
+ for i in reversed(range(w)):
+ if not (mask[:, i] == 0).all():
+ break
+ crop_right += 1
+
+ crop_top = 0
+ for i in range(h):
+ if not (mask[i] == 0).all():
+ break
+ crop_top += 1
+
+ crop_bottom = 0
+ for i in reversed(range(h)):
+ if not (mask[i] == 0).all():
+ break
+ crop_bottom += 1
+
+ return (
+ int(max(crop_left-pad, 0)),
+ int(max(crop_top-pad, 0)),
+ int(min(w - crop_right + pad, w)),
+ int(min(h - crop_bottom + pad, h))
+ )
+
+
+def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):
+ """expands crop region get_crop_region() to match the ratio of the image the region will processed in; returns expanded region
+ for example, if user drew mask in a 128x32 region, and the dimensions for processing are 512x512, the region will be expanded to 128x128."""
+
+ x1, y1, x2, y2 = crop_region
+
+ ratio_crop_region = (x2 - x1) / (y2 - y1)
+ ratio_processing = processing_width / processing_height
+
+ if ratio_crop_region > ratio_processing:
+ desired_height = (x2 - x1) / ratio_processing
+ desired_height_diff = int(desired_height - (y2-y1))
+ y1 -= desired_height_diff//2
+ y2 += desired_height_diff - desired_height_diff//2
+ if y2 >= image_height:
+ diff = y2 - image_height
+ y2 -= diff
+ y1 -= diff
+ if y1 < 0:
+ y2 -= y1
+ y1 -= y1
+ if y2 >= image_height:
+ y2 = image_height
+ else:
+ desired_width = (y2 - y1) * ratio_processing
+ desired_width_diff = int(desired_width - (x2-x1))
+ x1 -= desired_width_diff//2
+ x2 += desired_width_diff - desired_width_diff//2
+ if x2 >= image_width:
+ diff = x2 - image_width
+ x2 -= diff
+ x1 -= diff
+ if x1 < 0:
+ x2 -= x1
+ x1 -= x1
+ if x2 >= image_width:
+ x2 = image_width
+
+ return x1, y1, x2, y2
+
+
+def fill(image, mask):
+ """fills masked regions with colors from image using blur. Not extremely effective."""
+
+ image_mod = Image.new('RGBA', (image.width, image.height))
+
+ image_masked = Image.new('RGBa', (image.width, image.height))
+ image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
+
+ image_masked = image_masked.convert('RGBa')
+
+ for radius, repeats in [(256, 1), (64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
+ blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
+ for _ in range(repeats):
+ image_mod.alpha_composite(blurred)
+
+ return image_mod.convert("RGB")
+
diff --git a/stable-diffusion-webui/modules/memmon.py b/stable-diffusion-webui/modules/memmon.py
new file mode 100644
index 0000000000000000000000000000000000000000..4018edcc7ef1c36d6f71fddc43f773aba6c078e4
--- /dev/null
+++ b/stable-diffusion-webui/modules/memmon.py
@@ -0,0 +1,92 @@
+import threading
+import time
+from collections import defaultdict
+
+import torch
+
+
+class MemUsageMonitor(threading.Thread):
+ run_flag = None
+ device = None
+ disabled = False
+ opts = None
+ data = None
+
+ def __init__(self, name, device, opts):
+ threading.Thread.__init__(self)
+ self.name = name
+ self.device = device
+ self.opts = opts
+
+ self.daemon = True
+ self.run_flag = threading.Event()
+ self.data = defaultdict(int)
+
+ try:
+ self.cuda_mem_get_info()
+ torch.cuda.memory_stats(self.device)
+ except Exception as e: # AMD or whatever
+ print(f"Warning: caught exception '{e}', memory monitor disabled")
+ self.disabled = True
+
+ def cuda_mem_get_info(self):
+ index = self.device.index if self.device.index is not None else torch.cuda.current_device()
+ return torch.cuda.mem_get_info(index)
+
+ def run(self):
+ if self.disabled:
+ return
+
+ while True:
+ self.run_flag.wait()
+
+ torch.cuda.reset_peak_memory_stats()
+ self.data.clear()
+
+ if self.opts.memmon_poll_rate <= 0:
+ self.run_flag.clear()
+ continue
+
+ self.data["min_free"] = self.cuda_mem_get_info()[0]
+
+ while self.run_flag.is_set():
+ free, total = self.cuda_mem_get_info()
+ self.data["min_free"] = min(self.data["min_free"], free)
+
+ time.sleep(1 / self.opts.memmon_poll_rate)
+
+ def dump_debug(self):
+ print(self, 'recorded data:')
+ for k, v in self.read().items():
+ print(k, -(v // -(1024 ** 2)))
+
+ print(self, 'raw torch memory stats:')
+ tm = torch.cuda.memory_stats(self.device)
+ for k, v in tm.items():
+ if 'bytes' not in k:
+ continue
+ print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2)))
+
+ print(torch.cuda.memory_summary())
+
+ def monitor(self):
+ self.run_flag.set()
+
+ def read(self):
+ if not self.disabled:
+ free, total = self.cuda_mem_get_info()
+ self.data["free"] = free
+ self.data["total"] = total
+
+ torch_stats = torch.cuda.memory_stats(self.device)
+ self.data["active"] = torch_stats["active.all.current"]
+ self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
+ self.data["reserved"] = torch_stats["reserved_bytes.all.current"]
+ self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
+ self.data["system_peak"] = total - self.data["min_free"]
+
+ return self.data
+
+ def stop(self):
+ self.run_flag.clear()
+ return self.read()
diff --git a/stable-diffusion-webui/modules/modelloader.py b/stable-diffusion-webui/modules/modelloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..098bcb7933632c8f9cb798dcc038f1834fdb128f
--- /dev/null
+++ b/stable-diffusion-webui/modules/modelloader.py
@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+import os
+import shutil
+import importlib
+from urllib.parse import urlparse
+
+from modules import shared
+from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
+from modules.paths import script_path, models_path
+
+
+def load_file_from_url(
+ url: str,
+ *,
+ model_dir: str,
+ progress: bool = True,
+ file_name: str | None = None,
+) -> str:
+ """Download a file from `url` into `model_dir`, using the file present if possible.
+
+ Returns the path to the downloaded file.
+ """
+ os.makedirs(model_dir, exist_ok=True)
+ if not file_name:
+ parts = urlparse(url)
+ file_name = os.path.basename(parts.path)
+ cached_file = os.path.abspath(os.path.join(model_dir, file_name))
+ if not os.path.exists(cached_file):
+ print(f'Downloading: "{url}" to {cached_file}\n')
+ from torch.hub import download_url_to_file
+ download_url_to_file(url, cached_file, progress=progress)
+ return cached_file
+
+
+def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
+ """
+ A one-and done loader to try finding the desired models in specified directories.
+
+ @param download_name: Specify to download from model_url immediately.
+ @param model_url: If no other models are found, this will be downloaded on upscale.
+ @param model_path: The location to store/find models in.
+ @param command_path: A command-line argument to search for models in first.
+ @param ext_filter: An optional list of filename extensions to filter by
+ @return: A list of paths containing the desired model(s)
+ """
+ output = []
+
+ try:
+ places = []
+
+ if command_path is not None and command_path != model_path:
+ pretrained_path = os.path.join(command_path, 'experiments/pretrained_models')
+ if os.path.exists(pretrained_path):
+ print(f"Appending path: {pretrained_path}")
+ places.append(pretrained_path)
+ elif os.path.exists(command_path):
+ places.append(command_path)
+
+ places.append(model_path)
+
+ for place in places:
+ for full_path in shared.walk_files(place, allowed_extensions=ext_filter):
+ if os.path.islink(full_path) and not os.path.exists(full_path):
+ print(f"Skipping broken symlink: {full_path}")
+ continue
+ if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
+ continue
+ if full_path not in output:
+ output.append(full_path)
+
+ if model_url is not None and len(output) == 0:
+ if download_name is not None:
+ output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
+ else:
+ output.append(model_url)
+
+ except Exception:
+ pass
+
+ return output
+
+
+def friendly_name(file: str):
+ if file.startswith("http"):
+ file = urlparse(file).path
+
+ file = os.path.basename(file)
+ model_name, extension = os.path.splitext(file)
+ return model_name
+
+
+def cleanup_models():
+ # This code could probably be more efficient if we used a tuple list or something to store the src/destinations
+ # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
+ # somehow auto-register and just do these things...
+ root_path = script_path
+ src_path = models_path
+ dest_path = os.path.join(models_path, "Stable-diffusion")
+ move_files(src_path, dest_path, ".ckpt")
+ move_files(src_path, dest_path, ".safetensors")
+ src_path = os.path.join(root_path, "ESRGAN")
+ dest_path = os.path.join(models_path, "ESRGAN")
+ move_files(src_path, dest_path)
+ src_path = os.path.join(models_path, "BSRGAN")
+ dest_path = os.path.join(models_path, "ESRGAN")
+ move_files(src_path, dest_path, ".pth")
+ src_path = os.path.join(root_path, "gfpgan")
+ dest_path = os.path.join(models_path, "GFPGAN")
+ move_files(src_path, dest_path)
+ src_path = os.path.join(root_path, "SwinIR")
+ dest_path = os.path.join(models_path, "SwinIR")
+ move_files(src_path, dest_path)
+ src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
+ dest_path = os.path.join(models_path, "LDSR")
+ move_files(src_path, dest_path)
+
+
+def move_files(src_path: str, dest_path: str, ext_filter: str = None):
+ try:
+ os.makedirs(dest_path, exist_ok=True)
+ if os.path.exists(src_path):
+ for file in os.listdir(src_path):
+ fullpath = os.path.join(src_path, file)
+ if os.path.isfile(fullpath):
+ if ext_filter is not None:
+ if ext_filter not in file:
+ continue
+ print(f"Moving {file} from {src_path} to {dest_path}.")
+ try:
+ shutil.move(fullpath, dest_path)
+ except Exception:
+ pass
+ if len(os.listdir(src_path)) == 0:
+ print(f"Removing empty folder: {src_path}")
+ shutil.rmtree(src_path, True)
+ except Exception:
+ pass
+
+
+def load_upscalers():
+ # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
+ # so we'll try to import any _model.py files before looking in __subclasses__
+ modules_dir = os.path.join(shared.script_path, "modules")
+ for file in os.listdir(modules_dir):
+ if "_model.py" in file:
+ model_name = file.replace("_model.py", "")
+ full_model = f"modules.{model_name}_model"
+ try:
+ importlib.import_module(full_model)
+ except Exception:
+ pass
+
+ datas = []
+ commandline_options = vars(shared.cmd_opts)
+
+ # some of upscaler classes will not go away after reloading their modules, and we'll end
+ # up with two copies of those classes. The newest copy will always be the last in the list,
+ # so we go from end to beginning and ignore duplicates
+ used_classes = {}
+ for cls in reversed(Upscaler.__subclasses__()):
+ classname = str(cls)
+ if classname not in used_classes:
+ used_classes[classname] = cls
+
+ for cls in reversed(used_classes.values()):
+ name = cls.__name__
+ cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
+ commandline_model_path = commandline_options.get(cmd_name, None)
+ scaler = cls(commandline_model_path)
+ scaler.user_path = commandline_model_path
+ scaler.model_download_path = commandline_model_path or scaler.model_path
+ datas += scaler.scalers
+
+ shared.sd_upscalers = sorted(
+ datas,
+ # Special case for UpscalerNone keeps it at the beginning of the list.
+ key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
+ )
diff --git a/stable-diffusion-webui/modules/models/diffusion/ddpm_edit.py b/stable-diffusion-webui/modules/models/diffusion/ddpm_edit.py
new file mode 100644
index 0000000000000000000000000000000000000000..b892d5fc7b04755a3de6c17cf8787605df0faed3
--- /dev/null
+++ b/stable-diffusion-webui/modules/models/diffusion/ddpm_edit.py
@@ -0,0 +1,1455 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
+# See more details in LICENSE.
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager
+from functools import partial
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+ # classic DDPM with Gaussian diffusion, in image space
+ def __init__(self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule="linear",
+ loss_type="l2",
+ ckpt_path=None,
+ ignore_keys=None,
+ load_only_unet=False,
+ monitor="val/loss",
+ use_ema=True,
+ first_stage_key="image",
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.,
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.,
+ conditioning_key=None,
+ parameterization="eps", # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.,
+ load_ema=True,
+ ):
+ super().__init__()
+ assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+ self.parameterization = parameterization
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ self.cond_stage_model = None
+ self.clip_denoised = clip_denoised
+ self.log_every_t = log_every_t
+ self.first_stage_key = first_stage_key
+ self.image_size = image_size # try conv?
+ self.channels = channels
+ self.use_positional_encodings = use_positional_encodings
+ self.model = DiffusionWrapper(unet_config, conditioning_key)
+ count_params(self.model, verbose=True)
+ self.use_ema = use_ema
+
+ self.use_scheduler = scheduler_config is not None
+ if self.use_scheduler:
+ self.scheduler_config = scheduler_config
+
+ self.v_posterior = v_posterior
+ self.original_elbo_weight = original_elbo_weight
+ self.l_simple_weight = l_simple_weight
+
+ if monitor is not None:
+ self.monitor = monitor
+
+ if self.use_ema and load_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
+
+ # If initialing from EMA-only checkpoint, create EMA model after loading.
+ if self.use_ema and not load_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+ self.loss_type = loss_type
+
+ self.learn_logvar = learn_logvar
+ self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ if self.learn_logvar:
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if exists(given_betas):
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+ 1. - alphas_cumprod) + self.v_posterior * betas
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+ self.register_buffer('posterior_mean_coef1', to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+ self.register_buffer('posterior_mean_coef2', to_torch(
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+ if self.parameterization == "eps":
+ lvlb_weights = self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+ elif self.parameterization == "x0":
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ else:
+ raise NotImplementedError("mu not supported")
+ # TODO how to choose this term
+ lvlb_weights[0] = lvlb_weights[1]
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+ assert not torch.isnan(self.lvlb_weights).all()
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.model.parameters())
+ self.model_ema.copy_to(self.model)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.model.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
+ ignore_keys = ignore_keys or []
+
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+
+ # Our model adds additional channels to the first layer to condition on an input image.
+ # For the first layer, copy existing channel weights and initialize new channel weights to zero.
+ input_keys = [
+ "model.diffusion_model.input_blocks.0.0.weight",
+ "model_ema.diffusion_modelinput_blocks00weight",
+ ]
+
+ self_sd = self.state_dict()
+ for input_key in input_keys:
+ if input_key not in sd or input_key not in self_sd:
+ continue
+
+ input_weight = self_sd[input_key]
+
+ if input_weight.size() != sd[input_key].size():
+ print(f"Manual init: {input_key}")
+ input_weight.zero_()
+ input_weight[:, :4, :, :].copy_(sd[input_key])
+ ignore_keys.append(input_key)
+
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print(f"Deleting key {k} from state_dict.")
+ del sd[k]
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if missing:
+ print(f"Missing Keys: {missing}")
+ if unexpected:
+ print(f"Unexpected Keys: {unexpected}")
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def predict_start_from_noise(self, x_t, t, noise):
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ )
+
+ def q_posterior(self, x_start, x_t, t):
+ posterior_mean = (
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, x, t, clip_denoised: bool):
+ model_out = self.model(x, t)
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+ b, *_, device = *x.shape, x.device
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ noise = noise_like(x.shape, device, repeat_noise)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, shape, return_intermediates=False):
+ device = self.betas.device
+ b = shape[0]
+ img = torch.randn(shape, device=device)
+ intermediates = [img]
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised)
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+ intermediates.append(img)
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, batch_size=16, return_intermediates=False):
+ image_size = self.image_size
+ channels = self.channels
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates)
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_loss(self, pred, target, mean=True):
+ if self.loss_type == 'l1':
+ loss = (target - pred).abs()
+ if mean:
+ loss = loss.mean()
+ elif self.loss_type == 'l2':
+ if mean:
+ loss = torch.nn.functional.mse_loss(target, pred)
+ else:
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+ else:
+ raise NotImplementedError("unknown loss type '{loss_type}'")
+
+ return loss
+
+ def p_losses(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_out = self.model(x_noisy, t)
+
+ loss_dict = {}
+ if self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "x0":
+ target = x_start
+ else:
+ raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+ log_prefix = 'train' if self.training else 'val'
+
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+ loss_simple = loss.mean() * self.l_simple_weight
+
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+ loss_dict.update({f'{log_prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def forward(self, x, *args, **kwargs):
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ return self.p_losses(x, t, *args, **kwargs)
+
+ def get_input(self, batch, k):
+ return batch[k]
+
+ def shared_step(self, batch):
+ x = self.get_input(batch, self.first_stage_key)
+ loss, loss_dict = self(x)
+ return loss, loss_dict
+
+ def training_step(self, batch, batch_idx):
+ loss, loss_dict = self.shared_step(batch)
+
+ self.log_dict(loss_dict, prog_bar=True,
+ logger=True, on_step=True, on_epoch=True)
+
+ self.log("global_step", self.global_step,
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ if self.use_scheduler:
+ lr = self.optimizers().param_groups[0]['lr']
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ return loss
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ _, loss_dict_no_ema = self.shared_step(batch)
+ with self.ema_scope():
+ _, loss_dict_ema = self.shared_step(batch)
+ loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema}
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self.model)
+
+ def _get_rows_from_list(self, samples):
+ n_imgs_per_row = len(samples)
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ log = {}
+ x = self.get_input(batch, self.first_stage_key)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ x = x.to(self.device)[:N]
+ log["inputs"] = x
+
+ # get diffusion row
+ diffusion_row = []
+ x_start = x[:n_row]
+
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(x_start)
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ diffusion_row.append(x_noisy)
+
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+ log["samples"] = samples
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.learn_logvar:
+ params = params + [self.logvar]
+ opt = torch.optim.AdamW(params, lr=lr)
+ return opt
+
+
+class LatentDiffusion(DDPM):
+ """main class"""
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ load_ema=True,
+ *args, **kwargs):
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ if conditioning_key is None:
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
+ if cond_stage_config == '__is_unconditional__':
+ conditioning_key = None
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **kwargs)
+ self.concat_mode = concat_mode
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except Exception:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ self.instantiate_first_stage(first_stage_config)
+ self.instantiate_cond_stage(cond_stage_config)
+ self.cond_stage_forward = cond_stage_forward
+ self.clip_denoised = False
+ self.bbox_tokenizer = None
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+ self.restarted_from_ckpt = True
+
+ if self.use_ema and not load_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ @rank_zero_only
+ @torch.no_grad()
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+ # only for very first batch
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ # set rescale weight to 1./std of encodings
+ print("### USING STD-RESCALING ###")
+ x = super().get_input(batch, self.first_stage_key)
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ del self.scale_factor
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
+ print(f"setting self.scale_factor to {self.scale_factor}")
+ print("### USING STD-RESCALING ###")
+
+ def register_schedule(self,
+ given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
+ if self.shorten_cond_schedule:
+ self.make_cond_schedule()
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ if not self.cond_stage_trainable:
+ if config == "__is_first_stage__":
+ print("Using first stage also as cond stage.")
+ self.cond_stage_model = self.first_stage_model
+ elif config == "__is_unconditional__":
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
+ self.cond_stage_model = None
+ # self.be_unconditional = True
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ assert config != '__is_first_stage__'
+ assert config != '__is_unconditional__'
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization))
+ n_imgs_per_row = len(denoise_row)
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def meshgrid(self, h, w):
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+ arr = torch.cat([y, x], dim=-1)
+ return arr
+
+ def delta_border(self, h, w):
+ """
+ :param h: height
+ :param w: width
+ :return: normalized distance to image border,
+ wtith min distance = 0 at border and max dist = 0.5 at image center
+ """
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+ arr = self.meshgrid(h, w) / lower_right_corner
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ return edge_dist
+
+ def get_weighting(self, h, w, Ly, Lx, device):
+ weighting = self.delta_border(h, w)
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+ self.split_input_params["clip_max_weight"], )
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+ if self.split_input_params["tie_braker"]:
+ L_weighting = self.delta_border(Ly, Lx)
+ L_weighting = torch.clip(L_weighting,
+ self.split_input_params["clip_min_tie_weight"],
+ self.split_input_params["clip_max_tie_weight"])
+
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+ weighting = weighting * L_weighting
+ return weighting
+
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ """
+ :param x: img of size (bs, c, h, w)
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+ """
+ bs, nc, h, w = x.shape
+
+ # number of crops in image
+ Ly = (h - kernel_size[0]) // stride[0] + 1
+ Lx = (w - kernel_size[1]) // stride[1] + 1
+
+ if uf == 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+ elif uf > 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1, padding=0,
+ stride=(stride[0] * uf, stride[1] * uf))
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+ elif df > 1 and uf == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1, padding=0,
+ stride=(stride[0] // df, stride[1] // df))
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+ else:
+ raise NotImplementedError
+
+ return fold, unfold, normalization, weighting
+
+ @torch.no_grad()
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+ cond_key=None, return_original_cond=False, bs=None, uncond=0.05):
+ x = super().get_input(batch, k)
+ if bs is not None:
+ x = x[:bs]
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ cond_key = cond_key or self.cond_stage_key
+ xc = super().get_input(batch, cond_key)
+ if bs is not None:
+ xc["c_crossattn"] = xc["c_crossattn"][:bs]
+ xc["c_concat"] = xc["c_concat"][:bs]
+ cond = {}
+
+ # To support classifier-free guidance, randomly drop out only text conditioning 5%, only image conditioning 5%, and both 5%.
+ random = torch.rand(x.size(0), device=x.device)
+ prompt_mask = rearrange(random < 2 * uncond, "n -> n 1 1")
+ input_mask = 1 - rearrange((random >= uncond).float() * (random < 3 * uncond).float(), "n -> n 1 1 1")
+
+ null_prompt = self.get_learned_conditioning([""])
+ cond["c_crossattn"] = [torch.where(prompt_mask, null_prompt, self.get_learned_conditioning(xc["c_crossattn"]).detach())]
+ cond["c_concat"] = [input_mask * self.encode_first_stage((xc["c_concat"].to(self.device))).mode().detach()]
+
+ out = [z, cond]
+ if return_first_stage_outputs:
+ xrec = self.decode_first_stage(z)
+ out.extend([x, xrec])
+ if return_original_cond:
+ out.append(xc)
+ return out
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+
+ if hasattr(self, "split_input_params"):
+ if self.split_input_params["patch_distributed_vq"]:
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+ uf = self.split_input_params["vqf"]
+ bs, nc, h, w = z.shape
+ if ks[0] > h or ks[1] > w:
+ ks = (min(ks[0], h), min(ks[1], w))
+ print("reducing Kernel")
+
+ if stride[0] > h or stride[1] > w:
+ stride = (min(stride[0], h), min(stride[1], w))
+ print("reducing stride")
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+ z = unfold(z) # (bn, nc * prod(**ks), L)
+ # 1. Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ # 2. apply model loop over last dim
+ if isinstance(self.first_stage_model, VQModelInterface):
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+ force_not_quantize=predict_cids or force_not_quantize)
+ for i in range(z.shape[-1])]
+ else:
+
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])]
+
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
+ o = o * weighting
+ # Reverse 1. reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ decoded = fold(o)
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
+ return decoded
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ # same as above but without decorator
+ def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+
+ if hasattr(self, "split_input_params"):
+ if self.split_input_params["patch_distributed_vq"]:
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+ uf = self.split_input_params["vqf"]
+ bs, nc, h, w = z.shape
+ if ks[0] > h or ks[1] > w:
+ ks = (min(ks[0], h), min(ks[1], w))
+ print("reducing Kernel")
+
+ if stride[0] > h or stride[1] > w:
+ stride = (min(stride[0], h), min(stride[1], w))
+ print("reducing stride")
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+ z = unfold(z) # (bn, nc * prod(**ks), L)
+ # 1. Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ # 2. apply model loop over last dim
+ if isinstance(self.first_stage_model, VQModelInterface):
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+ force_not_quantize=predict_cids or force_not_quantize)
+ for i in range(z.shape[-1])]
+ else:
+
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])]
+
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
+ o = o * weighting
+ # Reverse 1. reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ decoded = fold(o)
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
+ return decoded
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ else:
+ if isinstance(self.first_stage_model, VQModelInterface):
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+ else:
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ if hasattr(self, "split_input_params"):
+ if self.split_input_params["patch_distributed_vq"]:
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+ df = self.split_input_params["vqf"]
+ self.split_input_params['original_image_size'] = x.shape[-2:]
+ bs, nc, h, w = x.shape
+ if ks[0] > h or ks[1] > w:
+ ks = (min(ks[0], h), min(ks[1], w))
+ print("reducing Kernel")
+
+ if stride[0] > h or stride[1] > w:
+ stride = (min(stride[0], h), min(stride[1], w))
+ print("reducing stride")
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+ z = unfold(x) # (bn, nc * prod(**ks), L)
+ # Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
+ for i in range(z.shape[-1])]
+
+ o = torch.stack(output_list, axis=-1)
+ o = o * weighting
+
+ # Reverse reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ decoded = fold(o)
+ decoded = decoded / normalization
+ return decoded
+
+ else:
+ return self.first_stage_model.encode(x)
+ else:
+ return self.first_stage_model.encode(x)
+
+ def shared_step(self, batch, **kwargs):
+ x, c = self.get_input(batch, self.first_stage_key)
+ loss = self(x, c)
+ return loss
+
+ def forward(self, x, c, *args, **kwargs):
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ if self.model.conditioning_key is not None:
+ assert c is not None
+ if self.cond_stage_trainable:
+ c = self.get_learned_conditioning(c)
+ if self.shorten_cond_schedule: # TODO: drop this option
+ tc = self.cond_ids[t].to(self.device)
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ return self.p_losses(x, c, t, *args, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
+
+ if isinstance(cond, dict):
+ # hybrid case, cond is exptected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+
+ if hasattr(self, "split_input_params"):
+ assert len(cond) == 1 # todo can only deal with one conditioning atm
+ assert not return_ids
+ ks = self.split_input_params["ks"] # eg. (128, 128)
+ stride = self.split_input_params["stride"] # eg. (64, 64)
+
+ h, w = x_noisy.shape[-2:]
+
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+
+ z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
+ # Reshape to img shape
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+ z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
+
+ if self.cond_stage_key in ["image", "LR_image", "segmentation",
+ 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
+ c_key = next(iter(cond.keys())) # get key
+ c = next(iter(cond.values())) # get value
+ assert (len(c) == 1) # todo extend to list with more than one elem
+ c = c[0] # get element
+
+ c = unfold(c)
+ c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
+
+ cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+
+ elif self.cond_stage_key == 'coordinates_bbox':
+ assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+
+ # assuming padding of unfold is always 0 and its dilation is always 1
+ n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
+ full_img_h, full_img_w = self.split_input_params['original_image_size']
+ # as we are operating on latents, we need the factor from the original image size to the
+ # spatial latent size to properly rescale the crops for regenerating the bbox annotations
+ num_downs = self.first_stage_model.encoder.num_resolutions - 1
+ rescale_latent = 2 ** (num_downs)
+
+ # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+ # need to rescale the tl patch coordinates to be in between (0,1)
+ tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
+ rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
+ for patch_nr in range(z.shape[-1])]
+
+ # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
+ patch_limits = [(x_tl, y_tl,
+ rescale_latent * ks[0] / full_img_w,
+ rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+ # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
+
+ # tokenize crop coordinates for the bounding boxes of the respective patches
+ patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
+ for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
+ print(patch_limits_tknzd[0].shape)
+ # cut tknzd crop position from conditioning
+ assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+ cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
+ print(cut_cond.shape)
+
+ adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+ adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
+ print(adapted_cond.shape)
+ adapted_cond = self.get_learned_conditioning(adapted_cond)
+ print(adapted_cond.shape)
+ adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+ print(adapted_cond.shape)
+
+ cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
+
+ else:
+ cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
+
+ # apply model by loop over crops
+ output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
+ assert not isinstance(output_list[0],
+ tuple) # todo cant deal with multiple model outputs check this never happens
+
+ o = torch.stack(output_list, axis=-1)
+ o = o * weighting
+ # Reverse reshape to img shape
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
+ # stitch crops together
+ x_recon = fold(o) / normalization
+
+ else:
+ x_recon = self.model(x_noisy, t, **cond)
+
+ if isinstance(x_recon, tuple) and not return_ids:
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+ This term can't be optimized, as it only depends on the encoder.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def p_losses(self, x_start, cond, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_output = self.apply_model(x_noisy, t, cond)
+
+ loss_dict = {}
+ prefix = 'train' if self.training else 'val'
+
+ if self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "eps":
+ target = noise
+ else:
+ raise NotImplementedError()
+
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+ logvar_t = self.logvar[t].to(self.device)
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+ if self.learn_logvar:
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+ loss_dict.update({'logvar': self.logvar.data.mean()})
+
+ loss = self.l_simple_weight * loss.mean()
+
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+ loss += (self.original_elbo_weight * loss_vlb)
+ loss_dict.update({f'{prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
+ t_in = t
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if return_codebook_ids:
+ model_out, logits = model_out
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+ if quantize_denoised:
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ if return_codebook_ids:
+ return model_mean, posterior_variance, posterior_log_variance, logits
+ elif return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if return_codebook_ids:
+ raise DeprecationWarning("Support dropped.")
+ model_mean, _, model_log_variance, logits = outputs
+ elif return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_codebook_ids:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+ log_every_t=None):
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ timesteps = self.num_timesteps
+ if batch_size is not None:
+ b = batch_size if batch_size is not None else shape[0]
+ shape = [batch_size] + list(shape)
+ else:
+ b = batch_size = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=self.device)
+ else:
+ img = x_T
+ intermediates = []
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ [x[:batch_size] for x in cond[key]] for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+ total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+ if type(temperature) == float:
+ temperature = [temperature] * timesteps
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img, x0_partial = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised, return_x0=True,
+ temperature=temperature[i], noise_dropout=noise_dropout,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(x0_partial)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(img, i)
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, start_T=None,
+ log_every_t=None):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback:
+ callback(i)
+ if img_callback:
+ img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+ verbose=True, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, shape=None,**kwargs):
+ if shape is None:
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ [x[:batch_size] for x in cond[key]] for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ return self.p_sample_loop(cond,
+ shape,
+ return_intermediates=return_intermediates, x_T=x_T,
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+ mask=mask, x0=x0)
+
+ @torch.no_grad()
+ def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+
+ if ddim:
+ ddim_sampler = DDIMSampler(self)
+ shape = (self.channels, self.image_size, self.image_size)
+ samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
+ shape,cond,verbose=False,**kwargs)
+
+ else:
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+ return_intermediates=True,**kwargs)
+
+ return samples, intermediates
+
+
+ @torch.no_grad()
+ def log_images(self, batch, N=4, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+ quantize_denoised=True, inpaint=False, plot_denoise_rows=False, plot_progressive_rows=False,
+ plot_diffusion_rows=False, **kwargs):
+
+ use_ddim = False
+
+ log = {}
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ bs=N, uncond=0)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reals"] = xc["c_concat"]
+ log["reconstruction"] = xrec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
+ log["conditioning"] = xc
+ elif self.cond_stage_key == 'class_label':
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+ log['conditioning'] = xc
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = []
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+ ddim_steps=ddim_steps,eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+ self.first_stage_model, IdentityFirstStage):
+ # also display when quantizing x0 while sampling
+ with self.ema_scope("Plotting Quantized Denoised"):
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+ ddim_steps=ddim_steps,eta=ddim_eta,
+ quantize_denoised=True)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+ # quantize_denoised=True)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_x0_quantized"] = x_samples
+
+ if inpaint:
+ # make a simple center square
+ h, w = z.shape[2], z.shape[3]
+ mask = torch.ones(N, h, w).to(self.device)
+ # zeros will be filled in
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask = mask[:, None, ...]
+ with self.ema_scope("Plotting Inpaint"):
+
+ samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_inpainting"] = x_samples
+ log["mask"] = mask
+
+ # outpaint
+ with self.ema_scope("Plotting Outpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_outpainting"] = x_samples
+
+ if plot_progressive_rows:
+ with self.ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.cond_stage_trainable:
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ params = params + list(self.cond_stage_model.parameters())
+ if self.learn_logvar:
+ print('Diffusion model optimizing logvar')
+ params.append(self.logvar)
+ opt = torch.optim.AdamW(params, lr=lr)
+ if self.use_scheduler:
+ assert 'target' in self.scheduler_config
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ }]
+ return [opt], scheduler
+ return opt
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+ def __init__(self, diff_model_config, conditioning_key):
+ super().__init__()
+ self.diffusion_model = instantiate_from_config(diff_model_config)
+ self.conditioning_key = conditioning_key
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
+ if self.conditioning_key is None:
+ out = self.diffusion_model(x, t)
+ elif self.conditioning_key == 'concat':
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t)
+ elif self.conditioning_key == 'crossattn':
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc)
+ elif self.conditioning_key == 'hybrid':
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'adm':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, y=cc)
+ else:
+ raise NotImplementedError()
+
+ return out
+
+
+class Layout2ImgDiffusion(LatentDiffusion):
+ # TODO: move all layout-specific hacks to this class
+ def __init__(self, cond_stage_key, *args, **kwargs):
+ assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+ super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
+
+ def log_images(self, batch, N=8, *args, **kwargs):
+ logs = super().log_images(*args, batch=batch, N=N, **kwargs)
+
+ key = 'train' if self.training else 'validation'
+ dset = self.trainer.datamodule.datasets[key]
+ mapper = dset.conditional_builders[self.cond_stage_key]
+
+ bbox_imgs = []
+ map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+ for tknzd_bbox in batch[self.cond_stage_key][:N]:
+ bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+ bbox_imgs.append(bboximg)
+
+ cond_img = torch.stack(bbox_imgs, dim=0)
+ logs['bbox_image'] = cond_img
+ return logs
diff --git a/stable-diffusion-webui/modules/models/diffusion/uni_pc/__init__.py b/stable-diffusion-webui/modules/models/diffusion/uni_pc/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbb35964ce08f4d3e1b2f77d2a5359c87bf6f3fa
--- /dev/null
+++ b/stable-diffusion-webui/modules/models/diffusion/uni_pc/__init__.py
@@ -0,0 +1 @@
+from .sampler import UniPCSampler # noqa: F401
diff --git a/stable-diffusion-webui/modules/models/diffusion/uni_pc/sampler.py b/stable-diffusion-webui/modules/models/diffusion/uni_pc/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a9defa108b8e61adb8665b031c9770158654714
--- /dev/null
+++ b/stable-diffusion-webui/modules/models/diffusion/uni_pc/sampler.py
@@ -0,0 +1,101 @@
+"""SAMPLING ONLY."""
+
+import torch
+
+from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
+from modules import shared, devices
+
+
+class UniPCSampler(object):
+ def __init__(self, model, **kwargs):
+ super().__init__()
+ self.model = model
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+ self.before_sample = None
+ self.after_sample = None
+ self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != devices.device:
+ attr = attr.to(devices.device)
+ setattr(self, name, attr)
+
+ def set_hooks(self, before_sample, after_sample, after_update):
+ self.before_sample = before_sample
+ self.after_sample = after_sample
+ self.after_update = after_update
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ ctmp = conditioning[list(conditioning.keys())[0]]
+ while isinstance(ctmp, list):
+ ctmp = ctmp[0]
+ cbs = ctmp.shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ elif isinstance(conditioning, list):
+ for ctmp in conditioning:
+ if ctmp.shape[0] != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ # print(f'Data shape for UniPC sampling is {size}')
+
+ device = self.model.betas.device
+ if x_T is None:
+ img = torch.randn(size, device=device)
+ else:
+ img = x_T
+
+ ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+ # SD 1.X is "noise", SD 2.X is "v"
+ model_type = "v" if self.model.parameterization == "v" else "noise"
+
+ model_fn = model_wrapper(
+ lambda x, t, c: self.model.apply_model(x, t, c),
+ ns,
+ model_type=model_type,
+ guidance_type="classifier-free",
+ #condition=conditioning,
+ #unconditional_condition=unconditional_conditioning,
+ guidance_scale=unconditional_guidance_scale,
+ )
+
+ uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
+ x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+
+ return x.to(device), None
diff --git a/stable-diffusion-webui/modules/models/diffusion/uni_pc/uni_pc.py b/stable-diffusion-webui/modules/models/diffusion/uni_pc/uni_pc.py
new file mode 100644
index 0000000000000000000000000000000000000000..d257a7286fc6be7da4612041287313a3290cf45e
--- /dev/null
+++ b/stable-diffusion-webui/modules/models/diffusion/uni_pc/uni_pc.py
@@ -0,0 +1,863 @@
+import torch
+import math
+import tqdm
+
+
+class NoiseScheduleVP:
+ def __init__(
+ self,
+ schedule='discrete',
+ betas=None,
+ alphas_cumprod=None,
+ continuous_beta_0=0.1,
+ continuous_beta_1=20.,
+ ):
+ """Create a wrapper class for the forward SDE (VP type).
+
+ ***
+ Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+ We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+ ***
+
+ The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+ We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+ Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+
+ log_alpha_t = self.marginal_log_mean_coeff(t)
+ sigma_t = self.marginal_std(t)
+ lambda_t = self.marginal_lambda(t)
+
+ Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+
+ t = self.inverse_lambda(lambda_t)
+
+ ===============================================================
+
+ We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+
+ 1. For discrete-time DPMs:
+
+ For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+ t_i = (i + 1) / N
+ e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+ We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+
+ Args:
+ betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+ alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+
+ Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+
+ **Important**: Please pay special attention for the args for `alphas_cumprod`:
+ The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+ q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+ Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+ alpha_{t_n} = \sqrt{\hat{alpha_n}},
+ and
+ log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+
+
+ 2. For continuous-time DPMs:
+
+ We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+ schedule are the default settings in DDPM and improved-DDPM:
+
+ Args:
+ beta_min: A `float` number. The smallest beta for the linear schedule.
+ beta_max: A `float` number. The largest beta for the linear schedule.
+ cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+ cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+ T: A `float` number. The ending time of the forward process.
+
+ ===============================================================
+
+ Args:
+ schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+ 'linear' or 'cosine' for continuous-time DPMs.
+ Returns:
+ A wrapper object of the forward SDE (VP type).
+
+ ===============================================================
+
+ Example:
+
+ # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', betas=betas)
+
+ # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+
+ # For continuous-time DPMs (VPSDE), linear schedule:
+ >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+
+ """
+
+ if schedule not in ['discrete', 'linear', 'cosine']:
+ raise ValueError(f"Unsupported noise schedule {schedule}. The schedule needs to be 'discrete' or 'linear' or 'cosine'")
+
+ self.schedule = schedule
+ if schedule == 'discrete':
+ if betas is not None:
+ log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+ else:
+ assert alphas_cumprod is not None
+ log_alphas = 0.5 * torch.log(alphas_cumprod)
+ self.total_N = len(log_alphas)
+ self.T = 1.
+ self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+ self.log_alpha_array = log_alphas.reshape((1, -1,))
+ else:
+ self.total_N = 1000
+ self.beta_0 = continuous_beta_0
+ self.beta_1 = continuous_beta_1
+ self.cosine_s = 0.008
+ self.cosine_beta_max = 999.
+ self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+ self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+ self.schedule = schedule
+ if schedule == 'cosine':
+ # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+ # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+ self.T = 0.9946
+ else:
+ self.T = 1.
+
+ def marginal_log_mean_coeff(self, t):
+ """
+ Compute log(alpha_t) of a given continuous-time label t in [0, T].
+ """
+ if self.schedule == 'discrete':
+ return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
+ elif self.schedule == 'linear':
+ return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+ elif self.schedule == 'cosine':
+ log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+ log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
+ return log_alpha_t
+
+ def marginal_alpha(self, t):
+ """
+ Compute alpha_t of a given continuous-time label t in [0, T].
+ """
+ return torch.exp(self.marginal_log_mean_coeff(t))
+
+ def marginal_std(self, t):
+ """
+ Compute sigma_t of a given continuous-time label t in [0, T].
+ """
+ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+ def marginal_lambda(self, t):
+ """
+ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+ """
+ log_mean_coeff = self.marginal_log_mean_coeff(t)
+ log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+ return log_mean_coeff - log_std
+
+ def inverse_lambda(self, lamb):
+ """
+ Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+ """
+ if self.schedule == 'linear':
+ tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ Delta = self.beta_0**2 + tmp
+ return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+ elif self.schedule == 'discrete':
+ log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+ t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
+ return t.reshape((-1,))
+ else:
+ log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+ t = t_fn(log_alpha)
+ return t
+
+
+def model_wrapper(
+ model,
+ noise_schedule,
+ model_type="noise",
+ model_kwargs=None,
+ guidance_type="uncond",
+ #condition=None,
+ #unconditional_condition=None,
+ guidance_scale=1.,
+ classifier_fn=None,
+ classifier_kwargs=None,
+):
+ """Create a wrapper function for the noise prediction model.
+
+ DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+ firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+
+ We support four types of the diffusion model by setting `model_type`:
+
+ 1. "noise": noise prediction model. (Trained by predicting noise).
+
+ 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+
+ 3. "v": velocity prediction model. (Trained by predicting the velocity).
+ The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+
+ [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+ arXiv preprint arXiv:2202.00512 (2022).
+ [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+ arXiv preprint arXiv:2210.02303 (2022).
+
+ 4. "score": marginal score function. (Trained by denoising score matching).
+ Note that the score function and the noise prediction model follows a simple relationship:
+ ```
+ noise(x_t, t) = -sigma_t * score(x_t, t)
+ ```
+
+ We support three types of guided sampling by DPMs by setting `guidance_type`:
+ 1. "uncond": unconditional sampling by DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+
+ 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+
+ The input `classifier_fn` has the following format:
+ ``
+ classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+ ``
+
+ [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+ in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+
+ 3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+ ``
+ And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+
+ [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+ arXiv preprint arXiv:2207.12598 (2022).
+
+
+ The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+ or continuous-time labels (i.e. epsilon to T).
+
+ We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+ ``
+ def model_fn(x, t_continuous) -> noise:
+ t_input = get_model_input_time(t_continuous)
+ return noise_pred(model, x, t_input, **model_kwargs)
+ ``
+ where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+
+ ===============================================================
+
+ Args:
+ model: A diffusion model with the corresponding format described above.
+ noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+ model_type: A `str`. The parameterization type of the diffusion model.
+ "noise" or "x_start" or "v" or "score".
+ model_kwargs: A `dict`. A dict for the other inputs of the model function.
+ guidance_type: A `str`. The type of the guidance for sampling.
+ "uncond" or "classifier" or "classifier-free".
+ condition: A pytorch tensor. The condition for the guided sampling.
+ Only used for "classifier" or "classifier-free" guidance type.
+ unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+ Only used for "classifier-free" guidance type.
+ guidance_scale: A `float`. The scale for the guided sampling.
+ classifier_fn: A classifier function. Only used for the classifier guidance.
+ classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+ Returns:
+ A noise prediction model that accepts the noised data and the continuous time as the inputs.
+ """
+
+ model_kwargs = model_kwargs or {}
+ classifier_kwargs = classifier_kwargs or {}
+
+ def get_model_input_time(t_continuous):
+ """
+ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+ For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+ For continuous-time DPMs, we just use `t_continuous`.
+ """
+ if noise_schedule.schedule == 'discrete':
+ return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+ else:
+ return t_continuous
+
+ def noise_pred_fn(x, t_continuous, cond=None):
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ t_input = get_model_input_time(t_continuous)
+ if cond is None:
+ output = model(x, t_input, None, **model_kwargs)
+ else:
+ output = model(x, t_input, cond, **model_kwargs)
+ if model_type == "noise":
+ return output
+ elif model_type == "x_start":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+ elif model_type == "v":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+ elif model_type == "score":
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return -expand_dims(sigma_t, dims) * output
+
+ def cond_grad_fn(x, t_input, condition):
+ """
+ Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+ """
+ with torch.enable_grad():
+ x_in = x.detach().requires_grad_(True)
+ log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+ return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+ def model_fn(x, t_continuous, condition, unconditional_condition):
+ """
+ The noise predicition model function that is used for DPM-Solver.
+ """
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ if guidance_type == "uncond":
+ return noise_pred_fn(x, t_continuous)
+ elif guidance_type == "classifier":
+ assert classifier_fn is not None
+ t_input = get_model_input_time(t_continuous)
+ cond_grad = cond_grad_fn(x, t_input, condition)
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ noise = noise_pred_fn(x, t_continuous)
+ return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+ elif guidance_type == "classifier-free":
+ if guidance_scale == 1. or unconditional_condition is None:
+ return noise_pred_fn(x, t_continuous, cond=condition)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t_continuous] * 2)
+ if isinstance(condition, dict):
+ assert isinstance(unconditional_condition, dict)
+ c_in = {}
+ for k in condition:
+ if isinstance(condition[k], list):
+ c_in[k] = [torch.cat([
+ unconditional_condition[k][i],
+ condition[k][i]]) for i in range(len(condition[k]))]
+ else:
+ c_in[k] = torch.cat([
+ unconditional_condition[k],
+ condition[k]])
+ elif isinstance(condition, list):
+ c_in = []
+ assert isinstance(unconditional_condition, list)
+ for i in range(len(condition)):
+ c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
+ else:
+ c_in = torch.cat([unconditional_condition, condition])
+ noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+ return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+ assert model_type in ["noise", "x_start", "v"]
+ assert guidance_type in ["uncond", "classifier", "classifier-free"]
+ return model_fn
+
+
+class UniPC:
+ def __init__(
+ self,
+ model_fn,
+ noise_schedule,
+ predict_x0=True,
+ thresholding=False,
+ max_val=1.,
+ variant='bh1',
+ condition=None,
+ unconditional_condition=None,
+ before_sample=None,
+ after_sample=None,
+ after_update=None
+ ):
+ """Construct a UniPC.
+
+ We support both data_prediction and noise_prediction.
+ """
+ self.model_fn_ = model_fn
+ self.noise_schedule = noise_schedule
+ self.variant = variant
+ self.predict_x0 = predict_x0
+ self.thresholding = thresholding
+ self.max_val = max_val
+ self.condition = condition
+ self.unconditional_condition = unconditional_condition
+ self.before_sample = before_sample
+ self.after_sample = after_sample
+ self.after_update = after_update
+
+ def dynamic_thresholding_fn(self, x0, t=None):
+ """
+ The dynamic thresholding method.
+ """
+ dims = x0.dim()
+ p = self.dynamic_thresholding_ratio
+ s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+ s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)
+ x0 = torch.clamp(x0, -s, s) / s
+ return x0
+
+ def model(self, x, t):
+ cond = self.condition
+ uncond = self.unconditional_condition
+ if self.before_sample is not None:
+ x, t, cond, uncond = self.before_sample(x, t, cond, uncond)
+ res = self.model_fn_(x, t, cond, uncond)
+ if self.after_sample is not None:
+ x, t, cond, uncond, res = self.after_sample(x, t, cond, uncond, res)
+
+ if isinstance(res, tuple):
+ # (None, pred_x0)
+ res = res[1]
+
+ return res
+
+ def noise_prediction_fn(self, x, t):
+ """
+ Return the noise prediction model.
+ """
+ return self.model(x, t)
+
+ def data_prediction_fn(self, x, t):
+ """
+ Return the data prediction model (with thresholding).
+ """
+ noise = self.noise_prediction_fn(x, t)
+ dims = x.dim()
+ alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+ x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+ if self.thresholding:
+ p = 0.995 # A hyperparameter in the paper of "Imagen" [1].
+ s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+ s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+ x0 = torch.clamp(x0, -s, s) / s
+ return x0
+
+ def model_fn(self, x, t):
+ """
+ Convert the model to the noise prediction model or the data prediction model.
+ """
+ if self.predict_x0:
+ return self.data_prediction_fn(x, t)
+ else:
+ return self.noise_prediction_fn(x, t)
+
+ def get_time_steps(self, skip_type, t_T, t_0, N, device):
+ """Compute the intermediate time steps for sampling.
+ """
+ if skip_type == 'logSNR':
+ lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+ lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+ logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+ return self.noise_schedule.inverse_lambda(logSNR_steps)
+ elif skip_type == 'time_uniform':
+ return torch.linspace(t_T, t_0, N + 1).to(device)
+ elif skip_type == 'time_quadratic':
+ t_order = 2
+ t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
+ return t
+ else:
+ raise ValueError(f"Unsupported skip_type {skip_type}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'")
+
+ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+ """
+ Get the order of each step for sampling by the singlestep DPM-Solver.
+ """
+ if order == 3:
+ K = steps // 3 + 1
+ if steps % 3 == 0:
+ orders = [3,] * (K - 2) + [2, 1]
+ elif steps % 3 == 1:
+ orders = [3,] * (K - 1) + [1]
+ else:
+ orders = [3,] * (K - 1) + [2]
+ elif order == 2:
+ if steps % 2 == 0:
+ K = steps // 2
+ orders = [2,] * K
+ else:
+ K = steps // 2 + 1
+ orders = [2,] * (K - 1) + [1]
+ elif order == 1:
+ K = steps
+ orders = [1,] * steps
+ else:
+ raise ValueError("'order' must be '1' or '2' or '3'.")
+ if skip_type == 'logSNR':
+ # To reproduce the results in DPM-Solver paper
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+ else:
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]
+ return timesteps_outer, orders
+
+ def denoise_to_zero_fn(self, x, s):
+ """
+ Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
+ """
+ return self.data_prediction_fn(x, s)
+
+ def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):
+ if len(t.shape) == 0:
+ t = t.view(-1)
+ if 'bh' in self.variant:
+ return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+ else:
+ assert self.variant == 'vary_coeff'
+ return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+
+ def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
+ #print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
+ ns = self.noise_schedule
+ assert order <= len(model_prev_list)
+
+ # first compute rks
+ t_prev_0 = t_prev_list[-1]
+ lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+ lambda_t = ns.marginal_lambda(t)
+ model_prev_0 = model_prev_list[-1]
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ log_alpha_t = ns.marginal_log_mean_coeff(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h = lambda_t - lambda_prev_0
+
+ rks = []
+ D1s = []
+ for i in range(1, order):
+ t_prev_i = t_prev_list[-(i + 1)]
+ model_prev_i = model_prev_list[-(i + 1)]
+ lambda_prev_i = ns.marginal_lambda(t_prev_i)
+ rk = (lambda_prev_i - lambda_prev_0) / h
+ rks.append(rk)
+ D1s.append((model_prev_i - model_prev_0) / rk)
+
+ rks.append(1.)
+ rks = torch.tensor(rks, device=x.device)
+
+ K = len(rks)
+ # build C matrix
+ C = []
+
+ col = torch.ones_like(rks)
+ for k in range(1, K + 1):
+ C.append(col)
+ col = col * rks / (k + 1)
+ C = torch.stack(C, dim=1)
+
+ if len(D1s) > 0:
+ D1s = torch.stack(D1s, dim=1) # (B, K)
+ C_inv_p = torch.linalg.inv(C[:-1, :-1])
+ A_p = C_inv_p
+
+ if use_corrector:
+ #print('using corrector')
+ C_inv = torch.linalg.inv(C)
+ A_c = C_inv
+
+ hh = -h if self.predict_x0 else h
+ h_phi_1 = torch.expm1(hh)
+ h_phi_ks = []
+ factorial_k = 1
+ h_phi_k = h_phi_1
+ for k in range(1, K + 2):
+ h_phi_ks.append(h_phi_k)
+ h_phi_k = h_phi_k / hh - 1 / factorial_k
+ factorial_k *= (k + 1)
+
+ model_t = None
+ if self.predict_x0:
+ x_t_ = (
+ sigma_t / sigma_prev_0 * x
+ - alpha_t * h_phi_1 * model_prev_0
+ )
+ # now predictor
+ x_t = x_t_
+ if len(D1s) > 0:
+ # compute the residuals for predictor
+ for k in range(K - 1):
+ x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+ # now corrector
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_
+ k = 0
+ for k in range(K - 1):
+ x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+ x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+ else:
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ x_t_ = (
+ (torch.exp(log_alpha_t - log_alpha_prev_0)) * x
+ - (sigma_t * h_phi_1) * model_prev_0
+ )
+ # now predictor
+ x_t = x_t_
+ if len(D1s) > 0:
+ # compute the residuals for predictor
+ for k in range(K - 1):
+ x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+ # now corrector
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_
+ k = 0
+ for k in range(K - 1):
+ x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+ x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+ return x_t, model_t
+
+ def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
+ #print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
+ ns = self.noise_schedule
+ assert order <= len(model_prev_list)
+ dims = x.dim()
+
+ # first compute rks
+ t_prev_0 = t_prev_list[-1]
+ lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+ lambda_t = ns.marginal_lambda(t)
+ model_prev_0 = model_prev_list[-1]
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h = lambda_t - lambda_prev_0
+
+ rks = []
+ D1s = []
+ for i in range(1, order):
+ t_prev_i = t_prev_list[-(i + 1)]
+ model_prev_i = model_prev_list[-(i + 1)]
+ lambda_prev_i = ns.marginal_lambda(t_prev_i)
+ rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
+ rks.append(rk)
+ D1s.append((model_prev_i - model_prev_0) / rk)
+
+ rks.append(1.)
+ rks = torch.tensor(rks, device=x.device)
+
+ R = []
+ b = []
+
+ hh = -h[0] if self.predict_x0 else h[0]
+ h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
+ h_phi_k = h_phi_1 / hh - 1
+
+ factorial_i = 1
+
+ if self.variant == 'bh1':
+ B_h = hh
+ elif self.variant == 'bh2':
+ B_h = torch.expm1(hh)
+ else:
+ raise NotImplementedError()
+
+ for i in range(1, order + 1):
+ R.append(torch.pow(rks, i - 1))
+ b.append(h_phi_k * factorial_i / B_h)
+ factorial_i *= (i + 1)
+ h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+ R = torch.stack(R)
+ b = torch.tensor(b, device=x.device)
+
+ # now predictor
+ use_predictor = len(D1s) > 0 and x_t is None
+ if len(D1s) > 0:
+ D1s = torch.stack(D1s, dim=1) # (B, K)
+ if x_t is None:
+ # for order 2, we use a simplified version
+ if order == 2:
+ rhos_p = torch.tensor([0.5], device=b.device)
+ else:
+ rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+ else:
+ D1s = None
+
+ if use_corrector:
+ #print('using corrector')
+ # for order 1, we use a simplified version
+ if order == 1:
+ rhos_c = torch.tensor([0.5], device=b.device)
+ else:
+ rhos_c = torch.linalg.solve(R, b)
+
+ model_t = None
+ if self.predict_x0:
+ x_t_ = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0
+ )
+
+ if x_t is None:
+ if use_predictor:
+ pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+ else:
+ pred_res = 0
+ x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
+
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ if D1s is not None:
+ corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+ else:
+ corr_res = 0
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+ else:
+ x_t_ = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
+ )
+ if x_t is None:
+ if use_predictor:
+ pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+ else:
+ pred_res = 0
+ x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res
+
+ if use_corrector:
+ model_t = self.model_fn(x_t, t)
+ if D1s is not None:
+ corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+ else:
+ corr_res = 0
+ D1_t = (model_t - model_prev_0)
+ x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+ return x_t, model_t
+
+
+ def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+ method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+ atol=0.0078, rtol=0.05, corrector=False,
+ ):
+ t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+ t_T = self.noise_schedule.T if t_start is None else t_start
+ device = x.device
+ if method == 'multistep':
+ assert steps >= order, "UniPC order must be < sampling steps"
+ timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+ #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
+ assert timesteps.shape[0] - 1 == steps
+ with torch.no_grad():
+ vec_t = timesteps[0].expand((x.shape[0]))
+ model_prev_list = [self.model_fn(x, vec_t)]
+ t_prev_list = [vec_t]
+ with tqdm.tqdm(total=steps) as pbar:
+ # Init the first `order` values by lower order multistep DPM-Solver.
+ for init_order in range(1, order):
+ vec_t = timesteps[init_order].expand(x.shape[0])
+ x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
+ if model_x is None:
+ model_x = self.model_fn(x, vec_t)
+ if self.after_update is not None:
+ self.after_update(x, model_x)
+ model_prev_list.append(model_x)
+ t_prev_list.append(vec_t)
+ pbar.update()
+
+ for step in range(order, steps + 1):
+ vec_t = timesteps[step].expand(x.shape[0])
+ if lower_order_final:
+ step_order = min(order, steps + 1 - step)
+ else:
+ step_order = order
+ #print('this step order:', step_order)
+ if step == steps:
+ #print('do not run corrector at the last step')
+ use_corrector = False
+ else:
+ use_corrector = True
+ x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
+ if self.after_update is not None:
+ self.after_update(x, model_x)
+ for i in range(order - 1):
+ t_prev_list[i] = t_prev_list[i + 1]
+ model_prev_list[i] = model_prev_list[i + 1]
+ t_prev_list[-1] = vec_t
+ # We do not need to evaluate the final model value.
+ if step < steps:
+ if model_x is None:
+ model_x = self.model_fn(x, vec_t)
+ model_prev_list[-1] = model_x
+ pbar.update()
+ else:
+ raise NotImplementedError()
+ if denoise_to_zero:
+ x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+ return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+ """
+ A piecewise linear function y = f(x), using xp and yp as keypoints.
+ We implement f(x) in a differentiable way (i.e. applicable for autograd).
+ The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+
+ Args:
+ x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+ xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+ yp: PyTorch tensor with shape [C, K].
+ Returns:
+ The function values f(x), with shape [N, C].
+ """
+ N, K = x.shape[0], xp.shape[1]
+ all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+ sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+ x_idx = torch.argmin(x_indices, dim=2)
+ cand_start_idx = x_idx - 1
+ start_idx = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(1, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+ start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+ end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+ start_idx2 = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(0, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+ start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+ end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+ cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+ return cand
+
+
+def expand_dims(v, dims):
+ """
+ Expand the tensor `v` to the dim `dims`.
+
+ Args:
+ `v`: a PyTorch tensor with shape [N].
+ `dim`: a `int`.
+ Returns:
+ a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+ """
+ return v[(...,) + (None,)*(dims - 1)]
diff --git a/stable-diffusion-webui/modules/ngrok.py b/stable-diffusion-webui/modules/ngrok.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c713e2765d293186313661474e21bd8779199ec
--- /dev/null
+++ b/stable-diffusion-webui/modules/ngrok.py
@@ -0,0 +1,30 @@
+import ngrok
+
+# Connect to ngrok for ingress
+def connect(token, port, options):
+ account = None
+ if token is None:
+ token = 'None'
+ else:
+ if ':' in token:
+ # token = authtoken:username:password
+ token, username, password = token.split(':', 2)
+ account = f"{username}:{password}"
+
+ # For all options see: https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py
+ if not options.get('authtoken_from_env'):
+ options['authtoken'] = token
+ if account:
+ options['basic_auth'] = account
+ if not options.get('session_metadata'):
+ options['session_metadata'] = 'stable-diffusion-webui'
+
+
+ try:
+ public_url = ngrok.connect(f"127.0.0.1:{port}", **options).url()
+ except Exception as e:
+ print(f'Invalid ngrok authtoken? ngrok connection aborted due to: {e}\n'
+ f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
+ else:
+ print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
+ 'You can use this link after the launch is complete.')
diff --git a/stable-diffusion-webui/modules/options.py b/stable-diffusion-webui/modules/options.py
new file mode 100644
index 0000000000000000000000000000000000000000..0616ee95b05038c8c5659b49aacd9e5fcf2b7f61
--- /dev/null
+++ b/stable-diffusion-webui/modules/options.py
@@ -0,0 +1,245 @@
+import json
+import sys
+
+import gradio as gr
+
+from modules import errors
+from modules.shared_cmd_options import cmd_opts
+
+
+class OptionInfo:
+ def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False):
+ self.default = default
+ self.label = label
+ self.component = component
+ self.component_args = component_args
+ self.onchange = onchange
+ self.section = section
+ self.refresh = refresh
+ self.do_not_save = False
+
+ self.comment_before = comment_before
+ """HTML text that will be added after label in UI"""
+
+ self.comment_after = comment_after
+ """HTML text that will be added before label in UI"""
+
+ self.infotext = infotext
+
+ self.restrict_api = restrict_api
+ """If True, the setting will not be accessible via API"""
+
+ def link(self, label, url):
+ self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
+ return self
+
+ def js(self, label, js_func):
+ self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
+ return self
+
+ def info(self, info):
+ self.comment_after += f"<span class='info'>({info})</span>"
+ return self
+
+ def html(self, html):
+ self.comment_after += html
+ return self
+
+ def needs_restart(self):
+ self.comment_after += " <span class='info'>(requires restart)</span>"
+ return self
+
+ def needs_reload_ui(self):
+ self.comment_after += " <span class='info'>(requires Reload UI)</span>"
+ return self
+
+
+class OptionHTML(OptionInfo):
+ def __init__(self, text):
+ super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))
+
+ self.do_not_save = True
+
+
+def options_section(section_identifier, options_dict):
+ for v in options_dict.values():
+ v.section = section_identifier
+
+ return options_dict
+
+
+options_builtin_fields = {"data_labels", "data", "restricted_opts", "typemap"}
+
+
+class Options:
+ typemap = {int: float}
+
+ def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
+ self.data_labels = data_labels
+ self.data = {k: v.default for k, v in self.data_labels.items()}
+ self.restricted_opts = restricted_opts
+
+ def __setattr__(self, key, value):
+ if key in options_builtin_fields:
+ return super(Options, self).__setattr__(key, value)
+
+ if self.data is not None:
+ if key in self.data or key in self.data_labels:
+ assert not cmd_opts.freeze_settings, "changing settings is disabled"
+
+ info = self.data_labels.get(key, None)
+ if info.do_not_save:
+ return
+
+ comp_args = info.component_args if info else None
+ if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
+ raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+ if cmd_opts.hide_ui_dir_config and key in self.restricted_opts:
+ raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+ self.data[key] = value
+ return
+
+ return super(Options, self).__setattr__(key, value)
+
+ def __getattr__(self, item):
+ if item in options_builtin_fields:
+ return super(Options, self).__getattribute__(item)
+
+ if self.data is not None:
+ if item in self.data:
+ return self.data[item]
+
+ if item in self.data_labels:
+ return self.data_labels[item].default
+
+ return super(Options, self).__getattribute__(item)
+
+ def set(self, key, value, is_api=False, run_callbacks=True):
+ """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
+
+ oldval = self.data.get(key, None)
+ if oldval == value:
+ return False
+
+ option = self.data_labels[key]
+ if option.do_not_save:
+ return False
+
+ if is_api and option.restrict_api:
+ return False
+
+ try:
+ setattr(self, key, value)
+ except RuntimeError:
+ return False
+
+ if run_callbacks and option.onchange is not None:
+ try:
+ option.onchange()
+ except Exception as e:
+ errors.display(e, f"changing setting {key} to {value}")
+ setattr(self, key, oldval)
+ return False
+
+ return True
+
+ def get_default(self, key):
+ """returns the default value for the key"""
+
+ data_label = self.data_labels.get(key)
+ if data_label is None:
+ return None
+
+ return data_label.default
+
+ def save(self, filename):
+ assert not cmd_opts.freeze_settings, "saving settings is disabled"
+
+ with open(filename, "w", encoding="utf8") as file:
+ json.dump(self.data, file, indent=4)
+
+ def same_type(self, x, y):
+ if x is None or y is None:
+ return True
+
+ type_x = self.typemap.get(type(x), type(x))
+ type_y = self.typemap.get(type(y), type(y))
+
+ return type_x == type_y
+
+ def load(self, filename):
+ with open(filename, "r", encoding="utf8") as file:
+ self.data = json.load(file)
+
+ # 1.6.0 VAE defaults
+ if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
+ self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')
+
+ # 1.1.1 quicksettings list migration
+ if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
+ self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
+
+ # 1.4.0 ui_reorder
+ if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
+ self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
+
+ bad_settings = 0
+ for k, v in self.data.items():
+ info = self.data_labels.get(k, None)
+ if info is not None and not self.same_type(info.default, v):
+ print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
+ bad_settings += 1
+
+ if bad_settings > 0:
+ print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
+
+ def onchange(self, key, func, call=True):
+ item = self.data_labels.get(key)
+ item.onchange = func
+
+ if call:
+ func()
+
+ def dumpjson(self):
+ d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
+ d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
+ d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
+ return json.dumps(d)
+
+ def add_option(self, key, info):
+ self.data_labels[key] = info
+
+ def reorder(self):
+ """reorder settings so that all items related to section always go together"""
+
+ section_ids = {}
+ settings_items = self.data_labels.items()
+ for _, item in settings_items:
+ if item.section not in section_ids:
+ section_ids[item.section] = len(section_ids)
+
+ self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
+
+ def cast_value(self, key, value):
+ """casts an arbitrary to the same type as this setting's value with key
+ Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
+ """
+
+ if value is None:
+ return None
+
+ default_value = self.data_labels[key].default
+ if default_value is None:
+ default_value = getattr(self, key, None)
+ if default_value is None:
+ return None
+
+ expected_type = type(default_value)
+ if expected_type == bool and value == "False":
+ value = False
+ else:
+ value = expected_type(value)
+
+ return value
diff --git a/stable-diffusion-webui/modules/patches.py b/stable-diffusion-webui/modules/patches.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d36194a3ccddfd852c22cc234601ce6b50a1297
--- /dev/null
+++ b/stable-diffusion-webui/modules/patches.py
@@ -0,0 +1,64 @@
+from collections import defaultdict
+
+
+def patch(key, obj, field, replacement):
+ """Replaces a function in a module or a class.
+
+ Also stores the original function in this module, possible to be retrieved via original(key, obj, field).
+ If the function is already replaced by this caller (key), an exception is raised -- use undo() before that.
+
+ Arguments:
+ key: identifying information for who is doing the replacement. You can use __name__.
+ obj: the module or the class
+ field: name of the function as a string
+ replacement: the new function
+
+ Returns:
+ the original function
+ """
+
+ patch_key = (obj, field)
+ if patch_key in originals[key]:
+ raise RuntimeError(f"patch for {field} is already applied")
+
+ original_func = getattr(obj, field)
+ originals[key][patch_key] = original_func
+
+ setattr(obj, field, replacement)
+
+ return original_func
+
+
+def undo(key, obj, field):
+ """Undoes the peplacement by the patch().
+
+ If the function is not replaced, raises an exception.
+
+ Arguments:
+ key: identifying information for who is doing the replacement. You can use __name__.
+ obj: the module or the class
+ field: name of the function as a string
+
+ Returns:
+ Always None
+ """
+
+ patch_key = (obj, field)
+
+ if patch_key not in originals[key]:
+ raise RuntimeError(f"there is no patch for {field} to undo")
+
+ original_func = originals[key].pop(patch_key)
+ setattr(obj, field, original_func)
+
+ return None
+
+
+def original(key, obj, field):
+ """Returns the original function for the patch created by the patch() function"""
+ patch_key = (obj, field)
+
+ return originals[key].get(patch_key, None)
+
+
+originals = defaultdict(dict)
diff --git a/stable-diffusion-webui/modules/paths.py b/stable-diffusion-webui/modules/paths.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fecc5eb01daeedece36c469e80ac6c89fa95623
--- /dev/null
+++ b/stable-diffusion-webui/modules/paths.py
@@ -0,0 +1,65 @@
+import os
+import sys
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir # noqa: F401
+
+import modules.safe # noqa: F401
+
+
+def mute_sdxl_imports():
+ """create fake modules that SDXL wants to import but doesn't actually use for our purposes"""
+
+ class Dummy:
+ pass
+
+ module = Dummy()
+ module.LPIPS = None
+ sys.modules['taming.modules.losses.lpips'] = module
+
+ module = Dummy()
+ module.StableDataModuleFromConfig = None
+ sys.modules['sgm.data'] = module
+
+
+# data_path = cmd_opts_pre.data
+sys.path.insert(0, script_path)
+
+# search for directory of stable diffusion in following places
+sd_path = None
+possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion-stability-ai'), '.', os.path.dirname(script_path)]
+for possible_sd_path in possible_sd_paths:
+ if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
+ sd_path = os.path.abspath(possible_sd_path)
+ break
+
+assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
+
+mute_sdxl_imports()
+
+path_dirs = [
+ (sd_path, 'ldm', 'Stable Diffusion', []),
+ (os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
+ (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
+ (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
+ (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
+]
+
+paths = {}
+
+for d, must_exist, what, options in path_dirs:
+ must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
+ if not os.path.exists(must_exist_path):
+ print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)
+ else:
+ d = os.path.abspath(d)
+ if "atstart" in options:
+ sys.path.insert(0, d)
+ elif "sgm" in options:
+ # Stable Diffusion XL repo has scripts dir with __init__.py in it which ruins every extension's scripts dir, so we
+ # import sgm and remove it from sys.path so that when a script imports scripts.something, it doesbn't use sgm's scripts dir.
+
+ sys.path.insert(0, d)
+ import sgm # noqa: F401
+ sys.path.pop(0)
+ else:
+ sys.path.append(d)
+ paths[what] = d
diff --git a/stable-diffusion-webui/modules/paths_internal.py b/stable-diffusion-webui/modules/paths_internal.py
new file mode 100644
index 0000000000000000000000000000000000000000..530dfab465b8f7c46ecd9fab531c69fadec4c9c4
--- /dev/null
+++ b/stable-diffusion-webui/modules/paths_internal.py
@@ -0,0 +1,31 @@
+"""this module defines internal paths used by program and is safe to import before dependencies are installed in launch.py"""
+
+import argparse
+import os
+import sys
+import shlex
+
+commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+sys.argv += shlex.split(commandline_args)
+
+modules_path = os.path.dirname(os.path.realpath(__file__))
+script_path = os.path.dirname(modules_path)
+
+sd_configs_path = os.path.join(script_path, "configs")
+sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
+sd_model_file = os.path.join(script_path, 'model.ckpt')
+default_sd_model_file = sd_model_file
+
+# Parse the --data-dir flag first so we can use it as a base for our other argument default values
+parser_pre = argparse.ArgumentParser(add_help=False)
+parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
+cmd_opts_pre = parser_pre.parse_known_args()[0]
+
+data_path = cmd_opts_pre.data_dir
+
+models_path = os.path.join(data_path, "models")
+extensions_dir = os.path.join(data_path, "extensions")
+extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
+config_states_dir = os.path.join(script_path, "config_states")
+
+roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')
diff --git a/stable-diffusion-webui/modules/postprocessing.py b/stable-diffusion-webui/modules/postprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db614b250e821ad729c426a96445bdfb74910eb
--- /dev/null
+++ b/stable-diffusion-webui/modules/postprocessing.py
@@ -0,0 +1,108 @@
+import os
+
+from PIL import Image
+
+from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
+from modules.shared import opts
+
+
+def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
+ devices.torch_gc()
+
+ shared.state.begin(job="extras")
+
+ outputs = []
+
+ def get_images(extras_mode, image, image_folder, input_dir):
+ if extras_mode == 1:
+ for img in image_folder:
+ if isinstance(img, Image.Image):
+ image = img
+ fn = ''
+ else:
+ image = Image.open(os.path.abspath(img.name))
+ fn = os.path.splitext(img.orig_name)[0]
+ yield image, fn
+ elif extras_mode == 2:
+ assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+ assert input_dir, 'input directory not selected'
+
+ image_list = shared.listfiles(input_dir)
+ for filename in image_list:
+ try:
+ image = Image.open(filename)
+ except Exception:
+ continue
+ yield image, filename
+ else:
+ assert image, 'image not selected'
+ yield image, None
+
+ if extras_mode == 2 and output_dir != '':
+ outpath = output_dir
+ else:
+ outpath = opts.outdir_samples or opts.outdir_extras_samples
+
+ infotext = ''
+
+ for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
+ image_data: Image.Image
+
+ shared.state.textinfo = name
+
+ parameters, existing_pnginfo = images.read_info_from_image(image_data)
+ if parameters:
+ existing_pnginfo["parameters"] = parameters
+
+ pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
+
+ scripts.scripts_postproc.run(pp, args)
+
+ if opts.use_original_name_batch and name is not None:
+ basename = os.path.splitext(os.path.basename(name))[0]
+ else:
+ basename = ''
+
+ infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
+
+ if opts.enable_pnginfo:
+ pp.image.info = existing_pnginfo
+ pp.image.info["postprocessing"] = infotext
+
+ if save_output:
+ images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
+
+ if extras_mode != 2 or show_extras_results:
+ outputs.append(pp.image)
+
+ image_data.close()
+
+ devices.torch_gc()
+
+ return outputs, ui_common.plaintext_to_html(infotext), ''
+
+
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
+ """old handler for API"""
+
+ args = scripts.scripts_postproc.create_args_for_run({
+ "Upscale": {
+ "upscale_mode": resize_mode,
+ "upscale_by": upscaling_resize,
+ "upscale_to_width": upscaling_resize_w,
+ "upscale_to_height": upscaling_resize_h,
+ "upscale_crop": upscaling_crop,
+ "upscaler_1_name": extras_upscaler_1,
+ "upscaler_2_name": extras_upscaler_2,
+ "upscaler_2_visibility": extras_upscaler_2_visibility,
+ },
+ "GFPGAN": {
+ "gfpgan_visibility": gfpgan_visibility,
+ },
+ "CodeFormer": {
+ "codeformer_visibility": codeformer_visibility,
+ "codeformer_weight": codeformer_weight,
+ },
+ })
+
+ return run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output=save_output)
diff --git a/stable-diffusion-webui/modules/processing.py b/stable-diffusion-webui/modules/processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc0a479515774375f203212574ab2a8c65480b85
--- /dev/null
+++ b/stable-diffusion-webui/modules/processing.py
@@ -0,0 +1,1539 @@
+from __future__ import annotations
+import json
+import logging
+import math
+import os
+import sys
+import hashlib
+from dataclasses import dataclass, field
+
+import torch
+import numpy as np
+from PIL import Image, ImageOps
+import random
+import cv2
+from skimage import exposure
+from typing import Any
+
+import modules.sd_hijack
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
+from modules.rng import slerp # noqa: F401
+from modules.sd_hijack import model_hijack
+from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
+from modules.shared import opts, cmd_opts, state
+import modules.shared as shared
+import modules.paths as paths
+import modules.face_restoration
+import modules.images as images
+import modules.styles
+import modules.sd_models as sd_models
+import modules.sd_vae as sd_vae
+from ldm.data.util import AddMiDaS
+from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
+
+from einops import repeat, rearrange
+from blendmodes.blend import blendLayers, BlendType
+
+
+# some of those options should not be changed at all because they would break the model, so I removed them from options.
+opt_C = 4
+opt_f = 8
+
+
+def setup_color_correction(image):
+ logging.info("Calibrating color correction.")
+ correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
+ return correction_target
+
+
+def apply_color_correction(correction, original_image):
+ logging.info("Applying color correction.")
+ image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
+ cv2.cvtColor(
+ np.asarray(original_image),
+ cv2.COLOR_RGB2LAB
+ ),
+ correction,
+ channel_axis=2
+ ), cv2.COLOR_LAB2RGB).astype("uint8"))
+
+ image = blendLayers(image, original_image, BlendType.LUMINOSITY)
+
+ return image.convert('RGB')
+
+
+def apply_overlay(image, paste_loc, index, overlays):
+ if overlays is None or index >= len(overlays):
+ return image
+
+ overlay = overlays[index]
+
+ if paste_loc is not None:
+ x, y, w, h = paste_loc
+ base_image = Image.new('RGBA', (overlay.width, overlay.height))
+ image = images.resize_image(1, image, w, h)
+ base_image.paste(image, (x, y))
+ image = base_image
+
+ image = image.convert('RGBA')
+ image.alpha_composite(overlay)
+ image = image.convert('RGB')
+
+ return image
+
+def create_binary_mask(image):
+ if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
+ image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+ else:
+ image = image.convert('L')
+ return image
+
+def txt2img_image_conditioning(sd_model, x, width, height):
+ if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+
+ # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
+ image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
+ image_conditioning = images_tensor_to_samples(image_conditioning, approximation_indexes.get(opts.sd_vae_encode_method))
+
+ # Add the fake full 1s mask to the first dimension.
+ image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+ image_conditioning = image_conditioning.to(x.dtype)
+
+ return image_conditioning
+
+ elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
+
+ return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+
+ else:
+ # Dummy zero conditioning if we're not using inpainting or unclip models.
+ # Still takes up a bit of memory, but no encoder call.
+ # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+ return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+
+@dataclass(repr=False)
+class StableDiffusionProcessing:
+ sd_model: object = None
+ outpath_samples: str = None
+ outpath_grids: str = None
+ prompt: str = ""
+ prompt_for_display: str = None
+ negative_prompt: str = ""
+ styles: list[str] = None
+ seed: int = -1
+ subseed: int = -1
+ subseed_strength: float = 0
+ seed_resize_from_h: int = -1
+ seed_resize_from_w: int = -1
+ seed_enable_extras: bool = True
+ sampler_name: str = None
+ batch_size: int = 1
+ n_iter: int = 1
+ steps: int = 50
+ cfg_scale: float = 7.0
+ width: int = 512
+ height: int = 512
+ restore_faces: bool = None
+ tiling: bool = None
+ do_not_save_samples: bool = False
+ do_not_save_grid: bool = False
+ extra_generation_params: dict[str, Any] = None
+ overlay_images: list = None
+ eta: float = None
+ do_not_reload_embeddings: bool = False
+ denoising_strength: float = 0
+ ddim_discretize: str = None
+ s_min_uncond: float = None
+ s_churn: float = None
+ s_tmax: float = None
+ s_tmin: float = None
+ s_noise: float = None
+ override_settings: dict[str, Any] = None
+ override_settings_restore_afterwards: bool = True
+ sampler_index: int = None
+ refiner_checkpoint: str = None
+ refiner_switch_at: float = None
+ token_merging_ratio = 0
+ token_merging_ratio_hr = 0
+ disable_extra_networks: bool = False
+
+ scripts_value: scripts.ScriptRunner = field(default=None, init=False)
+ script_args_value: list = field(default=None, init=False)
+ scripts_setup_complete: bool = field(default=False, init=False)
+
+ cached_uc = [None, None]
+ cached_c = [None, None]
+
+ comments: dict = None
+ sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
+ is_using_inpainting_conditioning: bool = field(default=False, init=False)
+ paste_to: tuple | None = field(default=None, init=False)
+
+ is_hr_pass: bool = field(default=False, init=False)
+
+ c: tuple = field(default=None, init=False)
+ uc: tuple = field(default=None, init=False)
+
+ rng: rng.ImageRNG | None = field(default=None, init=False)
+ step_multiplier: int = field(default=1, init=False)
+ color_corrections: list = field(default=None, init=False)
+
+ all_prompts: list = field(default=None, init=False)
+ all_negative_prompts: list = field(default=None, init=False)
+ all_seeds: list = field(default=None, init=False)
+ all_subseeds: list = field(default=None, init=False)
+ iteration: int = field(default=0, init=False)
+ main_prompt: str = field(default=None, init=False)
+ main_negative_prompt: str = field(default=None, init=False)
+
+ prompts: list = field(default=None, init=False)
+ negative_prompts: list = field(default=None, init=False)
+ seeds: list = field(default=None, init=False)
+ subseeds: list = field(default=None, init=False)
+ extra_network_data: dict = field(default=None, init=False)
+
+ user: str = field(default=None, init=False)
+
+ sd_model_name: str = field(default=None, init=False)
+ sd_model_hash: str = field(default=None, init=False)
+ sd_vae_name: str = field(default=None, init=False)
+ sd_vae_hash: str = field(default=None, init=False)
+
+ is_api: bool = field(default=False, init=False)
+
+ def __post_init__(self):
+ if self.sampler_index is not None:
+ print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
+
+ self.comments = {}
+
+ if self.styles is None:
+ self.styles = []
+
+ self.sampler_noise_scheduler_override = None
+ self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
+ self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
+ self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
+ self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
+ self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
+
+ self.extra_generation_params = self.extra_generation_params or {}
+ self.override_settings = self.override_settings or {}
+ self.script_args = self.script_args or {}
+
+ self.refiner_checkpoint_info = None
+
+ if not self.seed_enable_extras:
+ self.subseed = -1
+ self.subseed_strength = 0
+ self.seed_resize_from_h = 0
+ self.seed_resize_from_w = 0
+
+ self.cached_uc = StableDiffusionProcessing.cached_uc
+ self.cached_c = StableDiffusionProcessing.cached_c
+
+ @property
+ def sd_model(self):
+ return shared.sd_model
+
+ @sd_model.setter
+ def sd_model(self, value):
+ pass
+
+ @property
+ def scripts(self):
+ return self.scripts_value
+
+ @scripts.setter
+ def scripts(self, value):
+ self.scripts_value = value
+
+ if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+ self.setup_scripts()
+
+ @property
+ def script_args(self):
+ return self.script_args_value
+
+ @script_args.setter
+ def script_args(self, value):
+ self.script_args_value = value
+
+ if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+ self.setup_scripts()
+
+ def setup_scripts(self):
+ self.scripts_setup_complete = True
+
+ self.scripts.setup_scrips(self, is_ui=not self.is_api)
+
+ def comment(self, text):
+ self.comments[text] = 1
+
+ def txt2img_image_conditioning(self, x, width=None, height=None):
+ self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
+
+ return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
+
+ def depth2img_image_conditioning(self, source_image):
+ # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
+ transformer = AddMiDaS(model_type="dpt_hybrid")
+ transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
+ midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
+ midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
+
+ conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+ conditioning = torch.nn.functional.interpolate(
+ self.sd_model.depth_model(midas_in),
+ size=conditioning_image.shape[2:],
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ (depth_min, depth_max) = torch.aminmax(conditioning)
+ conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
+ return conditioning
+
+ def edit_image_conditioning(self, source_image):
+ conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+
+ return conditioning_image
+
+ def unclip_image_conditioning(self, source_image):
+ c_adm = self.sd_model.embedder(source_image)
+ if self.sd_model.noise_augmentor is not None:
+ noise_level = 0 # TODO: Allow other noise levels?
+ c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+ c_adm = torch.cat((c_adm, noise_level_emb), 1)
+ return c_adm
+
+ def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
+ self.is_using_inpainting_conditioning = True
+
+ # Handle the different mask inputs
+ if image_mask is not None:
+ if torch.is_tensor(image_mask):
+ conditioning_mask = image_mask
+ else:
+ conditioning_mask = np.array(image_mask.convert("L"))
+ conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+ conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+ # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+ conditioning_mask = torch.round(conditioning_mask)
+ else:
+ conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
+
+ # Create another latent image, this time with a masked version of the original input.
+ # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
+ conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
+ conditioning_image = torch.lerp(
+ source_image,
+ source_image * (1.0 - conditioning_mask),
+ getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+ )
+
+ # Encode the new masked image using first stage of network.
+ conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+ # Create the concatenated conditioning tensor to be fed to `c_concat`
+ conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+ conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+ image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+ image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+ return image_conditioning
+
+ def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+ source_image = devices.cond_cast_float(source_image)
+
+ # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
+ # identify itself with a field common to all models. The conditioning_key is also hybrid.
+ if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
+ return self.depth2img_image_conditioning(source_image)
+
+ if self.sd_model.cond_stage_key == "edit":
+ return self.edit_image_conditioning(source_image)
+
+ if self.sampler.conditioning_key in {'hybrid', 'concat'}:
+ return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
+ if self.sampler.conditioning_key == "crossattn-adm":
+ return self.unclip_image_conditioning(source_image)
+
+ # Dummy zero conditioning if we're not using inpainting or depth model.
+ return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
+
+ def init(self, all_prompts, all_seeds, all_subseeds):
+ pass
+
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ raise NotImplementedError()
+
+ def close(self):
+ self.sampler = None
+ self.c = None
+ self.uc = None
+ if not opts.persistent_cond_cache:
+ StableDiffusionProcessing.cached_c = [None, None]
+ StableDiffusionProcessing.cached_uc = [None, None]
+
+ def get_token_merging_ratio(self, for_hr=False):
+ if for_hr:
+ return self.token_merging_ratio_hr or opts.token_merging_ratio_hr or self.token_merging_ratio or opts.token_merging_ratio
+
+ return self.token_merging_ratio or opts.token_merging_ratio
+
+ def setup_prompts(self):
+ if isinstance(self.prompt,list):
+ self.all_prompts = self.prompt
+ elif isinstance(self.negative_prompt, list):
+ self.all_prompts = [self.prompt] * len(self.negative_prompt)
+ else:
+ self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
+
+ if isinstance(self.negative_prompt, list):
+ self.all_negative_prompts = self.negative_prompt
+ else:
+ self.all_negative_prompts = [self.negative_prompt] * len(self.all_prompts)
+
+ if len(self.all_prompts) != len(self.all_negative_prompts):
+ raise RuntimeError(f"Received a different number of prompts ({len(self.all_prompts)}) and negative prompts ({len(self.all_negative_prompts)})")
+
+ self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
+ self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
+
+ self.main_prompt = self.all_prompts[0]
+ self.main_negative_prompt = self.all_negative_prompts[0]
+
+ def cached_params(self, required_prompts, steps, extra_network_data, hires_steps=None, use_old_scheduling=False):
+ """Returns parameters that invalidate the cond cache if changed"""
+
+ return (
+ required_prompts,
+ steps,
+ hires_steps,
+ use_old_scheduling,
+ opts.CLIP_stop_at_last_layers,
+ shared.sd_model.sd_checkpoint_info,
+ extra_network_data,
+ opts.sdxl_crop_left,
+ opts.sdxl_crop_top,
+ self.width,
+ self.height,
+ )
+
+ def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
+ """
+ Returns the result of calling function(shared.sd_model, required_prompts, steps)
+ using a cache to store the result if the same arguments have been used before.
+
+ cache is an array containing two elements. The first element is a tuple
+ representing the previously used arguments, or None if no arguments
+ have been used before. The second element is where the previously
+ computed result is stored.
+
+ caches is a list with items described above.
+ """
+
+ if shared.opts.use_old_scheduling:
+ old_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, False)
+ new_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, True)
+ if old_schedules != new_schedules:
+ self.extra_generation_params["Old prompt editing timelines"] = True
+
+ cached_params = self.cached_params(required_prompts, steps, extra_network_data, hires_steps, shared.opts.use_old_scheduling)
+
+ for cache in caches:
+ if cache[0] is not None and cached_params == cache[0]:
+ return cache[1]
+
+ cache = caches[0]
+
+ with devices.autocast():
+ cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
+
+ cache[0] = cached_params
+ return cache[1]
+
+ def setup_conds(self):
+ prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
+ negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
+
+ sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
+ total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps
+ self.step_multiplier = total_steps // self.steps
+ self.firstpass_steps = total_steps
+
+ self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)
+ self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)
+
+ def get_conds(self):
+ return self.c, self.uc
+
+ def parse_extra_network_prompts(self):
+ self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
+
+ def save_samples(self) -> bool:
+ """Returns whether generated images need to be written to disk"""
+ return opts.samples_save and not self.do_not_save_samples and (opts.save_incomplete_images or not state.interrupted and not state.skipped)
+
+
+class Processed:
+ def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
+ self.images = images_list
+ self.prompt = p.prompt
+ self.negative_prompt = p.negative_prompt
+ self.seed = seed
+ self.subseed = subseed
+ self.subseed_strength = p.subseed_strength
+ self.info = info
+ self.comments = "".join(f"{comment}\n" for comment in p.comments)
+ self.width = p.width
+ self.height = p.height
+ self.sampler_name = p.sampler_name
+ self.cfg_scale = p.cfg_scale
+ self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+ self.steps = p.steps
+ self.batch_size = p.batch_size
+ self.restore_faces = p.restore_faces
+ self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
+ self.sd_model_name = p.sd_model_name
+ self.sd_model_hash = p.sd_model_hash
+ self.sd_vae_name = p.sd_vae_name
+ self.sd_vae_hash = p.sd_vae_hash
+ self.seed_resize_from_w = p.seed_resize_from_w
+ self.seed_resize_from_h = p.seed_resize_from_h
+ self.denoising_strength = getattr(p, 'denoising_strength', None)
+ self.extra_generation_params = p.extra_generation_params
+ self.index_of_first_image = index_of_first_image
+ self.styles = p.styles
+ self.job_timestamp = state.job_timestamp
+ self.clip_skip = opts.CLIP_stop_at_last_layers
+ self.token_merging_ratio = p.token_merging_ratio
+ self.token_merging_ratio_hr = p.token_merging_ratio_hr
+
+ self.eta = p.eta
+ self.ddim_discretize = p.ddim_discretize
+ self.s_churn = p.s_churn
+ self.s_tmin = p.s_tmin
+ self.s_tmax = p.s_tmax
+ self.s_noise = p.s_noise
+ self.s_min_uncond = p.s_min_uncond
+ self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
+ self.prompt = self.prompt if not isinstance(self.prompt, list) else self.prompt[0]
+ self.negative_prompt = self.negative_prompt if not isinstance(self.negative_prompt, list) else self.negative_prompt[0]
+ self.seed = int(self.seed if not isinstance(self.seed, list) else self.seed[0]) if self.seed is not None else -1
+ self.subseed = int(self.subseed if not isinstance(self.subseed, list) else self.subseed[0]) if self.subseed is not None else -1
+ self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
+
+ self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
+ self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
+ self.all_seeds = all_seeds or p.all_seeds or [self.seed]
+ self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
+ self.infotexts = infotexts or [info]
+
+ def js(self):
+ obj = {
+ "prompt": self.all_prompts[0],
+ "all_prompts": self.all_prompts,
+ "negative_prompt": self.all_negative_prompts[0],
+ "all_negative_prompts": self.all_negative_prompts,
+ "seed": self.seed,
+ "all_seeds": self.all_seeds,
+ "subseed": self.subseed,
+ "all_subseeds": self.all_subseeds,
+ "subseed_strength": self.subseed_strength,
+ "width": self.width,
+ "height": self.height,
+ "sampler_name": self.sampler_name,
+ "cfg_scale": self.cfg_scale,
+ "steps": self.steps,
+ "batch_size": self.batch_size,
+ "restore_faces": self.restore_faces,
+ "face_restoration_model": self.face_restoration_model,
+ "sd_model_name": self.sd_model_name,
+ "sd_model_hash": self.sd_model_hash,
+ "sd_vae_name": self.sd_vae_name,
+ "sd_vae_hash": self.sd_vae_hash,
+ "seed_resize_from_w": self.seed_resize_from_w,
+ "seed_resize_from_h": self.seed_resize_from_h,
+ "denoising_strength": self.denoising_strength,
+ "extra_generation_params": self.extra_generation_params,
+ "index_of_first_image": self.index_of_first_image,
+ "infotexts": self.infotexts,
+ "styles": self.styles,
+ "job_timestamp": self.job_timestamp,
+ "clip_skip": self.clip_skip,
+ "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
+ }
+
+ return json.dumps(obj)
+
+ def infotext(self, p: StableDiffusionProcessing, index):
+ return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
+
+ def get_token_merging_ratio(self, for_hr=False):
+ return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
+
+
+def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+ g = rng.ImageRNG(shape, seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w)
+ return g.next()
+
+
+class DecodedSamples(list):
+ already_decoded = True
+
+
+def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
+ samples = DecodedSamples()
+
+ for i in range(batch.shape[0]):
+ sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+ if check_for_nans:
+ try:
+ devices.test_for_nans(sample, "vae")
+ except devices.NansException as e:
+ if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+ raise e
+
+ errors.print_error_explanation(
+ "A tensor with all NaNs was produced in VAE.\n"
+ "Web UI will now convert VAE into 32-bit float and retry.\n"
+ "To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.\n"
+ "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+ )
+
+ devices.dtype_vae = torch.float32
+ model.first_stage_model.to(devices.dtype_vae)
+ batch = batch.to(devices.dtype_vae)
+
+ sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+ if target_device is not None:
+ sample = sample.to(target_device)
+
+ samples.append(sample)
+
+ return samples
+
+
+def get_fixed_seed(seed):
+ if seed == '' or seed is None:
+ seed = -1
+ elif isinstance(seed, str):
+ try:
+ seed = int(seed)
+ except Exception:
+ seed = -1
+
+ if seed == -1:
+ return int(random.randrange(4294967294))
+
+ return seed
+
+
+def fix_seed(p):
+ p.seed = get_fixed_seed(p.seed)
+ p.subseed = get_fixed_seed(p.subseed)
+
+
+def program_version():
+ import launch
+
+ res = launch.git_tag()
+ if res == "<none>":
+ res = None
+
+ return res
+
+
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):
+ if index is None:
+ index = position_in_batch + iteration * p.batch_size
+
+ if all_negative_prompts is None:
+ all_negative_prompts = p.all_negative_prompts
+
+ clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
+ enable_hr = getattr(p, 'enable_hr', False)
+ token_merging_ratio = p.get_token_merging_ratio()
+ token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
+
+ uses_ensd = opts.eta_noise_seed_delta != 0
+ if uses_ensd:
+ uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
+
+ generation_params = {
+ "Steps": p.steps,
+ "Sampler": p.sampler_name,
+ "CFG scale": p.cfg_scale,
+ "Image CFG scale": getattr(p, 'image_cfg_scale', None),
+ "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
+ "Face restoration": opts.face_restoration_model if p.restore_faces else None,
+ "Size": f"{p.width}x{p.height}",
+ "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
+ "Model": p.sd_model_name if opts.add_model_name_to_info else None,
+ "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
+ "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
+ "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
+ "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
+ "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
+ "Denoising strength": getattr(p, 'denoising_strength', None),
+ "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
+ "Clip skip": None if clip_skip <= 1 else clip_skip,
+ "ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
+ "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
+ "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
+ "Init image hash": getattr(p, 'init_img_hash', None),
+ "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+ "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
+ "Tiling": "True" if p.tiling else None,
+ **p.extra_generation_params,
+ "Version": program_version() if opts.add_version_to_infotext else None,
+ "User": p.user if opts.add_user_name_to_info else None,
+ }
+
+ generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
+
+ prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
+ negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
+
+ return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()
+
+
+def process_images(p: StableDiffusionProcessing) -> Processed:
+ if p.scripts is not None:
+ p.scripts.before_process(p)
+
+ stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
+
+ try:
+ # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
+ # and if after running refiner, the refiner model is not unloaded - webui swaps back to main model here, if model over is present it will be reloaded afterwards
+ if sd_models.checkpoint_aliases.get(p.override_settings.get('sd_model_checkpoint')) is None:
+ p.override_settings.pop('sd_model_checkpoint', None)
+ sd_models.reload_model_weights()
+
+ for k, v in p.override_settings.items():
+ opts.set(k, v, is_api=True, run_callbacks=False)
+
+ if k == 'sd_model_checkpoint':
+ sd_models.reload_model_weights()
+
+ if k == 'sd_vae':
+ sd_vae.reload_vae_weights()
+
+ sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
+
+ res = process_images_inner(p)
+
+ finally:
+ sd_models.apply_token_merging(p.sd_model, 0)
+
+ # restore opts to original state
+ if p.override_settings_restore_afterwards:
+ for k, v in stored_opts.items():
+ setattr(opts, k, v)
+
+ if k == 'sd_vae':
+ sd_vae.reload_vae_weights()
+
+ return res
+
+
+def process_images_inner(p: StableDiffusionProcessing) -> Processed:
+ """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
+
+ if isinstance(p.prompt, list):
+ assert(len(p.prompt) > 0)
+ else:
+ assert p.prompt is not None
+
+ devices.torch_gc()
+
+ seed = get_fixed_seed(p.seed)
+ subseed = get_fixed_seed(p.subseed)
+
+ if p.restore_faces is None:
+ p.restore_faces = opts.face_restoration
+
+ if p.tiling is None:
+ p.tiling = opts.tiling
+
+ if p.refiner_checkpoint not in (None, "", "None", "none"):
+ p.refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(p.refiner_checkpoint)
+ if p.refiner_checkpoint_info is None:
+ raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
+
+ p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
+ p.sd_model_hash = shared.sd_model.sd_model_hash
+ p.sd_vae_name = sd_vae.get_loaded_vae_name()
+ p.sd_vae_hash = sd_vae.get_loaded_vae_hash()
+
+ modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+ modules.sd_hijack.model_hijack.clear_comments()
+
+ p.setup_prompts()
+
+ if isinstance(seed, list):
+ p.all_seeds = seed
+ else:
+ p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
+
+ if isinstance(subseed, list):
+ p.all_subseeds = subseed
+ else:
+ p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
+
+ if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
+ model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+ if p.scripts is not None:
+ p.scripts.process(p)
+
+ infotexts = []
+ output_images = []
+
+ with torch.no_grad(), p.sd_model.ema_scope():
+ with devices.autocast():
+ p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
+
+ # for OSX, loading the model during sampling changes the generated picture, so it is loaded here
+ if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
+ sd_vae_approx.model()
+
+ sd_unet.apply_unet()
+
+ if state.job_count == -1:
+ state.job_count = p.n_iter
+
+ for n in range(p.n_iter):
+ p.iteration = n
+
+ if state.skipped:
+ state.skipped = False
+
+ if state.interrupted:
+ break
+
+ sd_models.reload_model_weights() # model can be changed for example by refiner
+
+ p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+ p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+
+ p.rng = rng.ImageRNG((opt_C, p.height // opt_f, p.width // opt_f), p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
+
+ if p.scripts is not None:
+ p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
+
+ if len(p.prompts) == 0:
+ break
+
+ p.parse_extra_network_prompts()
+
+ if not p.disable_extra_networks:
+ with devices.autocast():
+ extra_networks.activate(p, p.extra_network_data)
+
+ if p.scripts is not None:
+ p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
+
+ # params.txt should be saved after scripts.process_batch, since the
+ # infotext could be modified by that callback
+ # Example: a wildcard processed by process_batch sets an extra model
+ # strength, which is saved as "Model Strength: 1.0" in the infotext
+ if n == 0:
+ with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+ processed = Processed(p, [])
+ file.write(processed.infotext(p, 0))
+
+ p.setup_conds()
+
+ for comment in model_hijack.comments:
+ p.comment(comment)
+
+ p.extra_generation_params.update(model_hijack.extra_generation_params)
+
+ if p.n_iter > 1:
+ shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+
+ with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
+ samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
+
+ if getattr(samples_ddim, 'already_decoded', False):
+ x_samples_ddim = samples_ddim
+ else:
+ if opts.sd_vae_decode_method != 'Full':
+ p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
+
+ x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
+
+ x_samples_ddim = torch.stack(x_samples_ddim).float()
+ x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+
+ del samples_ddim
+
+ if lowvram.is_enabled(shared.sd_model):
+ lowvram.send_everything_to_cpu()
+
+ devices.torch_gc()
+
+ if p.scripts is not None:
+ p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
+
+ p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+ p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+
+ batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+ p.scripts.postprocess_batch_list(p, batch_params, batch_number=n)
+ x_samples_ddim = batch_params.images
+
+ def infotext(index=0, use_main_prompt=False):
+ return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)
+
+ save_samples = p.save_samples()
+
+ for i, x_sample in enumerate(x_samples_ddim):
+ p.batch_index = i
+
+ x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+ x_sample = x_sample.astype(np.uint8)
+
+ if p.restore_faces:
+ if save_samples and opts.save_images_before_face_restoration:
+ images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")
+
+ devices.torch_gc()
+
+ x_sample = modules.face_restoration.restore_faces(x_sample)
+ devices.torch_gc()
+
+ image = Image.fromarray(x_sample)
+
+ if p.scripts is not None:
+ pp = scripts.PostprocessImageArgs(image)
+ p.scripts.postprocess_image(p, pp)
+ image = pp.image
+ if p.color_corrections is not None and i < len(p.color_corrections):
+ if save_samples and opts.save_images_before_color_correction:
+ image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
+ images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
+ image = apply_color_correction(p.color_corrections[i], image)
+
+ image = apply_overlay(image, p.paste_to, i, p.overlay_images)
+
+ if save_samples:
+ images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
+
+ text = infotext(i)
+ infotexts.append(text)
+ if opts.enable_pnginfo:
+ image.info["parameters"] = text
+ output_images.append(image)
+ if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
+ image_mask = p.mask_for_overlay.convert('RGB')
+ image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
+
+ if opts.save_mask:
+ images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
+
+ if opts.save_mask_composite:
+ images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
+
+ if opts.return_mask:
+ output_images.append(image_mask)
+
+ if opts.return_mask_composite:
+ output_images.append(image_mask_composite)
+
+ del x_samples_ddim
+
+ devices.torch_gc()
+
+ state.nextjob()
+
+ p.color_corrections = None
+
+ index_of_first_image = 0
+ unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
+ if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
+ grid = images.image_grid(output_images, p.batch_size)
+
+ if opts.return_grid:
+ text = infotext(use_main_prompt=True)
+ infotexts.insert(0, text)
+ if opts.enable_pnginfo:
+ grid.info["parameters"] = text
+ output_images.insert(0, grid)
+ index_of_first_image = 1
+ if opts.grid_save:
+ images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(use_main_prompt=True), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+
+ if not p.disable_extra_networks and p.extra_network_data:
+ extra_networks.deactivate(p, p.extra_network_data)
+
+ devices.torch_gc()
+
+ res = Processed(
+ p,
+ images_list=output_images,
+ seed=p.all_seeds[0],
+ info=infotexts[0],
+ subseed=p.all_subseeds[0],
+ index_of_first_image=index_of_first_image,
+ infotexts=infotexts,
+ )
+
+ if p.scripts is not None:
+ p.scripts.postprocess(p, res)
+
+ return res
+
+
+def old_hires_fix_first_pass_dimensions(width, height):
+ """old algorithm for auto-calculating first pass size"""
+
+ desired_pixel_count = 512 * 512
+ actual_pixel_count = width * height
+ scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+ width = math.ceil(scale * width / 64) * 64
+ height = math.ceil(scale * height / 64) * 64
+
+ return width, height
+
+
+@dataclass(repr=False)
+class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
+ enable_hr: bool = False
+ denoising_strength: float = 0.75
+ firstphase_width: int = 0
+ firstphase_height: int = 0
+ hr_scale: float = 2.0
+ hr_upscaler: str = None
+ hr_second_pass_steps: int = 0
+ hr_resize_x: int = 0
+ hr_resize_y: int = 0
+ hr_checkpoint_name: str = None
+ hr_sampler_name: str = None
+ hr_prompt: str = ''
+ hr_negative_prompt: str = ''
+
+ cached_hr_uc = [None, None]
+ cached_hr_c = [None, None]
+
+ hr_checkpoint_info: dict = field(default=None, init=False)
+ hr_upscale_to_x: int = field(default=0, init=False)
+ hr_upscale_to_y: int = field(default=0, init=False)
+ truncate_x: int = field(default=0, init=False)
+ truncate_y: int = field(default=0, init=False)
+ applied_old_hires_behavior_to: tuple = field(default=None, init=False)
+ latent_scale_mode: dict = field(default=None, init=False)
+ hr_c: tuple | None = field(default=None, init=False)
+ hr_uc: tuple | None = field(default=None, init=False)
+ all_hr_prompts: list = field(default=None, init=False)
+ all_hr_negative_prompts: list = field(default=None, init=False)
+ hr_prompts: list = field(default=None, init=False)
+ hr_negative_prompts: list = field(default=None, init=False)
+ hr_extra_network_data: list = field(default=None, init=False)
+
+ def __post_init__(self):
+ super().__post_init__()
+
+ if self.firstphase_width != 0 or self.firstphase_height != 0:
+ self.hr_upscale_to_x = self.width
+ self.hr_upscale_to_y = self.height
+ self.width = self.firstphase_width
+ self.height = self.firstphase_height
+
+ self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
+ self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
+
+ def calculate_target_resolution(self):
+ if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
+ self.hr_resize_x = self.width
+ self.hr_resize_y = self.height
+ self.hr_upscale_to_x = self.width
+ self.hr_upscale_to_y = self.height
+
+ self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
+ self.applied_old_hires_behavior_to = (self.width, self.height)
+
+ if self.hr_resize_x == 0 and self.hr_resize_y == 0:
+ self.extra_generation_params["Hires upscale"] = self.hr_scale
+ self.hr_upscale_to_x = int(self.width * self.hr_scale)
+ self.hr_upscale_to_y = int(self.height * self.hr_scale)
+ else:
+ self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
+
+ if self.hr_resize_y == 0:
+ self.hr_upscale_to_x = self.hr_resize_x
+ self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+ elif self.hr_resize_x == 0:
+ self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+ self.hr_upscale_to_y = self.hr_resize_y
+ else:
+ target_w = self.hr_resize_x
+ target_h = self.hr_resize_y
+ src_ratio = self.width / self.height
+ dst_ratio = self.hr_resize_x / self.hr_resize_y
+
+ if src_ratio < dst_ratio:
+ self.hr_upscale_to_x = self.hr_resize_x
+ self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+ else:
+ self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+ self.hr_upscale_to_y = self.hr_resize_y
+
+ self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
+ self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
+
+ def init(self, all_prompts, all_seeds, all_subseeds):
+ if self.enable_hr:
+ if self.hr_checkpoint_name:
+ self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
+
+ if self.hr_checkpoint_info is None:
+ raise Exception(f'Could not find checkpoint with name {self.hr_checkpoint_name}')
+
+ self.extra_generation_params["Hires checkpoint"] = self.hr_checkpoint_info.short_title
+
+ if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
+ self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
+
+ if tuple(self.hr_prompt) != tuple(self.prompt):
+ self.extra_generation_params["Hires prompt"] = self.hr_prompt
+
+ if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
+ self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
+
+ self.latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+ if self.enable_hr and self.latent_scale_mode is None:
+ if not any(x.name == self.hr_upscaler for x in shared.sd_upscalers):
+ raise Exception(f"could not find upscaler named {self.hr_upscaler}")
+
+ self.calculate_target_resolution()
+
+ if not state.processing_has_refined_job_count:
+ if state.job_count == -1:
+ state.job_count = self.n_iter
+
+ shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
+ state.job_count = state.job_count * 2
+ state.processing_has_refined_job_count = True
+
+ if self.hr_second_pass_steps:
+ self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps
+
+ if self.hr_upscaler is not None:
+ self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
+
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
+
+ x = self.rng.next()
+ samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+ del x
+
+ if not self.enable_hr:
+ return samples
+
+ if self.latent_scale_mode is None:
+ decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
+ else:
+ decoded_samples = None
+
+ with sd_models.SkipWritingToConfig():
+ sd_models.reload_model_weights(info=self.hr_checkpoint_info)
+
+ devices.torch_gc()
+
+ return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
+
+ def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
+ if shared.state.interrupted:
+ return samples
+
+ self.is_hr_pass = True
+
+ target_width = self.hr_upscale_to_x
+ target_height = self.hr_upscale_to_y
+
+ def save_intermediate(image, index):
+ """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
+
+ if not self.save_samples() or not opts.save_images_before_highres_fix:
+ return
+
+ if not isinstance(image, Image.Image):
+ image = sd_samplers.sample_to_image(image, index, approximation=0)
+
+ info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
+ images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
+
+ img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
+ self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
+
+ if self.latent_scale_mode is not None:
+ for i in range(samples.shape[0]):
+ save_intermediate(samples, i)
+
+ samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=self.latent_scale_mode["mode"], antialias=self.latent_scale_mode["antialias"])
+
+ # Avoid making the inpainting conditioning unless necessary as
+ # this does need some extra compute to decode / encode the image again.
+ if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
+ image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
+ else:
+ image_conditioning = self.txt2img_image_conditioning(samples)
+ else:
+ lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+ batch_images = []
+ for i, x_sample in enumerate(lowres_samples):
+ x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+ x_sample = x_sample.astype(np.uint8)
+ image = Image.fromarray(x_sample)
+
+ save_intermediate(image, i)
+
+ image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
+ image = np.array(image).astype(np.float32) / 255.0
+ image = np.moveaxis(image, 2, 0)
+ batch_images.append(image)
+
+ decoded_samples = torch.from_numpy(np.array(batch_images))
+ decoded_samples = decoded_samples.to(shared.device, dtype=devices.dtype_vae)
+
+ if opts.sd_vae_encode_method != 'Full':
+ self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+ samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))
+
+ image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
+
+ shared.state.nextjob()
+
+ samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
+
+ self.rng = rng.ImageRNG(samples.shape[1:], self.seeds, subseeds=self.subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w)
+ noise = self.rng.next()
+
+ # GC now before running the next img2img to prevent running out of memory
+ devices.torch_gc()
+
+ if not self.disable_extra_networks:
+ with devices.autocast():
+ extra_networks.activate(self, self.hr_extra_network_data)
+
+ with devices.autocast():
+ self.calculate_hr_conds()
+
+ sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
+
+ if self.scripts is not None:
+ self.scripts.before_hr(self)
+
+ samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+
+ sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
+
+ self.sampler = None
+ devices.torch_gc()
+
+ decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
+
+ self.is_hr_pass = False
+
+ return decoded_samples
+
+ def close(self):
+ super().close()
+ self.hr_c = None
+ self.hr_uc = None
+ if not opts.persistent_cond_cache:
+ StableDiffusionProcessingTxt2Img.cached_hr_uc = [None, None]
+ StableDiffusionProcessingTxt2Img.cached_hr_c = [None, None]
+
+ def setup_prompts(self):
+ super().setup_prompts()
+
+ if not self.enable_hr:
+ return
+
+ if self.hr_prompt == '':
+ self.hr_prompt = self.prompt
+
+ if self.hr_negative_prompt == '':
+ self.hr_negative_prompt = self.negative_prompt
+
+ if isinstance(self.hr_prompt, list):
+ self.all_hr_prompts = self.hr_prompt
+ else:
+ self.all_hr_prompts = self.batch_size * self.n_iter * [self.hr_prompt]
+
+ if isinstance(self.hr_negative_prompt, list):
+ self.all_hr_negative_prompts = self.hr_negative_prompt
+ else:
+ self.all_hr_negative_prompts = self.batch_size * self.n_iter * [self.hr_negative_prompt]
+
+ self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
+ self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
+
+ def calculate_hr_conds(self):
+ if self.hr_c is not None:
+ return
+
+ hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
+ hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
+
+ sampler_config = sd_samplers.find_sampler_config(self.hr_sampler_name or self.sampler_name)
+ steps = self.hr_second_pass_steps or self.steps
+ total_steps = sampler_config.total_steps(steps) if sampler_config else steps
+
+ self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)
+ self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)
+
+ def setup_conds(self):
+ if self.is_hr_pass:
+ # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
+ self.hr_c = None
+ self.calculate_hr_conds()
+ return
+
+ super().setup_conds()
+
+ self.hr_uc = None
+ self.hr_c = None
+
+ if self.enable_hr and self.hr_checkpoint_info is None:
+ if shared.opts.hires_fix_use_firstpass_conds:
+ self.calculate_hr_conds()
+
+ elif lowvram.is_enabled(shared.sd_model) and shared.sd_model.sd_checkpoint_info == sd_models.select_checkpoint(): # if in lowvram mode, we need to calculate conds right away, before the cond NN is unloaded
+ with devices.autocast():
+ extra_networks.activate(self, self.hr_extra_network_data)
+
+ self.calculate_hr_conds()
+
+ with devices.autocast():
+ extra_networks.activate(self, self.extra_network_data)
+
+ def get_conds(self):
+ if self.is_hr_pass:
+ return self.hr_c, self.hr_uc
+
+ return super().get_conds()
+
+ def parse_extra_network_prompts(self):
+ res = super().parse_extra_network_prompts()
+
+ if self.enable_hr:
+ self.hr_prompts = self.all_hr_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+ self.hr_negative_prompts = self.all_hr_negative_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+
+ self.hr_prompts, self.hr_extra_network_data = extra_networks.parse_prompts(self.hr_prompts)
+
+ return res
+
+
+@dataclass(repr=False)
+class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
+ init_images: list = None
+ resize_mode: int = 0
+ denoising_strength: float = 0.75
+ image_cfg_scale: float = None
+ mask: Any = None
+ mask_blur_x: int = 4
+ mask_blur_y: int = 4
+ mask_blur: int = None
+ inpainting_fill: int = 0
+ inpaint_full_res: bool = True
+ inpaint_full_res_padding: int = 0
+ inpainting_mask_invert: int = 0
+ initial_noise_multiplier: float = None
+ latent_mask: Image = None
+
+ image_mask: Any = field(default=None, init=False)
+
+ nmask: torch.Tensor = field(default=None, init=False)
+ image_conditioning: torch.Tensor = field(default=None, init=False)
+ init_img_hash: str = field(default=None, init=False)
+ mask_for_overlay: Image = field(default=None, init=False)
+ init_latent: torch.Tensor = field(default=None, init=False)
+
+ def __post_init__(self):
+ super().__post_init__()
+
+ self.image_mask = self.mask
+ self.mask = None
+ self.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier
+
+ @property
+ def mask_blur(self):
+ if self.mask_blur_x == self.mask_blur_y:
+ return self.mask_blur_x
+ return None
+
+ @mask_blur.setter
+ def mask_blur(self, value):
+ if isinstance(value, int):
+ self.mask_blur_x = value
+ self.mask_blur_y = value
+
+ def init(self, all_prompts, all_seeds, all_subseeds):
+ self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
+
+ self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
+ crop_region = None
+
+ image_mask = self.image_mask
+
+ if image_mask is not None:
+ # image_mask is passed in as RGBA by Gradio to support alpha masks,
+ # but we still want to support binary masks.
+ image_mask = create_binary_mask(image_mask)
+
+ if self.inpainting_mask_invert:
+ image_mask = ImageOps.invert(image_mask)
+
+ if self.mask_blur_x > 0:
+ np_mask = np.array(image_mask)
+ kernel_size = 2 * int(2.5 * self.mask_blur_x + 0.5) + 1
+ np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
+ image_mask = Image.fromarray(np_mask)
+
+ if self.mask_blur_y > 0:
+ np_mask = np.array(image_mask)
+ kernel_size = 2 * int(2.5 * self.mask_blur_y + 0.5) + 1
+ np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
+ image_mask = Image.fromarray(np_mask)
+
+ if self.inpaint_full_res:
+ self.mask_for_overlay = image_mask
+ mask = image_mask.convert('L')
+ crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
+ crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
+ x1, y1, x2, y2 = crop_region
+
+ mask = mask.crop(crop_region)
+ image_mask = images.resize_image(2, mask, self.width, self.height)
+ self.paste_to = (x1, y1, x2-x1, y2-y1)
+ else:
+ image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
+ np_mask = np.array(image_mask)
+ np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
+ self.mask_for_overlay = Image.fromarray(np_mask)
+
+ self.overlay_images = []
+
+ latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
+
+ add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
+ if add_color_corrections:
+ self.color_corrections = []
+ imgs = []
+ for img in self.init_images:
+
+ # Save init image
+ if opts.save_init_img:
+ self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+ images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
+ image = images.flatten(img, opts.img2img_background_color)
+
+ if crop_region is None and self.resize_mode != 3:
+ image = images.resize_image(self.resize_mode, image, self.width, self.height)
+
+ if image_mask is not None:
+ image_masked = Image.new('RGBa', (image.width, image.height))
+ image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
+
+ self.overlay_images.append(image_masked.convert('RGBA'))
+
+ # crop_region is not None if we are doing inpaint full res
+ if crop_region is not None:
+ image = image.crop(crop_region)
+ image = images.resize_image(2, image, self.width, self.height)
+
+ if image_mask is not None:
+ if self.inpainting_fill != 1:
+ image = masking.fill(image, latent_mask)
+
+ if add_color_corrections:
+ self.color_corrections.append(setup_color_correction(image))
+
+ image = np.array(image).astype(np.float32) / 255.0
+ image = np.moveaxis(image, 2, 0)
+
+ imgs.append(image)
+
+ if len(imgs) == 1:
+ batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
+ if self.overlay_images is not None:
+ self.overlay_images = self.overlay_images * self.batch_size
+
+ if self.color_corrections is not None and len(self.color_corrections) == 1:
+ self.color_corrections = self.color_corrections * self.batch_size
+
+ elif len(imgs) <= self.batch_size:
+ self.batch_size = len(imgs)
+ batch_images = np.array(imgs)
+ else:
+ raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
+
+ image = torch.from_numpy(batch_images)
+ image = image.to(shared.device, dtype=devices.dtype_vae)
+
+ if opts.sd_vae_encode_method != 'Full':
+ self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+
+ self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
+ devices.torch_gc()
+
+ if self.resize_mode == 3:
+ self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+
+ if image_mask is not None:
+ init_mask = latent_mask
+ latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
+ latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
+ latmask = latmask[0]
+ latmask = np.around(latmask)
+ latmask = np.tile(latmask[None], (4, 1, 1))
+
+ self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
+ self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
+
+ # this needs to be fixed to be done in sample() using actual seeds for batches
+ if self.inpainting_fill == 2:
+ self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+ elif self.inpainting_fill == 3:
+ self.init_latent = self.init_latent * self.mask
+
+ self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
+
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ x = self.rng.next()
+
+ if self.initial_noise_multiplier != 1.0:
+ self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
+ x *= self.initial_noise_multiplier
+
+ samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
+
+ if self.mask is not None:
+ samples = samples * self.nmask + self.init_latent * self.mask
+
+ del x
+ devices.torch_gc()
+
+ return samples
+
+ def get_token_merging_ratio(self, for_hr=False):
+ return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and opts.token_merging_ratio) or opts.token_merging_ratio_img2img or opts.token_merging_ratio
diff --git a/stable-diffusion-webui/modules/processing_scripts/refiner.py b/stable-diffusion-webui/modules/processing_scripts/refiner.py
new file mode 100644
index 0000000000000000000000000000000000000000..00e035053ad18c18092499a8a1a63faeab397e8e
--- /dev/null
+++ b/stable-diffusion-webui/modules/processing_scripts/refiner.py
@@ -0,0 +1,49 @@
+import gradio as gr
+
+from modules import scripts, sd_models
+from modules.ui_common import create_refresh_button
+from modules.ui_components import InputAccordion
+
+
+class ScriptRefiner(scripts.ScriptBuiltinUI):
+ section = "accordions"
+ create_group = False
+
+ def __init__(self):
+ pass
+
+ def title(self):
+ return "Refiner"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ with InputAccordion(False, label="Refiner", elem_id=self.elem_id("enable")) as enable_refiner:
+ with gr.Row():
+ refiner_checkpoint = gr.Dropdown(label='Checkpoint', elem_id=self.elem_id("checkpoint"), choices=sd_models.checkpoint_tiles(), value='', tooltip="switch to another model in the middle of generation")
+ create_refresh_button(refiner_checkpoint, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, self.elem_id("checkpoint_refresh"))
+
+ refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the switch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")
+
+ def lookup_checkpoint(title):
+ info = sd_models.get_closet_checkpoint_match(title)
+ return None if info is None else info.title
+
+ self.infotext_fields = [
+ (enable_refiner, lambda d: 'Refiner' in d),
+ (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
+ (refiner_switch_at, 'Refiner switch at'),
+ ]
+
+ return enable_refiner, refiner_checkpoint, refiner_switch_at
+
+ def setup(self, p, enable_refiner, refiner_checkpoint, refiner_switch_at):
+ # the actual implementation is in sd_samplers_common.py, apply_refiner
+
+ if not enable_refiner or refiner_checkpoint in (None, "", "None"):
+ p.refiner_checkpoint = None
+ p.refiner_switch_at = None
+ else:
+ p.refiner_checkpoint = refiner_checkpoint
+ p.refiner_switch_at = refiner_switch_at
diff --git a/stable-diffusion-webui/modules/processing_scripts/seed.py b/stable-diffusion-webui/modules/processing_scripts/seed.py
new file mode 100644
index 0000000000000000000000000000000000000000..265087fd85e48764e3fad5493afbacddabf7b2fb
--- /dev/null
+++ b/stable-diffusion-webui/modules/processing_scripts/seed.py
@@ -0,0 +1,111 @@
+import json
+
+import gradio as gr
+
+from modules import scripts, ui, errors
+from modules.shared import cmd_opts
+from modules.ui_components import ToolButton
+
+
+class ScriptSeed(scripts.ScriptBuiltinUI):
+ section = "seed"
+ create_group = False
+
+ def __init__(self):
+ self.seed = None
+ self.reuse_seed = None
+ self.reuse_subseed = None
+
+ def title(self):
+ return "Seed"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ with gr.Row(elem_id=self.elem_id("seed_row")):
+ if cmd_opts.use_textbox_seed:
+ self.seed = gr.Textbox(label='Seed', value="", elem_id=self.elem_id("seed"), min_width=100)
+ else:
+ self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
+
+ random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), label='Random seed')
+ reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), label='Reuse seed')
+
+ seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)
+
+ with gr.Group(visible=False, elem_id=self.elem_id("seed_extras")) as seed_extras:
+ with gr.Row(elem_id=self.elem_id("subseed_row")):
+ subseed = gr.Number(label='Variation seed', value=-1, elem_id=self.elem_id("subseed"), precision=0)
+ random_subseed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_subseed"))
+ reuse_subseed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_subseed"))
+ subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=self.elem_id("subseed_strength"))
+
+ with gr.Row(elem_id=self.elem_id("seed_resize_from_row")):
+ seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=self.elem_id("seed_resize_from_w"))
+ seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=self.elem_id("seed_resize_from_h"))
+
+ random_seed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("seed") + "')}", show_progress=False, inputs=[], outputs=[])
+ random_subseed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("subseed") + "')}", show_progress=False, inputs=[], outputs=[])
+
+ seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
+
+ self.infotext_fields = [
+ (self.seed, "Seed"),
+ (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
+ (subseed, "Variation seed"),
+ (subseed_strength, "Variation seed strength"),
+ (seed_resize_from_w, "Seed resize from-1"),
+ (seed_resize_from_h, "Seed resize from-2"),
+ ]
+
+ self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
+ self.on_after_component(lambda x: connect_reuse_seed(subseed, reuse_subseed, x.component, True), elem_id=f'generation_info_{self.tabname}')
+
+ return self.seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h
+
+ def setup(self, p, seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h):
+ p.seed = seed
+
+ if seed_checkbox and subseed_strength > 0:
+ p.subseed = subseed
+ p.subseed_strength = subseed_strength
+
+ if seed_checkbox and seed_resize_from_w > 0 and seed_resize_from_h > 0:
+ p.seed_resize_from_w = seed_resize_from_w
+ p.seed_resize_from_h = seed_resize_from_h
+
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
+ """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+ (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+ was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+
+ def copy_seed(gen_info_string: str, index):
+ res = -1
+
+ try:
+ gen_info = json.loads(gen_info_string)
+ index -= gen_info.get('index_of_first_image', 0)
+
+ if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+ all_subseeds = gen_info.get('all_subseeds', [-1])
+ res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+ else:
+ all_seeds = gen_info.get('all_seeds', [-1])
+ res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+ except json.decoder.JSONDecodeError:
+ if gen_info_string:
+ errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+
+ return [res, gr.update()]
+
+ reuse_seed.click(
+ fn=copy_seed,
+ _js="(x, y) => [x, selected_gallery_index()]",
+ show_progress=False,
+ inputs=[generation_info, seed],
+ outputs=[seed, seed]
+ )
diff --git a/stable-diffusion-webui/modules/progress.py b/stable-diffusion-webui/modules/progress.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc8bb9996d9d0969933f5f375e5b5c3847083e56
--- /dev/null
+++ b/stable-diffusion-webui/modules/progress.py
@@ -0,0 +1,134 @@
+import base64
+import io
+import time
+
+import gradio as gr
+from pydantic import BaseModel, Field
+
+from modules.shared import opts
+
+import modules.shared as shared
+
+
+current_task = None
+pending_tasks = {}
+finished_tasks = []
+recorded_results = []
+recorded_results_limit = 2
+
+
+def start_task(id_task):
+ global current_task
+
+ current_task = id_task
+ pending_tasks.pop(id_task, None)
+
+
+def finish_task(id_task):
+ global current_task
+
+ if current_task == id_task:
+ current_task = None
+
+ finished_tasks.append(id_task)
+ if len(finished_tasks) > 16:
+ finished_tasks.pop(0)
+
+
+def record_results(id_task, res):
+ recorded_results.append((id_task, res))
+ if len(recorded_results) > recorded_results_limit:
+ recorded_results.pop(0)
+
+
+def add_task_to_queue(id_job):
+ pending_tasks[id_job] = time.time()
+
+
+class ProgressRequest(BaseModel):
+ id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
+ id_live_preview: int = Field(default=-1, title="Live preview image ID", description="id of last received last preview image")
+ live_preview: bool = Field(default=True, title="Include live preview", description="boolean flag indicating whether to include the live preview image")
+
+
+class ProgressResponse(BaseModel):
+ active: bool = Field(title="Whether the task is being worked on right now")
+ queued: bool = Field(title="Whether the task is in queue")
+ completed: bool = Field(title="Whether the task has already finished")
+ progress: float = Field(default=None, title="Progress", description="The progress with a range of 0 to 1")
+ eta: float = Field(default=None, title="ETA in secs")
+ live_preview: str = Field(default=None, title="Live preview image", description="Current live preview; a data: uri")
+ id_live_preview: int = Field(default=None, title="Live preview image ID", description="Send this together with next request to prevent receiving same image")
+ textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
+
+
+def setup_progress_api(app):
+ return app.add_api_route("/internal/progress", progressapi, methods=["POST"], response_model=ProgressResponse)
+
+
+def progressapi(req: ProgressRequest):
+ active = req.id_task == current_task
+ queued = req.id_task in pending_tasks
+ completed = req.id_task in finished_tasks
+
+ if not active:
+ textinfo = "Waiting..."
+ if queued:
+ sorted_queued = sorted(pending_tasks.keys(), key=lambda x: pending_tasks[x])
+ queue_index = sorted_queued.index(req.id_task)
+ textinfo = "In queue: {}/{}".format(queue_index + 1, len(sorted_queued))
+ return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo=textinfo)
+
+ progress = 0
+
+ job_count, job_no = shared.state.job_count, shared.state.job_no
+ sampling_steps, sampling_step = shared.state.sampling_steps, shared.state.sampling_step
+
+ if job_count > 0:
+ progress += job_no / job_count
+ if sampling_steps > 0 and job_count > 0:
+ progress += 1 / job_count * sampling_step / sampling_steps
+
+ progress = min(progress, 1)
+
+ elapsed_since_start = time.time() - shared.state.time_start
+ predicted_duration = elapsed_since_start / progress if progress > 0 else None
+ eta = predicted_duration - elapsed_since_start if predicted_duration is not None else None
+
+ live_preview = None
+ id_live_preview = req.id_live_preview
+
+ if opts.live_previews_enable and req.live_preview:
+ shared.state.set_current_image()
+ if shared.state.id_live_preview != req.id_live_preview:
+ image = shared.state.current_image
+ if image is not None:
+ buffered = io.BytesIO()
+
+ if opts.live_previews_image_format == "png":
+ # using optimize for large images takes an enormous amount of time
+ if max(*image.size) <= 256:
+ save_kwargs = {"optimize": True}
+ else:
+ save_kwargs = {"optimize": False, "compress_level": 1}
+
+ else:
+ save_kwargs = {}
+
+ image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
+ base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
+ live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
+ id_live_preview = shared.state.id_live_preview
+
+ return ProgressResponse(active=active, queued=queued, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo)
+
+
+def restore_progress(id_task):
+ while id_task == current_task or id_task in pending_tasks:
+ time.sleep(0.1)
+
+ res = next(iter([x[1] for x in recorded_results if id_task == x[0]]), None)
+ if res is not None:
+ return res
+
+ return gr.update(), gr.update(), gr.update(), f"Couldn't restore progress for {id_task}: results either have been discarded or never were obtained"
diff --git a/stable-diffusion-webui/modules/prompt_parser.py b/stable-diffusion-webui/modules/prompt_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea0421dec0fa81a3f1eced529fab32626ab2c9f2
--- /dev/null
+++ b/stable-diffusion-webui/modules/prompt_parser.py
@@ -0,0 +1,465 @@
+from __future__ import annotations
+
+import re
+from collections import namedtuple
+from typing import List
+import lark
+
+# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
+# will be represented with prompt_schedule like this (assuming steps=100):
+# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
+# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
+# [60, 'fantasy landscape with a lake and an oak in foreground in background masterful']
+# [75, 'fantasy landscape with a lake and an oak in background masterful']
+# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
+
+schedule_parser = lark.Lark(r"""
+!start: (prompt | /[][():]/+)*
+prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
+!emphasized: "(" prompt ")"
+ | "(" prompt ":" prompt ")"
+ | "[" prompt "]"
+scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER [WHITESPACE] "]"
+alternate: "[" prompt ("|" [prompt])+ "]"
+WHITESPACE: /\s+/
+plain: /([^\\\[\]():|]|\\.)+/
+%import common.SIGNED_NUMBER -> NUMBER
+""")
+
+def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False):
+ """
+ >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
+ >>> g("test")
+ [[10, 'test']]
+ >>> g("a [b:3]")
+ [[3, 'a '], [10, 'a b']]
+ >>> g("a [b: 3]")
+ [[3, 'a '], [10, 'a b']]
+ >>> g("a [[[b]]:2]")
+ [[2, 'a '], [10, 'a [[b]]']]
+ >>> g("[(a:2):3]")
+ [[3, ''], [10, '(a:2)']]
+ >>> g("a [b : c : 1] d")
+ [[1, 'a b d'], [10, 'a c d']]
+ >>> g("a[b:[c:d:2]:1]e")
+ [[1, 'abe'], [2, 'ace'], [10, 'ade']]
+ >>> g("a [unbalanced")
+ [[10, 'a [unbalanced']]
+ >>> g("a [b:.5] c")
+ [[5, 'a c'], [10, 'a b c']]
+ >>> g("a [{b|d{:.5] c") # not handling this right now
+ [[5, 'a c'], [10, 'a {b|d{ c']]
+ >>> g("((a][:b:c [d:3]")
+ [[3, '((a][:b:c '], [10, '((a][:b:c d']]
+ >>> g("[a|(b:1.1)]")
+ [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
+ >>> g("[fe|]male")
+ [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
+ >>> g("[fe|||]male")
+ [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
+ >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]
+ >>> g("a [b:.5] c")
+ [[10, 'a b c']]
+ >>> g("a [b:1.5] c")
+ [[5, 'a c'], [10, 'a b c']]
+ """
+
+ if hires_steps is None or use_old_scheduling:
+ int_offset = 0
+ flt_offset = 0
+ steps = base_steps
+ else:
+ int_offset = base_steps
+ flt_offset = 1.0
+ steps = hires_steps
+
+ def collect_steps(steps, tree):
+ res = [steps]
+
+ class CollectSteps(lark.Visitor):
+ def scheduled(self, tree):
+ s = tree.children[-2]
+ v = float(s)
+ if use_old_scheduling:
+ v = v*steps if v<1 else v
+ else:
+ if "." in s:
+ v = (v - flt_offset) * steps
+ else:
+ v = (v - int_offset)
+ tree.children[-2] = min(steps, int(v))
+ if tree.children[-2] >= 1:
+ res.append(tree.children[-2])
+
+ def alternate(self, tree):
+ res.extend(range(1, steps+1))
+
+ CollectSteps().visit(tree)
+ return sorted(set(res))
+
+ def at_step(step, tree):
+ class AtStep(lark.Transformer):
+ def scheduled(self, args):
+ before, after, _, when, _ = args
+ yield before or () if step <= when else after
+ def alternate(self, args):
+ args = ["" if not arg else arg for arg in args]
+ yield args[(step - 1) % len(args)]
+ def start(self, args):
+ def flatten(x):
+ if isinstance(x, str):
+ yield x
+ else:
+ for gen in x:
+ yield from flatten(gen)
+ return ''.join(flatten(args))
+ def plain(self, args):
+ yield args[0].value
+ def __default__(self, data, children, meta):
+ for child in children:
+ yield child
+ return AtStep().transform(tree)
+
+ def get_schedule(prompt):
+ try:
+ tree = schedule_parser.parse(prompt)
+ except lark.exceptions.LarkError:
+ if 0:
+ import traceback
+ traceback.print_exc()
+ return [[steps, prompt]]
+ return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
+
+ promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
+ return [promptdict[prompt] for prompt in prompts]
+
+
+ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
+
+
+class SdConditioning(list):
+ """
+ A list with prompts for stable diffusion's conditioner model.
+ Can also specify width and height of created image - SDXL needs it.
+ """
+ def __init__(self, prompts, is_negative_prompt=False, width=None, height=None, copy_from=None):
+ super().__init__()
+ self.extend(prompts)
+
+ if copy_from is None:
+ copy_from = prompts
+
+ self.is_negative_prompt = is_negative_prompt or getattr(copy_from, 'is_negative_prompt', False)
+ self.width = width or getattr(copy_from, 'width', None)
+ self.height = height or getattr(copy_from, 'height', None)
+
+
+
+def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):
+ """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
+ and the sampling step at which this condition is to be replaced by the next one.
+
+ Input:
+ (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
+
+ Output:
+ [
+ [
+ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0'))
+ ],
+ [
+ ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')),
+ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0'))
+ ]
+ ]
+ """
+ res = []
+
+ prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)
+ cache = {}
+
+ for prompt, prompt_schedule in zip(prompts, prompt_schedules):
+
+ cached = cache.get(prompt, None)
+ if cached is not None:
+ res.append(cached)
+ continue
+
+ texts = SdConditioning([x[1] for x in prompt_schedule], copy_from=prompts)
+ conds = model.get_learned_conditioning(texts)
+
+ cond_schedule = []
+ for i, (end_at_step, _) in enumerate(prompt_schedule):
+ if isinstance(conds, dict):
+ cond = {k: v[i] for k, v in conds.items()}
+ else:
+ cond = conds[i]
+
+ cond_schedule.append(ScheduledPromptConditioning(end_at_step, cond))
+
+ cache[prompt] = cond_schedule
+ res.append(cond_schedule)
+
+ return res
+
+
+re_AND = re.compile(r"\bAND\b")
+re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+
+
+def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
+ res_indexes = []
+
+ prompt_indexes = {}
+ prompt_flat_list = SdConditioning(prompts)
+ prompt_flat_list.clear()
+
+ for prompt in prompts:
+ subprompts = re_AND.split(prompt)
+
+ indexes = []
+ for subprompt in subprompts:
+ match = re_weight.search(subprompt)
+
+ text, weight = match.groups() if match is not None else (subprompt, 1.0)
+
+ weight = float(weight) if weight is not None else 1.0
+
+ index = prompt_indexes.get(text, None)
+ if index is None:
+ index = len(prompt_flat_list)
+ prompt_flat_list.append(text)
+ prompt_indexes[text] = index
+
+ indexes.append((index, weight))
+
+ res_indexes.append(indexes)
+
+ return res_indexes, prompt_flat_list, prompt_indexes
+
+
+class ComposableScheduledPromptConditioning:
+ def __init__(self, schedules, weight=1.0):
+ self.schedules: List[ScheduledPromptConditioning] = schedules
+ self.weight: float = weight
+
+
+class MulticondLearnedConditioning:
+ def __init__(self, shape, batch):
+ self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
+ self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
+
+
+def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
+ """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
+ For each prompt, the list is obtained by splitting the prompt using the AND separator.
+
+ https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
+ """
+
+ res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
+
+ learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling)
+
+ res = []
+ for indexes in res_indexes:
+ res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
+
+ return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
+
+
+class DictWithShape(dict):
+ def __init__(self, x, shape):
+ super().__init__()
+ self.update(x)
+
+ @property
+ def shape(self):
+ return self["crossattn"].shape
+
+
+def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
+ param = c[0][0].cond
+ is_dict = isinstance(param, dict)
+
+ if is_dict:
+ dict_cond = param
+ res = {k: torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for k, param in dict_cond.items()}
+ res = DictWithShape(res, (len(c),) + dict_cond['crossattn'].shape)
+ else:
+ res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
+
+ for i, cond_schedule in enumerate(c):
+ target_index = 0
+ for current, entry in enumerate(cond_schedule):
+ if current_step <= entry.end_at_step:
+ target_index = current
+ break
+
+ if is_dict:
+ for k, param in cond_schedule[target_index].cond.items():
+ res[k][i] = param
+ else:
+ res[i] = cond_schedule[target_index].cond
+
+ return res
+
+
+def stack_conds(tensors):
+ # if prompts have wildly different lengths above the limit we'll get tensors of different shapes
+ # and won't be able to torch.stack them. So this fixes that.
+ token_count = max([x.shape[0] for x in tensors])
+ for i in range(len(tensors)):
+ if tensors[i].shape[0] != token_count:
+ last_vector = tensors[i][-1:]
+ last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+ tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+
+ return torch.stack(tensors)
+
+
+
+def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
+ param = c.batch[0][0].schedules[0].cond
+
+ tensors = []
+ conds_list = []
+
+ for composable_prompts in c.batch:
+ conds_for_batch = []
+
+ for composable_prompt in composable_prompts:
+ target_index = 0
+ for current, entry in enumerate(composable_prompt.schedules):
+ if current_step <= entry.end_at_step:
+ target_index = current
+ break
+
+ conds_for_batch.append((len(tensors), composable_prompt.weight))
+ tensors.append(composable_prompt.schedules[target_index].cond)
+
+ conds_list.append(conds_for_batch)
+
+ if isinstance(tensors[0], dict):
+ keys = list(tensors[0].keys())
+ stacked = {k: stack_conds([x[k] for x in tensors]) for k in keys}
+ stacked = DictWithShape(stacked, stacked['crossattn'].shape)
+ else:
+ stacked = stack_conds(tensors).to(device=param.device, dtype=param.dtype)
+
+ return conds_list, stacked
+
+
+re_attention = re.compile(r"""
+\\\(|
+\\\)|
+\\\[|
+\\]|
+\\\\|
+\\|
+\(|
+\[|
+:\s*([+-]?[.\d]+)\s*\)|
+\)|
+]|
+[^\\()\[\]:]+|
+:
+""", re.X)
+
+re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
+
+def parse_prompt_attention(text):
+ """
+ Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+ Accepted tokens are:
+ (abc) - increases attention to abc by a multiplier of 1.1
+ (abc:3.12) - increases attention to abc by a multiplier of 3.12
+ [abc] - decreases attention to abc by a multiplier of 1.1
+ \( - literal character '('
+ \[ - literal character '['
+ \) - literal character ')'
+ \] - literal character ']'
+ \\ - literal character '\'
+ anything else - just text
+
+ >>> parse_prompt_attention('normal text')
+ [['normal text', 1.0]]
+ >>> parse_prompt_attention('an (important) word')
+ [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+ >>> parse_prompt_attention('(unbalanced')
+ [['unbalanced', 1.1]]
+ >>> parse_prompt_attention('\(literal\]')
+ [['(literal]', 1.0]]
+ >>> parse_prompt_attention('(unnecessary)(parens)')
+ [['unnecessaryparens', 1.1]]
+ >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+ [['a ', 1.0],
+ ['house', 1.5730000000000004],
+ [' ', 1.1],
+ ['on', 1.0],
+ [' a ', 1.1],
+ ['hill', 0.55],
+ [', sun, ', 1.1],
+ ['sky', 1.4641000000000006],
+ ['.', 1.1]]
+ """
+
+ res = []
+ round_brackets = []
+ square_brackets = []
+
+ round_bracket_multiplier = 1.1
+ square_bracket_multiplier = 1 / 1.1
+
+ def multiply_range(start_position, multiplier):
+ for p in range(start_position, len(res)):
+ res[p][1] *= multiplier
+
+ for m in re_attention.finditer(text):
+ text = m.group(0)
+ weight = m.group(1)
+
+ if text.startswith('\\'):
+ res.append([text[1:], 1.0])
+ elif text == '(':
+ round_brackets.append(len(res))
+ elif text == '[':
+ square_brackets.append(len(res))
+ elif weight is not None and round_brackets:
+ multiply_range(round_brackets.pop(), float(weight))
+ elif text == ')' and round_brackets:
+ multiply_range(round_brackets.pop(), round_bracket_multiplier)
+ elif text == ']' and square_brackets:
+ multiply_range(square_brackets.pop(), square_bracket_multiplier)
+ else:
+ parts = re.split(re_break, text)
+ for i, part in enumerate(parts):
+ if i > 0:
+ res.append(["BREAK", -1])
+ res.append([part, 1.0])
+
+ for pos in round_brackets:
+ multiply_range(pos, round_bracket_multiplier)
+
+ for pos in square_brackets:
+ multiply_range(pos, square_bracket_multiplier)
+
+ if len(res) == 0:
+ res = [["", 1.0]]
+
+ # merge runs of identical weights
+ i = 0
+ while i + 1 < len(res):
+ if res[i][1] == res[i + 1][1]:
+ res[i][0] += res[i + 1][0]
+ res.pop(i + 1)
+ else:
+ i += 1
+
+ return res
+
+if __name__ == "__main__":
+ import doctest
+ doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
+else:
+ import torch # doctest faster
diff --git a/stable-diffusion-webui/modules/realesrgan_model.py b/stable-diffusion-webui/modules/realesrgan_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf69d3d2044e07c5fc662742dacc5de8a32fc2ed
--- /dev/null
+++ b/stable-diffusion-webui/modules/realesrgan_model.py
@@ -0,0 +1,133 @@
+import os
+
+import numpy as np
+from PIL import Image
+from realesrgan import RealESRGANer
+
+from modules.upscaler import Upscaler, UpscalerData
+from modules.shared import cmd_opts, opts
+from modules import modelloader, errors
+
+
+class UpscalerRealESRGAN(Upscaler):
+ def __init__(self, path):
+ self.name = "RealESRGAN"
+ self.user_path = path
+ super().__init__()
+ try:
+ from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
+ from realesrgan import RealESRGANer # noqa: F401
+ from realesrgan.archs.srvgg_arch import SRVGGNetCompact # noqa: F401
+ self.enable = True
+ self.scalers = []
+ scalers = self.load_models(path)
+
+ local_model_paths = self.find_models(ext_filter=[".pth"])
+ for scaler in scalers:
+ if scaler.local_data_path.startswith("http"):
+ filename = modelloader.friendly_name(scaler.local_data_path)
+ local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
+ if local_model_candidates:
+ scaler.local_data_path = local_model_candidates[0]
+
+ if scaler.name in opts.realesrgan_enabled_models:
+ self.scalers.append(scaler)
+
+ except Exception:
+ errors.report("Error importing Real-ESRGAN", exc_info=True)
+ self.enable = False
+ self.scalers = []
+
+ def do_upscale(self, img, path):
+ if not self.enable:
+ return img
+
+ try:
+ info = self.load_model(path)
+ except Exception:
+ errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
+ return img
+
+ upsampler = RealESRGANer(
+ scale=info.scale,
+ model_path=info.local_data_path,
+ model=info.model(),
+ half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
+ tile=opts.ESRGAN_tile,
+ tile_pad=opts.ESRGAN_tile_overlap,
+ device=self.device,
+ )
+
+ upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
+
+ image = Image.fromarray(upsampled)
+ return image
+
+ def load_model(self, path):
+ for scaler in self.scalers:
+ if scaler.data_path == path:
+ if scaler.local_data_path.startswith("http"):
+ scaler.local_data_path = modelloader.load_file_from_url(
+ scaler.data_path,
+ model_dir=self.model_download_path,
+ )
+ if not os.path.exists(scaler.local_data_path):
+ raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
+ return scaler
+ raise ValueError(f"Unable to find model info: {path}")
+
+ def load_models(self, _):
+ return get_realesrgan_models(self)
+
+
+def get_realesrgan_models(scaler):
+ try:
+ from basicsr.archs.rrdbnet_arch import RRDBNet
+ from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+ models = [
+ UpscalerData(
+ name="R-ESRGAN General 4xV3",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+ scale=4,
+ upscaler=scaler,
+ model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+ ),
+ UpscalerData(
+ name="R-ESRGAN General WDN 4xV3",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+ scale=4,
+ upscaler=scaler,
+ model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+ ),
+ UpscalerData(
+ name="R-ESRGAN AnimeVideo",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+ scale=4,
+ upscaler=scaler,
+ model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+ ),
+ UpscalerData(
+ name="R-ESRGAN 4x+",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+ scale=4,
+ upscaler=scaler,
+ model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+ ),
+ UpscalerData(
+ name="R-ESRGAN 4x+ Anime6B",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+ scale=4,
+ upscaler=scaler,
+ model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+ ),
+ UpscalerData(
+ name="R-ESRGAN 2x+",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+ scale=2,
+ upscaler=scaler,
+ model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+ ),
+ ]
+ return models
+ except Exception:
+ errors.report("Error making Real-ESRGAN models list", exc_info=True)
diff --git a/stable-diffusion-webui/modules/restart.py b/stable-diffusion-webui/modules/restart.py
new file mode 100644
index 0000000000000000000000000000000000000000..18eacaf377ee4f13c5dc3e12d7a2d818143cc69d
--- /dev/null
+++ b/stable-diffusion-webui/modules/restart.py
@@ -0,0 +1,23 @@
+import os
+from pathlib import Path
+
+from modules.paths_internal import script_path
+
+
+def is_restartable() -> bool:
+ """
+ Return True if the webui is restartable (i.e. there is something watching to restart it with)
+ """
+ return bool(os.environ.get('SD_WEBUI_RESTART'))
+
+
+def restart_program() -> None:
+ """creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""
+
+ (Path(script_path) / "tmp" / "restart").touch()
+
+ stop_program()
+
+
+def stop_program() -> None:
+ os._exit(0)
diff --git a/stable-diffusion-webui/modules/rng.py b/stable-diffusion-webui/modules/rng.py
new file mode 100644
index 0000000000000000000000000000000000000000..21113c9449c4a0610b688b9dab615e5a25a70f10
--- /dev/null
+++ b/stable-diffusion-webui/modules/rng.py
@@ -0,0 +1,170 @@
+import torch
+
+from modules import devices, rng_philox, shared
+
+
+def randn(seed, shape, generator=None):
+ """Generate a tensor with random numbers from a normal distribution using seed.
+
+ Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
+
+ manual_seed(seed)
+
+ if shared.opts.randn_source == "NV":
+ return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
+
+ if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
+ return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
+
+ return torch.randn(shape, device=devices.device, generator=generator)
+
+
+def randn_local(seed, shape):
+ """Generate a tensor with random numbers from a normal distribution using seed.
+
+ Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
+
+ if shared.opts.randn_source == "NV":
+ rng = rng_philox.Generator(seed)
+ return torch.asarray(rng.randn(shape), device=devices.device)
+
+ local_device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
+ local_generator = torch.Generator(local_device).manual_seed(int(seed))
+ return torch.randn(shape, device=local_device, generator=local_generator).to(devices.device)
+
+
+def randn_like(x):
+ """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+ Use either randn() or manual_seed() to initialize the generator."""
+
+ if shared.opts.randn_source == "NV":
+ return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
+
+ if shared.opts.randn_source == "CPU" or x.device.type == 'mps':
+ return torch.randn_like(x, device=devices.cpu).to(x.device)
+
+ return torch.randn_like(x)
+
+
+def randn_without_seed(shape, generator=None):
+ """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+ Use either randn() or manual_seed() to initialize the generator."""
+
+ if shared.opts.randn_source == "NV":
+ return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
+
+ if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
+ return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
+
+ return torch.randn(shape, device=devices.device, generator=generator)
+
+
+def manual_seed(seed):
+ """Set up a global random number generator using the specified seed."""
+
+ if shared.opts.randn_source == "NV":
+ global nv_rng
+ nv_rng = rng_philox.Generator(seed)
+ return
+
+ torch.manual_seed(seed)
+
+
+def create_generator(seed):
+ if shared.opts.randn_source == "NV":
+ return rng_philox.Generator(seed)
+
+ device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
+ generator = torch.Generator(device).manual_seed(int(seed))
+ return generator
+
+
+# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
+def slerp(val, low, high):
+ low_norm = low/torch.norm(low, dim=1, keepdim=True)
+ high_norm = high/torch.norm(high, dim=1, keepdim=True)
+ dot = (low_norm*high_norm).sum(1)
+
+ if dot.mean() > 0.9995:
+ return low * val + high * (1 - val)
+
+ omega = torch.acos(dot)
+ so = torch.sin(omega)
+ res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+ return res
+
+
+class ImageRNG:
+ def __init__(self, shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
+ self.shape = tuple(map(int, shape))
+ self.seeds = seeds
+ self.subseeds = subseeds
+ self.subseed_strength = subseed_strength
+ self.seed_resize_from_h = seed_resize_from_h
+ self.seed_resize_from_w = seed_resize_from_w
+
+ self.generators = [create_generator(seed) for seed in seeds]
+
+ self.is_first = True
+
+ def first(self):
+ noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
+
+ xs = []
+
+ for i, (seed, generator) in enumerate(zip(self.seeds, self.generators)):
+ subnoise = None
+ if self.subseeds is not None and self.subseed_strength != 0:
+ subseed = 0 if i >= len(self.subseeds) else self.subseeds[i]
+ subnoise = randn(subseed, noise_shape)
+
+ if noise_shape != self.shape:
+ noise = randn(seed, noise_shape)
+ else:
+ noise = randn(seed, self.shape, generator=generator)
+
+ if subnoise is not None:
+ noise = slerp(self.subseed_strength, noise, subnoise)
+
+ if noise_shape != self.shape:
+ x = randn(seed, self.shape, generator=generator)
+ dx = (self.shape[2] - noise_shape[2]) // 2
+ dy = (self.shape[1] - noise_shape[1]) // 2
+ w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
+ h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
+ tx = 0 if dx < 0 else dx
+ ty = 0 if dy < 0 else dy
+ dx = max(-dx, 0)
+ dy = max(-dy, 0)
+
+ x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
+ noise = x
+
+ xs.append(noise)
+
+ eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
+ if eta_noise_seed_delta:
+ self.generators = [create_generator(seed + eta_noise_seed_delta) for seed in self.seeds]
+
+ return torch.stack(xs).to(shared.device)
+
+ def next(self):
+ if self.is_first:
+ self.is_first = False
+ return self.first()
+
+ xs = []
+ for generator in self.generators:
+ x = randn_without_seed(self.shape, generator=generator)
+ xs.append(x)
+
+ return torch.stack(xs).to(shared.device)
+
+
+devices.randn = randn
+devices.randn_local = randn_local
+devices.randn_like = randn_like
+devices.randn_without_seed = randn_without_seed
+devices.manual_seed = manual_seed
diff --git a/stable-diffusion-webui/modules/rng_philox.py b/stable-diffusion-webui/modules/rng_philox.py
new file mode 100644
index 0000000000000000000000000000000000000000..8897dc3ac54beec0eb43e315e2201ccd6613576e
--- /dev/null
+++ b/stable-diffusion-webui/modules/rng_philox.py
@@ -0,0 +1,102 @@
+"""RNG imitiating torch cuda randn on CPU. You are welcome.
+
+Usage:
+
+```
+g = Generator(seed=0)
+print(g.randn(shape=(3, 4)))
+```
+
+Expected output:
+```
+[[-0.92466259 -0.42534415 -2.6438457 0.14518388]
+ [-0.12086647 -0.57972564 -0.62285122 -0.32838709]
+ [-1.07454231 -0.36314407 -1.67105067 2.26550497]]
+```
+"""
+
+import numpy as np
+
+philox_m = [0xD2511F53, 0xCD9E8D57]
+philox_w = [0x9E3779B9, 0xBB67AE85]
+
+two_pow32_inv = np.array([2.3283064e-10], dtype=np.float32)
+two_pow32_inv_2pi = np.array([2.3283064e-10 * 6.2831855], dtype=np.float32)
+
+
+def uint32(x):
+ """Converts (N,) np.uint64 array into (2, N) np.unit32 array."""
+ return x.view(np.uint32).reshape(-1, 2).transpose(1, 0)
+
+
+def philox4_round(counter, key):
+ """A single round of the Philox 4x32 random number generator."""
+
+ v1 = uint32(counter[0].astype(np.uint64) * philox_m[0])
+ v2 = uint32(counter[2].astype(np.uint64) * philox_m[1])
+
+ counter[0] = v2[1] ^ counter[1] ^ key[0]
+ counter[1] = v2[0]
+ counter[2] = v1[1] ^ counter[3] ^ key[1]
+ counter[3] = v1[0]
+
+
+def philox4_32(counter, key, rounds=10):
+ """Generates 32-bit random numbers using the Philox 4x32 random number generator.
+
+ Parameters:
+ counter (numpy.ndarray): A 4xN array of 32-bit integers representing the counter values (offset into generation).
+ key (numpy.ndarray): A 2xN array of 32-bit integers representing the key values (seed).
+ rounds (int): The number of rounds to perform.
+
+ Returns:
+ numpy.ndarray: A 4xN array of 32-bit integers containing the generated random numbers.
+ """
+
+ for _ in range(rounds - 1):
+ philox4_round(counter, key)
+
+ key[0] = key[0] + philox_w[0]
+ key[1] = key[1] + philox_w[1]
+
+ philox4_round(counter, key)
+ return counter
+
+
+def box_muller(x, y):
+ """Returns just the first out of two numbers generated by Box–Muller transform algorithm."""
+ u = x * two_pow32_inv + two_pow32_inv / 2
+ v = y * two_pow32_inv_2pi + two_pow32_inv_2pi / 2
+
+ s = np.sqrt(-2.0 * np.log(u))
+
+ r1 = s * np.sin(v)
+ return r1.astype(np.float32)
+
+
+class Generator:
+ """RNG that produces same outputs as torch.randn(..., device='cuda') on CPU"""
+
+ def __init__(self, seed):
+ self.seed = seed
+ self.offset = 0
+
+ def randn(self, shape):
+ """Generate a sequence of n standard normal random variables using the Philox 4x32 random number generator and the Box-Muller transform."""
+
+ n = 1
+ for x in shape:
+ n *= x
+
+ counter = np.zeros((4, n), dtype=np.uint32)
+ counter[0] = self.offset
+ counter[2] = np.arange(n, dtype=np.uint32) # up to 2^32 numbers can be generated - if you want more you'd need to spill into counter[3]
+ self.offset += 1
+
+ key = np.empty(n, dtype=np.uint64)
+ key.fill(self.seed)
+ key = uint32(key)
+
+ g = philox4_32(counter, key)
+
+ return box_muller(g[0], g[1]).reshape(shape) # discard g[2] and g[3]
diff --git a/stable-diffusion-webui/modules/safe.py b/stable-diffusion-webui/modules/safe.py
new file mode 100644
index 0000000000000000000000000000000000000000..00df31348376ebd7daef9b39f858eace22caa105
--- /dev/null
+++ b/stable-diffusion-webui/modules/safe.py
@@ -0,0 +1,196 @@
+# this code is adapted from the script contributed by anon from /h/
+
+import pickle
+import collections
+
+import torch
+import numpy
+import _codecs
+import zipfile
+import re
+
+
+# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+from modules import errors
+
+TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
+
+def encode(*args):
+ out = _codecs.encode(*args)
+ return out
+
+
+class RestrictedUnpickler(pickle.Unpickler):
+ extra_handler = None
+
+ def persistent_load(self, saved_id):
+ assert saved_id[0] == 'storage'
+
+ try:
+ return TypedStorage(_internal=True)
+ except TypeError:
+ return TypedStorage() # PyTorch before 2.0 does not have the _internal argument
+
+ def find_class(self, module, name):
+ if self.extra_handler is not None:
+ res = self.extra_handler(module, name)
+ if res is not None:
+ return res
+
+ if module == 'collections' and name == 'OrderedDict':
+ return getattr(collections, name)
+ if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:
+ return getattr(torch._utils, name)
+ if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32', 'BFloat16Storage']:
+ return getattr(torch, name)
+ if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
+ return getattr(torch.nn.modules.container, name)
+ if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:
+ return getattr(numpy.core.multiarray, name)
+ if module == 'numpy' and name in ['dtype', 'ndarray']:
+ return getattr(numpy, name)
+ if module == '_codecs' and name == 'encode':
+ return encode
+ if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
+ import pytorch_lightning.callbacks
+ return pytorch_lightning.callbacks.model_checkpoint
+ if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
+ import pytorch_lightning.callbacks.model_checkpoint
+ return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
+ if module == "__builtin__" and name == 'set':
+ return set
+
+ # Forbid everything else.
+ raise Exception(f"global '{module}/{name}' is forbidden")
+
+
+# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/<number>'
+allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
+data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
+
+def check_zip_filenames(filename, names):
+ for name in names:
+ if allowed_zip_names_re.match(name):
+ continue
+
+ raise Exception(f"bad file inside {filename}: {name}")
+
+
+def check_pt(filename, extra_handler):
+ try:
+
+ # new pytorch format is a zip file
+ with zipfile.ZipFile(filename) as z:
+ check_zip_filenames(filename, z.namelist())
+
+ # find filename of data.pkl in zip file: '<directory name>/data.pkl'
+ data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
+ if len(data_pkl_filenames) == 0:
+ raise Exception(f"data.pkl not found in {filename}")
+ if len(data_pkl_filenames) > 1:
+ raise Exception(f"Multiple data.pkl found in {filename}")
+ with z.open(data_pkl_filenames[0]) as file:
+ unpickler = RestrictedUnpickler(file)
+ unpickler.extra_handler = extra_handler
+ unpickler.load()
+
+ except zipfile.BadZipfile:
+
+ # if it's not a zip file, it's an old pytorch format, with five objects written to pickle
+ with open(filename, "rb") as file:
+ unpickler = RestrictedUnpickler(file)
+ unpickler.extra_handler = extra_handler
+ for _ in range(5):
+ unpickler.load()
+
+
+def load(filename, *args, **kwargs):
+ return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs)
+
+
+def load_with_extra(filename, extra_handler=None, *args, **kwargs):
+ """
+ this function is intended to be used by extensions that want to load models with
+ some extra classes in them that the usual unpickler would find suspicious.
+
+ Use the extra_handler argument to specify a function that takes module and field name as text,
+ and returns that field's value:
+
+ ```python
+ def extra(module, name):
+ if module == 'collections' and name == 'OrderedDict':
+ return collections.OrderedDict
+
+ return None
+
+ safe.load_with_extra('model.pt', extra_handler=extra)
+ ```
+
+ The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is
+ definitely unsafe.
+ """
+
+ from modules import shared
+
+ try:
+ if not shared.cmd_opts.disable_safe_unpickle:
+ check_pt(filename, extra_handler)
+
+ except pickle.UnpicklingError:
+ errors.report(
+ f"Error verifying pickled file from {filename}\n"
+ "-----> !!!! The file is most likely corrupted !!!! <-----\n"
+ "You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n",
+ exc_info=True,
+ )
+ return None
+ except Exception:
+ errors.report(
+ f"Error verifying pickled file from {filename}\n"
+ f"The file may be malicious, so the program is not going to read it.\n"
+ f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n",
+ exc_info=True,
+ )
+ return None
+
+ return unsafe_torch_load(filename, *args, **kwargs)
+
+
+class Extra:
+ """
+ A class for temporarily setting the global handler for when you can't explicitly call load_with_extra
+ (because it's not your code making the torch.load call). The intended use is like this:
+
+```
+import torch
+from modules import safe
+
+def handler(module, name):
+ if module == 'torch' and name in ['float64', 'float16']:
+ return getattr(torch, name)
+
+ return None
+
+with safe.Extra(handler):
+ x = torch.load('model.pt')
+```
+ """
+
+ def __init__(self, handler):
+ self.handler = handler
+
+ def __enter__(self):
+ global global_extra_handler
+
+ assert global_extra_handler is None, 'already inside an Extra() block'
+ global_extra_handler = self.handler
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ global global_extra_handler
+
+ global_extra_handler = None
+
+
+unsafe_torch_load = torch.load
+torch.load = load
+global_extra_handler = None
diff --git a/stable-diffusion-webui/modules/script_callbacks.py b/stable-diffusion-webui/modules/script_callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dab09ecb9e102c8e2e1cc6d87eab62247629caa
--- /dev/null
+++ b/stable-diffusion-webui/modules/script_callbacks.py
@@ -0,0 +1,482 @@
+import inspect
+import os
+from collections import namedtuple
+from typing import Optional, Dict, Any
+
+from fastapi import FastAPI
+from gradio import Blocks
+
+from modules import errors, timer
+
+
+def report_exception(c, job):
+ errors.report(f"Error executing callback {job} for {c.script}", exc_info=True)
+
+
+class ImageSaveParams:
+ def __init__(self, image, p, filename, pnginfo):
+ self.image = image
+ """the PIL image itself"""
+
+ self.p = p
+ """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
+
+ self.filename = filename
+ """name of file that the image would be saved to"""
+
+ self.pnginfo = pnginfo
+ """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
+
+
+class ExtraNoiseParams:
+ def __init__(self, noise, x, xi):
+ self.noise = noise
+ """Random noise generated by the seed"""
+
+ self.x = x
+ """Latent representation of the image"""
+
+ self.xi = xi
+ """Noisy latent representation of the image"""
+
+
+class CFGDenoiserParams:
+ def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.image_cond = image_cond
+ """Conditioning image"""
+
+ self.sigma = sigma
+ """Current sigma noise step value"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+ self.text_cond = text_cond
+ """ Encoder hidden states of text conditioning from prompt"""
+
+ self.text_uncond = text_uncond
+ """ Encoder hidden states of text conditioning from negative prompt"""
+
+
+class CFGDenoisedParams:
+ def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+ self.inner_model = inner_model
+ """Inner model reference used for denoising"""
+
+
+class AfterCFGCallbackParams:
+ def __init__(self, x, sampling_step, total_sampling_steps):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+
+class UiTrainTabParams:
+ def __init__(self, txt2img_preview_params):
+ self.txt2img_preview_params = txt2img_preview_params
+
+
+class ImageGridLoopParams:
+ def __init__(self, imgs, cols, rows):
+ self.imgs = imgs
+ self.cols = cols
+ self.rows = rows
+
+
+ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
+callback_map = dict(
+ callbacks_app_started=[],
+ callbacks_model_loaded=[],
+ callbacks_ui_tabs=[],
+ callbacks_ui_train_tabs=[],
+ callbacks_ui_settings=[],
+ callbacks_before_image_saved=[],
+ callbacks_image_saved=[],
+ callbacks_extra_noise=[],
+ callbacks_cfg_denoiser=[],
+ callbacks_cfg_denoised=[],
+ callbacks_cfg_after_cfg=[],
+ callbacks_before_component=[],
+ callbacks_after_component=[],
+ callbacks_image_grid=[],
+ callbacks_infotext_pasted=[],
+ callbacks_script_unloaded=[],
+ callbacks_before_ui=[],
+ callbacks_on_reload=[],
+ callbacks_list_optimizers=[],
+ callbacks_list_unets=[],
+)
+
+
+def clear_callbacks():
+ for callback_list in callback_map.values():
+ callback_list.clear()
+
+
+def app_started_callback(demo: Optional[Blocks], app: FastAPI):
+ for c in callback_map['callbacks_app_started']:
+ try:
+ c.callback(demo, app)
+ timer.startup_timer.record(os.path.basename(c.script))
+ except Exception:
+ report_exception(c, 'app_started_callback')
+
+
+def app_reload_callback():
+ for c in callback_map['callbacks_on_reload']:
+ try:
+ c.callback()
+ except Exception:
+ report_exception(c, 'callbacks_on_reload')
+
+
+def model_loaded_callback(sd_model):
+ for c in callback_map['callbacks_model_loaded']:
+ try:
+ c.callback(sd_model)
+ except Exception:
+ report_exception(c, 'model_loaded_callback')
+
+
+def ui_tabs_callback():
+ res = []
+
+ for c in callback_map['callbacks_ui_tabs']:
+ try:
+ res += c.callback() or []
+ except Exception:
+ report_exception(c, 'ui_tabs_callback')
+
+ return res
+
+
+def ui_train_tabs_callback(params: UiTrainTabParams):
+ for c in callback_map['callbacks_ui_train_tabs']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'callbacks_ui_train_tabs')
+
+
+def ui_settings_callback():
+ for c in callback_map['callbacks_ui_settings']:
+ try:
+ c.callback()
+ except Exception:
+ report_exception(c, 'ui_settings_callback')
+
+
+def before_image_saved_callback(params: ImageSaveParams):
+ for c in callback_map['callbacks_before_image_saved']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'before_image_saved_callback')
+
+
+def image_saved_callback(params: ImageSaveParams):
+ for c in callback_map['callbacks_image_saved']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'image_saved_callback')
+
+
+def extra_noise_callback(params: ExtraNoiseParams):
+ for c in callback_map['callbacks_extra_noise']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'callbacks_extra_noise')
+
+
+def cfg_denoiser_callback(params: CFGDenoiserParams):
+ for c in callback_map['callbacks_cfg_denoiser']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'cfg_denoiser_callback')
+
+
+def cfg_denoised_callback(params: CFGDenoisedParams):
+ for c in callback_map['callbacks_cfg_denoised']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'cfg_denoised_callback')
+
+
+def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
+ for c in callback_map['callbacks_cfg_after_cfg']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'cfg_after_cfg_callback')
+
+
+def before_component_callback(component, **kwargs):
+ for c in callback_map['callbacks_before_component']:
+ try:
+ c.callback(component, **kwargs)
+ except Exception:
+ report_exception(c, 'before_component_callback')
+
+
+def after_component_callback(component, **kwargs):
+ for c in callback_map['callbacks_after_component']:
+ try:
+ c.callback(component, **kwargs)
+ except Exception:
+ report_exception(c, 'after_component_callback')
+
+
+def image_grid_callback(params: ImageGridLoopParams):
+ for c in callback_map['callbacks_image_grid']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'image_grid')
+
+
+def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
+ for c in callback_map['callbacks_infotext_pasted']:
+ try:
+ c.callback(infotext, params)
+ except Exception:
+ report_exception(c, 'infotext_pasted')
+
+
+def script_unloaded_callback():
+ for c in reversed(callback_map['callbacks_script_unloaded']):
+ try:
+ c.callback()
+ except Exception:
+ report_exception(c, 'script_unloaded')
+
+
+def before_ui_callback():
+ for c in reversed(callback_map['callbacks_before_ui']):
+ try:
+ c.callback()
+ except Exception:
+ report_exception(c, 'before_ui')
+
+
+def list_optimizers_callback():
+ res = []
+
+ for c in callback_map['callbacks_list_optimizers']:
+ try:
+ c.callback(res)
+ except Exception:
+ report_exception(c, 'list_optimizers')
+
+ return res
+
+
+def list_unets_callback():
+ res = []
+
+ for c in callback_map['callbacks_list_unets']:
+ try:
+ c.callback(res)
+ except Exception:
+ report_exception(c, 'list_unets')
+
+ return res
+
+
+def add_callback(callbacks, fun):
+ stack = [x for x in inspect.stack() if x.filename != __file__]
+ filename = stack[0].filename if stack else 'unknown file'
+
+ callbacks.append(ScriptCallback(filename, fun))
+
+
+def remove_current_script_callbacks():
+ stack = [x for x in inspect.stack() if x.filename != __file__]
+ filename = stack[0].filename if stack else 'unknown file'
+ if filename == 'unknown file':
+ return
+ for callback_list in callback_map.values():
+ for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
+ callback_list.remove(callback_to_remove)
+
+
+def remove_callbacks_for_function(callback_func):
+ for callback_list in callback_map.values():
+ for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
+ callback_list.remove(callback_to_remove)
+
+
+def on_app_started(callback):
+ """register a function to be called when the webui started, the gradio `Block` component and
+ fastapi `FastAPI` object are passed as the arguments"""
+ add_callback(callback_map['callbacks_app_started'], callback)
+
+
+def on_before_reload(callback):
+ """register a function to be called just before the server reloads."""
+ add_callback(callback_map['callbacks_on_reload'], callback)
+
+
+def on_model_loaded(callback):
+ """register a function to be called when the stable diffusion model is created; the model is
+ passed as an argument; this function is also called when the script is reloaded. """
+ add_callback(callback_map['callbacks_model_loaded'], callback)
+
+
+def on_ui_tabs(callback):
+ """register a function to be called when the UI is creating new tabs.
+ The function must either return a None, which means no new tabs to be added, or a list, where
+ each element is a tuple:
+ (gradio_component, title, elem_id)
+
+ gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
+ title is tab text displayed to user in the UI
+ elem_id is HTML id for the tab
+ """
+ add_callback(callback_map['callbacks_ui_tabs'], callback)
+
+
+def on_ui_train_tabs(callback):
+ """register a function to be called when the UI is creating new tabs for the train tab.
+ Create your new tabs with gr.Tab.
+ """
+ add_callback(callback_map['callbacks_ui_train_tabs'], callback)
+
+
+def on_ui_settings(callback):
+ """register a function to be called before UI settings are populated; add your settings
+ by using shared.opts.add_option(shared.OptionInfo(...)) """
+ add_callback(callback_map['callbacks_ui_settings'], callback)
+
+
+def on_before_image_saved(callback):
+ """register a function to be called before an image is saved to a file.
+ The callback is called with one argument:
+ - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
+ """
+ add_callback(callback_map['callbacks_before_image_saved'], callback)
+
+
+def on_image_saved(callback):
+ """register a function to be called after an image is saved to a file.
+ The callback is called with one argument:
+ - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
+ """
+ add_callback(callback_map['callbacks_image_saved'], callback)
+
+
+def on_extra_noise(callback):
+ """register a function to be called before adding extra noise in img2img or hires fix;
+ The callback is called with one argument:
+ - params: ExtraNoiseParams - contains noise determined by seed and latent representation of image
+ """
+ add_callback(callback_map['callbacks_extra_noise'], callback)
+
+
+def on_cfg_denoiser(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+ The callback is called with one argument:
+ - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
+ """
+ add_callback(callback_map['callbacks_cfg_denoiser'], callback)
+
+
+def on_cfg_denoised(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+ The callback is called with one argument:
+ - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
+ """
+ add_callback(callback_map['callbacks_cfg_denoised'], callback)
+
+
+def on_cfg_after_cfg(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
+ The callback is called with one argument:
+ - params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
+ """
+ add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
+
+
+def on_before_component(callback):
+ """register a function to be called before a component is created.
+ The callback is called with arguments:
+ - component - gradio component that is about to be created.
+ - **kwargs - args to gradio.components.IOComponent.__init__ function
+
+ Use elem_id/label fields of kwargs to figure out which component it is.
+ This can be useful to inject your own components somewhere in the middle of vanilla UI.
+ """
+ add_callback(callback_map['callbacks_before_component'], callback)
+
+
+def on_after_component(callback):
+ """register a function to be called after a component is created. See on_before_component for more."""
+ add_callback(callback_map['callbacks_after_component'], callback)
+
+
+def on_image_grid(callback):
+ """register a function to be called before making an image grid.
+ The callback is called with one argument:
+ - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
+ """
+ add_callback(callback_map['callbacks_image_grid'], callback)
+
+
+def on_infotext_pasted(callback):
+ """register a function to be called before applying an infotext.
+ The callback is called with two arguments:
+ - infotext: str - raw infotext.
+ - result: Dict[str, any] - parsed infotext parameters.
+ """
+ add_callback(callback_map['callbacks_infotext_pasted'], callback)
+
+
+def on_script_unloaded(callback):
+ """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
+ the script did should be reverted here"""
+
+ add_callback(callback_map['callbacks_script_unloaded'], callback)
+
+
+def on_before_ui(callback):
+ """register a function to be called before the UI is created."""
+
+ add_callback(callback_map['callbacks_before_ui'], callback)
+
+
+def on_list_optimizers(callback):
+ """register a function to be called when UI is making a list of cross attention optimization options.
+ The function will be called with one argument, a list, and shall add objects of type modules.sd_hijack_optimizations.SdOptimization
+ to it."""
+
+ add_callback(callback_map['callbacks_list_optimizers'], callback)
+
+
+def on_list_unets(callback):
+ """register a function to be called when UI is making a list of alternative options for unet.
+ The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
+
+ add_callback(callback_map['callbacks_list_unets'], callback)
diff --git a/stable-diffusion-webui/modules/script_loading.py b/stable-diffusion-webui/modules/script_loading.py
new file mode 100644
index 0000000000000000000000000000000000000000..02ae522a806c760a393e548a60083e6901c891c8
--- /dev/null
+++ b/stable-diffusion-webui/modules/script_loading.py
@@ -0,0 +1,31 @@
+import os
+import importlib.util
+
+from modules import errors
+
+
+def load_module(path):
+ module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
+ module = importlib.util.module_from_spec(module_spec)
+ module_spec.loader.exec_module(module)
+
+ return module
+
+
+def preload_extensions(extensions_dir, parser, extension_list=None):
+ if not os.path.isdir(extensions_dir):
+ return
+
+ extensions = extension_list if extension_list is not None else os.listdir(extensions_dir)
+ for dirname in sorted(extensions):
+ preload_script = os.path.join(extensions_dir, dirname, "preload.py")
+ if not os.path.isfile(preload_script):
+ continue
+
+ try:
+ module = load_module(preload_script)
+ if hasattr(module, 'preload'):
+ module.preload(parser)
+
+ except Exception:
+ errors.report(f"Error running preload() for {preload_script}", exc_info=True)
diff --git a/stable-diffusion-webui/modules/scripts.py b/stable-diffusion-webui/modules/scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..08745bf65769c48164096b1952e0ea270e4c53ff
--- /dev/null
+++ b/stable-diffusion-webui/modules/scripts.py
@@ -0,0 +1,758 @@
+import os
+import re
+import sys
+import inspect
+from collections import namedtuple
+from dataclasses import dataclass
+
+import gradio as gr
+
+from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing, errors, timer
+
+AlwaysVisible = object()
+
+
+class PostprocessImageArgs:
+ def __init__(self, image):
+ self.image = image
+
+
+class PostprocessBatchListArgs:
+ def __init__(self, images):
+ self.images = images
+
+
+@dataclass
+class OnComponent:
+ component: gr.blocks.Block
+
+
+class Script:
+ name = None
+ """script's internal name derived from title"""
+
+ section = None
+ """name of UI section that the script's controls will be placed into"""
+
+ filename = None
+ args_from = None
+ args_to = None
+ alwayson = False
+
+ is_txt2img = False
+ is_img2img = False
+ tabname = None
+
+ group = None
+ """A gr.Group component that has all script's UI inside it."""
+
+ create_group = True
+ """If False, for alwayson scripts, a group component will not be created."""
+
+ infotext_fields = None
+ """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
+ parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
+ """
+
+ paste_field_names = None
+ """if set in ui(), this is a list of names of infotext fields; the fields will be sent through the
+ various "Send to <X>" buttons when clicked
+ """
+
+ api_info = None
+ """Generated value of type modules.api.models.ScriptInfo with information about the script for API"""
+
+ on_before_component_elem_id = None
+ """list of callbacks to be called before a component with an elem_id is created"""
+
+ on_after_component_elem_id = None
+ """list of callbacks to be called after a component with an elem_id is created"""
+
+ setup_for_ui_only = False
+ """If true, the script setup will only be run in Gradio UI, not in API"""
+
+ def title(self):
+ """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
+
+ raise NotImplementedError()
+
+ def ui(self, is_img2img):
+ """this function should create gradio UI elements. See https://gradio.app/docs/#components
+ The return value should be an array of all components that are used in processing.
+ Values of those returned components will be passed to run() and process() functions.
+ """
+
+ pass
+
+ def show(self, is_img2img):
+ """
+ is_img2img is True if this function is called for the img2img interface, and Fasle otherwise
+
+ This function should return:
+ - False if the script should not be shown in UI at all
+ - True if the script should be shown in UI if it's selected in the scripts dropdown
+ - script.AlwaysVisible if the script should be shown in UI at all times
+ """
+
+ return True
+
+ def run(self, p, *args):
+ """
+ This function is called if the script has been selected in the script dropdown.
+ It must do all processing and return the Processed object with results, same as
+ one returned by processing.process_images.
+
+ Usually the processing is done by calling the processing.process_images function.
+
+ args contains all values returned by components from ui()
+ """
+
+ pass
+
+ def setup(self, p, *args):
+ """For AlwaysVisible scripts, this function is called when the processing object is set up, before any processing starts.
+ args contains all values returned by components from ui().
+ """
+ pass
+
+
+ def before_process(self, p, *args):
+ """
+ This function is called very early during processing begins for AlwaysVisible scripts.
+ You can modify the processing object (p) here, inject hooks, etc.
+ args contains all values returned by components from ui()
+ """
+
+ pass
+
+ def process(self, p, *args):
+ """
+ This function is called before processing begins for AlwaysVisible scripts.
+ You can modify the processing object (p) here, inject hooks, etc.
+ args contains all values returned by components from ui()
+ """
+
+ pass
+
+ def before_process_batch(self, p, *args, **kwargs):
+ """
+ Called before extra networks are parsed from the prompt, so you can add
+ new extra network keywords to the prompt with this callback.
+
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ """
+
+ pass
+
+ def after_extra_networks_activate(self, p, *args, **kwargs):
+ """
+ Called after extra networks activation, before conds calculation
+ allow modification of the network after extra networks activation been applied
+ won't be call if p.disable_extra_networks
+
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ - extra_network_data - list of ExtraNetworkParams for current stage
+ """
+ pass
+
+ def process_batch(self, p, *args, **kwargs):
+ """
+ Same as process(), but called for every batch.
+
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ """
+
+ pass
+
+ def postprocess_batch(self, p, *args, **kwargs):
+ """
+ Same as process_batch(), but called for every batch after it has been generated.
+
+ **kwargs will have same items as process_batch, and also:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - images - torch tensor with all generated images, with values ranging from 0 to 1;
+ """
+
+ pass
+
+ def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
+ """
+ Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
+ This is useful when you want to update the entire batch instead of individual images.
+
+ You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
+ If the number of images is different from the batch size when returning,
+ then the script has the responsibility to also update the following attributes in the processing object (p):
+ - p.prompts
+ - p.negative_prompts
+ - p.seeds
+ - p.subseeds
+
+ **kwargs will have same items as process_batch, and also:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ """
+
+ pass
+
+ def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
+ """
+ Called for every image after it has been generated.
+ """
+
+ pass
+
+ def postprocess(self, p, processed, *args):
+ """
+ This function is called after processing ends for AlwaysVisible scripts.
+ args contains all values returned by components from ui()
+ """
+
+ pass
+
+ def before_component(self, component, **kwargs):
+ """
+ Called before a component is created.
+ Use elem_id/label fields of kwargs to figure out which component it is.
+ This can be useful to inject your own components somewhere in the middle of vanilla UI.
+ You can return created components in the ui() function to add them to the list of arguments for your processing functions
+ """
+
+ pass
+
+ def after_component(self, component, **kwargs):
+ """
+ Called after a component is created. Same as above.
+ """
+
+ pass
+
+ def on_before_component(self, callback, *, elem_id):
+ """
+ Calls callback before a component is created. The callback function is called with a single argument of type OnComponent.
+
+ May be called in show() or ui() - but it may be too late in latter as some components may already be created.
+
+ This function is an alternative to before_component in that it also cllows to run before a component is created, but
+ it doesn't require to be called for every created component - just for the one you need.
+ """
+ if self.on_before_component_elem_id is None:
+ self.on_before_component_elem_id = []
+
+ self.on_before_component_elem_id.append((elem_id, callback))
+
+ def on_after_component(self, callback, *, elem_id):
+ """
+ Calls callback after a component is created. The callback function is called with a single argument of type OnComponent.
+ """
+ if self.on_after_component_elem_id is None:
+ self.on_after_component_elem_id = []
+
+ self.on_after_component_elem_id.append((elem_id, callback))
+
+ def describe(self):
+ """unused"""
+ return ""
+
+ def elem_id(self, item_id):
+ """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
+
+ need_tabname = self.show(True) == self.show(False)
+ tabkind = 'img2img' if self.is_img2img else 'txt2img'
+ tabname = f"{tabkind}_" if need_tabname else ""
+ title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
+
+ return f'script_{tabname}{title}_{item_id}'
+
+ def before_hr(self, p, *args):
+ """
+ This function is called before hires fix start.
+ """
+ pass
+
+
+class ScriptBuiltinUI(Script):
+ setup_for_ui_only = True
+
+ def elem_id(self, item_id):
+ """helper function to generate id for a HTML element, constructs final id out of tab and user-supplied item_id"""
+
+ need_tabname = self.show(True) == self.show(False)
+ tabname = ('img2img' if self.is_img2img else 'txt2img') + "_" if need_tabname else ""
+
+ return f'{tabname}{item_id}'
+
+
+current_basedir = paths.script_path
+
+
+def basedir():
+ """returns the base directory for the current script. For scripts in the main scripts directory,
+ this is the main directory (where webui.py resides), and for scripts in extensions directory
+ (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic)
+ """
+ return current_basedir
+
+
+ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
+
+scripts_data = []
+postprocessing_scripts_data = []
+ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
+
+
+def list_scripts(scriptdirname, extension, *, include_extensions=True):
+ scripts_list = []
+
+ basedir = os.path.join(paths.script_path, scriptdirname)
+ if os.path.exists(basedir):
+ for filename in sorted(os.listdir(basedir)):
+ scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
+
+ if include_extensions:
+ for ext in extensions.active():
+ scripts_list += ext.list_files(scriptdirname, extension)
+
+ scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
+
+ return scripts_list
+
+
+def list_files_with_name(filename):
+ res = []
+
+ dirs = [paths.script_path] + [ext.path for ext in extensions.active()]
+
+ for dirpath in dirs:
+ if not os.path.isdir(dirpath):
+ continue
+
+ path = os.path.join(dirpath, filename)
+ if os.path.isfile(path):
+ res.append(path)
+
+ return res
+
+
+def load_scripts():
+ global current_basedir
+ scripts_data.clear()
+ postprocessing_scripts_data.clear()
+ script_callbacks.clear_callbacks()
+
+ scripts_list = list_scripts("scripts", ".py") + list_scripts("modules/processing_scripts", ".py", include_extensions=False)
+
+ syspath = sys.path
+
+ def register_scripts_from_module(module):
+ for script_class in module.__dict__.values():
+ if not inspect.isclass(script_class):
+ continue
+
+ if issubclass(script_class, Script):
+ scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
+ elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
+ postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
+
+ def orderby(basedir):
+ # 1st webui, 2nd extensions-builtin, 3rd extensions
+ priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+ for key in priority:
+ if basedir.startswith(key):
+ return priority[key]
+ return 9999
+
+ for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
+ try:
+ if scriptfile.basedir != paths.script_path:
+ sys.path = [scriptfile.basedir] + sys.path
+ current_basedir = scriptfile.basedir
+
+ script_module = script_loading.load_module(scriptfile.path)
+ register_scripts_from_module(script_module)
+
+ except Exception:
+ errors.report(f"Error loading script: {scriptfile.filename}", exc_info=True)
+
+ finally:
+ sys.path = syspath
+ current_basedir = paths.script_path
+ timer.startup_timer.record(scriptfile.filename)
+
+ global scripts_txt2img, scripts_img2img, scripts_postproc
+
+ scripts_txt2img = ScriptRunner()
+ scripts_img2img = ScriptRunner()
+ scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+
+
+def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
+ try:
+ return func(*args, **kwargs)
+ except Exception:
+ errors.report(f"Error calling: {filename}/{funcname}", exc_info=True)
+
+ return default
+
+
+class ScriptRunner:
+ def __init__(self):
+ self.scripts = []
+ self.selectable_scripts = []
+ self.alwayson_scripts = []
+ self.titles = []
+ self.title_map = {}
+ self.infotext_fields = []
+ self.paste_field_names = []
+ self.inputs = [None]
+
+ self.on_before_component_elem_id = {}
+ """dict of callbacks to be called before an element is created; key=elem_id, value=list of callbacks"""
+
+ self.on_after_component_elem_id = {}
+ """dict of callbacks to be called after an element is created; key=elem_id, value=list of callbacks"""
+
+ def initialize_scripts(self, is_img2img):
+ from modules import scripts_auto_postprocessing
+
+ self.scripts.clear()
+ self.alwayson_scripts.clear()
+ self.selectable_scripts.clear()
+
+ auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
+
+ for script_data in auto_processing_scripts + scripts_data:
+ script = script_data.script_class()
+ script.filename = script_data.path
+ script.is_txt2img = not is_img2img
+ script.is_img2img = is_img2img
+ script.tabname = "img2img" if is_img2img else "txt2img"
+
+ visibility = script.show(script.is_img2img)
+
+ if visibility == AlwaysVisible:
+ self.scripts.append(script)
+ self.alwayson_scripts.append(script)
+ script.alwayson = True
+
+ elif visibility:
+ self.scripts.append(script)
+ self.selectable_scripts.append(script)
+
+ self.apply_on_before_component_callbacks()
+
+ def apply_on_before_component_callbacks(self):
+ for script in self.scripts:
+ on_before = script.on_before_component_elem_id or []
+ on_after = script.on_after_component_elem_id or []
+
+ for elem_id, callback in on_before:
+ if elem_id not in self.on_before_component_elem_id:
+ self.on_before_component_elem_id[elem_id] = []
+
+ self.on_before_component_elem_id[elem_id].append((callback, script))
+
+ for elem_id, callback in on_after:
+ if elem_id not in self.on_after_component_elem_id:
+ self.on_after_component_elem_id[elem_id] = []
+
+ self.on_after_component_elem_id[elem_id].append((callback, script))
+
+ on_before.clear()
+ on_after.clear()
+
+ def create_script_ui(self, script):
+ import modules.api.models as api_models
+
+ script.args_from = len(self.inputs)
+ script.args_to = len(self.inputs)
+
+ controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
+
+ if controls is None:
+ return
+
+ script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+ api_args = []
+
+ for control in controls:
+ control.custom_script_source = os.path.basename(script.filename)
+
+ arg_info = api_models.ScriptArg(label=control.label or "")
+
+ for field in ("value", "minimum", "maximum", "step", "choices"):
+ v = getattr(control, field, None)
+ if v is not None:
+ setattr(arg_info, field, v)
+
+ api_args.append(arg_info)
+
+ script.api_info = api_models.ScriptInfo(
+ name=script.name,
+ is_img2img=script.is_img2img,
+ is_alwayson=script.alwayson,
+ args=api_args,
+ )
+
+ if script.infotext_fields is not None:
+ self.infotext_fields += script.infotext_fields
+
+ if script.paste_field_names is not None:
+ self.paste_field_names += script.paste_field_names
+
+ self.inputs += controls
+ script.args_to = len(self.inputs)
+
+ def setup_ui_for_section(self, section, scriptlist=None):
+ if scriptlist is None:
+ scriptlist = self.alwayson_scripts
+
+ for script in scriptlist:
+ if script.alwayson and script.section != section:
+ continue
+
+ if script.create_group:
+ with gr.Group(visible=script.alwayson) as group:
+ self.create_script_ui(script)
+
+ script.group = group
+ else:
+ self.create_script_ui(script)
+
+ def prepare_ui(self):
+ self.inputs = [None]
+
+ def setup_ui(self):
+ all_titles = [wrap_call(script.title, script.filename, "title") or script.filename for script in self.scripts]
+ self.title_map = {title.lower(): script for title, script in zip(all_titles, self.scripts)}
+ self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
+
+ self.setup_ui_for_section(None)
+
+ dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
+ self.inputs[0] = dropdown
+
+ self.setup_ui_for_section(None, self.selectable_scripts)
+
+ def select_script(script_index):
+ selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
+
+ return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
+
+ def init_field(title):
+ """called when an initial value is set from ui-config.json to show script's UI components"""
+
+ if title == 'None':
+ return
+
+ script_index = self.titles.index(title)
+ self.selectable_scripts[script_index].group.visible = True
+
+ dropdown.init_field = init_field
+
+ dropdown.change(
+ fn=select_script,
+ inputs=[dropdown],
+ outputs=[script.group for script in self.selectable_scripts]
+ )
+
+ self.script_load_ctr = 0
+
+ def onload_script_visibility(params):
+ title = params.get('Script', None)
+ if title:
+ title_index = self.titles.index(title)
+ visibility = title_index == self.script_load_ctr
+ self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
+ return gr.update(visible=visibility)
+ else:
+ return gr.update(visible=False)
+
+ self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
+ self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])
+
+ self.apply_on_before_component_callbacks()
+
+ return self.inputs
+
+ def run(self, p, *args):
+ script_index = args[0]
+
+ if script_index == 0:
+ return None
+
+ script = self.selectable_scripts[script_index-1]
+
+ if script is None:
+ return None
+
+ script_args = args[script.args_from:script.args_to]
+ processed = script.run(p, *script_args)
+
+ shared.total_tqdm.clear()
+
+ return processed
+
+ def before_process(self, p):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_process(p, *script_args)
+ except Exception:
+ errors.report(f"Error running before_process: {script.filename}", exc_info=True)
+
+ def process(self, p):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.process(p, *script_args)
+ except Exception:
+ errors.report(f"Error running process: {script.filename}", exc_info=True)
+
+ def before_process_batch(self, p, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_process_batch(p, *script_args, **kwargs)
+ except Exception:
+ errors.report(f"Error running before_process_batch: {script.filename}", exc_info=True)
+
+ def after_extra_networks_activate(self, p, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.after_extra_networks_activate(p, *script_args, **kwargs)
+ except Exception:
+ errors.report(f"Error running after_extra_networks_activate: {script.filename}", exc_info=True)
+
+ def process_batch(self, p, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.process_batch(p, *script_args, **kwargs)
+ except Exception:
+ errors.report(f"Error running process_batch: {script.filename}", exc_info=True)
+
+ def postprocess(self, p, processed):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess(p, processed, *script_args)
+ except Exception:
+ errors.report(f"Error running postprocess: {script.filename}", exc_info=True)
+
+ def postprocess_batch(self, p, images, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess_batch(p, *script_args, images=images, **kwargs)
+ except Exception:
+ errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
+
+ def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess_batch_list(p, pp, *script_args, **kwargs)
+ except Exception:
+ errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
+
+ def postprocess_image(self, p, pp: PostprocessImageArgs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess_image(p, pp, *script_args)
+ except Exception:
+ errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
+
+ def before_component(self, component, **kwargs):
+ for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
+ try:
+ callback(OnComponent(component=component))
+ except Exception:
+ errors.report(f"Error running on_before_component: {script.filename}", exc_info=True)
+
+ for script in self.scripts:
+ try:
+ script.before_component(component, **kwargs)
+ except Exception:
+ errors.report(f"Error running before_component: {script.filename}", exc_info=True)
+
+ def after_component(self, component, **kwargs):
+ for callback, script in self.on_after_component_elem_id.get(component.elem_id, []):
+ try:
+ callback(OnComponent(component=component))
+ except Exception:
+ errors.report(f"Error running on_after_component: {script.filename}", exc_info=True)
+
+ for script in self.scripts:
+ try:
+ script.after_component(component, **kwargs)
+ except Exception:
+ errors.report(f"Error running after_component: {script.filename}", exc_info=True)
+
+ def script(self, title):
+ return self.title_map.get(title.lower())
+
+ def reload_sources(self, cache):
+ for si, script in list(enumerate(self.scripts)):
+ args_from = script.args_from
+ args_to = script.args_to
+ filename = script.filename
+
+ module = cache.get(filename, None)
+ if module is None:
+ module = script_loading.load_module(script.filename)
+ cache[filename] = module
+
+ for script_class in module.__dict__.values():
+ if type(script_class) == type and issubclass(script_class, Script):
+ self.scripts[si] = script_class()
+ self.scripts[si].filename = filename
+ self.scripts[si].args_from = args_from
+ self.scripts[si].args_to = args_to
+
+ def before_hr(self, p):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_hr(p, *script_args)
+ except Exception:
+ errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
+
+ def setup_scrips(self, p, *, is_ui=True):
+ for script in self.alwayson_scripts:
+ if not is_ui and script.setup_for_ui_only:
+ continue
+
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.setup(p, *script_args)
+ except Exception:
+ errors.report(f"Error running setup: {script.filename}", exc_info=True)
+
+
+scripts_txt2img: ScriptRunner = None
+scripts_img2img: ScriptRunner = None
+scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None
+scripts_current: ScriptRunner = None
+
+
+def reload_script_body_only():
+ cache = {}
+ scripts_txt2img.reload_sources(cache)
+ scripts_img2img.reload_sources(cache)
+
+
+reload_scripts = load_scripts # compatibility alias
diff --git a/stable-diffusion-webui/modules/scripts_auto_postprocessing.py b/stable-diffusion-webui/modules/scripts_auto_postprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..610139b767c37d032daea709d4c841fd257cddb8
--- /dev/null
+++ b/stable-diffusion-webui/modules/scripts_auto_postprocessing.py
@@ -0,0 +1,42 @@
+from modules import scripts, scripts_postprocessing, shared
+
+
+class ScriptPostprocessingForMainUI(scripts.Script):
+ def __init__(self, script_postproc):
+ self.script: scripts_postprocessing.ScriptPostprocessing = script_postproc
+ self.postprocessing_controls = None
+
+ def title(self):
+ return self.script.name
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def ui(self, is_img2img):
+ self.postprocessing_controls = self.script.ui()
+ return self.postprocessing_controls.values()
+
+ def postprocess_image(self, p, script_pp, *args):
+ args_dict = dict(zip(self.postprocessing_controls, args))
+
+ pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
+ pp.info = {}
+ self.script.process(pp, **args_dict)
+ p.extra_generation_params.update(pp.info)
+ script_pp.image = pp.image
+
+
+def create_auto_preprocessing_script_data():
+ from modules import scripts
+
+ res = []
+
+ for name in shared.opts.postprocessing_enable_in_main_ui:
+ script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
+ if script is None:
+ continue
+
+ constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class())
+ res.append(scripts.ScriptClassData(script_class=constructor, path=script.path, basedir=script.basedir, module=script.module))
+
+ return res
diff --git a/stable-diffusion-webui/modules/scripts_postprocessing.py b/stable-diffusion-webui/modules/scripts_postprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9edf3705c3843fa02cde88c025239fa23fae379
--- /dev/null
+++ b/stable-diffusion-webui/modules/scripts_postprocessing.py
@@ -0,0 +1,152 @@
+import os
+import gradio as gr
+
+from modules import errors, shared
+
+
+class PostprocessedImage:
+ def __init__(self, image):
+ self.image = image
+ self.info = {}
+
+
+class ScriptPostprocessing:
+ filename = None
+ controls = None
+ args_from = None
+ args_to = None
+
+ order = 1000
+ """scripts will be ordred by this value in postprocessing UI"""
+
+ name = None
+ """this function should return the title of the script."""
+
+ group = None
+ """A gr.Group component that has all script's UI inside it"""
+
+ def ui(self):
+ """
+ This function should create gradio UI elements. See https://gradio.app/docs/#components
+ The return value should be a dictionary that maps parameter names to components used in processing.
+ Values of those components will be passed to process() function.
+ """
+
+ pass
+
+ def process(self, pp: PostprocessedImage, **args):
+ """
+ This function is called to postprocess the image.
+ args contains a dictionary with all values returned by components from ui()
+ """
+
+ pass
+
+ def image_changed(self):
+ pass
+
+
+
+
+def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
+ try:
+ res = func(*args, **kwargs)
+ return res
+ except Exception as e:
+ errors.display(e, f"calling {filename}/{funcname}")
+
+ return default
+
+
+class ScriptPostprocessingRunner:
+ def __init__(self):
+ self.scripts = None
+ self.ui_created = False
+
+ def initialize_scripts(self, scripts_data):
+ self.scripts = []
+
+ for script_data in scripts_data:
+ script: ScriptPostprocessing = script_data.script_class()
+ script.filename = script_data.path
+
+ if script.name == "Simple Upscale":
+ continue
+
+ self.scripts.append(script)
+
+ def create_script_ui(self, script, inputs):
+ script.args_from = len(inputs)
+ script.args_to = len(inputs)
+
+ script.controls = wrap_call(script.ui, script.filename, "ui")
+
+ for control in script.controls.values():
+ control.custom_script_source = os.path.basename(script.filename)
+
+ inputs += list(script.controls.values())
+ script.args_to = len(inputs)
+
+ def scripts_in_preferred_order(self):
+ if self.scripts is None:
+ import modules.scripts
+ self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
+
+ scripts_order = shared.opts.postprocessing_operation_order
+
+ def script_score(name):
+ for i, possible_match in enumerate(scripts_order):
+ if possible_match == name:
+ return i
+
+ return len(self.scripts)
+
+ script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
+
+ return sorted(self.scripts, key=lambda x: script_scores[x.name])
+
+ def setup_ui(self):
+ inputs = []
+
+ for script in self.scripts_in_preferred_order():
+ with gr.Row() as group:
+ self.create_script_ui(script, inputs)
+
+ script.group = group
+
+ self.ui_created = True
+ return inputs
+
+ def run(self, pp: PostprocessedImage, args):
+ for script in self.scripts_in_preferred_order():
+ shared.state.job = script.name
+
+ script_args = args[script.args_from:script.args_to]
+
+ process_args = {}
+ for (name, _component), value in zip(script.controls.items(), script_args):
+ process_args[name] = value
+
+ script.process(pp, **process_args)
+
+ def create_args_for_run(self, scripts_args):
+ if not self.ui_created:
+ with gr.Blocks(analytics_enabled=False):
+ self.setup_ui()
+
+ scripts = self.scripts_in_preferred_order()
+ args = [None] * max([x.args_to for x in scripts])
+
+ for script in scripts:
+ script_args_dict = scripts_args.get(script.name, None)
+ if script_args_dict is not None:
+
+ for i, name in enumerate(script.controls):
+ args[script.args_from + i] = script_args_dict.get(name, None)
+
+ return args
+
+ def image_changed(self):
+ for script in self.scripts_in_preferred_order():
+ script.image_changed()
+
diff --git a/stable-diffusion-webui/modules/sd_disable_initialization.py b/stable-diffusion-webui/modules/sd_disable_initialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..4364f5408103762fbe0ae3da7befa115ec0dfaf1
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_disable_initialization.py
@@ -0,0 +1,232 @@
+import ldm.modules.encoders.modules
+import open_clip
+import torch
+import transformers.utils.hub
+
+from modules import shared
+
+
+class ReplaceHelper:
+ def __init__(self):
+ self.replaced = []
+
+ def replace(self, obj, field, func):
+ original = getattr(obj, field, None)
+ if original is None:
+ return None
+
+ self.replaced.append((obj, field, original))
+ setattr(obj, field, func)
+
+ return original
+
+ def restore(self):
+ for obj, field, original in self.replaced:
+ setattr(obj, field, original)
+
+ self.replaced.clear()
+
+
+class DisableInitialization(ReplaceHelper):
+ """
+ When an object of this class enters a `with` block, it starts:
+ - preventing torch's layer initialization functions from working
+ - changes CLIP and OpenCLIP to not download model weights
+ - changes CLIP to not make requests to check if there is a new version of a file you already have
+
+ When it leaves the block, it reverts everything to how it was before.
+
+ Use it like this:
+ ```
+ with DisableInitialization():
+ do_things()
+ ```
+ """
+
+ def __init__(self, disable_clip=True):
+ super().__init__()
+ self.disable_clip = disable_clip
+
+ def replace(self, obj, field, func):
+ original = getattr(obj, field, None)
+ if original is None:
+ return None
+
+ self.replaced.append((obj, field, original))
+ setattr(obj, field, func)
+
+ return original
+
+ def __enter__(self):
+ def do_nothing(*args, **kwargs):
+ pass
+
+ def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
+ return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
+
+ def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
+ res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
+ res.name_or_path = pretrained_model_name_or_path
+ return res
+
+ def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
+ args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
+ return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
+
+ def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
+
+ # this file is always 404, prevent making request
+ if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+ return None
+
+ try:
+ res = original(url, *args, local_files_only=True, **kwargs)
+ if res is None:
+ res = original(url, *args, local_files_only=False, **kwargs)
+ return res
+ except Exception:
+ return original(url, *args, local_files_only=False, **kwargs)
+
+ def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
+ return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
+
+ def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
+ return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
+
+ def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
+ return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
+
+ self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
+ self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
+
+ if self.disable_clip:
+ self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
+ self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
+ self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
+ self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
+ self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
+ self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.restore()
+
+
+class InitializeOnMeta(ReplaceHelper):
+ """
+ Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,
+ which results in those parameters having no values and taking no memory. model.to() will be broken and
+ will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.
+
+ Usage:
+ ```
+ with sd_disable_initialization.InitializeOnMeta():
+ sd_model = instantiate_from_config(sd_config.model)
+ ```
+ """
+
+ def __enter__(self):
+ if shared.cmd_opts.disable_model_loading_ram_optimization:
+ return
+
+ def set_device(x):
+ x["device"] = "meta"
+ return x
+
+ linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))
+ conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))
+ mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))
+ self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.restore()
+
+
+class LoadStateDictOnMeta(ReplaceHelper):
+ """
+ Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.
+ As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.
+ Meant to be used together with InitializeOnMeta above.
+
+ Usage:
+ ```
+ with sd_disable_initialization.LoadStateDictOnMeta(state_dict):
+ model.load_state_dict(state_dict, strict=False)
+ ```
+ """
+
+ def __init__(self, state_dict, device, weight_dtype_conversion=None):
+ super().__init__()
+ self.state_dict = state_dict
+ self.device = device
+ self.weight_dtype_conversion = weight_dtype_conversion or {}
+ self.default_dtype = self.weight_dtype_conversion.get('')
+
+ def get_weight_dtype(self, key):
+ key_first_term, _ = key.split('.', 1)
+ return self.weight_dtype_conversion.get(key_first_term, self.default_dtype)
+
+ def __enter__(self):
+ if shared.cmd_opts.disable_model_loading_ram_optimization:
+ return
+
+ sd = self.state_dict
+ device = self.device
+
+ def load_from_state_dict(original, module, state_dict, prefix, *args, **kwargs):
+ used_param_keys = []
+
+ for name, param in module._parameters.items():
+ if param is None:
+ continue
+
+ key = prefix + name
+ sd_param = sd.pop(key, None)
+ if sd_param is not None:
+ state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key))
+ used_param_keys.append(key)
+
+ if param.is_meta:
+ dtype = sd_param.dtype if sd_param is not None else param.dtype
+ module._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
+
+ for name in module._buffers:
+ key = prefix + name
+
+ sd_param = sd.pop(key, None)
+ if sd_param is not None:
+ state_dict[key] = sd_param
+ used_param_keys.append(key)
+
+ original(module, state_dict, prefix, *args, **kwargs)
+
+ for key in used_param_keys:
+ state_dict.pop(key, None)
+
+ def load_state_dict(original, module, state_dict, strict=True):
+ """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help
+ because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with
+ all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes.
+
+ In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd).
+
+ The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads
+ the function and does not call the original) the state dict will just fail to load because weights
+ would be on the meta device.
+ """
+
+ if state_dict == sd:
+ state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
+
+ original(module, state_dict, strict=strict)
+
+ module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs))
+ module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs))
+ linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
+ conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
+ mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+ layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs))
+ group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs))
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.restore()
diff --git a/stable-diffusion-webui/modules/sd_hijack.py b/stable-diffusion-webui/modules/sd_hijack.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f2ae48b04c83900bf26adb5db133231250f35e
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack.py
@@ -0,0 +1,364 @@
+import torch
+from torch.nn.functional import silu
+from types import MethodType
+
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
+from modules.hypernetworks import hypernetwork
+from modules.shared import cmd_opts
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
+
+import ldm.modules.attention
+import ldm.modules.diffusionmodules.model
+import ldm.modules.diffusionmodules.openaimodel
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+import ldm.modules.encoders.modules
+
+import sgm.modules.attention
+import sgm.modules.diffusionmodules.model
+import sgm.modules.diffusionmodules.openaimodel
+import sgm.modules.encoders.modules
+
+attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
+diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
+
+# new memory efficient cross attention blocks do not support hypernets and we already
+# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention
+ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention
+ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention
+
+# silence new console spam from SD2
+ldm.modules.attention.print = shared.ldm_print
+ldm.modules.diffusionmodules.model.print = shared.ldm_print
+ldm.util.print = shared.ldm_print
+ldm.models.diffusion.ddpm.print = shared.ldm_print
+
+optimizers = []
+current_optimizer: sd_hijack_optimizations.SdOptimization = None
+
+
+def list_optimizers():
+ new_optimizers = script_callbacks.list_optimizers_callback()
+
+ new_optimizers = [x for x in new_optimizers if x.is_available()]
+
+ new_optimizers = sorted(new_optimizers, key=lambda x: x.priority, reverse=True)
+
+ optimizers.clear()
+ optimizers.extend(new_optimizers)
+
+
+def apply_optimizations(option=None):
+ global current_optimizer
+
+ undo_optimizations()
+
+ if len(optimizers) == 0:
+ # a script can access the model very early, and optimizations would not be filled by then
+ current_optimizer = None
+ return ''
+
+ ldm.modules.diffusionmodules.model.nonlinearity = silu
+ ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+
+ sgm.modules.diffusionmodules.model.nonlinearity = silu
+ sgm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+
+ if current_optimizer is not None:
+ current_optimizer.undo()
+ current_optimizer = None
+
+ selection = option or shared.opts.cross_attention_optimization
+ if selection == "Automatic" and len(optimizers) > 0:
+ matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
+ else:
+ matching_optimizer = next(iter([x for x in optimizers if x.title() == selection]), None)
+
+ if selection == "None":
+ matching_optimizer = None
+ elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:
+ matching_optimizer = None
+ elif matching_optimizer is None:
+ matching_optimizer = optimizers[0]
+
+ if matching_optimizer is not None:
+ print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
+ matching_optimizer.apply()
+ print("done.")
+ current_optimizer = matching_optimizer
+ return current_optimizer.name
+ else:
+ print("Disabling attention optimization")
+ return ''
+
+
+def undo_optimizations():
+ ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+ ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
+ sgm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+ sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
+
+def fix_checkpoint():
+ """checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want
+ checkpoints to be added when not training (there's a warning)"""
+
+ pass
+
+
+def weighted_loss(sd_model, pred, target, mean=True):
+ #Calculate the weight normally, but ignore the mean
+ loss = sd_model._old_get_loss(pred, target, mean=False)
+
+ #Check if we have weights available
+ weight = getattr(sd_model, '_custom_loss_weight', None)
+ if weight is not None:
+ loss *= weight
+
+ #Return the loss, as mean if specified
+ return loss.mean() if mean else loss
+
+def weighted_forward(sd_model, x, c, w, *args, **kwargs):
+ try:
+ #Temporarily append weights to a place accessible during loss calc
+ sd_model._custom_loss_weight = w
+
+ #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
+ #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
+ if not hasattr(sd_model, '_old_get_loss'):
+ sd_model._old_get_loss = sd_model.get_loss
+ sd_model.get_loss = MethodType(weighted_loss, sd_model)
+
+ #Run the standard forward function, but with the patched 'get_loss'
+ return sd_model.forward(x, c, *args, **kwargs)
+ finally:
+ try:
+ #Delete temporary weights if appended
+ del sd_model._custom_loss_weight
+ except AttributeError:
+ pass
+
+ #If we have an old loss function, reset the loss function to the original one
+ if hasattr(sd_model, '_old_get_loss'):
+ sd_model.get_loss = sd_model._old_get_loss
+ del sd_model._old_get_loss
+
+def apply_weighted_forward(sd_model):
+ #Add new function 'weighted_forward' that can be called to calc weighted loss
+ sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
+
+def undo_weighted_forward(sd_model):
+ try:
+ del sd_model.weighted_forward
+ except AttributeError:
+ pass
+
+
+class StableDiffusionModelHijack:
+ fixes = None
+ layers = None
+ circular_enabled = False
+ clip = None
+ optimization_method = None
+
+ def __init__(self):
+ import modules.textual_inversion.textual_inversion
+
+ self.extra_generation_params = {}
+ self.comments = []
+
+ self.embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
+ self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
+
+ def apply_optimizations(self, option=None):
+ try:
+ self.optimization_method = apply_optimizations(option)
+ except Exception as e:
+ errors.display(e, "applying cross attention optimization")
+ undo_optimizations()
+
+ def hijack(self, m):
+ conditioner = getattr(m, 'conditioner', None)
+ if conditioner:
+ text_cond_models = []
+
+ for i in range(len(conditioner.embedders)):
+ embedder = conditioner.embedders[i]
+ typename = type(embedder).__name__
+ if typename == 'FrozenOpenCLIPEmbedder':
+ embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self)
+ conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(embedder, self)
+ text_cond_models.append(conditioner.embedders[i])
+ if typename == 'FrozenCLIPEmbedder':
+ model_embeddings = embedder.transformer.text_model.embeddings
+ model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+ conditioner.embedders[i] = sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords(embedder, self)
+ text_cond_models.append(conditioner.embedders[i])
+ if typename == 'FrozenOpenCLIPEmbedder2':
+ embedder.model.token_embedding = EmbeddingsWithFixes(embedder.model.token_embedding, self, textual_inversion_key='clip_g')
+ conditioner.embedders[i] = sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords(embedder, self)
+ text_cond_models.append(conditioner.embedders[i])
+
+ if len(text_cond_models) == 1:
+ m.cond_stage_model = text_cond_models[0]
+ else:
+ m.cond_stage_model = conditioner
+
+ if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+ model_embeddings = m.cond_stage_model.roberta.embeddings
+ model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+ m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
+
+ elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+ model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+ model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
+ m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+
+ elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
+ m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
+ m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+
+ apply_weighted_forward(m)
+ if m.cond_stage_key == "edit":
+ sd_hijack_unet.hijack_ddpm_edit()
+
+ self.apply_optimizations()
+
+ self.clip = m.cond_stage_model
+
+ def flatten(el):
+ flattened = [flatten(children) for children in el.children()]
+ res = [el]
+ for c in flattened:
+ res += c
+ return res
+
+ self.layers = flatten(m)
+
+ if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+ ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+ ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
+
+ def undo_hijack(self, m):
+ conditioner = getattr(m, 'conditioner', None)
+ if conditioner:
+ for i in range(len(conditioner.embedders)):
+ embedder = conditioner.embedders[i]
+ if isinstance(embedder, (sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords, sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords)):
+ embedder.wrapped.model.token_embedding = embedder.wrapped.model.token_embedding.wrapped
+ conditioner.embedders[i] = embedder.wrapped
+ if isinstance(embedder, sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords):
+ embedder.wrapped.transformer.text_model.embeddings.token_embedding = embedder.wrapped.transformer.text_model.embeddings.token_embedding.wrapped
+ conditioner.embedders[i] = embedder.wrapped
+
+ if hasattr(m, 'cond_stage_model'):
+ delattr(m, 'cond_stage_model')
+
+ elif type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
+ m.cond_stage_model = m.cond_stage_model.wrapped
+
+ elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
+ m.cond_stage_model = m.cond_stage_model.wrapped
+
+ model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
+ if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
+ model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+ elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords:
+ m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
+ m.cond_stage_model = m.cond_stage_model.wrapped
+
+ undo_optimizations()
+ undo_weighted_forward(m)
+
+ self.apply_circular(False)
+ self.layers = None
+ self.clip = None
+
+ ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+
+ def apply_circular(self, enable):
+ if self.circular_enabled == enable:
+ return
+
+ self.circular_enabled = enable
+
+ for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
+ layer.padding_mode = 'circular' if enable else 'zeros'
+
+ def clear_comments(self):
+ self.comments = []
+ self.extra_generation_params = {}
+
+ def get_prompt_lengths(self, text):
+ if self.clip is None:
+ return "-", "-"
+
+ _, token_count = self.clip.process_texts([text])
+
+ return token_count, self.clip.get_target_prompt_token_count(token_count)
+
+ def redo_hijack(self, m):
+ self.undo_hijack(m)
+ self.hijack(m)
+
+
+class EmbeddingsWithFixes(torch.nn.Module):
+ def __init__(self, wrapped, embeddings, textual_inversion_key='clip_l'):
+ super().__init__()
+ self.wrapped = wrapped
+ self.embeddings = embeddings
+ self.textual_inversion_key = textual_inversion_key
+
+ def forward(self, input_ids):
+ batch_fixes = self.embeddings.fixes
+ self.embeddings.fixes = None
+
+ inputs_embeds = self.wrapped(input_ids)
+
+ if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0:
+ return inputs_embeds
+
+ vecs = []
+ for fixes, tensor in zip(batch_fixes, inputs_embeds):
+ for offset, embedding in fixes:
+ vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
+ emb = devices.cond_cast_unet(vec)
+ emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
+ tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
+
+ vecs.append(tensor)
+
+ return torch.stack(vecs)
+
+
+def add_circular_option_to_conv_2d():
+ conv2d_constructor = torch.nn.Conv2d.__init__
+
+ def conv2d_constructor_circular(self, *args, **kwargs):
+ return conv2d_constructor(self, *args, padding_mode='circular', **kwargs)
+
+ torch.nn.Conv2d.__init__ = conv2d_constructor_circular
+
+
+model_hijack = StableDiffusionModelHijack()
+
+
+def register_buffer(self, name, attr):
+ """
+ Fix register buffer bug for Mac OS.
+ """
+
+ if type(attr) == torch.Tensor:
+ if attr.device != devices.device:
+ attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None))
+
+ setattr(self, name, attr)
+
+
+ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer
+ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer
diff --git a/stable-diffusion-webui/modules/sd_hijack_checkpoint.py b/stable-diffusion-webui/modules/sd_hijack_checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..2604d969f910ffdd65aff66acc0b6ab09b793b38
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_checkpoint.py
@@ -0,0 +1,46 @@
+from torch.utils.checkpoint import checkpoint
+
+import ldm.modules.attention
+import ldm.modules.diffusionmodules.openaimodel
+
+
+def BasicTransformerBlock_forward(self, x, context=None):
+ return checkpoint(self._forward, x, context)
+
+
+def AttentionBlock_forward(self, x):
+ return checkpoint(self._forward, x)
+
+
+def ResBlock_forward(self, x, emb):
+ return checkpoint(self._forward, x, emb)
+
+
+stored = []
+
+
+def add():
+ if len(stored) != 0:
+ return
+
+ stored.extend([
+ ldm.modules.attention.BasicTransformerBlock.forward,
+ ldm.modules.diffusionmodules.openaimodel.ResBlock.forward,
+ ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward
+ ])
+
+ ldm.modules.attention.BasicTransformerBlock.forward = BasicTransformerBlock_forward
+ ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = ResBlock_forward
+ ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = AttentionBlock_forward
+
+
+def remove():
+ if len(stored) == 0:
+ return
+
+ ldm.modules.attention.BasicTransformerBlock.forward = stored[0]
+ ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = stored[1]
+ ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = stored[2]
+
+ stored.clear()
+
diff --git a/stable-diffusion-webui/modules/sd_hijack_clip.py b/stable-diffusion-webui/modules/sd_hijack_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8b369975c2c12d8438118c21874a970b99b9fb3
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_clip.py
@@ -0,0 +1,356 @@
+import math
+from collections import namedtuple
+
+import torch
+
+from modules import prompt_parser, devices, sd_hijack
+from modules.shared import opts
+
+
+class PromptChunk:
+ """
+ This object contains token ids, weight (multipliers:1.4) and textual inversion embedding info for a chunk of prompt.
+ If a prompt is short, it is represented by one PromptChunk, otherwise, multiple are necessary.
+ Each PromptChunk contains an exact amount of tokens - 77, which includes one for start and end token,
+ so just 75 tokens from prompt.
+ """
+
+ def __init__(self):
+ self.tokens = []
+ self.multipliers = []
+ self.fixes = []
+
+
+PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
+"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
+chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
+are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
+
+
+class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
+ """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
+ have unlimited prompt length and assign weights to tokens in prompt.
+ """
+
+ def __init__(self, wrapped, hijack):
+ super().__init__()
+
+ self.wrapped = wrapped
+ """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
+ depending on model."""
+
+ self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
+ self.chunk_length = 75
+
+ self.is_trainable = getattr(wrapped, 'is_trainable', False)
+ self.input_key = getattr(wrapped, 'input_key', 'txt')
+ self.legacy_ucg_val = None
+
+ def empty_chunk(self):
+ """creates an empty PromptChunk and returns it"""
+
+ chunk = PromptChunk()
+ chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
+ chunk.multipliers = [1.0] * (self.chunk_length + 2)
+ return chunk
+
+ def get_target_prompt_token_count(self, token_count):
+ """returns the maximum number of tokens a prompt of a known length can have before it requires one more PromptChunk to be represented"""
+
+ return math.ceil(max(token_count, 1) / self.chunk_length) * self.chunk_length
+
+ def tokenize(self, texts):
+ """Converts a batch of texts into a batch of token ids"""
+
+ raise NotImplementedError
+
+ def encode_with_transformers(self, tokens):
+ """
+ converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
+ All python lists with tokens are assumed to have same length, usually 77.
+ if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
+ model - can be 768 and 1024.
+ Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
+ """
+
+ raise NotImplementedError
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ """Converts text into a tensor with this text's tokens' embeddings. Note that those are embeddings before they are passed through
+ transformers. nvpt is used as a maximum length in tokens. If text produces less teokens than nvpt, only this many is returned."""
+
+ raise NotImplementedError
+
+ def tokenize_line(self, line):
+ """
+ this transforms a single prompt into a list of PromptChunk objects - as many as needed to
+ represent the prompt.
+ Returns the list and the total number of tokens in the prompt.
+ """
+
+ if opts.enable_emphasis:
+ parsed = prompt_parser.parse_prompt_attention(line)
+ else:
+ parsed = [[line, 1.0]]
+
+ tokenized = self.tokenize([text for text, _ in parsed])
+
+ chunks = []
+ chunk = PromptChunk()
+ token_count = 0
+ last_comma = -1
+
+ def next_chunk(is_last=False):
+ """puts current chunk into the list of results and produces the next one - empty;
+ if is_last is true, tokens <end-of-text> tokens at the end won't add to token_count"""
+ nonlocal token_count
+ nonlocal last_comma
+ nonlocal chunk
+
+ if is_last:
+ token_count += len(chunk.tokens)
+ else:
+ token_count += self.chunk_length
+
+ to_add = self.chunk_length - len(chunk.tokens)
+ if to_add > 0:
+ chunk.tokens += [self.id_end] * to_add
+ chunk.multipliers += [1.0] * to_add
+
+ chunk.tokens = [self.id_start] + chunk.tokens + [self.id_end]
+ chunk.multipliers = [1.0] + chunk.multipliers + [1.0]
+
+ last_comma = -1
+ chunks.append(chunk)
+ chunk = PromptChunk()
+
+ for tokens, (text, weight) in zip(tokenized, parsed):
+ if text == 'BREAK' and weight == -1:
+ next_chunk()
+ continue
+
+ position = 0
+ while position < len(tokens):
+ token = tokens[position]
+
+ if token == self.comma_token:
+ last_comma = len(chunk.tokens)
+
+ # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
+ # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
+ elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
+ break_location = last_comma + 1
+
+ reloc_tokens = chunk.tokens[break_location:]
+ reloc_mults = chunk.multipliers[break_location:]
+
+ chunk.tokens = chunk.tokens[:break_location]
+ chunk.multipliers = chunk.multipliers[:break_location]
+
+ next_chunk()
+ chunk.tokens = reloc_tokens
+ chunk.multipliers = reloc_mults
+
+ if len(chunk.tokens) == self.chunk_length:
+ next_chunk()
+
+ embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, position)
+ if embedding is None:
+ chunk.tokens.append(token)
+ chunk.multipliers.append(weight)
+ position += 1
+ continue
+
+ emb_len = int(embedding.vectors)
+ if len(chunk.tokens) + emb_len > self.chunk_length:
+ next_chunk()
+
+ chunk.fixes.append(PromptChunkFix(len(chunk.tokens), embedding))
+
+ chunk.tokens += [0] * emb_len
+ chunk.multipliers += [weight] * emb_len
+ position += embedding_length_in_tokens
+
+ if chunk.tokens or not chunks:
+ next_chunk(is_last=True)
+
+ return chunks, token_count
+
+ def process_texts(self, texts):
+ """
+ Accepts a list of texts and calls tokenize_line() on each, with cache. Returns the list of results and maximum
+ length, in tokens, of all texts.
+ """
+
+ token_count = 0
+
+ cache = {}
+ batch_chunks = []
+ for line in texts:
+ if line in cache:
+ chunks = cache[line]
+ else:
+ chunks, current_token_count = self.tokenize_line(line)
+ token_count = max(current_token_count, token_count)
+
+ cache[line] = chunks
+
+ batch_chunks.append(chunks)
+
+ return batch_chunks, token_count
+
+ def forward(self, texts):
+ """
+ Accepts an array of texts; Passes texts through transformers network to create a tensor with numerical representation of those texts.
+ Returns a tensor with shape of (B, T, C), where B is length of the array; T is length, in tokens, of texts (including padding) - T will
+ be a multiple of 77; and C is dimensionality of each token - for SD1 it's 768, for SD2 it's 1024, and for SDXL it's 1280.
+ An example shape returned by this function can be: (2, 77, 768).
+ For SDXL, instead of returning one tensor avobe, it returns a tuple with two: the other one with shape (B, 1280) with pooled values.
+ Webui usually sends just one text at a time through this function - the only time when texts is an array with more than one elemenet
+ is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
+ """
+
+ if opts.use_old_emphasis_implementation:
+ import modules.sd_hijack_clip_old
+ return modules.sd_hijack_clip_old.forward_old(self, texts)
+
+ batch_chunks, token_count = self.process_texts(texts)
+
+ used_embeddings = {}
+ chunk_count = max([len(x) for x in batch_chunks])
+
+ zs = []
+ for i in range(chunk_count):
+ batch_chunk = [chunks[i] if i < len(chunks) else self.empty_chunk() for chunks in batch_chunks]
+
+ tokens = [x.tokens for x in batch_chunk]
+ multipliers = [x.multipliers for x in batch_chunk]
+ self.hijack.fixes = [x.fixes for x in batch_chunk]
+
+ for fixes in self.hijack.fixes:
+ for _position, embedding in fixes:
+ used_embeddings[embedding.name] = embedding
+
+ z = self.process_tokens(tokens, multipliers)
+ zs.append(z)
+
+ if opts.textual_inversion_add_hashes_to_infotext and used_embeddings:
+ hashes = []
+ for name, embedding in used_embeddings.items():
+ shorthash = embedding.shorthash
+ if not shorthash:
+ continue
+
+ name = name.replace(":", "").replace(",", "")
+ hashes.append(f"{name}: {shorthash}")
+
+ if hashes:
+ if self.hijack.extra_generation_params.get("TI hashes"):
+ hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
+ self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
+
+ if getattr(self.wrapped, 'return_pooled', False):
+ return torch.hstack(zs), zs[0].pooled
+ else:
+ return torch.hstack(zs)
+
+ def process_tokens(self, remade_batch_tokens, batch_multipliers):
+ """
+ sends one single prompt chunk to be encoded by transformers neural network.
+ remade_batch_tokens is a batch of tokens - a list, where every element is a list of tokens; usually
+ there are exactly 77 tokens in the list. batch_multipliers is the same but for multipliers instead of tokens.
+ Multipliers are used to give more or less weight to the outputs of transformers network. Each multiplier
+ corresponds to one token.
+ """
+ tokens = torch.asarray(remade_batch_tokens).to(devices.device)
+
+ # this is for SD2: SD1 uses the same token for padding and end of text, while SD2 uses different ones.
+ if self.id_end != self.id_pad:
+ for batch_pos in range(len(remade_batch_tokens)):
+ index = remade_batch_tokens[batch_pos].index(self.id_end)
+ tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
+
+ z = self.encode_with_transformers(tokens)
+
+ pooled = getattr(z, 'pooled', None)
+
+ # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+ batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
+ original_mean = z.mean()
+ z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+ new_mean = z.mean()
+ z = z * (original_mean / new_mean)
+
+ if pooled is not None:
+ z.pooled = pooled
+
+ return z
+
+
+class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+ self.tokenizer = wrapped.tokenizer
+
+ vocab = self.tokenizer.get_vocab()
+
+ self.comma_token = vocab.get(',</w>', None)
+
+ self.token_mults = {}
+ tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
+ for text, ident in tokens_with_parens:
+ mult = 1.0
+ for c in text:
+ if c == '[':
+ mult /= 1.1
+ if c == ']':
+ mult *= 1.1
+ if c == '(':
+ mult *= 1.1
+ if c == ')':
+ mult /= 1.1
+
+ if mult != 1.0:
+ self.token_mults[ident] = mult
+
+ self.id_start = self.wrapped.tokenizer.bos_token_id
+ self.id_end = self.wrapped.tokenizer.eos_token_id
+ self.id_pad = self.id_end
+
+ def tokenize(self, texts):
+ tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
+
+ return tokenized
+
+ def encode_with_transformers(self, tokens):
+ outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+
+ if opts.CLIP_stop_at_last_layers > 1:
+ z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
+ z = self.wrapped.transformer.text_model.final_layer_norm(z)
+ else:
+ z = outputs.last_hidden_state
+
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ embedding_layer = self.wrapped.transformer.text_model.embeddings
+ ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+ embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
+
+ return embedded
+
+
+class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ def encode_with_transformers(self, tokens):
+ outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
+
+ if self.wrapped.layer == "last":
+ z = outputs.last_hidden_state
+ else:
+ z = outputs.hidden_states[self.wrapped.layer_idx]
+
+ return z
diff --git a/stable-diffusion-webui/modules/sd_hijack_clip_old.py b/stable-diffusion-webui/modules/sd_hijack_clip_old.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bd207671c5517de3d7f809d71c16904a2b0ca34
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_clip_old.py
@@ -0,0 +1,82 @@
+from modules import sd_hijack_clip
+from modules import shared
+
+
+def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
+ id_start = self.id_start
+ id_end = self.id_end
+ maxlen = self.wrapped.max_length # you get to stay at 77
+ used_custom_terms = []
+ remade_batch_tokens = []
+ hijack_comments = []
+ hijack_fixes = []
+ token_count = 0
+
+ cache = {}
+ batch_tokens = self.tokenize(texts)
+ batch_multipliers = []
+ for tokens in batch_tokens:
+ tuple_tokens = tuple(tokens)
+
+ if tuple_tokens in cache:
+ remade_tokens, fixes, multipliers = cache[tuple_tokens]
+ else:
+ fixes = []
+ remade_tokens = []
+ multipliers = []
+ mult = 1.0
+
+ i = 0
+ while i < len(tokens):
+ token = tokens[i]
+
+ embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
+
+ mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
+ if mult_change is not None:
+ mult *= mult_change
+ i += 1
+ elif embedding is None:
+ remade_tokens.append(token)
+ multipliers.append(mult)
+ i += 1
+ else:
+ emb_len = int(embedding.vec.shape[0])
+ fixes.append((len(remade_tokens), embedding))
+ remade_tokens += [0] * emb_len
+ multipliers += [mult] * emb_len
+ used_custom_terms.append((embedding.name, embedding.checksum()))
+ i += embedding_length_in_tokens
+
+ if len(remade_tokens) > maxlen - 2:
+ vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
+ ovf = remade_tokens[maxlen - 2:]
+ overflowing_words = [vocab.get(int(x), "") for x in ovf]
+ overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
+ hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
+
+ token_count = len(remade_tokens)
+ remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
+ remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+ cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
+
+ multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
+ multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+
+ remade_batch_tokens.append(remade_tokens)
+ hijack_fixes.append(fixes)
+ batch_multipliers.append(multipliers)
+ return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
+
+
+def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, texts):
+ batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = process_text_old(self, texts)
+
+ self.hijack.comments += hijack_comments
+
+ if used_custom_terms:
+ embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
+ self.hijack.comments.append(f"Used embeddings: {embedding_names}")
+
+ self.hijack.fixes = hijack_fixes
+ return self.process_tokens(remade_batch_tokens, batch_multipliers)
diff --git a/stable-diffusion-webui/modules/sd_hijack_ip2p.py b/stable-diffusion-webui/modules/sd_hijack_ip2p.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fe6b6ffec64d78a615e001398f3272decf348a5
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_ip2p.py
@@ -0,0 +1,10 @@
+import os.path
+
+
+def should_hijack_ip2p(checkpoint_info):
+ from modules import sd_models_config
+
+ ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
+ cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
+
+ return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename
diff --git a/stable-diffusion-webui/modules/sd_hijack_open_clip.py b/stable-diffusion-webui/modules/sd_hijack_open_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..703490ee4c21e56a2a208e6b67d0f89d0a897856
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_open_clip.py
@@ -0,0 +1,71 @@
+import open_clip.tokenizer
+import torch
+
+from modules import sd_hijack_clip, devices
+from modules.shared import opts
+
+tokenizer = open_clip.tokenizer._tokenizer
+
+
+class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
+ self.id_start = tokenizer.encoder["<start_of_text>"]
+ self.id_end = tokenizer.encoder["<end_of_text>"]
+ self.id_pad = 0
+
+ def tokenize(self, texts):
+ assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
+
+ tokenized = [tokenizer.encode(text) for text in texts]
+
+ return tokenized
+
+ def encode_with_transformers(self, tokens):
+ # set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
+ z = self.wrapped.encode_with_transformer(tokens)
+
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ ids = tokenizer.encode(init_text)
+ ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
+ embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
+
+ return embedded
+
+
+class FrozenOpenCLIPEmbedder2WithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
+ self.id_start = tokenizer.encoder["<start_of_text>"]
+ self.id_end = tokenizer.encoder["<end_of_text>"]
+ self.id_pad = 0
+
+ def tokenize(self, texts):
+ assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
+
+ tokenized = [tokenizer.encode(text) for text in texts]
+
+ return tokenized
+
+ def encode_with_transformers(self, tokens):
+ d = self.wrapped.encode_with_transformer(tokens)
+ z = d[self.wrapped.layer]
+
+ pooled = d.get("pooled")
+ if pooled is not None:
+ z.pooled = pooled
+
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ ids = tokenizer.encode(init_text)
+ ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
+ embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
+
+ return embedded
diff --git a/stable-diffusion-webui/modules/sd_hijack_optimizations.py b/stable-diffusion-webui/modules/sd_hijack_optimizations.py
new file mode 100644
index 0000000000000000000000000000000000000000..41ffc513caa2aed1a5cef9663e17b1313ac54d6a
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_optimizations.py
@@ -0,0 +1,675 @@
+from __future__ import annotations
+import math
+import psutil
+import platform
+
+import torch
+from torch import einsum
+
+from ldm.util import default
+from einops import rearrange
+
+from modules import shared, errors, devices, sub_quadratic_attention
+from modules.hypernetworks import hypernetwork
+
+import ldm.modules.attention
+import ldm.modules.diffusionmodules.model
+
+import sgm.modules.attention
+import sgm.modules.diffusionmodules.model
+
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
+sgm_diffusionmodules_model_AttnBlock_forward = sgm.modules.diffusionmodules.model.AttnBlock.forward
+
+
+class SdOptimization:
+ name: str = None
+ label: str | None = None
+ cmd_opt: str | None = None
+ priority: int = 0
+
+ def title(self):
+ if self.label is None:
+ return self.name
+
+ return f"{self.name} - {self.label}"
+
+ def is_available(self):
+ return True
+
+ def apply(self):
+ pass
+
+ def undo(self):
+ ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
+ sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sgm_diffusionmodules_model_AttnBlock_forward
+
+
+class SdOptimizationXformers(SdOptimization):
+ name = "xformers"
+ cmd_opt = "xformers"
+ priority = 100
+
+ def is_available(self):
+ return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.cuda.is_available() and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = xformers_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
+
+
+class SdOptimizationSdpNoMem(SdOptimization):
+ name = "sdp-no-mem"
+ label = "scaled dot product without memory efficient attention"
+ cmd_opt = "opt_sdp_no_mem_attention"
+ priority = 80
+
+ def is_available(self):
+ return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
+
+
+class SdOptimizationSdp(SdOptimizationSdpNoMem):
+ name = "sdp"
+ label = "scaled dot product"
+ cmd_opt = "opt_sdp_attention"
+ priority = 70
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
+
+
+class SdOptimizationSubQuad(SdOptimization):
+ name = "sub-quadratic"
+ cmd_opt = "opt_sub_quad_attention"
+
+ @property
+ def priority(self):
+ return 1000 if shared.device.type == 'mps' else 10
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
+
+
+class SdOptimizationV1(SdOptimization):
+ name = "V1"
+ label = "original v1"
+ cmd_opt = "opt_split_attention_v1"
+ priority = 10
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
+ sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
+
+
+class SdOptimizationInvokeAI(SdOptimization):
+ name = "InvokeAI"
+ cmd_opt = "opt_split_attention_invokeai"
+
+ @property
+ def priority(self):
+ return 1000 if shared.device.type != 'mps' and not torch.cuda.is_available() else 10
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
+ sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
+
+
+class SdOptimizationDoggettx(SdOptimization):
+ name = "Doggettx"
+ cmd_opt = "opt_split_attention"
+ priority = 90
+
+ def apply(self):
+ ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+ sgm.modules.attention.CrossAttention.forward = split_cross_attention_forward
+ sgm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+
+
+def list_optimizers(res):
+ res.extend([
+ SdOptimizationXformers(),
+ SdOptimizationSdpNoMem(),
+ SdOptimizationSdp(),
+ SdOptimizationSubQuad(),
+ SdOptimizationV1(),
+ SdOptimizationInvokeAI(),
+ SdOptimizationDoggettx(),
+ ])
+
+
+if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
+ try:
+ import xformers.ops
+ shared.xformers_available = True
+ except Exception:
+ errors.report("Cannot import xformers", exc_info=True)
+
+
+def get_available_vram():
+ if shared.device.type == 'cuda':
+ stats = torch.cuda.memory_stats(shared.device)
+ mem_active = stats['active_bytes.all.current']
+ mem_reserved = stats['reserved_bytes.all.current']
+ mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+ mem_free_torch = mem_reserved - mem_active
+ mem_free_total = mem_free_cuda + mem_free_torch
+ return mem_free_total
+ else:
+ return psutil.virtual_memory().available
+
+
+# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
+def split_cross_attention_forward_v1(self, x, context=None, mask=None, **kwargs):
+ h = self.heads
+
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+ del context, context_k, context_v, x
+
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
+ del q_in, k_in, v_in
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+
+ with devices.without_autocast(disable=not shared.opts.upcast_attn):
+ r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+ for i in range(0, q.shape[0], 2):
+ end = i + 2
+ s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+ s1 *= self.scale
+
+ s2 = s1.softmax(dim=-1)
+ del s1
+
+ r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+ del s2
+ del q, k, v
+
+ r1 = r1.to(dtype)
+
+ r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+ del r1
+
+ return self.to_out(r2)
+
+
+# taken from https://github.com/Doggettx/stable-diffusion and modified
+def split_cross_attention_forward(self, x, context=None, mask=None, **kwargs):
+ h = self.heads
+
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+
+ dtype = q_in.dtype
+ if shared.opts.upcast_attn:
+ q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()
+
+ with devices.without_autocast(disable=not shared.opts.upcast_attn):
+ k_in = k_in * self.scale
+
+ del context, x
+
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
+ del q_in, k_in, v_in
+
+ r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+ mem_free_total = get_available_vram()
+
+ gb = 1024 ** 3
+ tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+ modifier = 3 if q.element_size() == 2 else 2.5
+ mem_required = tensor_size * modifier
+ steps = 1
+
+ if mem_required > mem_free_total:
+ steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+ # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+ # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+ if steps > 64:
+ max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+ raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+ f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+
+ slice_size = q.shape[1] // steps
+ for i in range(0, q.shape[1], slice_size):
+ end = min(i + slice_size, q.shape[1])
+ s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
+
+ s2 = s1.softmax(dim=-1, dtype=q.dtype)
+ del s1
+
+ r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+ del s2
+
+ del q, k, v
+
+ r1 = r1.to(dtype)
+
+ r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
+ del r1
+
+ return self.to_out(r2)
+
+
+# -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
+mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+
+
+def einsum_op_compvis(q, k, v):
+ s = einsum('b i d, b j d -> b i j', q, k)
+ s = s.softmax(dim=-1, dtype=s.dtype)
+ return einsum('b i j, b j d -> b i d', s, v)
+
+
+def einsum_op_slice_0(q, k, v, slice_size):
+ r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+ for i in range(0, q.shape[0], slice_size):
+ end = i + slice_size
+ r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
+ return r
+
+
+def einsum_op_slice_1(q, k, v, slice_size):
+ r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+ for i in range(0, q.shape[1], slice_size):
+ end = i + slice_size
+ r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
+ return r
+
+
+def einsum_op_mps_v1(q, k, v):
+ if q.shape[0] * q.shape[1] <= 2**16: # (512x512) max q.shape[1]: 4096
+ return einsum_op_compvis(q, k, v)
+ else:
+ slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+ if slice_size % 4096 == 0:
+ slice_size -= 1
+ return einsum_op_slice_1(q, k, v, slice_size)
+
+
+def einsum_op_mps_v2(q, k, v):
+ if mem_total_gb > 8 and q.shape[0] * q.shape[1] <= 2**16:
+ return einsum_op_compvis(q, k, v)
+ else:
+ return einsum_op_slice_0(q, k, v, 1)
+
+
+def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
+ size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
+ if size_mb <= max_tensor_mb:
+ return einsum_op_compvis(q, k, v)
+ div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
+ if div <= q.shape[0]:
+ return einsum_op_slice_0(q, k, v, q.shape[0] // div)
+ return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
+
+
+def einsum_op_cuda(q, k, v):
+ stats = torch.cuda.memory_stats(q.device)
+ mem_active = stats['active_bytes.all.current']
+ mem_reserved = stats['reserved_bytes.all.current']
+ mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
+ mem_free_torch = mem_reserved - mem_active
+ mem_free_total = mem_free_cuda + mem_free_torch
+ # Divide factor of safety as there's copying and fragmentation
+ return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+
+
+def einsum_op(q, k, v):
+ if q.device.type == 'cuda':
+ return einsum_op_cuda(q, k, v)
+
+ if q.device.type == 'mps':
+ if mem_total_gb >= 32 and q.shape[0] % 32 != 0 and q.shape[0] * q.shape[1] < 2**18:
+ return einsum_op_mps_v1(q, k, v)
+ return einsum_op_mps_v2(q, k, v)
+
+ # Smaller slices are faster due to L2/L3/SLC caches.
+ # Tested on i7 with 8MB L3 cache.
+ return einsum_op_tensor_mem(q, k, v, 32)
+
+
+def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None, **kwargs):
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k = self.to_k(context_k)
+ v = self.to_v(context_v)
+ del context, context_k, context_v, x
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()
+
+ with devices.without_autocast(disable=not shared.opts.upcast_attn):
+ k = k * self.scale
+
+ q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
+ r = einsum_op(q, k, v)
+ r = r.to(dtype)
+ return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
+
+# -- End of code from https://github.com/invoke-ai/InvokeAI --
+
+
+# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
+# The sub_quad_attention_forward function is under the MIT License listed under Memory Efficient Attention in the Licenses section of the web UI interface
+def sub_quad_attention_forward(self, x, context=None, mask=None, **kwargs):
+ assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
+
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k = self.to_k(context_k)
+ v = self.to_v(context_v)
+ del context, context_k, context_v, x
+
+ q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+ k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+ v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+
+ if q.device.type == 'mps':
+ q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k = q.float(), k.float()
+
+ x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
+
+ x = x.to(dtype)
+
+ x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
+
+ out_proj, dropout = self.to_out
+ x = out_proj(x)
+ x = dropout(x)
+
+ return x
+
+
+def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold=None, use_checkpoint=True):
+ bytes_per_token = torch.finfo(q.dtype).bits//8
+ batch_x_heads, q_tokens, _ = q.shape
+ _, k_tokens, _ = k.shape
+ qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
+
+ if chunk_threshold is None:
+ if q.device.type == 'mps':
+ chunk_threshold_bytes = 268435456 * (2 if platform.processor() == 'i386' else bytes_per_token)
+ else:
+ chunk_threshold_bytes = int(get_available_vram() * 0.7)
+ elif chunk_threshold == 0:
+ chunk_threshold_bytes = None
+ else:
+ chunk_threshold_bytes = int(0.01 * chunk_threshold * get_available_vram())
+
+ if kv_chunk_size_min is None and chunk_threshold_bytes is not None:
+ kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))
+ elif kv_chunk_size_min == 0:
+ kv_chunk_size_min = None
+
+ if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
+ # the big matmul fits into our memory limit; do everything in 1 chunk,
+ # i.e. send it down the unchunked fast-path
+ kv_chunk_size = k_tokens
+
+ with devices.without_autocast(disable=q.dtype == v.dtype):
+ return sub_quadratic_attention.efficient_dot_product_attention(
+ q,
+ k,
+ v,
+ query_chunk_size=q_chunk_size,
+ kv_chunk_size=kv_chunk_size,
+ kv_chunk_size_min = kv_chunk_size_min,
+ use_checkpoint=use_checkpoint,
+ )
+
+
+def get_xformers_flash_attention_op(q, k, v):
+ if not shared.cmd_opts.xformers_flash_attention:
+ return None
+
+ try:
+ flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
+ fw, bw = flash_attention_op
+ if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
+ return flash_attention_op
+ except Exception as e:
+ errors.display_once(e, "enabling flash attention")
+
+ return None
+
+
+def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
+ h = self.heads
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+
+ q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
+ del q_in, k_in, v_in
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
+
+ out = out.to(dtype)
+
+ out = rearrange(out, 'b n h d -> b n (h d)', h=h)
+ return self.to_out(out)
+
+
+# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
+# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
+def scaled_dot_product_attention_forward(self, x, context=None, mask=None, **kwargs):
+ batch_size, sequence_length, inner_dim = x.shape
+
+ if mask is not None:
+ mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
+ mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
+
+ h = self.heads
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+
+ head_dim = inner_dim // h
+ q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+ k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+ v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+
+ del q_in, k_in, v_in
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+
+ # the output of sdp = (batch, num_heads, seq_len, head_dim)
+ hidden_states = torch.nn.functional.scaled_dot_product_attention(
+ q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
+ )
+
+ hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
+ hidden_states = hidden_states.to(dtype)
+
+ # linear proj
+ hidden_states = self.to_out[0](hidden_states)
+ # dropout
+ hidden_states = self.to_out[1](hidden_states)
+ return hidden_states
+
+
+def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None, **kwargs):
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+ return scaled_dot_product_attention_forward(self, x, context, mask)
+
+
+def cross_attention_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q1 = self.q(h_)
+ k1 = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b, c, h, w = q1.shape
+
+ q2 = q1.reshape(b, c, h*w)
+ del q1
+
+ q = q2.permute(0, 2, 1) # b,hw,c
+ del q2
+
+ k = k1.reshape(b, c, h*w) # b,c,hw
+ del k1
+
+ h_ = torch.zeros_like(k, device=q.device)
+
+ mem_free_total = get_available_vram()
+
+ tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+ mem_required = tensor_size * 2.5
+ steps = 1
+
+ if mem_required > mem_free_total:
+ steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+ slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+ for i in range(0, q.shape[1], slice_size):
+ end = i + slice_size
+
+ w1 = torch.bmm(q[:, i:end], k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+ w2 = w1 * (int(c)**(-0.5))
+ del w1
+ w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
+ del w2
+
+ # attend to values
+ v1 = v.reshape(b, c, h*w)
+ w4 = w3.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
+ del w3
+
+ h_[:, :, i:end] = torch.bmm(v1, w4) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+ del v1, w4
+
+ h2 = h_.reshape(b, c, h, w)
+ del h_
+
+ h3 = self.proj_out(h2)
+ del h2
+
+ h3 += x
+
+ return h3
+
+
+def xformers_attnblock_forward(self, x):
+ try:
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k = q.float(), k.float()
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
+ out = out.to(dtype)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+ out = self.proj_out(out)
+ return x + out
+ except NotImplementedError:
+ return cross_attention_attnblock_forward(self, x)
+
+
+def sdp_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
+ out = out.to(dtype)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+ out = self.proj_out(out)
+ return x + out
+
+
+def sdp_no_mem_attnblock_forward(self, x):
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+ return sdp_attnblock_forward(self, x)
+
+
+def sub_quad_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+ q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+ out = self.proj_out(out)
+ return x + out
diff --git a/stable-diffusion-webui/modules/sd_hijack_unet.py b/stable-diffusion-webui/modules/sd_hijack_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..119a89dbdfa04de0ff6d2011e6280d0798f309b8
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_unet.py
@@ -0,0 +1,85 @@
+import torch
+from packaging import version
+
+from modules import devices
+from modules.sd_hijack_utils import CondFunc
+
+
+class TorchHijackForUnet:
+ """
+ This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
+ this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
+ """
+
+ def __getattr__(self, item):
+ if item == 'cat':
+ return self.cat
+
+ if hasattr(torch, item):
+ return getattr(torch, item)
+
+ raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+ def cat(self, tensors, *args, **kwargs):
+ if len(tensors) == 2:
+ a, b = tensors
+ if a.shape[-2:] != b.shape[-2:]:
+ a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
+
+ tensors = (a, b)
+
+ return torch.cat(tensors, *args, **kwargs)
+
+
+th = TorchHijackForUnet()
+
+
+# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
+def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
+
+ if isinstance(cond, dict):
+ for y in cond.keys():
+ if isinstance(cond[y], list):
+ cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+ else:
+ cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
+
+ with devices.autocast():
+ return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
+
+
+class GELUHijack(torch.nn.GELU, torch.nn.Module):
+ def __init__(self, *args, **kwargs):
+ torch.nn.GELU.__init__(self, *args, **kwargs)
+ def forward(self, x):
+ if devices.unet_needs_upcast:
+ return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)
+ else:
+ return torch.nn.GELU.forward(self, x)
+
+
+ddpm_edit_hijack = None
+def hijack_ddpm_edit():
+ global ddpm_edit_hijack
+ if not ddpm_edit_hijack:
+ CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+ CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
+ ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+
+
+unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
+if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
+ CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
+ CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
+ CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
+
+first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16
+first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
+
+CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
+CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
diff --git a/stable-diffusion-webui/modules/sd_hijack_utils.py b/stable-diffusion-webui/modules/sd_hijack_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..179ebc78e6a3d16e7a4318b8644fee690b447d12
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_utils.py
@@ -0,0 +1,28 @@
+import importlib
+
+class CondFunc:
+ def __new__(cls, orig_func, sub_func, cond_func):
+ self = super(CondFunc, cls).__new__(cls)
+ if isinstance(orig_func, str):
+ func_path = orig_func.split('.')
+ for i in range(len(func_path)-1, -1, -1):
+ try:
+ resolved_obj = importlib.import_module('.'.join(func_path[:i]))
+ break
+ except ImportError:
+ pass
+ for attr_name in func_path[i:-1]:
+ resolved_obj = getattr(resolved_obj, attr_name)
+ orig_func = getattr(resolved_obj, func_path[-1])
+ setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
+ self.__init__(orig_func, sub_func, cond_func)
+ return lambda *args, **kwargs: self(*args, **kwargs)
+ def __init__(self, orig_func, sub_func, cond_func):
+ self.__orig_func = orig_func
+ self.__sub_func = sub_func
+ self.__cond_func = cond_func
+ def __call__(self, *args, **kwargs):
+ if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
+ return self.__sub_func(self.__orig_func, *args, **kwargs)
+ else:
+ return self.__orig_func(*args, **kwargs)
diff --git a/stable-diffusion-webui/modules/sd_hijack_xlmr.py b/stable-diffusion-webui/modules/sd_hijack_xlmr.py
new file mode 100644
index 0000000000000000000000000000000000000000..2aa646aeae3f0af39122b02b7b081ff7a3daf8ee
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_hijack_xlmr.py
@@ -0,0 +1,32 @@
+import torch
+
+from modules import sd_hijack_clip, devices
+
+
+class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
+ def __init__(self, wrapped, hijack):
+ super().__init__(wrapped, hijack)
+
+ self.id_start = wrapped.config.bos_token_id
+ self.id_end = wrapped.config.eos_token_id
+ self.id_pad = wrapped.config.pad_token_id
+
+ self.comma_token = self.tokenizer.get_vocab().get(',', None) # alt diffusion doesn't have </w> bits for comma
+
+ def encode_with_transformers(self, tokens):
+ # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
+ # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
+ # layer to work with - you have to use the last
+
+ attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
+ features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
+ z = features['projection_state']
+
+ return z
+
+ def encode_embedding_init_text(self, init_text, nvpt):
+ embedding_layer = self.wrapped.roberta.embeddings
+ ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+ embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+
+ return embedded
diff --git a/stable-diffusion-webui/modules/sd_models.py b/stable-diffusion-webui/modules/sd_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..efc9f447517386dee4c7e7d0f4b10c7c6148e5fa
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_models.py
@@ -0,0 +1,818 @@
+import collections
+import os.path
+import sys
+import gc
+import threading
+
+import torch
+import re
+import safetensors.torch
+from omegaconf import OmegaConf
+from os import mkdir
+from urllib import request
+import ldm.modules.midas as midas
+
+from ldm.util import instantiate_from_config
+
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack
+from modules.timer import Timer
+import tomesd
+
+model_dir = "Stable-diffusion"
+model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
+
+checkpoints_list = {}
+checkpoint_aliases = {}
+checkpoint_alisases = checkpoint_aliases # for compatibility with old name
+checkpoints_loaded = collections.OrderedDict()
+
+
+def replace_key(d, key, new_key, value):
+ keys = list(d.keys())
+
+ d[new_key] = value
+
+ if key not in keys:
+ return d
+
+ index = keys.index(key)
+ keys[index] = new_key
+
+ new_d = {k: d[k] for k in keys}
+
+ d.clear()
+ d.update(new_d)
+ return d
+
+
+class CheckpointInfo:
+ def __init__(self, filename):
+ self.filename = filename
+ abspath = os.path.abspath(filename)
+
+ self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
+ if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
+ name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
+ elif abspath.startswith(model_path):
+ name = abspath.replace(model_path, '')
+ else:
+ name = os.path.basename(filename)
+
+ if name.startswith("\\") or name.startswith("/"):
+ name = name[1:]
+
+ def read_metadata():
+ metadata = read_metadata_from_safetensors(filename)
+ self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
+
+ return metadata
+
+ self.metadata = {}
+ if self.is_safetensors:
+ try:
+ self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
+ except Exception as e:
+ errors.display(e, f"reading metadata for {filename}")
+
+ self.name = name
+ self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
+ self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
+ self.hash = model_hash(filename)
+
+ self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
+ self.shorthash = self.sha256[0:10] if self.sha256 else None
+
+ self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
+ self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
+
+ self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
+ if self.shorthash:
+ self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
+
+ def register(self):
+ checkpoints_list[self.title] = self
+ for id in self.ids:
+ checkpoint_aliases[id] = self
+
+ def calculate_shorthash(self):
+ self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
+ if self.sha256 is None:
+ return
+
+ shorthash = self.sha256[0:10]
+ if self.shorthash == self.sha256[0:10]:
+ return self.shorthash
+
+ self.shorthash = shorthash
+
+ if self.shorthash not in self.ids:
+ self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
+
+ old_title = self.title
+ self.title = f'{self.name} [{self.shorthash}]'
+ self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
+
+ replace_key(checkpoints_list, old_title, self.title, self)
+ self.register()
+
+ return self.shorthash
+
+
+try:
+ # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
+ from transformers import logging, CLIPModel # noqa: F401
+
+ logging.set_verbosity_error()
+except Exception:
+ pass
+
+
+def setup_model():
+ os.makedirs(model_path, exist_ok=True)
+
+ enable_midas_autodownload()
+
+
+def checkpoint_tiles(use_short=False):
+ return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
+
+
+def list_models():
+ checkpoints_list.clear()
+ checkpoint_aliases.clear()
+
+ cmd_ckpt = shared.cmd_opts.ckpt
+ if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
+ model_url = None
+ else:
+ model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+
+ model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
+
+ if os.path.exists(cmd_ckpt):
+ checkpoint_info = CheckpointInfo(cmd_ckpt)
+ checkpoint_info.register()
+
+ shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
+ elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
+ print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
+
+ for filename in model_list:
+ checkpoint_info = CheckpointInfo(filename)
+ checkpoint_info.register()
+
+
+re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
+
+
+def get_closet_checkpoint_match(search_string):
+ if not search_string:
+ return None
+
+ checkpoint_info = checkpoint_aliases.get(search_string, None)
+ if checkpoint_info is not None:
+ return checkpoint_info
+
+ found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
+ if found:
+ return found[0]
+
+ search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
+ found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
+ if found:
+ return found[0]
+
+ return None
+
+
+def model_hash(filename):
+ """old hash that only looks at a small part of the file and is prone to collisions"""
+
+ try:
+ with open(filename, "rb") as file:
+ import hashlib
+ m = hashlib.sha256()
+
+ file.seek(0x100000)
+ m.update(file.read(0x10000))
+ return m.hexdigest()[0:8]
+ except FileNotFoundError:
+ return 'NOFILE'
+
+
+def select_checkpoint():
+ """Raises `FileNotFoundError` if no checkpoints are found."""
+ model_checkpoint = shared.opts.sd_model_checkpoint
+
+ checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
+ if checkpoint_info is not None:
+ return checkpoint_info
+
+ if len(checkpoints_list) == 0:
+ error_message = "No checkpoints found. When searching for checkpoints, looked at:"
+ if shared.cmd_opts.ckpt is not None:
+ error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
+ error_message += f"\n - directory {model_path}"
+ if shared.cmd_opts.ckpt_dir is not None:
+ error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
+ error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
+ raise FileNotFoundError(error_message)
+
+ checkpoint_info = next(iter(checkpoints_list.values()))
+ if model_checkpoint is not None:
+ print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
+
+ return checkpoint_info
+
+
+checkpoint_dict_replacements = {
+ 'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
+ 'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
+ 'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
+}
+
+
+def transform_checkpoint_dict_key(k):
+ for text, replacement in checkpoint_dict_replacements.items():
+ if k.startswith(text):
+ k = replacement + k[len(text):]
+
+ return k
+
+
+def get_state_dict_from_checkpoint(pl_sd):
+ pl_sd = pl_sd.pop("state_dict", pl_sd)
+ pl_sd.pop("state_dict", None)
+
+ sd = {}
+ for k, v in pl_sd.items():
+ new_key = transform_checkpoint_dict_key(k)
+
+ if new_key is not None:
+ sd[new_key] = v
+
+ pl_sd.clear()
+ pl_sd.update(sd)
+
+ return pl_sd
+
+
+def read_metadata_from_safetensors(filename):
+ import json
+
+ with open(filename, mode="rb") as file:
+ metadata_len = file.read(8)
+ metadata_len = int.from_bytes(metadata_len, "little")
+ json_start = file.read(2)
+
+ assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
+ json_data = json_start + file.read(metadata_len-2)
+ json_obj = json.loads(json_data)
+
+ res = {}
+ for k, v in json_obj.get("__metadata__", {}).items():
+ res[k] = v
+ if isinstance(v, str) and v[0:1] == '{':
+ try:
+ res[k] = json.loads(v)
+ except Exception:
+ pass
+
+ return res
+
+
+def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
+ _, extension = os.path.splitext(checkpoint_file)
+ if extension.lower() == ".safetensors":
+ device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
+
+ if not shared.opts.disable_mmap_load_safetensors:
+ pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
+ else:
+ pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
+ pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
+ else:
+ pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
+
+ if print_global_state and "global_step" in pl_sd:
+ print(f"Global Step: {pl_sd['global_step']}")
+
+ sd = get_state_dict_from_checkpoint(pl_sd)
+ return sd
+
+
+def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
+ sd_model_hash = checkpoint_info.calculate_shorthash()
+ timer.record("calculate hash")
+
+ if checkpoint_info in checkpoints_loaded:
+ # use checkpoint cache
+ print(f"Loading weights [{sd_model_hash}] from cache")
+ return checkpoints_loaded[checkpoint_info]
+
+ print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
+ res = read_state_dict(checkpoint_info.filename)
+ timer.record("load weights from disk")
+
+ return res
+
+
+class SkipWritingToConfig:
+ """This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""
+
+ skip = False
+ previous = None
+
+ def __enter__(self):
+ self.previous = SkipWritingToConfig.skip
+ SkipWritingToConfig.skip = True
+ return self
+
+ def __exit__(self, exc_type, exc_value, exc_traceback):
+ SkipWritingToConfig.skip = self.previous
+
+
+def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
+ sd_model_hash = checkpoint_info.calculate_shorthash()
+ timer.record("calculate hash")
+
+ if not SkipWritingToConfig.skip:
+ shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
+
+ if state_dict is None:
+ state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+
+ model.is_sdxl = hasattr(model, 'conditioner')
+ model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
+ model.is_sd1 = not model.is_sdxl and not model.is_sd2
+
+ if model.is_sdxl:
+ sd_models_xl.extend_sdxl(model)
+
+ model.load_state_dict(state_dict, strict=False)
+ timer.record("apply weights to model")
+
+ if shared.opts.sd_checkpoint_cache > 0:
+ # cache newly loaded model
+ checkpoints_loaded[checkpoint_info] = state_dict
+
+ del state_dict
+
+ if shared.cmd_opts.opt_channelslast:
+ model.to(memory_format=torch.channels_last)
+ timer.record("apply channels_last")
+
+ if shared.cmd_opts.no_half:
+ model.float()
+ devices.dtype_unet = torch.float32
+ timer.record("apply float()")
+ else:
+ vae = model.first_stage_model
+ depth_model = getattr(model, 'depth_model', None)
+
+ # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+ if shared.cmd_opts.no_half_vae:
+ model.first_stage_model = None
+ # with --upcast-sampling, don't convert the depth model weights to float16
+ if shared.cmd_opts.upcast_sampling and depth_model:
+ model.depth_model = None
+
+ model.half()
+ model.first_stage_model = vae
+ if depth_model:
+ model.depth_model = depth_model
+
+ devices.dtype_unet = torch.float16
+ timer.record("apply half()")
+
+ devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
+
+ model.first_stage_model.to(devices.dtype_vae)
+ timer.record("apply dtype to VAE")
+
+ # clean up cache if limit is reached
+ while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+ checkpoints_loaded.popitem(last=False)
+
+ model.sd_model_hash = sd_model_hash
+ model.sd_model_checkpoint = checkpoint_info.filename
+ model.sd_checkpoint_info = checkpoint_info
+ shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
+
+ if hasattr(model, 'logvar'):
+ model.logvar = model.logvar.to(devices.device) # fix for training
+
+ sd_vae.delete_base_vae()
+ sd_vae.clear_loaded_vae()
+ vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
+ sd_vae.load_vae(model, vae_file, vae_source)
+ timer.record("load VAE")
+
+
+def enable_midas_autodownload():
+ """
+ Gives the ldm.modules.midas.api.load_model function automatic downloading.
+
+ When the 512-depth-ema model, and other future models like it, is loaded,
+ it calls midas.api.load_model to load the associated midas depth model.
+ This function applies a wrapper to download the model to the correct
+ location automatically.
+ """
+
+ midas_path = os.path.join(paths.models_path, 'midas')
+
+ # stable-diffusion-stability-ai hard-codes the midas model path to
+ # a location that differs from where other scripts using this model look.
+ # HACK: Overriding the path here.
+ for k, v in midas.api.ISL_PATHS.items():
+ file_name = os.path.basename(v)
+ midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
+
+ midas_urls = {
+ "dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
+ "dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
+ "midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
+ "midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
+ }
+
+ midas.api.load_model_inner = midas.api.load_model
+
+ def load_model_wrapper(model_type):
+ path = midas.api.ISL_PATHS[model_type]
+ if not os.path.exists(path):
+ if not os.path.exists(midas_path):
+ mkdir(midas_path)
+
+ print(f"Downloading midas model weights for {model_type} to {path}")
+ request.urlretrieve(midas_urls[model_type], path)
+ print(f"{model_type} downloaded")
+
+ return midas.api.load_model_inner(model_type)
+
+ midas.api.load_model = load_model_wrapper
+
+
+def repair_config(sd_config):
+
+ if not hasattr(sd_config.model.params, "use_ema"):
+ sd_config.model.params.use_ema = False
+
+ if hasattr(sd_config.model.params, 'unet_config'):
+ if shared.cmd_opts.no_half:
+ sd_config.model.params.unet_config.params.use_fp16 = False
+ elif shared.cmd_opts.upcast_sampling:
+ sd_config.model.params.unet_config.params.use_fp16 = True
+
+ if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
+ sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
+
+ # For UnCLIP-L, override the hardcoded karlo directory
+ if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
+ karlo_path = os.path.join(paths.models_path, 'karlo')
+ sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
+
+
+sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
+sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
+sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
+sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
+
+
+class SdModelData:
+ def __init__(self):
+ self.sd_model = None
+ self.loaded_sd_models = []
+ self.was_loaded_at_least_once = False
+ self.lock = threading.Lock()
+
+ def get_sd_model(self):
+ if self.was_loaded_at_least_once:
+ return self.sd_model
+
+ if self.sd_model is None:
+ with self.lock:
+ if self.sd_model is not None or self.was_loaded_at_least_once:
+ return self.sd_model
+
+ try:
+ load_model()
+
+ except Exception as e:
+ errors.display(e, "loading stable diffusion model", full_traceback=True)
+ print("", file=sys.stderr)
+ print("Stable diffusion model failed to load", file=sys.stderr)
+ self.sd_model = None
+
+ return self.sd_model
+
+ def set_sd_model(self, v, already_loaded=False):
+ self.sd_model = v
+ if already_loaded:
+ sd_vae.base_vae = getattr(v, "base_vae", None)
+ sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None)
+ sd_vae.checkpoint_info = v.sd_checkpoint_info
+
+ try:
+ self.loaded_sd_models.remove(v)
+ except ValueError:
+ pass
+
+ if v is not None:
+ self.loaded_sd_models.insert(0, v)
+
+
+model_data = SdModelData()
+
+
+def get_empty_cond(sd_model):
+
+ p = processing.StableDiffusionProcessingTxt2Img()
+ extra_networks.activate(p, {})
+
+ if hasattr(sd_model, 'conditioner'):
+ d = sd_model.get_learned_conditioning([""])
+ return d['crossattn']
+ else:
+ return sd_model.cond_stage_model([""])
+
+
+def send_model_to_cpu(m):
+ if m.lowvram:
+ lowvram.send_everything_to_cpu()
+ else:
+ m.to(devices.cpu)
+
+ devices.torch_gc()
+
+
+def model_target_device(m):
+ if lowvram.is_needed(m):
+ return devices.cpu
+ else:
+ return devices.device
+
+
+def send_model_to_device(m):
+ lowvram.apply(m)
+
+ if not m.lowvram:
+ m.to(shared.device)
+
+
+def send_model_to_trash(m):
+ m.to(device="meta")
+ devices.torch_gc()
+
+
+def load_model(checkpoint_info=None, already_loaded_state_dict=None):
+ from modules import sd_hijack
+ checkpoint_info = checkpoint_info or select_checkpoint()
+
+ timer = Timer()
+
+ if model_data.sd_model:
+ send_model_to_trash(model_data.sd_model)
+ model_data.sd_model = None
+ devices.torch_gc()
+
+ timer.record("unload existing model")
+
+ if already_loaded_state_dict is not None:
+ state_dict = already_loaded_state_dict
+ else:
+ state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+
+ checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+ clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
+
+ timer.record("find config")
+
+ sd_config = OmegaConf.load(checkpoint_config)
+ repair_config(sd_config)
+
+ timer.record("load config")
+
+ print(f"Creating model from config: {checkpoint_config}")
+
+ sd_model = None
+ try:
+ with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
+ with sd_disable_initialization.InitializeOnMeta():
+ sd_model = instantiate_from_config(sd_config.model)
+
+ except Exception as e:
+ errors.display(e, "creating model quickly", full_traceback=True)
+
+ if sd_model is None:
+ print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
+
+ with sd_disable_initialization.InitializeOnMeta():
+ sd_model = instantiate_from_config(sd_config.model)
+
+ sd_model.used_config = checkpoint_config
+
+ timer.record("create model")
+
+ if shared.cmd_opts.no_half:
+ weight_dtype_conversion = None
+ else:
+ weight_dtype_conversion = {
+ 'first_stage_model': None,
+ '': torch.float16,
+ }
+
+ with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion):
+ load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+ timer.record("load weights from state dict")
+
+ send_model_to_device(sd_model)
+ timer.record("move model to device")
+
+ sd_hijack.model_hijack.hijack(sd_model)
+
+ timer.record("hijack")
+
+ sd_model.eval()
+ model_data.set_sd_model(sd_model)
+ model_data.was_loaded_at_least_once = True
+
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
+
+ timer.record("load textual inversion embeddings")
+
+ script_callbacks.model_loaded_callback(sd_model)
+
+ timer.record("scripts callbacks")
+
+ with devices.autocast(), torch.no_grad():
+ sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
+
+ timer.record("calculate empty prompt")
+
+ print(f"Model loaded in {timer.summary()}.")
+
+ return sd_model
+
+
+def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
+ """
+ Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
+ If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
+ If not, returns the model that can be used to load weights from checkpoint_info's file.
+ If no such model exists, returns None.
+ Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
+ """
+
+ already_loaded = None
+ for i in reversed(range(len(model_data.loaded_sd_models))):
+ loaded_model = model_data.loaded_sd_models[i]
+ if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+ already_loaded = loaded_model
+ continue
+
+ if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
+ print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
+ model_data.loaded_sd_models.pop()
+ send_model_to_trash(loaded_model)
+ timer.record("send model to trash")
+
+ if shared.opts.sd_checkpoints_keep_in_cpu:
+ send_model_to_cpu(sd_model)
+ timer.record("send model to cpu")
+
+ if already_loaded is not None:
+ send_model_to_device(already_loaded)
+ timer.record("send model to device")
+
+ model_data.set_sd_model(already_loaded, already_loaded=True)
+
+ if not SkipWritingToConfig.skip:
+ shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title
+ shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256
+
+ print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
+ sd_vae.reload_vae_weights(already_loaded)
+ return model_data.sd_model
+ elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
+ print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
+
+ model_data.sd_model = None
+ load_model(checkpoint_info)
+ return model_data.sd_model
+ elif len(model_data.loaded_sd_models) > 0:
+ sd_model = model_data.loaded_sd_models.pop()
+ model_data.sd_model = sd_model
+
+ sd_vae.base_vae = getattr(sd_model, "base_vae", None)
+ sd_vae.loaded_vae_file = getattr(sd_model, "loaded_vae_file", None)
+ sd_vae.checkpoint_info = sd_model.sd_checkpoint_info
+
+ print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
+ return sd_model
+ else:
+ return None
+
+
+def reload_model_weights(sd_model=None, info=None):
+ checkpoint_info = info or select_checkpoint()
+
+ timer = Timer()
+
+ if not sd_model:
+ sd_model = model_data.sd_model
+
+ if sd_model is None: # previous model load failed
+ current_checkpoint_info = None
+ else:
+ current_checkpoint_info = sd_model.sd_checkpoint_info
+ if sd_model.sd_model_checkpoint == checkpoint_info.filename:
+ return sd_model
+
+ sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
+ if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+ return sd_model
+
+ if sd_model is not None:
+ sd_unet.apply_unet("None")
+ send_model_to_cpu(sd_model)
+ sd_hijack.model_hijack.undo_hijack(sd_model)
+
+ state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+
+ checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+
+ timer.record("find config")
+
+ if sd_model is None or checkpoint_config != sd_model.used_config:
+ if sd_model is not None:
+ send_model_to_trash(sd_model)
+
+ load_model(checkpoint_info, already_loaded_state_dict=state_dict)
+ return model_data.sd_model
+
+ try:
+ load_model_weights(sd_model, checkpoint_info, state_dict, timer)
+ except Exception:
+ print("Failed to load checkpoint, restoring previous")
+ load_model_weights(sd_model, current_checkpoint_info, None, timer)
+ raise
+ finally:
+ sd_hijack.model_hijack.hijack(sd_model)
+ timer.record("hijack")
+
+ script_callbacks.model_loaded_callback(sd_model)
+ timer.record("script callbacks")
+
+ if not sd_model.lowvram:
+ sd_model.to(devices.device)
+ timer.record("move model to device")
+
+ print(f"Weights loaded in {timer.summary()}.")
+
+ model_data.set_sd_model(sd_model)
+ sd_unet.apply_unet()
+
+ return sd_model
+
+
+def unload_model_weights(sd_model=None, info=None):
+ timer = Timer()
+
+ if model_data.sd_model:
+ model_data.sd_model.to(devices.cpu)
+ sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+ model_data.sd_model = None
+ sd_model = None
+ gc.collect()
+ devices.torch_gc()
+
+ print(f"Unloaded weights {timer.summary()}.")
+
+ return sd_model
+
+
+def apply_token_merging(sd_model, token_merging_ratio):
+ """
+ Applies speed and memory optimizations from tomesd.
+ """
+
+ current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
+
+ if current_token_merging_ratio == token_merging_ratio:
+ return
+
+ if current_token_merging_ratio > 0:
+ tomesd.remove_patch(sd_model)
+
+ if token_merging_ratio > 0:
+ tomesd.apply_patch(
+ sd_model,
+ ratio=token_merging_ratio,
+ use_rand=False, # can cause issues with some samplers
+ merge_attn=True,
+ merge_crossattn=False,
+ merge_mlp=False
+ )
+
+ sd_model.applied_token_merged_ratio = token_merging_ratio
diff --git a/stable-diffusion-webui/modules/sd_models_config.py b/stable-diffusion-webui/modules/sd_models_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..941acab055b498d9953a9f6afe961e22b5be9906
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_models_config.py
@@ -0,0 +1,124 @@
+import os
+
+import torch
+
+from modules import shared, paths, sd_disable_initialization, devices
+
+sd_configs_path = shared.sd_configs_path
+sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
+sd_xl_repo_configs_path = os.path.join(paths.paths['Stable Diffusion XL'], "configs", "inference")
+
+
+config_default = shared.sd_default_config
+config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
+config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
+config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
+config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
+config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
+config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
+config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
+config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
+config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
+config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
+
+
+def is_using_v_parameterization_for_sd2(state_dict):
+ """
+ Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
+ """
+
+ import ldm.modules.diffusionmodules.openaimodel
+
+ device = devices.cpu
+
+ with sd_disable_initialization.DisableInitialization():
+ unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
+ use_checkpoint=True,
+ use_fp16=False,
+ image_size=32,
+ in_channels=4,
+ out_channels=4,
+ model_channels=320,
+ attention_resolutions=[4, 2, 1],
+ num_res_blocks=2,
+ channel_mult=[1, 2, 4, 4],
+ num_head_channels=64,
+ use_spatial_transformer=True,
+ use_linear_in_transformer=True,
+ transformer_depth=1,
+ context_dim=1024,
+ legacy=False
+ )
+ unet.eval()
+
+ with torch.no_grad():
+ unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
+ unet.load_state_dict(unet_sd, strict=True)
+ unet.to(device=device, dtype=torch.float)
+
+ test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
+ x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
+
+ out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
+
+ return out < -1
+
+
+def guess_model_config_from_state_dict(sd, filename):
+ sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
+ diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+ sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
+
+ if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
+ return config_sdxl
+ if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
+ return config_sdxl_refiner
+ elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+ return config_depth_model
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+ return config_unclip
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+ return config_unopenclip
+
+ if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
+ if diffusion_model_input.shape[1] == 9:
+ return config_sd2_inpainting
+ elif is_using_v_parameterization_for_sd2(sd):
+ return config_sd2v
+ else:
+ return config_sd2
+
+ if diffusion_model_input is not None:
+ if diffusion_model_input.shape[1] == 9:
+ return config_inpainting
+ if diffusion_model_input.shape[1] == 8:
+ return config_instruct_pix2pix
+
+ if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
+ return config_alt_diffusion
+
+ return config_default
+
+
+def find_checkpoint_config(state_dict, info):
+ if info is None:
+ return guess_model_config_from_state_dict(state_dict, "")
+
+ config = find_checkpoint_config_near_filename(info)
+ if config is not None:
+ return config
+
+ return guess_model_config_from_state_dict(state_dict, info.filename)
+
+
+def find_checkpoint_config_near_filename(info):
+ if info is None:
+ return None
+
+ config = f"{os.path.splitext(info.filename)[0]}.yaml"
+ if os.path.exists(config):
+ return config
+
+ return None
+
diff --git a/stable-diffusion-webui/modules/sd_models_types.py b/stable-diffusion-webui/modules/sd_models_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ff81d4c679d55340fe1f4349e23d38651db5fce
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_models_types.py
@@ -0,0 +1,31 @@
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+ from modules.sd_models import CheckpointInfo
+
+
+class WebuiSdModel(LatentDiffusion):
+ """This class is not actually instantinated, but its fields are created and fieeld by webui"""
+
+ lowvram: bool
+ """True if lowvram/medvram optimizations are enabled -- see modules.lowvram for more info"""
+
+ sd_model_hash: str
+ """short hash, 10 first characters of SHA1 hash of the model file; may be None if --no-hashing flag is used"""
+
+ sd_model_checkpoint: str
+ """path to the file on disk that model weights were obtained from"""
+
+ sd_checkpoint_info: 'CheckpointInfo'
+ """structure with additional information about the file with model's weights"""
+
+ is_sdxl: bool
+ """True if the model's architecture is SDXL"""
+
+ is_sd2: bool
+ """True if the model's architecture is SD 2.x"""
+
+ is_sd1: bool
+ """True if the model's architecture is SD 1.x"""
diff --git a/stable-diffusion-webui/modules/sd_models_xl.py b/stable-diffusion-webui/modules/sd_models_xl.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0cab56f8704cbec61d6b913afa2df851aa2a235
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_models_xl.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+import torch
+
+import sgm.models.diffusion
+import sgm.modules.diffusionmodules.denoiser_scaling
+import sgm.modules.diffusionmodules.discretizer
+from modules import devices, shared, prompt_parser
+
+
+def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
+ for embedder in self.conditioner.embedders:
+ embedder.ucg_rate = 0.0
+
+ width = getattr(batch, 'width', 1024)
+ height = getattr(batch, 'height', 1024)
+ is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
+ aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
+
+ devices_args = dict(device=devices.device, dtype=devices.dtype)
+
+ sdxl_conds = {
+ "txt": batch,
+ "original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+ "crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1),
+ "target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
+ "aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1),
+ }
+
+ force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch)
+ c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else [])
+
+ return c
+
+
+def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
+ return self.model(x, t, cond)
+
+
+def get_first_stage_encoding(self, x): # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility
+ return x
+
+
+sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
+sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
+sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
+
+
+def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt):
+ res = []
+
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]:
+ encoded = embedder.encode_embedding_init_text(init_text, nvpt)
+ res.append(encoded)
+
+ return torch.cat(res, dim=1)
+
+
+def tokenize(self: sgm.modules.GeneralConditioner, texts):
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
+ return embedder.tokenize(texts)
+
+ raise AssertionError('no tokenizer available')
+
+
+
+def process_texts(self, texts):
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
+ return embedder.process_texts(texts)
+
+
+def get_target_prompt_token_count(self, token_count):
+ for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]:
+ return embedder.get_target_prompt_token_count(token_count)
+
+
+# those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
+sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
+sgm.modules.GeneralConditioner.tokenize = tokenize
+sgm.modules.GeneralConditioner.process_texts = process_texts
+sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
+
+
+def extend_sdxl(model):
+ """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
+
+ dtype = next(model.model.diffusion_model.parameters()).dtype
+ model.model.diffusion_model.dtype = dtype
+ model.model.conditioning_key = 'crossattn'
+ model.cond_stage_key = 'txt'
+ # model.cond_stage_model will be set in sd_hijack
+
+ model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
+
+ discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
+ model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
+
+ model.conditioner.wrapped = torch.nn.Module()
+
+
+sgm.modules.attention.print = shared.ldm_print
+sgm.modules.diffusionmodules.model.print = shared.ldm_print
+sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
+sgm.modules.encoders.modules.print = shared.ldm_print
+
+# this gets the code to load the vanilla attention that we override
+sgm.modules.attention.SDP_IS_AVAILABLE = True
+sgm.modules.attention.XFORMERS_IS_AVAILABLE = False
diff --git a/stable-diffusion-webui/modules/sd_samplers.py b/stable-diffusion-webui/modules/sd_samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..576ff43d9cb5898ea546f6fb74b8759b0b72978d
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_samplers.py
@@ -0,0 +1,59 @@
+from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared
+
+# imports for functions that previously were here and are used by other modules
+from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # noqa: F401
+
+all_samplers = [
+ *sd_samplers_kdiffusion.samplers_data_k_diffusion,
+ *sd_samplers_timesteps.samplers_data_timesteps,
+]
+all_samplers_map = {x.name: x for x in all_samplers}
+
+samplers = []
+samplers_for_img2img = []
+samplers_map = {}
+samplers_hidden = {}
+
+
+def find_sampler_config(name):
+ if name is not None:
+ config = all_samplers_map.get(name, None)
+ else:
+ config = all_samplers[0]
+
+ return config
+
+
+def create_sampler(name, model):
+ config = find_sampler_config(name)
+
+ assert config is not None, f'bad sampler name: {name}'
+
+ if model.is_sdxl and config.options.get("no_sdxl", False):
+ raise Exception(f"Sampler {config.name} is not supported for SDXL")
+
+ sampler = config.constructor(model)
+ sampler.config = config
+
+ return sampler
+
+
+def set_samplers():
+ global samplers, samplers_for_img2img, samplers_hidden
+
+ samplers_hidden = set(shared.opts.hide_samplers)
+ samplers = all_samplers
+ samplers_for_img2img = all_samplers
+
+ samplers_map.clear()
+ for sampler in all_samplers:
+ samplers_map[sampler.name.lower()] = sampler.name
+ for alias in sampler.aliases:
+ samplers_map[alias.lower()] = sampler.name
+
+
+def visible_sampler_names():
+ return [x.name for x in samplers if x.name not in samplers_hidden]
+
+
+set_samplers()
diff --git a/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py b/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7a52ed33b2ab44546683555b8b4d8f47bf56c6b
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py
@@ -0,0 +1,230 @@
+import torch
+from modules import prompt_parser, devices, sd_samplers_common
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
+
+
+def catenate_conds(conds):
+ if not isinstance(conds[0], dict):
+ return torch.cat(conds)
+
+ return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
+
+
+def subscript_cond(cond, a, b):
+ if not isinstance(cond, dict):
+ return cond[a:b]
+
+ return {key: vec[a:b] for key, vec in cond.items()}
+
+
+def pad_cond(tensor, repeats, empty):
+ if not isinstance(tensor, dict):
+ return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+ tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+ return tensor
+
+
+class CFGDenoiser(torch.nn.Module):
+ """
+ Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+ that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+ instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+ negative prompt.
+ """
+
+ def __init__(self, sampler):
+ super().__init__()
+ self.model_wrap = None
+ self.mask = None
+ self.nmask = None
+ self.init_latent = None
+ self.steps = None
+ """number of steps as specified by user in UI"""
+
+ self.total_steps = None
+ """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
+
+ self.step = 0
+ self.image_cfg_scale = None
+ self.padded_cond_uncond = False
+ self.sampler = sampler
+ self.model_wrap = None
+ self.p = None
+ self.mask_before_denoising = False
+
+ @property
+ def inner_model(self):
+ raise NotImplementedError()
+
+ def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+ denoised_uncond = x_out[-uncond.shape[0]:]
+ denoised = torch.clone(denoised_uncond)
+
+ for i, conds in enumerate(conds_list):
+ for cond_index, weight in conds:
+ denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+ return denoised
+
+ def combine_denoised_for_edit_model(self, x_out, cond_scale):
+ out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+ denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+ return denoised
+
+ def get_pred_x0(self, x_in, x_out, sigma):
+ return x_out
+
+ def update_inner_model(self):
+ self.model_wrap = None
+
+ c, uc = self.p.get_conds()
+ self.sampler.sampler_extra_args['cond'] = c
+ self.sampler.sampler_extra_args['uncond'] = uc
+
+ def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
+ if state.interrupted or state.skipped:
+ raise sd_samplers_common.InterruptedException
+
+ if sd_samplers_common.apply_refiner(self):
+ cond = self.sampler.sampler_extra_args['cond']
+ uncond = self.sampler.sampler_extra_args['uncond']
+
+ # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+ # so is_edit_model is set to False to support AND composition.
+ is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
+ conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+ uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+ assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
+ if self.mask_before_denoising and self.mask is not None:
+ x = self.init_latent * self.mask + self.nmask * x
+
+ batch_size = len(conds_list)
+ repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+ if shared.sd_model.model.conditioning_key == "crossattn-adm":
+ image_uncond = torch.zeros_like(image_cond)
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
+ else:
+ image_uncond = image_cond
+ if isinstance(uncond, dict):
+ make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+ else:
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
+
+ if not is_edit_model:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
+ else:
+ x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+ sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
+
+ denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+ cfg_denoiser_callback(denoiser_params)
+ x_in = denoiser_params.x
+ image_cond_in = denoiser_params.image_cond
+ sigma_in = denoiser_params.sigma
+ tensor = denoiser_params.text_cond
+ uncond = denoiser_params.text_uncond
+ skip_uncond = False
+
+ # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+ if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+ skip_uncond = True
+ x_in = x_in[:-batch_size]
+ sigma_in = sigma_in[:-batch_size]
+
+ self.padded_cond_uncond = False
+ if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+ empty = shared.sd_model.cond_stage_model_empty_prompt
+ num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+ if num_repeats < 0:
+ tensor = pad_cond(tensor, -num_repeats, empty)
+ self.padded_cond_uncond = True
+ elif num_repeats > 0:
+ uncond = pad_cond(uncond, num_repeats, empty)
+ self.padded_cond_uncond = True
+
+ if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+ if is_edit_model:
+ cond_in = catenate_conds([tensor, uncond, uncond])
+ elif skip_uncond:
+ cond_in = tensor
+ else:
+ cond_in = catenate_conds([tensor, uncond])
+
+ if shared.opts.batch_cond_uncond:
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
+ else:
+ x_out = torch.zeros_like(x_in)
+ for batch_offset in range(0, x_out.shape[0], batch_size):
+ a = batch_offset
+ b = a + batch_size
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
+ else:
+ x_out = torch.zeros_like(x_in)
+ batch_size = batch_size*2 if shared.opts.batch_cond_uncond else batch_size
+ for batch_offset in range(0, tensor.shape[0], batch_size):
+ a = batch_offset
+ b = min(a + batch_size, tensor.shape[0])
+
+ if not is_edit_model:
+ c_crossattn = subscript_cond(tensor, a, b)
+ else:
+ c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+
+ if not skip_uncond:
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
+
+ denoised_image_indexes = [x[0][0] for x in conds_list]
+ if skip_uncond:
+ fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+ x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+
+ denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
+ cfg_denoised_callback(denoised_params)
+
+ devices.test_for_nans(x_out, "unet")
+
+ if is_edit_model:
+ denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+ elif skip_uncond:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+ else:
+ denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+ if not self.mask_before_denoising and self.mask is not None:
+ denoised = self.init_latent * self.mask + self.nmask * denoised
+
+ self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
+
+ if opts.live_preview_content == "Prompt":
+ preview = self.sampler.last_latent
+ elif opts.live_preview_content == "Negative prompt":
+ preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
+ else:
+ preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
+
+ sd_samplers_common.store_latent(preview)
+
+ after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+ cfg_after_cfg_callback(after_cfg_callback_params)
+ denoised = after_cfg_callback_params.x
+
+ self.step += 1
+ return denoised
+
diff --git a/stable-diffusion-webui/modules/sd_samplers_common.py b/stable-diffusion-webui/modules/sd_samplers_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..94c807e37ad6f999bf1ac7a4295e22f9d4566cdf
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_samplers_common.py
@@ -0,0 +1,337 @@
+import inspect
+from collections import namedtuple
+import numpy as np
+import torch
+from PIL import Image
+from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
+from modules.shared import opts, state
+import k_diffusion.sampling
+
+
+SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+class SamplerData(SamplerDataTuple):
+ def total_steps(self, steps):
+ if self.options.get("second_order", False):
+ steps = steps * 2
+
+ return steps
+
+
+def setup_img2img_steps(p, steps=None):
+ if opts.img2img_fix_steps or steps is not None:
+ requested_steps = (steps or p.steps)
+ steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
+ t_enc = requested_steps - 1
+ else:
+ steps = p.steps
+ t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+ return steps, t_enc
+
+
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
+
+
+def samples_to_images_tensor(sample, approximation=None, model=None):
+ """Transforms 4-channel latent space images into 3-channel RGB image tensors, with values in range [-1, 1]."""
+
+ if approximation is None or (shared.state.interrupted and opts.live_preview_fast_interrupt):
+ approximation = approximation_indexes.get(opts.show_progress_type, 0)
+
+ from modules import lowvram
+ if approximation == 0 and lowvram.is_enabled(shared.sd_model) and not shared.opts.live_preview_allow_lowvram_full:
+ approximation = 1
+
+ if approximation == 2:
+ x_sample = sd_vae_approx.cheap_approximation(sample)
+ elif approximation == 1:
+ x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach()
+ elif approximation == 3:
+ x_sample = sd_vae_taesd.decoder_model()(sample.to(devices.device, devices.dtype)).detach()
+ x_sample = x_sample * 2 - 1
+ else:
+ if model is None:
+ model = shared.sd_model
+ with devices.without_autocast(): # fixes an issue with unstable VAEs that are flaky even in fp32
+ x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype))
+
+ return x_sample
+
+
+def single_sample_to_image(sample, approximation=None):
+ x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
+
+ x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
+ x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+ x_sample = x_sample.astype(np.uint8)
+
+ return Image.fromarray(x_sample)
+
+
+def decode_first_stage(model, x):
+ x = x.to(devices.dtype_vae)
+ approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0)
+ return samples_to_images_tensor(x, approx_index, model)
+
+
+def sample_to_image(samples, index=0, approximation=None):
+ return single_sample_to_image(samples[index], approximation)
+
+
+def samples_to_image_grid(samples, approximation=None):
+ return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+
+
+def images_tensor_to_samples(image, approximation=None, model=None):
+ '''image[0, 1] -> latent'''
+ if approximation is None:
+ approximation = approximation_indexes.get(opts.sd_vae_encode_method, 0)
+
+ if approximation == 3:
+ image = image.to(devices.device, devices.dtype)
+ x_latent = sd_vae_taesd.encoder_model()(image)
+ else:
+ if model is None:
+ model = shared.sd_model
+ model.first_stage_model.to(devices.dtype_vae)
+
+ image = image.to(shared.device, dtype=devices.dtype_vae)
+ image = image * 2 - 1
+ if len(image) > 1:
+ x_latent = torch.stack([
+ model.get_first_stage_encoding(
+ model.encode_first_stage(torch.unsqueeze(img, 0))
+ )[0]
+ for img in image
+ ])
+ else:
+ x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+
+ return x_latent
+
+
+def store_latent(decoded):
+ state.current_latent = decoded
+
+ if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+ if not shared.parallel_processing_allowed:
+ shared.state.assign_current_image(sample_to_image(decoded))
+
+
+def is_sampler_using_eta_noise_seed_delta(p):
+ """returns whether sampler from config will use eta noise seed delta for image creation"""
+
+ sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
+
+ eta = p.eta
+
+ if eta is None and p.sampler is not None:
+ eta = p.sampler.eta
+
+ if eta is None and sampler_config is not None:
+ eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
+
+ if eta == 0:
+ return False
+
+ return sampler_config.options.get("uses_ensd", False)
+
+
+class InterruptedException(BaseException):
+ pass
+
+
+def replace_torchsde_browinan():
+ import torchsde._brownian.brownian_interval
+
+ def torchsde_randn(size, dtype, device, seed):
+ return devices.randn_local(seed, size).to(device=device, dtype=dtype)
+
+ torchsde._brownian.brownian_interval._randn = torchsde_randn
+
+
+replace_torchsde_browinan()
+
+
+def apply_refiner(cfg_denoiser):
+ completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+ refiner_switch_at = cfg_denoiser.p.refiner_switch_at
+ refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
+
+ if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
+ return False
+
+ if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
+ return False
+
+ if getattr(cfg_denoiser.p, "enable_hr", False):
+ is_second_pass = cfg_denoiser.p.is_hr_pass
+
+ if opts.hires_fix_refiner_pass == "first pass" and is_second_pass:
+ return False
+
+ if opts.hires_fix_refiner_pass == "second pass" and not is_second_pass:
+ return False
+
+ if opts.hires_fix_refiner_pass != "second pass":
+ cfg_denoiser.p.extra_generation_params['Hires refiner'] = opts.hires_fix_refiner_pass
+
+ cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
+ cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at
+
+ with sd_models.SkipWritingToConfig():
+ sd_models.reload_model_weights(info=refiner_checkpoint_info)
+
+ devices.torch_gc()
+ cfg_denoiser.p.setup_conds()
+ cfg_denoiser.update_inner_model()
+
+ return True
+
+
+class TorchHijack:
+ """This is here to replace torch.randn_like of k-diffusion.
+
+ k-diffusion has random_sampler argument for most samplers, but not for all, so
+ this is needed to properly replace every use of torch.randn_like.
+
+ We need to replace to make images generated in batches to be same as images generated individually."""
+
+ def __init__(self, p):
+ self.rng = p.rng
+
+ def __getattr__(self, item):
+ if item == 'randn_like':
+ return self.randn_like
+
+ if hasattr(torch, item):
+ return getattr(torch, item)
+
+ raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+ def randn_like(self, x):
+ return self.rng.next()
+
+
+class Sampler:
+ def __init__(self, funcname):
+ self.funcname = funcname
+ self.func = funcname
+ self.extra_params = []
+ self.sampler_noises = None
+ self.stop_at = None
+ self.eta = None
+ self.config: SamplerData = None # set by the function calling the constructor
+ self.last_latent = None
+ self.s_min_uncond = None
+ self.s_churn = 0.0
+ self.s_tmin = 0.0
+ self.s_tmax = float('inf')
+ self.s_noise = 1.0
+
+ self.eta_option_field = 'eta_ancestral'
+ self.eta_infotext_field = 'Eta'
+ self.eta_default = 1.0
+
+ self.conditioning_key = shared.sd_model.model.conditioning_key
+
+ self.p = None
+ self.model_wrap_cfg = None
+ self.sampler_extra_args = None
+ self.options = {}
+
+ def callback_state(self, d):
+ step = d['i']
+
+ if self.stop_at is not None and step > self.stop_at:
+ raise InterruptedException
+
+ state.sampling_step = step
+ shared.total_tqdm.update()
+
+ def launch_sampling(self, steps, func):
+ self.model_wrap_cfg.steps = steps
+ self.model_wrap_cfg.total_steps = self.config.total_steps(steps)
+ state.sampling_steps = steps
+ state.sampling_step = 0
+
+ try:
+ return func()
+ except RecursionError:
+ print(
+ 'Encountered RecursionError during sampling, returning last latent. '
+ 'rho >5 with a polyexponential scheduler may cause this error. '
+ 'You should try to use a smaller rho value instead.'
+ )
+ return self.last_latent
+ except InterruptedException:
+ return self.last_latent
+
+ def number_of_needed_noises(self, p):
+ return p.steps
+
+ def initialize(self, p) -> dict:
+ self.p = p
+ self.model_wrap_cfg.p = p
+ self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+ self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+ self.model_wrap_cfg.step = 0
+ self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+ self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0)
+ self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+
+ k_diffusion.sampling.torch = TorchHijack(p)
+
+ extra_params_kwargs = {}
+ for param_name in self.extra_params:
+ if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+ extra_params_kwargs[param_name] = getattr(p, param_name)
+
+ if 'eta' in inspect.signature(self.func).parameters:
+ if self.eta != self.eta_default:
+ p.extra_generation_params[self.eta_infotext_field] = self.eta
+
+ extra_params_kwargs['eta'] = self.eta
+
+ if len(self.extra_params) > 0:
+ s_churn = getattr(opts, 's_churn', p.s_churn)
+ s_tmin = getattr(opts, 's_tmin', p.s_tmin)
+ s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
+ s_noise = getattr(opts, 's_noise', p.s_noise)
+
+ if 's_churn' in extra_params_kwargs and s_churn != self.s_churn:
+ extra_params_kwargs['s_churn'] = s_churn
+ p.s_churn = s_churn
+ p.extra_generation_params['Sigma churn'] = s_churn
+ if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin:
+ extra_params_kwargs['s_tmin'] = s_tmin
+ p.s_tmin = s_tmin
+ p.extra_generation_params['Sigma tmin'] = s_tmin
+ if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax:
+ extra_params_kwargs['s_tmax'] = s_tmax
+ p.s_tmax = s_tmax
+ p.extra_generation_params['Sigma tmax'] = s_tmax
+ if 's_noise' in extra_params_kwargs and s_noise != self.s_noise:
+ extra_params_kwargs['s_noise'] = s_noise
+ p.s_noise = s_noise
+ p.extra_generation_params['Sigma noise'] = s_noise
+
+ return extra_params_kwargs
+
+ def create_noise_sampler(self, x, sigmas, p):
+ """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+ if shared.opts.no_dpmpp_sde_batch_determinism:
+ return None
+
+ from k_diffusion.sampling import BrownianTreeNoiseSampler
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
+ return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ raise NotImplementedError()
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ raise NotImplementedError()
diff --git a/stable-diffusion-webui/modules/sd_samplers_compvis.py b/stable-diffusion-webui/modules/sd_samplers_compvis.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/modules/sd_samplers_extra.py b/stable-diffusion-webui/modules/sd_samplers_extra.py
new file mode 100644
index 0000000000000000000000000000000000000000..24491ca1616d3d563e47e8d5dc0b7aa16152ef5f
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_samplers_extra.py
@@ -0,0 +1,74 @@
+import torch
+import tqdm
+import k_diffusion.sampling
+
+
+@torch.no_grad()
+def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list=None):
+ """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)
+ Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}
+ If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list
+ """
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ step_id = 0
+ from k_diffusion.sampling import to_d, get_sigmas_karras
+
+ def heun_step(x, old_sigma, new_sigma, second_order=True):
+ nonlocal step_id
+ denoised = model(x, old_sigma * s_in, **extra_args)
+ d = to_d(x, old_sigma, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
+ dt = new_sigma - old_sigma
+ if new_sigma == 0 or not second_order:
+ # Euler method
+ x = x + d * dt
+ else:
+ # Heun's method
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+ d_2 = to_d(x_2, new_sigma, denoised_2)
+ d_prime = (d + d_2) / 2
+ x = x + d_prime * dt
+ step_id += 1
+ return x
+
+ steps = sigmas.shape[0] - 1
+ if restart_list is None:
+ if steps >= 20:
+ restart_steps = 9
+ restart_times = 1
+ if steps >= 36:
+ restart_steps = steps // 4
+ restart_times = 2
+ sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), device=sigmas.device)
+ restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+ else:
+ restart_list = {}
+
+ restart_list = {int(torch.argmin(abs(sigmas - key), dim=0)): value for key, value in restart_list.items()}
+
+ step_list = []
+ for i in range(len(sigmas) - 1):
+ step_list.append((sigmas[i], sigmas[i + 1]))
+ if i + 1 in restart_list:
+ restart_steps, restart_times, restart_max = restart_list[i + 1]
+ min_idx = i + 1
+ max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+ if max_idx < min_idx:
+ sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
+ while restart_times > 0:
+ restart_times -= 1
+ step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+
+ last_sigma = None
+ for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
+ if last_sigma is None:
+ last_sigma = old_sigma
+ elif last_sigma < old_sigma:
+ x = x + k_diffusion.sampling.torch.randn_like(x) * s_noise * (old_sigma ** 2 - last_sigma ** 2) ** 0.5
+ x = heun_step(x, old_sigma, new_sigma)
+ last_sigma = new_sigma
+
+ return x
diff --git a/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py b/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cdbe441aa06aff841f40f0efc446fd75b05bb66
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py
@@ -0,0 +1,242 @@
+import torch
+import inspect
+import k_diffusion.sampling
+from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
+from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
+
+from modules.shared import opts
+import modules.shared as shared
+
+samplers_k_diffusion = [
+ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+ ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+ ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
+ ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+ ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
+ ('Euler', 'sample_euler', ['k_euler'], {}),
+ ('LMS', 'sample_lms', ['k_lms'], {}),
+ ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
+ ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
+ ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+ ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
+ ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+ ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
+ ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+ ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
+ ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
+ ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
+ ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
+ ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
+ ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
+ ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+ ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+ ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+ ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
+ ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
+]
+
+
+samplers_data_k_diffusion = [
+ sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+ for label, funcname, aliases, options in samplers_k_diffusion
+ if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
+]
+
+sampler_extra_params = {
+ 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+ 'sample_dpm_fast': ['s_noise'],
+ 'sample_dpm_2_ancestral': ['s_noise'],
+ 'sample_dpmpp_2s_ancestral': ['s_noise'],
+ 'sample_dpmpp_sde': ['s_noise'],
+ 'sample_dpmpp_2m_sde': ['s_noise'],
+ 'sample_dpmpp_3m_sde': ['s_noise'],
+}
+
+k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
+k_diffusion_scheduler = {
+ 'Automatic': None,
+ 'karras': k_diffusion.sampling.get_sigmas_karras,
+ 'exponential': k_diffusion.sampling.get_sigmas_exponential,
+ 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
+}
+
+
+class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
+ @property
+ def inner_model(self):
+ if self.model_wrap is None:
+ denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+ self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
+
+ return self.model_wrap
+
+
+class KDiffusionSampler(sd_samplers_common.Sampler):
+ def __init__(self, funcname, sd_model, options=None):
+ super().__init__(funcname)
+
+ self.extra_params = sampler_extra_params.get(funcname, [])
+
+ self.options = options or {}
+ self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
+
+ self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
+ self.model_wrap = self.model_wrap_cfg.inner_model
+
+ def get_sigmas(self, p, steps):
+ discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+ if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+ discard_next_to_last_sigma = True
+ p.extra_generation_params["Discard penultimate sigma"] = True
+
+ steps += 1 if discard_next_to_last_sigma else 0
+
+ if p.sampler_noise_scheduler_override:
+ sigmas = p.sampler_noise_scheduler_override(steps)
+ elif opts.k_sched_type != "Automatic":
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
+ sigmas_kwargs = {
+ 'sigma_min': sigma_min,
+ 'sigma_max': sigma_max,
+ }
+
+ sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
+ p.extra_generation_params["Schedule type"] = opts.k_sched_type
+
+ if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
+ sigmas_kwargs['sigma_min'] = opts.sigma_min
+ p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
+ if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
+ sigmas_kwargs['sigma_max'] = opts.sigma_max
+ p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
+
+ default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
+
+ if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
+ sigmas_kwargs['rho'] = opts.rho
+ p.extra_generation_params["Schedule rho"] = opts.rho
+
+ sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
+ elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+ sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+ sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+ elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
+ m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+ sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
+ else:
+ sigmas = self.model_wrap.get_sigmas(steps)
+
+ if discard_next_to_last_sigma:
+ sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
+ return sigmas
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+
+ sigmas = self.get_sigmas(p, steps)
+ sigma_sched = sigmas[steps - t_enc - 1:]
+
+ xi = x + noise * sigma_sched[0]
+
+ if opts.img2img_extra_noise > 0:
+ p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
+ extra_noise_params = ExtraNoiseParams(noise, x, xi)
+ extra_noise_callback(extra_noise_params)
+ noise = extra_noise_params.noise
+ xi += noise * opts.img2img_extra_noise
+
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+
+ if 'sigma_min' in parameters:
+ ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+ extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+ if 'sigma_max' in parameters:
+ extra_params_kwargs['sigma_max'] = sigma_sched[0]
+ if 'n' in parameters:
+ extra_params_kwargs['n'] = len(sigma_sched) - 1
+ if 'sigma_sched' in parameters:
+ extra_params_kwargs['sigma_sched'] = sigma_sched
+ if 'sigmas' in parameters:
+ extra_params_kwargs['sigmas'] = sigma_sched
+
+ if self.config.options.get('brownian_noise', False):
+ noise_sampler = self.create_noise_sampler(x, sigmas, p)
+ extra_params_kwargs['noise_sampler'] = noise_sampler
+
+ if self.config.options.get('solver_type', None) == 'heun':
+ extra_params_kwargs['solver_type'] = 'heun'
+
+ self.model_wrap_cfg.init_latent = x
+ self.last_latent = x
+ self.sampler_extra_args = {
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
+ }
+
+ samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ return samples
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps = steps or p.steps
+
+ sigmas = self.get_sigmas(p, steps)
+
+ if opts.sgm_noise_multiplier:
+ p.extra_generation_params["SGM noise multiplier"] = True
+ x = x * torch.sqrt(1.0 + sigmas[0] ** 2.0)
+ else:
+ x = x * sigmas[0]
+
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+
+ if 'n' in parameters:
+ extra_params_kwargs['n'] = steps
+
+ if 'sigma_min' in parameters:
+ extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+ extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+
+ if 'sigmas' in parameters:
+ extra_params_kwargs['sigmas'] = sigmas
+
+ if self.config.options.get('brownian_noise', False):
+ noise_sampler = self.create_noise_sampler(x, sigmas, p)
+ extra_params_kwargs['noise_sampler'] = noise_sampler
+
+ if self.config.options.get('solver_type', None) == 'heun':
+ extra_params_kwargs['solver_type'] = 'heun'
+
+ self.last_latent = x
+ self.sampler_extra_args = {
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
+ }
+
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ return samples
+
+
diff --git a/stable-diffusion-webui/modules/sd_samplers_timesteps.py b/stable-diffusion-webui/modules/sd_samplers_timesteps.py
new file mode 100644
index 0000000000000000000000000000000000000000..26e18d19da077cd724438b6969651dc3bde48e55
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_samplers_timesteps.py
@@ -0,0 +1,167 @@
+import torch
+import inspect
+import sys
+from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
+from modules.sd_samplers_cfg_denoiser import CFGDenoiser
+from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
+
+from modules.shared import opts
+import modules.shared as shared
+
+samplers_timesteps = [
+ ('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
+ ('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
+ ('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}),
+]
+
+
+samplers_data_timesteps = [
+ sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options)
+ for label, funcname, aliases, options in samplers_timesteps
+]
+
+
+class CompVisTimestepsDenoiser(torch.nn.Module):
+ def __init__(self, model, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.inner_model = model
+
+ def forward(self, input, timesteps, **kwargs):
+ return self.inner_model.apply_model(input, timesteps, **kwargs)
+
+
+class CompVisTimestepsVDenoiser(torch.nn.Module):
+ def __init__(self, model, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.inner_model = model
+
+ def predict_eps_from_z_and_v(self, x_t, t, v):
+ return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
+
+ def forward(self, input, timesteps, **kwargs):
+ model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
+ e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
+ return e_t
+
+
+class CFGDenoiserTimesteps(CFGDenoiser):
+
+ def __init__(self, sampler):
+ super().__init__(sampler)
+
+ self.alphas = shared.sd_model.alphas_cumprod
+ self.mask_before_denoising = True
+
+ def get_pred_x0(self, x_in, x_out, sigma):
+ ts = sigma.to(dtype=int)
+
+ a_t = self.alphas[ts][:, None, None, None]
+ sqrt_one_minus_at = (1 - a_t).sqrt()
+
+ pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
+
+ return pred_x0
+
+ @property
+ def inner_model(self):
+ if self.model_wrap is None:
+ denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
+ self.model_wrap = denoiser(shared.sd_model)
+
+ return self.model_wrap
+
+
+class CompVisSampler(sd_samplers_common.Sampler):
+ def __init__(self, funcname, sd_model):
+ super().__init__(funcname)
+
+ self.eta_option_field = 'eta_ddim'
+ self.eta_infotext_field = 'Eta DDIM'
+ self.eta_default = 0.0
+
+ self.model_wrap_cfg = CFGDenoiserTimesteps(self)
+
+ def get_timesteps(self, p, steps):
+ discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+ if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+ discard_next_to_last_sigma = True
+ p.extra_generation_params["Discard penultimate sigma"] = True
+
+ steps += 1 if discard_next_to_last_sigma else 0
+
+ timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999)
+
+ return timesteps
+
+ def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+
+ timesteps = self.get_timesteps(p, steps)
+ timesteps_sched = timesteps[:t_enc]
+
+ alphas_cumprod = shared.sd_model.alphas_cumprod
+ sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
+ sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
+
+ xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
+
+ if opts.img2img_extra_noise > 0:
+ p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
+ extra_noise_params = ExtraNoiseParams(noise, x, xi)
+ extra_noise_callback(extra_noise_params)
+ noise = extra_noise_params.noise
+ xi += noise * opts.img2img_extra_noise * sqrt_alpha_cumprod
+
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+
+ if 'timesteps' in parameters:
+ extra_params_kwargs['timesteps'] = timesteps_sched
+ if 'is_img2img' in parameters:
+ extra_params_kwargs['is_img2img'] = True
+
+ self.model_wrap_cfg.init_latent = x
+ self.last_latent = x
+ self.sampler_extra_args = {
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
+ }
+
+ samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ return samples
+
+ def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+ steps = steps or p.steps
+ timesteps = self.get_timesteps(p, steps)
+
+ extra_params_kwargs = self.initialize(p)
+ parameters = inspect.signature(self.func).parameters
+
+ if 'timesteps' in parameters:
+ extra_params_kwargs['timesteps'] = timesteps
+
+ self.last_latent = x
+ self.sampler_extra_args = {
+ 'cond': conditioning,
+ 'image_cond': image_conditioning,
+ 'uncond': unconditional_conditioning,
+ 'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
+ }
+ samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ return samples
+
+
+sys.modules['modules.sd_samplers_compvis'] = sys.modules[__name__]
+VanillaStableDiffusionSampler = CompVisSampler # temp. compatibility with older extensions
diff --git a/stable-diffusion-webui/modules/sd_samplers_timesteps_impl.py b/stable-diffusion-webui/modules/sd_samplers_timesteps_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b33232b4eb8e1be007629ed26a861039e5d1b86
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_samplers_timesteps_impl.py
@@ -0,0 +1,137 @@
+import torch
+import tqdm
+import k_diffusion.sampling
+import numpy as np
+
+from modules import shared
+from modules.models.diffusion.uni_pc import uni_pc
+
+
+@torch.no_grad()
+def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+ alphas = alphas_cumprod[timesteps]
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
+ sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+ sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
+
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones((x.shape[0]))
+ s_x = x.new_ones((x.shape[0], 1, 1, 1))
+ for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+ index = len(timesteps) - 1 - i
+
+ e_t = model(x, timesteps[index].item() * s_in, **extra_args)
+
+ a_t = alphas[index].item() * s_x
+ a_prev = alphas_prev[index].item() * s_x
+ sigma_t = sigmas[index].item() * s_x
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
+
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
+ noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
+ x = a_prev.sqrt() * pred_x0 + dir_xt + noise
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+ return x
+
+
+@torch.no_grad()
+def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+ alphas = alphas_cumprod[timesteps]
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
+ sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
+
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ s_x = x.new_ones((x.shape[0], 1, 1, 1))
+ old_eps = []
+
+ def get_x_prev_and_pred_x0(e_t, index):
+ # select parameters corresponding to the currently considered timestep
+ a_t = alphas[index].item() * s_x
+ a_prev = alphas_prev[index].item() * s_x
+ sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
+
+ # current prediction for x_0
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev).sqrt() * e_t
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt
+ return x_prev, pred_x0
+
+ for i in tqdm.trange(len(timesteps) - 1, disable=disable):
+ index = len(timesteps) - 1 - i
+ ts = timesteps[index].item() * s_in
+ t_next = timesteps[max(index - 1, 0)].item() * s_in
+
+ e_t = model(x, ts, **extra_args)
+
+ if len(old_eps) == 0:
+ # Pseudo Improved Euler (2nd order)
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+ e_t_next = model(x_prev, t_next, **extra_args)
+ e_t_prime = (e_t + e_t_next) / 2
+ elif len(old_eps) == 1:
+ # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (3 * e_t - old_eps[-1]) / 2
+ elif len(old_eps) == 2:
+ # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+ else:
+ # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+ old_eps.append(e_t)
+ if len(old_eps) >= 4:
+ old_eps.pop(0)
+
+ x = x_prev
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
+
+ return x
+
+
+class UniPCCFG(uni_pc.UniPC):
+ def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
+ super().__init__(None, *args, **kwargs)
+
+ def after_update(x, model_x):
+ callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
+ self.index += 1
+
+ self.cfg_model = cfg_model
+ self.extra_args = extra_args
+ self.callback = callback
+ self.index = 0
+ self.after_update = after_update
+
+ def get_model_input_time(self, t_continuous):
+ return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
+
+ def model(self, x, t):
+ t_input = self.get_model_input_time(t)
+
+ res = self.cfg_model(x, t_input, **self.extra_args)
+
+ return res
+
+
+def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False):
+ alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
+
+ ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+ t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None # this is likely off by a bit - if someone wants to fix it please by all means
+ unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
+ x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+
+ return x
diff --git a/stable-diffusion-webui/modules/sd_unet.py b/stable-diffusion-webui/modules/sd_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..430b9ae468bb84fb08d49a3c6bc9635ba23500cd
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_unet.py
@@ -0,0 +1,92 @@
+import torch.nn
+import ldm.modules.diffusionmodules.openaimodel
+
+from modules import script_callbacks, shared, devices
+
+unet_options = []
+current_unet_option = None
+current_unet = None
+
+
+def list_unets():
+ new_unets = script_callbacks.list_unets_callback()
+
+ unet_options.clear()
+ unet_options.extend(new_unets)
+
+
+def get_unet_option(option=None):
+ option = option or shared.opts.sd_unet
+
+ if option == "None":
+ return None
+
+ if option == "Automatic":
+ name = shared.sd_model.sd_checkpoint_info.model_name
+
+ options = [x for x in unet_options if x.model_name == name]
+
+ option = options[0].label if options else "None"
+
+ return next(iter([x for x in unet_options if x.label == option]), None)
+
+
+def apply_unet(option=None):
+ global current_unet_option
+ global current_unet
+
+ new_option = get_unet_option(option)
+ if new_option == current_unet_option:
+ return
+
+ if current_unet is not None:
+ print(f"Dectivating unet: {current_unet.option.label}")
+ current_unet.deactivate()
+
+ current_unet_option = new_option
+ if current_unet_option is None:
+ current_unet = None
+
+ if not shared.sd_model.lowvram:
+ shared.sd_model.model.diffusion_model.to(devices.device)
+
+ return
+
+ shared.sd_model.model.diffusion_model.to(devices.cpu)
+ devices.torch_gc()
+
+ current_unet = current_unet_option.create_unet()
+ current_unet.option = current_unet_option
+ print(f"Activating unet: {current_unet.option.label}")
+ current_unet.activate()
+
+
+class SdUnetOption:
+ model_name = None
+ """name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
+
+ label = None
+ """name of the unet in UI"""
+
+ def create_unet(self):
+ """returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
+ raise NotImplementedError()
+
+
+class SdUnet(torch.nn.Module):
+ def forward(self, x, timesteps, context, *args, **kwargs):
+ raise NotImplementedError()
+
+ def activate(self):
+ pass
+
+ def deactivate(self):
+ pass
+
+
+def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
+ if current_unet is not None:
+ return current_unet.forward(x, timesteps, context, *args, **kwargs)
+
+ return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
+
diff --git a/stable-diffusion-webui/modules/sd_vae.py b/stable-diffusion-webui/modules/sd_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..31306d8ba4bf10223df199b163a0dc386e322b03
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_vae.py
@@ -0,0 +1,282 @@
+import os
+import collections
+from dataclasses import dataclass
+
+from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks, lowvram, sd_hijack, hashes
+
+import glob
+from copy import deepcopy
+
+
+vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE"))
+vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
+vae_dict = {}
+
+
+base_vae = None
+loaded_vae_file = None
+checkpoint_info = None
+
+checkpoints_loaded = collections.OrderedDict()
+
+
+def get_loaded_vae_name():
+ if loaded_vae_file is None:
+ return None
+
+ return os.path.basename(loaded_vae_file)
+
+
+def get_loaded_vae_hash():
+ if loaded_vae_file is None:
+ return None
+
+ sha256 = hashes.sha256(loaded_vae_file, 'vae')
+
+ return sha256[0:10] if sha256 else None
+
+
+def get_base_vae(model):
+ if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
+ return base_vae
+ return None
+
+
+def store_base_vae(model):
+ global base_vae, checkpoint_info
+ if checkpoint_info != model.sd_checkpoint_info:
+ assert not loaded_vae_file, "Trying to store non-base VAE!"
+ base_vae = deepcopy(model.first_stage_model.state_dict())
+ checkpoint_info = model.sd_checkpoint_info
+
+
+def delete_base_vae():
+ global base_vae, checkpoint_info
+ base_vae = None
+ checkpoint_info = None
+
+
+def restore_base_vae(model):
+ global loaded_vae_file
+ if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
+ print("Restoring base VAE")
+ _load_vae_dict(model, base_vae)
+ loaded_vae_file = None
+ delete_base_vae()
+
+
+def get_filename(filepath):
+ return os.path.basename(filepath)
+
+
+def refresh_vae_list():
+ vae_dict.clear()
+
+ paths = [
+ os.path.join(sd_models.model_path, '**/*.vae.ckpt'),
+ os.path.join(sd_models.model_path, '**/*.vae.pt'),
+ os.path.join(sd_models.model_path, '**/*.vae.safetensors'),
+ os.path.join(vae_path, '**/*.ckpt'),
+ os.path.join(vae_path, '**/*.pt'),
+ os.path.join(vae_path, '**/*.safetensors'),
+ ]
+
+ if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
+ paths += [
+ os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
+ os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
+ os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
+ ]
+
+ if shared.cmd_opts.vae_dir is not None and os.path.isdir(shared.cmd_opts.vae_dir):
+ paths += [
+ os.path.join(shared.cmd_opts.vae_dir, '**/*.ckpt'),
+ os.path.join(shared.cmd_opts.vae_dir, '**/*.pt'),
+ os.path.join(shared.cmd_opts.vae_dir, '**/*.safetensors'),
+ ]
+
+ candidates = []
+ for path in paths:
+ candidates += glob.iglob(path, recursive=True)
+
+ for filepath in candidates:
+ name = get_filename(filepath)
+ vae_dict[name] = filepath
+
+ vae_dict.update(dict(sorted(vae_dict.items(), key=lambda item: shared.natural_sort_key(item[0]))))
+
+
+def find_vae_near_checkpoint(checkpoint_file):
+ checkpoint_path = os.path.basename(checkpoint_file).rsplit('.', 1)[0]
+ for vae_file in vae_dict.values():
+ if os.path.basename(vae_file).startswith(checkpoint_path):
+ return vae_file
+
+ return None
+
+
+@dataclass
+class VaeResolution:
+ vae: str = None
+ source: str = None
+ resolved: bool = True
+
+ def tuple(self):
+ return self.vae, self.source
+
+
+def is_automatic():
+ return shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config
+
+
+def resolve_vae_from_setting() -> VaeResolution:
+ if shared.opts.sd_vae == "None":
+ return VaeResolution()
+
+ vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
+ if vae_from_options is not None:
+ return VaeResolution(vae_from_options, 'specified in settings')
+
+ if not is_automatic():
+ print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
+
+ return VaeResolution(resolved=False)
+
+
+def resolve_vae_from_user_metadata(checkpoint_file) -> VaeResolution:
+ metadata = extra_networks.get_user_metadata(checkpoint_file)
+ vae_metadata = metadata.get("vae", None)
+ if vae_metadata is not None and vae_metadata != "Automatic":
+ if vae_metadata == "None":
+ return VaeResolution()
+
+ vae_from_metadata = vae_dict.get(vae_metadata, None)
+ if vae_from_metadata is not None:
+ return VaeResolution(vae_from_metadata, "from user metadata")
+
+ return VaeResolution(resolved=False)
+
+
+def resolve_vae_near_checkpoint(checkpoint_file) -> VaeResolution:
+ vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
+ if vae_near_checkpoint is not None and (not shared.opts.sd_vae_overrides_per_model_preferences or is_automatic()):
+ return VaeResolution(vae_near_checkpoint, 'found near the checkpoint')
+
+ return VaeResolution(resolved=False)
+
+
+def resolve_vae(checkpoint_file) -> VaeResolution:
+ if shared.cmd_opts.vae_path is not None:
+ return VaeResolution(shared.cmd_opts.vae_path, 'from commandline argument')
+
+ if shared.opts.sd_vae_overrides_per_model_preferences and not is_automatic():
+ return resolve_vae_from_setting()
+
+ res = resolve_vae_from_user_metadata(checkpoint_file)
+ if res.resolved:
+ return res
+
+ res = resolve_vae_near_checkpoint(checkpoint_file)
+ if res.resolved:
+ return res
+
+ res = resolve_vae_from_setting()
+
+ return res
+
+
+def load_vae_dict(filename, map_location):
+ vae_ckpt = sd_models.read_state_dict(filename, map_location=map_location)
+ vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+ return vae_dict_1
+
+
+def load_vae(model, vae_file=None, vae_source="from unknown source"):
+ global vae_dict, base_vae, loaded_vae_file
+ # save_settings = False
+
+ cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
+
+ if vae_file:
+ if cache_enabled and vae_file in checkpoints_loaded:
+ # use vae checkpoint cache
+ print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}")
+ store_base_vae(model)
+ _load_vae_dict(model, checkpoints_loaded[vae_file])
+ else:
+ assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}"
+ print(f"Loading VAE weights {vae_source}: {vae_file}")
+ store_base_vae(model)
+
+ vae_dict_1 = load_vae_dict(vae_file, map_location=shared.weight_load_location)
+ _load_vae_dict(model, vae_dict_1)
+
+ if cache_enabled:
+ # cache newly loaded vae
+ checkpoints_loaded[vae_file] = vae_dict_1.copy()
+
+ # clean up cache if limit is reached
+ if cache_enabled:
+ while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
+ checkpoints_loaded.popitem(last=False) # LRU
+
+ # If vae used is not in dict, update it
+ # It will be removed on refresh though
+ vae_opt = get_filename(vae_file)
+ if vae_opt not in vae_dict:
+ vae_dict[vae_opt] = vae_file
+
+ elif loaded_vae_file:
+ restore_base_vae(model)
+
+ loaded_vae_file = vae_file
+ model.base_vae = base_vae
+ model.loaded_vae_file = loaded_vae_file
+
+
+# don't call this from outside
+def _load_vae_dict(model, vae_dict_1):
+ model.first_stage_model.load_state_dict(vae_dict_1)
+ model.first_stage_model.to(devices.dtype_vae)
+
+
+def clear_loaded_vae():
+ global loaded_vae_file
+ loaded_vae_file = None
+
+
+unspecified = object()
+
+
+def reload_vae_weights(sd_model=None, vae_file=unspecified):
+ if not sd_model:
+ sd_model = shared.sd_model
+
+ checkpoint_info = sd_model.sd_checkpoint_info
+ checkpoint_file = checkpoint_info.filename
+
+ if vae_file == unspecified:
+ vae_file, vae_source = resolve_vae(checkpoint_file).tuple()
+ else:
+ vae_source = "from function argument"
+
+ if loaded_vae_file == vae_file:
+ return
+
+ if sd_model.lowvram:
+ lowvram.send_everything_to_cpu()
+ else:
+ sd_model.to(devices.cpu)
+
+ sd_hijack.model_hijack.undo_hijack(sd_model)
+
+ load_vae(sd_model, vae_file, vae_source)
+
+ sd_hijack.model_hijack.hijack(sd_model)
+ script_callbacks.model_loaded_callback(sd_model)
+
+ if not sd_model.lowvram:
+ sd_model.to(devices.device)
+
+ print("VAE weights loaded.")
+ return sd_model
diff --git a/stable-diffusion-webui/modules/sd_vae_approx.py b/stable-diffusion-webui/modules/sd_vae_approx.py
new file mode 100644
index 0000000000000000000000000000000000000000..1412bc8e02b9791938a0fb54d776ab853019e0e3
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_vae_approx.py
@@ -0,0 +1,86 @@
+import os
+
+import torch
+from torch import nn
+from modules import devices, paths, shared
+
+sd_vae_approx_models = {}
+
+
+class VAEApprox(nn.Module):
+ def __init__(self):
+ super(VAEApprox, self).__init__()
+ self.conv1 = nn.Conv2d(4, 8, (7, 7))
+ self.conv2 = nn.Conv2d(8, 16, (5, 5))
+ self.conv3 = nn.Conv2d(16, 32, (3, 3))
+ self.conv4 = nn.Conv2d(32, 64, (3, 3))
+ self.conv5 = nn.Conv2d(64, 32, (3, 3))
+ self.conv6 = nn.Conv2d(32, 16, (3, 3))
+ self.conv7 = nn.Conv2d(16, 8, (3, 3))
+ self.conv8 = nn.Conv2d(8, 3, (3, 3))
+
+ def forward(self, x):
+ extra = 11
+ x = nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))
+ x = nn.functional.pad(x, (extra, extra, extra, extra))
+
+ for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8, ]:
+ x = layer(x)
+ x = nn.functional.leaky_relu(x, 0.1)
+
+ return x
+
+
+def download_model(model_path, model_url):
+ if not os.path.exists(model_path):
+ os.makedirs(os.path.dirname(model_path), exist_ok=True)
+
+ print(f'Downloading VAEApprox model to: {model_path}')
+ torch.hub.download_url_to_file(model_url, model_path)
+
+
+def model():
+ model_name = "vaeapprox-sdxl.pt" if getattr(shared.sd_model, 'is_sdxl', False) else "model.pt"
+ loaded_model = sd_vae_approx_models.get(model_name)
+
+ if loaded_model is None:
+ model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
+ if not os.path.exists(model_path):
+ model_path = os.path.join(paths.script_path, "models", "VAE-approx", model_name)
+
+ if not os.path.exists(model_path):
+ model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
+ download_model(model_path, 'https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/download/v1.0.0-pre/' + model_name)
+
+ loaded_model = VAEApprox()
+ loaded_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
+ loaded_model.eval()
+ loaded_model.to(devices.device, devices.dtype)
+ sd_vae_approx_models[model_name] = loaded_model
+
+ return loaded_model
+
+
+def cheap_approximation(sample):
+ # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
+
+ if shared.sd_model.is_sdxl:
+ coeffs = [
+ [ 0.3448, 0.4168, 0.4395],
+ [-0.1953, -0.0290, 0.0250],
+ [ 0.1074, 0.0886, -0.0163],
+ [-0.3730, -0.2499, -0.2088],
+ ]
+ else:
+ coeffs = [
+ [ 0.298, 0.207, 0.208],
+ [ 0.187, 0.286, 0.173],
+ [-0.158, 0.189, 0.264],
+ [-0.184, -0.271, -0.473],
+ ]
+
+ coefs = torch.tensor(coeffs).to(sample.device)
+
+ x_sample = torch.einsum("...lxy,lr -> ...rxy", sample, coefs)
+
+ return x_sample
diff --git a/stable-diffusion-webui/modules/sd_vae_taesd.py b/stable-diffusion-webui/modules/sd_vae_taesd.py
new file mode 100644
index 0000000000000000000000000000000000000000..808eb3624fd40daa56bcbdb5f8ad771ae5557346
--- /dev/null
+++ b/stable-diffusion-webui/modules/sd_vae_taesd.py
@@ -0,0 +1,124 @@
+"""
+Tiny AutoEncoder for Stable Diffusion
+(DNN for encoding / decoding SD's latent space)
+
+https://github.com/madebyollin/taesd
+"""
+import os
+import torch
+import torch.nn as nn
+
+from modules import devices, paths_internal, shared
+
+sd_vae_taesd_models = {}
+
+
+def conv(n_in, n_out, **kwargs):
+ return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+
+class Clamp(nn.Module):
+ @staticmethod
+ def forward(x):
+ return torch.tanh(x / 3) * 3
+
+
+class Block(nn.Module):
+ def __init__(self, n_in, n_out):
+ super().__init__()
+ self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
+ self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+ self.fuse = nn.ReLU()
+
+ def forward(self, x):
+ return self.fuse(self.conv(x) + self.skip(x))
+
+
+def decoder():
+ return nn.Sequential(
+ Clamp(), conv(4, 64), nn.ReLU(),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), conv(64, 3),
+ )
+
+
+def encoder():
+ return nn.Sequential(
+ conv(3, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 4),
+ )
+
+
+class TAESDDecoder(nn.Module):
+ latent_magnitude = 3
+ latent_shift = 0.5
+
+ def __init__(self, decoder_path="taesd_decoder.pth"):
+ """Initialize pretrained TAESD on the given device from the given checkpoints."""
+ super().__init__()
+ self.decoder = decoder()
+ self.decoder.load_state_dict(
+ torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
+
+
+class TAESDEncoder(nn.Module):
+ latent_magnitude = 3
+ latent_shift = 0.5
+
+ def __init__(self, encoder_path="taesd_encoder.pth"):
+ """Initialize pretrained TAESD on the given device from the given checkpoints."""
+ super().__init__()
+ self.encoder = encoder()
+ self.encoder.load_state_dict(
+ torch.load(encoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
+
+
+def download_model(model_path, model_url):
+ if not os.path.exists(model_path):
+ os.makedirs(os.path.dirname(model_path), exist_ok=True)
+
+ print(f'Downloading TAESD model to: {model_path}')
+ torch.hub.download_url_to_file(model_url, model_path)
+
+
+def decoder_model():
+ model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_decoder.pth"
+ loaded_model = sd_vae_taesd_models.get(model_name)
+
+ if loaded_model is None:
+ model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
+ download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
+
+ if os.path.exists(model_path):
+ loaded_model = TAESDDecoder(model_path)
+ loaded_model.eval()
+ loaded_model.to(devices.device, devices.dtype)
+ sd_vae_taesd_models[model_name] = loaded_model
+ else:
+ raise FileNotFoundError('TAESD model not found')
+
+ return loaded_model.decoder
+
+
+def encoder_model():
+ model_name = "taesdxl_encoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_encoder.pth"
+ loaded_model = sd_vae_taesd_models.get(model_name)
+
+ if loaded_model is None:
+ model_path = os.path.join(paths_internal.models_path, "VAE-taesd", model_name)
+ download_model(model_path, 'https://github.com/madebyollin/taesd/raw/main/' + model_name)
+
+ if os.path.exists(model_path):
+ loaded_model = TAESDEncoder(model_path)
+ loaded_model.eval()
+ loaded_model.to(devices.device, devices.dtype)
+ sd_vae_taesd_models[model_name] = loaded_model
+ else:
+ raise FileNotFoundError('TAESD model not found')
+
+ return loaded_model.encoder
diff --git a/stable-diffusion-webui/modules/shared.py b/stable-diffusion-webui/modules/shared.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6d08cad7206c8f2526716dcbe844941de965698
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared.py
@@ -0,0 +1,87 @@
+import sys
+
+import gradio as gr
+
+from modules import shared_cmd_options, shared_gradio_themes, options, shared_items, sd_models_types
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
+from modules import util
+
+cmd_opts = shared_cmd_options.cmd_opts
+parser = shared_cmd_options.parser
+
+batch_cond_uncond = True # old field, unused now in favor of shared.opts.batch_cond_uncond
+parallel_processing_allowed = True
+styles_filename = cmd_opts.styles_file
+config_filename = cmd_opts.ui_settings_file
+hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+
+demo = None
+
+device = None
+
+weight_load_location = None
+
+xformers_available = False
+
+hypernetworks = {}
+
+loaded_hypernetworks = []
+
+state = None
+
+prompt_styles = None
+
+interrogator = None
+
+face_restorers = []
+
+options_templates = None
+opts = None
+restricted_opts = None
+
+sd_model: sd_models_types.WebuiSdModel = None
+
+settings_components = None
+"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
+
+tab_names = []
+
+latent_upscale_default_mode = "Latent"
+latent_upscale_modes = {
+ "Latent": {"mode": "bilinear", "antialias": False},
+ "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
+ "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
+ "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
+ "Latent (nearest)": {"mode": "nearest", "antialias": False},
+ "Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
+}
+
+sd_upscalers = []
+
+clip_model = None
+
+progress_print_out = sys.stdout
+
+gradio_theme = gr.themes.Base()
+
+total_tqdm = None
+
+mem_mon = None
+
+options_section = options.options_section
+OptionInfo = options.OptionInfo
+OptionHTML = options.OptionHTML
+
+natural_sort_key = util.natural_sort_key
+listfiles = util.listfiles
+html_path = util.html_path
+html = util.html
+walk_files = util.walk_files
+ldm_print = util.ldm_print
+
+reload_gradio_theme = shared_gradio_themes.reload_gradio_theme
+
+list_checkpoint_tiles = shared_items.list_checkpoint_tiles
+refresh_checkpoints = shared_items.refresh_checkpoints
+list_samplers = shared_items.list_samplers
+reload_hypernetworks = shared_items.reload_hypernetworks
diff --git a/stable-diffusion-webui/modules/shared_cmd_options.py b/stable-diffusion-webui/modules/shared_cmd_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9dcacd8abb4882c3c23f95cfe275d5daf15366c
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared_cmd_options.py
@@ -0,0 +1,18 @@
+import os
+
+import launch
+from modules import cmd_args, script_loading
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
+
+parser = cmd_args.parser
+
+script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
+script_loading.preload_extensions(extensions_builtin_dir, parser)
+
+if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
+ cmd_opts = parser.parse_args()
+else:
+ cmd_opts, _ = parser.parse_known_args()
+
+
+cmd_opts.disable_extension_access = any([cmd_opts.share, cmd_opts.listen, cmd_opts.ngrok, cmd_opts.server_name]) and not cmd_opts.enable_insecure_extension_access
diff --git a/stable-diffusion-webui/modules/shared_gradio_themes.py b/stable-diffusion-webui/modules/shared_gradio_themes.py
new file mode 100644
index 0000000000000000000000000000000000000000..4520cfe319f8cce867313bf3d5c4b0f4fef83aac
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared_gradio_themes.py
@@ -0,0 +1,67 @@
+import os
+
+import gradio as gr
+
+from modules import errors, shared
+from modules.paths_internal import script_path
+
+
+# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
+gradio_hf_hub_themes = [
+ "gradio/base",
+ "gradio/glass",
+ "gradio/monochrome",
+ "gradio/seafoam",
+ "gradio/soft",
+ "gradio/dracula_test",
+ "abidlabs/dracula_test",
+ "abidlabs/Lime",
+ "abidlabs/pakistan",
+ "Ama434/neutral-barlow",
+ "dawood/microsoft_windows",
+ "finlaymacklon/smooth_slate",
+ "Franklisi/darkmode",
+ "freddyaboulton/dracula_revamped",
+ "freddyaboulton/test-blue",
+ "gstaff/xkcd",
+ "Insuz/Mocha",
+ "Insuz/SimpleIndigo",
+ "JohnSmith9982/small_and_pretty",
+ "nota-ai/theme",
+ "nuttea/Softblue",
+ "ParityError/Anime",
+ "reilnuud/polite",
+ "remilia/Ghostly",
+ "rottenlittlecreature/Moon_Goblin",
+ "step-3-profit/Midnight-Deep",
+ "Taithrah/Minimal",
+ "ysharma/huggingface",
+ "ysharma/steampunk",
+ "NoCrypt/miku"
+]
+
+
+def reload_gradio_theme(theme_name=None):
+ if not theme_name:
+ theme_name = shared.opts.gradio_theme
+
+ default_theme_args = dict(
+ font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
+ font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
+ )
+
+ if theme_name == "Default":
+ shared.gradio_theme = gr.themes.Default(**default_theme_args)
+ else:
+ try:
+ theme_cache_dir = os.path.join(script_path, 'tmp', 'gradio_themes')
+ theme_cache_path = os.path.join(theme_cache_dir, f'{theme_name.replace("/", "_")}.json')
+ if shared.opts.gradio_themes_cache and os.path.exists(theme_cache_path):
+ shared.gradio_theme = gr.themes.ThemeClass.load(theme_cache_path)
+ else:
+ os.makedirs(theme_cache_dir, exist_ok=True)
+ shared.gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+ shared.gradio_theme.dump(theme_cache_path)
+ except Exception as e:
+ errors.display(e, "changing gradio theme")
+ shared.gradio_theme = gr.themes.Default(**default_theme_args)
diff --git a/stable-diffusion-webui/modules/shared_init.py b/stable-diffusion-webui/modules/shared_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..acd698c291936148753be1d2faf70d123c0fa8aa
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared_init.py
@@ -0,0 +1,49 @@
+import os
+
+import torch
+
+from modules import shared
+from modules.shared import cmd_opts
+
+
+def initialize():
+ """Initializes fields inside the shared module in a controlled manner.
+
+ Should be called early because some other modules you can import mingt need these fields to be already set.
+ """
+
+ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
+
+ from modules import options, shared_options
+ shared.options_templates = shared_options.options_templates
+ shared.opts = options.Options(shared_options.options_templates, shared_options.restricted_opts)
+ shared.restricted_opts = shared_options.restricted_opts
+ if os.path.exists(shared.config_filename):
+ shared.opts.load(shared.config_filename)
+
+ from modules import devices
+ devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
+ (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
+
+ devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
+ devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
+
+ shared.device = devices.device
+ shared.weight_load_location = None if cmd_opts.lowram else "cpu"
+
+ from modules import shared_state
+ shared.state = shared_state.State()
+
+ from modules import styles
+ shared.prompt_styles = styles.StyleDatabase(shared.styles_filename)
+
+ from modules import interrogate
+ shared.interrogator = interrogate.InterrogateModels("interrogate")
+
+ from modules import shared_total_tqdm
+ shared.total_tqdm = shared_total_tqdm.TotalTQDM()
+
+ from modules import memmon, devices
+ shared.mem_mon = memmon.MemUsageMonitor("MemMon", devices.device, shared.opts)
+ shared.mem_mon.start()
+
diff --git a/stable-diffusion-webui/modules/shared_items.py b/stable-diffusion-webui/modules/shared_items.py
new file mode 100644
index 0000000000000000000000000000000000000000..11d89c6f2e2dfd08846e478626eb81eaa0d50564
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared_items.py
@@ -0,0 +1,119 @@
+import sys
+
+from modules.shared_cmd_options import cmd_opts
+
+
+def realesrgan_models_names():
+ import modules.realesrgan_model
+ return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
+
+
+def postprocessing_scripts():
+ import modules.scripts
+
+ return modules.scripts.scripts_postproc.scripts
+
+
+def sd_vae_items():
+ import modules.sd_vae
+
+ return ["Automatic", "None"] + list(modules.sd_vae.vae_dict)
+
+
+def refresh_vae_list():
+ import modules.sd_vae
+
+ modules.sd_vae.refresh_vae_list()
+
+
+def cross_attention_optimizations():
+ import modules.sd_hijack
+
+ return ["Automatic"] + [x.title() for x in modules.sd_hijack.optimizers] + ["None"]
+
+
+def sd_unet_items():
+ import modules.sd_unet
+
+ return ["Automatic"] + [x.label for x in modules.sd_unet.unet_options] + ["None"]
+
+
+def refresh_unet_list():
+ import modules.sd_unet
+
+ modules.sd_unet.list_unets()
+
+
+def list_checkpoint_tiles():
+ import modules.sd_models
+ return modules.sd_models.checkpoint_tiles()
+
+
+def refresh_checkpoints():
+ import modules.sd_models
+ return modules.sd_models.list_models()
+
+
+def list_samplers():
+ import modules.sd_samplers
+ return modules.sd_samplers.all_samplers
+
+
+def reload_hypernetworks():
+ from modules.hypernetworks import hypernetwork
+ from modules import shared
+
+ shared.hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+
+
+ui_reorder_categories_builtin_items = [
+ "inpaint",
+ "sampler",
+ "accordions",
+ "checkboxes",
+ "dimensions",
+ "cfg",
+ "denoising",
+ "seed",
+ "batch",
+ "override_settings",
+]
+
+
+def ui_reorder_categories():
+ from modules import scripts
+
+ yield from ui_reorder_categories_builtin_items
+
+ sections = {}
+ for script in scripts.scripts_txt2img.scripts + scripts.scripts_img2img.scripts:
+ if isinstance(script.section, str) and script.section not in ui_reorder_categories_builtin_items:
+ sections[script.section] = 1
+
+ yield from sections
+
+ yield "scripts"
+
+
+class Shared(sys.modules[__name__].__class__):
+ """
+ this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
+ at program startup.
+ """
+
+ sd_model_val = None
+
+ @property
+ def sd_model(self):
+ import modules.sd_models
+
+ return modules.sd_models.model_data.get_sd_model()
+
+ @sd_model.setter
+ def sd_model(self, value):
+ import modules.sd_models
+
+ modules.sd_models.model_data.set_sd_model(value)
+
+
+sys.modules['modules.shared'].__class__ = Shared
diff --git a/stable-diffusion-webui/modules/shared_options.py b/stable-diffusion-webui/modules/shared_options.py
new file mode 100644
index 0000000000000000000000000000000000000000..7739b80291fa55ae6f0b0a5b49ee058ce20ec501
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared_options.py
@@ -0,0 +1,332 @@
+import gradio as gr
+
+from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
+from modules.shared_cmd_options import cmd_opts
+from modules.options import options_section, OptionInfo, OptionHTML
+
+options_templates = {}
+hide_dirs = shared.hide_dirs
+
+restricted_opts = {
+ "samples_filename_pattern",
+ "directories_filename_pattern",
+ "outdir_samples",
+ "outdir_txt2img_samples",
+ "outdir_img2img_samples",
+ "outdir_extras_samples",
+ "outdir_grids",
+ "outdir_txt2img_grids",
+ "outdir_save",
+ "outdir_init_images"
+}
+
+options_templates.update(options_section(('saving-images', "Saving images/grids"), {
+ "samples_save": OptionInfo(True, "Always save all generated images"),
+ "samples_format": OptionInfo('png', 'File format for images'),
+ "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
+ "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
+
+ "grid_save": OptionInfo(True, "Always save all generated image grids"),
+ "grid_format": OptionInfo('png', 'File format for grids'),
+ "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
+ "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+ "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
+ "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
+ "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
+ "font": OptionInfo("", "Font for image grids that have text"),
+ "grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
+ "grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
+ "grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
+
+ "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
+ "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
+ "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
+ "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
+ "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
+ "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
+ "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
+ "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+ "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
+ "export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
+ "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
+ "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
+ "img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),
+
+ "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
+ "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
+ "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
+ "save_init_img": OptionInfo(False, "Save init images when using img2img"),
+
+ "temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
+ "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
+
+ "save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
+}))
+
+options_templates.update(options_section(('saving-paths', "Paths for saving"), {
+ "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
+ "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
+ "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
+ "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
+ "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
+ "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
+ "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
+ "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
+ "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
+}))
+
+options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
+ "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
+ "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
+ "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
+ "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
+ "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
+}))
+
+options_templates.update(options_section(('upscaling', "Upscaling"), {
+ "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
+ "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
+ "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
+ "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
+}))
+
+options_templates.update(options_section(('face-restoration', "Face restoration"), {
+ "face_restoration": OptionInfo(False, "Restore faces", infotext='Face restoration').info("will use a third-party model on generation result to reconstruct faces"),
+ "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
+ "code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
+ "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
+}))
+
+options_templates.update(options_section(('system', "System"), {
+ "auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
+ "show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
+ "show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
+ "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
+ "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
+ "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
+ "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
+ "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
+ "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
+ "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
+}))
+
+options_templates.update(options_section(('API', "API"), {
+ "api_enable_requests": OptionInfo(True, "Allow http:// and https:// URLs for input images in API", restrict_api=True),
+ "api_forbid_local_requests": OptionInfo(True, "Forbid URLs to local resources", restrict_api=True),
+ "api_useragent": OptionInfo("", "User agent for requests", restrict_api=True),
+}))
+
+options_templates.update(options_section(('training', "Training"), {
+ "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+ "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
+ "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
+ "save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
+ "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
+ "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
+ "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+ "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+ "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
+ "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
+ "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
+ "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
+}))
+
+options_templates.update(options_section(('sd', "Stable Diffusion"), {
+ "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext='Model hash'),
+ "sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
+ "sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
+ "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
+ "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
+ "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
+ "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+ "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
+ "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
+ "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}, infotext="Clip skip").link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
+ "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+ "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}, infotext="RNG").info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
+ "tiling": OptionInfo(False, "Tiling", infotext='Tiling').info("produce a tileable picture"),
+ "hires_fix_refiner_pass": OptionInfo("second pass", "Hires fix: which pass to enable refiner for", gr.Radio, {"choices": ["first pass", "second pass", "both passes"]}, infotext="Hires refiner"),
+}))
+
+options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
+ "sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
+ "sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
+ "sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
+ "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
+}))
+
+options_templates.update(options_section(('vae', "VAE"), {
+ "sd_vae_explanation": OptionHTML("""
+<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
+image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
+(i.e. when the progress bar is between empty and full). For txt2img, VAE is used to create a resulting image after the sampling is finished.
+For img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.
+"""),
+ "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+ "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list, infotext='VAE').info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
+ "sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),
+ "auto_vae_precision": OptionInfo(True, "Automatically revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
+ "sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Encoder').info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
+ "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),
+}))
+
+options_templates.update(options_section(('img2img', "img2img"), {
+ "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Conditional mask weight'),
+ "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.0, "maximum": 1.5, "step": 0.001}, infotext='Noise multiplier'),
+ "img2img_extra_noise": OptionInfo(0.0, "Extra noise multiplier for img2img and hires fix", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Extra noise').info("0 = disabled (default); should be lower than denoising strength"),
+ "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
+ "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
+ "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
+ "img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_reload_ui(),
+ "img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_reload_ui(),
+ "img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker, {}).info("brush color of inpaint mask").needs_reload_ui(),
+ "img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
+ "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
+ "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
+}))
+
+options_templates.update(options_section(('optimizations', "Optimizations"), {
+ "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
+ "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
+ "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
+ "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
+ "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+ "persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("do not recalculate conds from prompts if prompts have not changed since previous calculation"),
+ "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
+}))
+
+options_templates.update(options_section(('compatibility', "Compatibility"), {
+ "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
+ "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
+ "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
+ "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
+ "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
+ "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
+ "use_old_scheduling": OptionInfo(False, "Use old prompt editing timelines.", infotext="Old prompt editing timelines").info("For [red:green:N]; old: If N < 1, it's a fraction of steps (and hires fix uses range from 0 to 1), if N >= 1, it's an absolute number of steps; new: If N has a decimal point in it, it's a fraction of steps (and hires fix uses range from 1 to 2), othewrwise it's an absolute number of steps"),
+}))
+
+options_templates.update(options_section(('interrogate', "Interrogate"), {
+ "interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
+ "interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
+ "interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+ "interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+ "interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+ "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
+ "interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": interrogate.category_types()}, refresh=interrogate.category_types),
+ "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
+ "deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
+ "deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
+ "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
+}))
+
+options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+ "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
+ "extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
+ "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
+ "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
+ "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
+ "extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
+ "extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
+ "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
+ "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
+ "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
+ "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
+ "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
+}))
+
+options_templates.update(options_section(('ui', "User interface"), {
+ "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
+ "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
+ "gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
+ "gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("an be any valid CSS value").needs_reload_ui(),
+ "return_grid": OptionInfo(True, "Show grid in results for web"),
+ "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
+ "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
+ "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
+ "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
+ "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+ "js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
+ "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
+ "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+ "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
+ "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
+ "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+ "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+ "keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
+ "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
+ "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
+ "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
+ "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
+ "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
+ "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
+ "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
+ "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
+}))
+
+
+options_templates.update(options_section(('infotext', "Infotext"), {
+ "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+ "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
+ "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
+ "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
+ "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
+ "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
+<li>Ignore: keep prompt and styles dropdown as it is.</li>
+<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
+<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
+<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
+</ul>"""),
+
+}))
+
+options_templates.update(options_section(('ui', "Live previews"), {
+ "show_progressbar": OptionInfo(True, "Show progressbar"),
+ "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
+ "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
+ "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
+ "show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
+ "show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
+ "live_preview_allow_lowvram_full": OptionInfo(False, "Allow Full live preview method with lowvram/medvram").info("If not, Approx NN will be used instead; Full live preview method is very detrimental to speed if lowvram/medvram optimizations are enabled"),
+ "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
+ "live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
+ "live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
+}))
+
+options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+ "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
+ "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unpredictable results"),
+ "eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
+ "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
+ 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}, infotext='Sigma churn').info('amount of stochasticity; only applies to Euler, Heun, and DPM2'),
+ 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}, infotext='Sigma tmin').info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
+ 's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
+ 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling'),
+ 'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
+ 'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule max sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+ 'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule min sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
+ 'rho': OptionInfo(0.0, "rho", gr.Number, infotext='Schedule rho').info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
+ 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}, infotext='ENSD').info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
+ 'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma", infotext='Discard penultimate sigma').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
+ 'sgm_noise_multiplier': OptionInfo(False, "SGM noise multiplier", infotext='SGM noise multplier').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818").info("Match initial noise to official SDXL implementation - only useful for reproducing images"),
+ 'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}, infotext='UniPC variant'),
+ 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, infotext='UniPC skip type'),
+ 'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
+ 'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
+}))
+
+options_templates.update(options_section(('postprocessing', "Postprocessing"), {
+ 'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+ 'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+ 'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+}))
+
+options_templates.update(options_section((None, "Hidden options"), {
+ "disabled_extensions": OptionInfo([], "Disable these extensions"),
+ "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
+ "restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
+ "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
+}))
+
diff --git a/stable-diffusion-webui/modules/shared_state.py b/stable-diffusion-webui/modules/shared_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..91740d38fb134921a79b15edb1d0a707511b14a8
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared_state.py
@@ -0,0 +1,159 @@
+import datetime
+import logging
+import threading
+import time
+
+from modules import errors, shared, devices
+from typing import Optional
+
+log = logging.getLogger(__name__)
+
+
+class State:
+ skipped = False
+ interrupted = False
+ job = ""
+ job_no = 0
+ job_count = 0
+ processing_has_refined_job_count = False
+ job_timestamp = '0'
+ sampling_step = 0
+ sampling_steps = 0
+ current_latent = None
+ current_image = None
+ current_image_sampling_step = 0
+ id_live_preview = 0
+ textinfo = None
+ time_start = None
+ server_start = None
+ _server_command_signal = threading.Event()
+ _server_command: Optional[str] = None
+
+ def __init__(self):
+ self.server_start = time.time()
+
+ @property
+ def need_restart(self) -> bool:
+ # Compatibility getter for need_restart.
+ return self.server_command == "restart"
+
+ @need_restart.setter
+ def need_restart(self, value: bool) -> None:
+ # Compatibility setter for need_restart.
+ if value:
+ self.server_command = "restart"
+
+ @property
+ def server_command(self):
+ return self._server_command
+
+ @server_command.setter
+ def server_command(self, value: Optional[str]) -> None:
+ """
+ Set the server command to `value` and signal that it's been set.
+ """
+ self._server_command = value
+ self._server_command_signal.set()
+
+ def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
+ """
+ Wait for server command to get set; return and clear the value and signal.
+ """
+ if self._server_command_signal.wait(timeout):
+ self._server_command_signal.clear()
+ req = self._server_command
+ self._server_command = None
+ return req
+ return None
+
+ def request_restart(self) -> None:
+ self.interrupt()
+ self.server_command = "restart"
+ log.info("Received restart request")
+
+ def skip(self):
+ self.skipped = True
+ log.info("Received skip request")
+
+ def interrupt(self):
+ self.interrupted = True
+ log.info("Received interrupt request")
+
+ def nextjob(self):
+ if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps == -1:
+ self.do_set_current_image()
+
+ self.job_no += 1
+ self.sampling_step = 0
+ self.current_image_sampling_step = 0
+
+ def dict(self):
+ obj = {
+ "skipped": self.skipped,
+ "interrupted": self.interrupted,
+ "job": self.job,
+ "job_count": self.job_count,
+ "job_timestamp": self.job_timestamp,
+ "job_no": self.job_no,
+ "sampling_step": self.sampling_step,
+ "sampling_steps": self.sampling_steps,
+ }
+
+ return obj
+
+ def begin(self, job: str = "(unknown)"):
+ self.sampling_step = 0
+ self.job_count = -1
+ self.processing_has_refined_job_count = False
+ self.job_no = 0
+ self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+ self.current_latent = None
+ self.current_image = None
+ self.current_image_sampling_step = 0
+ self.id_live_preview = 0
+ self.skipped = False
+ self.interrupted = False
+ self.textinfo = None
+ self.time_start = time.time()
+ self.job = job
+ devices.torch_gc()
+ log.info("Starting job %s", job)
+
+ def end(self):
+ duration = time.time() - self.time_start
+ log.info("Ending job %s (%.2f seconds)", self.job, duration)
+ self.job = ""
+ self.job_count = 0
+
+ devices.torch_gc()
+
+ def set_current_image(self):
+ """if enough sampling steps have been made after the last call to this, sets self.current_image from self.current_latent, and modifies self.id_live_preview accordingly"""
+ if not shared.parallel_processing_allowed:
+ return
+
+ if self.sampling_step - self.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps != -1:
+ self.do_set_current_image()
+
+ def do_set_current_image(self):
+ if self.current_latent is None:
+ return
+
+ import modules.sd_samplers
+
+ try:
+ if shared.opts.show_progress_grid:
+ self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
+ else:
+ self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
+
+ self.current_image_sampling_step = self.sampling_step
+
+ except Exception:
+ # when switching models during genration, VAE would be on CPU, so creating an image will fail.
+ # we silently ignore this error
+ errors.record_exception()
+
+ def assign_current_image(self, image):
+ self.current_image = image
+ self.id_live_preview += 1
diff --git a/stable-diffusion-webui/modules/shared_total_tqdm.py b/stable-diffusion-webui/modules/shared_total_tqdm.py
new file mode 100644
index 0000000000000000000000000000000000000000..a023b1588f0fce8cc47ae9e72217733aedb16581
--- /dev/null
+++ b/stable-diffusion-webui/modules/shared_total_tqdm.py
@@ -0,0 +1,37 @@
+import tqdm
+
+from modules import shared
+
+
+class TotalTQDM:
+ def __init__(self):
+ self._tqdm = None
+
+ def reset(self):
+ self._tqdm = tqdm.tqdm(
+ desc="Total progress",
+ total=shared.state.job_count * shared.state.sampling_steps,
+ position=1,
+ file=shared.progress_print_out
+ )
+
+ def update(self):
+ if not shared.opts.multiple_tqdm or shared.cmd_opts.disable_console_progressbars:
+ return
+ if self._tqdm is None:
+ self.reset()
+ self._tqdm.update()
+
+ def updateTotal(self, new_total):
+ if not shared.opts.multiple_tqdm or shared.cmd_opts.disable_console_progressbars:
+ return
+ if self._tqdm is None:
+ self.reset()
+ self._tqdm.total = new_total
+
+ def clear(self):
+ if self._tqdm is not None:
+ self._tqdm.refresh()
+ self._tqdm.close()
+ self._tqdm = None
+
diff --git a/stable-diffusion-webui/modules/styles.py b/stable-diffusion-webui/modules/styles.py
new file mode 100644
index 0000000000000000000000000000000000000000..b633772d5fc07a86ce3156262d038c677dfae577
--- /dev/null
+++ b/stable-diffusion-webui/modules/styles.py
@@ -0,0 +1,136 @@
+import csv
+import os
+import os.path
+import re
+import typing
+import shutil
+
+
+class PromptStyle(typing.NamedTuple):
+ name: str
+ prompt: str
+ negative_prompt: str
+
+
+def merge_prompts(style_prompt: str, prompt: str) -> str:
+ if "{prompt}" in style_prompt:
+ res = style_prompt.replace("{prompt}", prompt)
+ else:
+ parts = filter(None, (prompt.strip(), style_prompt.strip()))
+ res = ", ".join(parts)
+
+ return res
+
+
+def apply_styles_to_prompt(prompt, styles):
+ for style in styles:
+ prompt = merge_prompts(style, prompt)
+
+ return prompt
+
+
+re_spaces = re.compile(" +")
+
+
+def extract_style_text_from_prompt(style_text, prompt):
+ stripped_prompt = re.sub(re_spaces, " ", prompt.strip())
+ stripped_style_text = re.sub(re_spaces, " ", style_text.strip())
+ if "{prompt}" in stripped_style_text:
+ left, right = stripped_style_text.split("{prompt}", 2)
+ if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
+ prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
+ return True, prompt
+ else:
+ if stripped_prompt.endswith(stripped_style_text):
+ prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
+
+ if prompt.endswith(', '):
+ prompt = prompt[:-2]
+
+ return True, prompt
+
+ return False, prompt
+
+
+def extract_style_from_prompts(style: PromptStyle, prompt, negative_prompt):
+ if not style.prompt and not style.negative_prompt:
+ return False, prompt, negative_prompt
+
+ match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
+ if not match_positive:
+ return False, prompt, negative_prompt
+
+ match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
+ if not match_negative:
+ return False, prompt, negative_prompt
+
+ return True, extracted_positive, extracted_negative
+
+
+class StyleDatabase:
+ def __init__(self, path: str):
+ self.no_style = PromptStyle("None", "", "")
+ self.styles = {}
+ self.path = path
+
+ self.reload()
+
+ def reload(self):
+ self.styles.clear()
+
+ if not os.path.exists(self.path):
+ return
+
+ with open(self.path, "r", encoding="utf-8-sig", newline='') as file:
+ reader = csv.DictReader(file, skipinitialspace=True)
+ for row in reader:
+ # Support loading old CSV format with "name, text"-columns
+ prompt = row["prompt"] if "prompt" in row else row["text"]
+ negative_prompt = row.get("negative_prompt", "")
+ self.styles[row["name"]] = PromptStyle(row["name"], prompt, negative_prompt)
+
+ def get_style_prompts(self, styles):
+ return [self.styles.get(x, self.no_style).prompt for x in styles]
+
+ def get_negative_style_prompts(self, styles):
+ return [self.styles.get(x, self.no_style).negative_prompt for x in styles]
+
+ def apply_styles_to_prompt(self, prompt, styles):
+ return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).prompt for x in styles])
+
+ def apply_negative_styles_to_prompt(self, prompt, styles):
+ return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
+
+ def save_styles(self, path: str) -> None:
+ # Always keep a backup file around
+ if os.path.exists(path):
+ shutil.copy(path, f"{path}.bak")
+
+ with open(path, "w", encoding="utf-8-sig", newline='') as file:
+ writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
+ writer.writeheader()
+ writer.writerows(style._asdict() for k, style in self.styles.items())
+
+ def extract_styles_from_prompt(self, prompt, negative_prompt):
+ extracted = []
+
+ applicable_styles = list(self.styles.values())
+
+ while True:
+ found_style = None
+
+ for style in applicable_styles:
+ is_match, new_prompt, new_neg_prompt = extract_style_from_prompts(style, prompt, negative_prompt)
+ if is_match:
+ found_style = style
+ prompt = new_prompt
+ negative_prompt = new_neg_prompt
+ break
+
+ if not found_style:
+ break
+
+ applicable_styles.remove(found_style)
+ extracted.append(found_style.name)
+
+ return list(reversed(extracted)), prompt, negative_prompt
diff --git a/stable-diffusion-webui/modules/sub_quadratic_attention.py b/stable-diffusion-webui/modules/sub_quadratic_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae4ee4bbec061b72cf20bfc369f3e14ca4188c7a
--- /dev/null
+++ b/stable-diffusion-webui/modules/sub_quadratic_attention.py
@@ -0,0 +1,215 @@
+# original source:
+# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
+# license:
+# MIT License (see Memory Efficient Attention under the Licenses section in the web UI interface for the full license)
+# credit:
+# Amin Rezaei (original author)
+# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
+# brkirch (modified to use torch.narrow instead of dynamic_slice implementation)
+# implementation of:
+# Self-attention Does Not Need O(n2) Memory":
+# https://arxiv.org/abs/2112.05682v2
+
+from functools import partial
+import torch
+from torch import Tensor
+from torch.utils.checkpoint import checkpoint
+import math
+from typing import Optional, NamedTuple, List
+
+
+def narrow_trunc(
+ input: Tensor,
+ dim: int,
+ start: int,
+ length: int
+) -> Tensor:
+ return torch.narrow(input, dim, start, length if input.shape[dim] >= start + length else input.shape[dim] - start)
+
+
+class AttnChunk(NamedTuple):
+ exp_values: Tensor
+ exp_weights_sum: Tensor
+ max_score: Tensor
+
+
+class SummarizeChunk:
+ @staticmethod
+ def __call__(
+ query: Tensor,
+ key: Tensor,
+ value: Tensor,
+ ) -> AttnChunk: ...
+
+
+class ComputeQueryChunkAttn:
+ @staticmethod
+ def __call__(
+ query: Tensor,
+ key: Tensor,
+ value: Tensor,
+ ) -> Tensor: ...
+
+
+def _summarize_chunk(
+ query: Tensor,
+ key: Tensor,
+ value: Tensor,
+ scale: float,
+) -> AttnChunk:
+ attn_weights = torch.baddbmm(
+ torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
+ query,
+ key.transpose(1,2),
+ alpha=scale,
+ beta=0,
+ )
+ max_score, _ = torch.max(attn_weights, -1, keepdim=True)
+ max_score = max_score.detach()
+ exp_weights = torch.exp(attn_weights - max_score)
+ exp_values = torch.bmm(exp_weights, value) if query.device.type == 'mps' else torch.bmm(exp_weights, value.to(exp_weights.dtype)).to(value.dtype)
+ max_score = max_score.squeeze(-1)
+ return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
+
+
+def _query_chunk_attention(
+ query: Tensor,
+ key: Tensor,
+ value: Tensor,
+ summarize_chunk: SummarizeChunk,
+ kv_chunk_size: int,
+) -> Tensor:
+ batch_x_heads, k_tokens, k_channels_per_head = key.shape
+ _, _, v_channels_per_head = value.shape
+
+ def chunk_scanner(chunk_idx: int) -> AttnChunk:
+ key_chunk = narrow_trunc(
+ key,
+ 1,
+ chunk_idx,
+ kv_chunk_size
+ )
+ value_chunk = narrow_trunc(
+ value,
+ 1,
+ chunk_idx,
+ kv_chunk_size
+ )
+ return summarize_chunk(query, key_chunk, value_chunk)
+
+ chunks: List[AttnChunk] = [
+ chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
+ ]
+ acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
+ chunk_values, chunk_weights, chunk_max = acc_chunk
+
+ global_max, _ = torch.max(chunk_max, 0, keepdim=True)
+ max_diffs = torch.exp(chunk_max - global_max)
+ chunk_values *= torch.unsqueeze(max_diffs, -1)
+ chunk_weights *= max_diffs
+
+ all_values = chunk_values.sum(dim=0)
+ all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
+ return all_values / all_weights
+
+
+# TODO: refactor CrossAttention#get_attention_scores to share code with this
+def _get_attention_scores_no_kv_chunking(
+ query: Tensor,
+ key: Tensor,
+ value: Tensor,
+ scale: float,
+) -> Tensor:
+ attn_scores = torch.baddbmm(
+ torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
+ query,
+ key.transpose(1,2),
+ alpha=scale,
+ beta=0,
+ )
+ attn_probs = attn_scores.softmax(dim=-1)
+ del attn_scores
+ hidden_states_slice = torch.bmm(attn_probs, value) if query.device.type == 'mps' else torch.bmm(attn_probs, value.to(attn_probs.dtype)).to(value.dtype)
+ return hidden_states_slice
+
+
+class ScannedChunk(NamedTuple):
+ chunk_idx: int
+ attn_chunk: AttnChunk
+
+
+def efficient_dot_product_attention(
+ query: Tensor,
+ key: Tensor,
+ value: Tensor,
+ query_chunk_size=1024,
+ kv_chunk_size: Optional[int] = None,
+ kv_chunk_size_min: Optional[int] = None,
+ use_checkpoint=True,
+):
+ """Computes efficient dot-product attention given query, key, and value.
+ This is efficient version of attention presented in
+ https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
+ Args:
+ query: queries for calculating attention with shape of
+ `[batch * num_heads, tokens, channels_per_head]`.
+ key: keys for calculating attention with shape of
+ `[batch * num_heads, tokens, channels_per_head]`.
+ value: values to be used in attention with shape of
+ `[batch * num_heads, tokens, channels_per_head]`.
+ query_chunk_size: int: query chunks size
+ kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
+ kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
+ use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
+ Returns:
+ Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
+ """
+ batch_x_heads, q_tokens, q_channels_per_head = query.shape
+ _, k_tokens, _ = key.shape
+ scale = q_channels_per_head ** -0.5
+
+ kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
+ if kv_chunk_size_min is not None:
+ kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
+
+ def get_query_chunk(chunk_idx: int) -> Tensor:
+ return narrow_trunc(
+ query,
+ 1,
+ chunk_idx,
+ min(query_chunk_size, q_tokens)
+ )
+
+ summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
+ summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
+ compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
+ _get_attention_scores_no_kv_chunking,
+ scale=scale
+ ) if k_tokens <= kv_chunk_size else (
+ # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
+ partial(
+ _query_chunk_attention,
+ kv_chunk_size=kv_chunk_size,
+ summarize_chunk=summarize_chunk,
+ )
+ )
+
+ if q_tokens <= query_chunk_size:
+ # fast-path for when there's just 1 query chunk
+ return compute_query_chunk_attn(
+ query=query,
+ key=key,
+ value=value,
+ )
+
+ res = torch.zeros_like(query)
+ for i in range(math.ceil(q_tokens / query_chunk_size)):
+ attn_scores = compute_query_chunk_attn(
+ query=get_query_chunk(i * query_chunk_size),
+ key=key,
+ value=value,
+ )
+
+ res[:, i * query_chunk_size:i * query_chunk_size + attn_scores.shape[1], :] = attn_scores
+
+ return res
diff --git a/stable-diffusion-webui/modules/sysinfo.py b/stable-diffusion-webui/modules/sysinfo.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdad85b8a79e97b97b48eb2819554648986ebc7c
--- /dev/null
+++ b/stable-diffusion-webui/modules/sysinfo.py
@@ -0,0 +1,176 @@
+import json
+import os
+import sys
+import traceback
+
+import platform
+import hashlib
+import pkg_resources
+import psutil
+import re
+
+import launch
+from modules import paths_internal, timer, shared, extensions, errors
+
+checksum_token = "DontStealMyGamePlz__WINNERS_DONT_USE_DRUGS__DONT_COPY_THAT_FLOPPY"
+environment_whitelist = {
+ "GIT",
+ "INDEX_URL",
+ "WEBUI_LAUNCH_LIVE_OUTPUT",
+ "GRADIO_ANALYTICS_ENABLED",
+ "PYTHONPATH",
+ "TORCH_INDEX_URL",
+ "TORCH_COMMAND",
+ "REQS_FILE",
+ "XFORMERS_PACKAGE",
+ "CLIP_PACKAGE",
+ "OPENCLIP_PACKAGE",
+ "STABLE_DIFFUSION_REPO",
+ "K_DIFFUSION_REPO",
+ "CODEFORMER_REPO",
+ "BLIP_REPO",
+ "STABLE_DIFFUSION_COMMIT_HASH",
+ "K_DIFFUSION_COMMIT_HASH",
+ "CODEFORMER_COMMIT_HASH",
+ "BLIP_COMMIT_HASH",
+ "COMMANDLINE_ARGS",
+ "IGNORE_CMD_ARGS_ERRORS",
+}
+
+
+def pretty_bytes(num, suffix="B"):
+ for unit in ["", "K", "M", "G", "T", "P", "E", "Z", "Y"]:
+ if abs(num) < 1024 or unit == 'Y':
+ return f"{num:.0f}{unit}{suffix}"
+ num /= 1024
+
+
+def get():
+ res = get_dict()
+
+ text = json.dumps(res, ensure_ascii=False, indent=4)
+
+ h = hashlib.sha256(text.encode("utf8"))
+ text = text.replace(checksum_token, h.hexdigest())
+
+ return text
+
+
+re_checksum = re.compile(r'"Checksum": "([0-9a-fA-F]{64})"')
+
+
+def check(x):
+ m = re.search(re_checksum, x)
+ if not m:
+ return False
+
+ replaced = re.sub(re_checksum, f'"Checksum": "{checksum_token}"', x)
+
+ h = hashlib.sha256(replaced.encode("utf8"))
+ return h.hexdigest() == m.group(1)
+
+
+def get_dict():
+ ram = psutil.virtual_memory()
+
+ res = {
+ "Platform": platform.platform(),
+ "Python": platform.python_version(),
+ "Version": launch.git_tag(),
+ "Commit": launch.commit_hash(),
+ "Script path": paths_internal.script_path,
+ "Data path": paths_internal.data_path,
+ "Extensions dir": paths_internal.extensions_dir,
+ "Checksum": checksum_token,
+ "Commandline": get_argv(),
+ "Torch env info": get_torch_sysinfo(),
+ "Exceptions": get_exceptions(),
+ "CPU": {
+ "model": platform.processor(),
+ "count logical": psutil.cpu_count(logical=True),
+ "count physical": psutil.cpu_count(logical=False),
+ },
+ "RAM": {
+ x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0
+ },
+ "Extensions": get_extensions(enabled=True),
+ "Inactive extensions": get_extensions(enabled=False),
+ "Environment": get_environment(),
+ "Config": get_config(),
+ "Startup": timer.startup_record,
+ "Packages": sorted([f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set]),
+ }
+
+ return res
+
+
+def format_traceback(tb):
+ return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
+
+
+def format_exception(e, tb):
+ return {"exception": str(e), "traceback": format_traceback(tb)}
+
+
+def get_exceptions():
+ try:
+ return list(reversed(errors.exception_records))
+ except Exception as e:
+ return str(e)
+
+
+def get_environment():
+ return {k: os.environ[k] for k in sorted(os.environ) if k in environment_whitelist}
+
+
+def get_argv():
+ res = []
+
+ for v in sys.argv:
+ if shared.cmd_opts.gradio_auth and shared.cmd_opts.gradio_auth == v:
+ res.append("<hidden>")
+ continue
+
+ if shared.cmd_opts.api_auth and shared.cmd_opts.api_auth == v:
+ res.append("<hidden>")
+ continue
+
+ res.append(v)
+
+ return res
+
+re_newline = re.compile(r"\r*\n")
+
+
+def get_torch_sysinfo():
+ try:
+ import torch.utils.collect_env
+ info = torch.utils.collect_env.get_env_info()._asdict()
+
+ return {k: re.split(re_newline, str(v)) if "\n" in str(v) else v for k, v in info.items()}
+ except Exception as e:
+ return str(e)
+
+
+def get_extensions(*, enabled):
+
+ try:
+ def to_json(x: extensions.Extension):
+ return {
+ "name": x.name,
+ "path": x.path,
+ "version": x.version,
+ "branch": x.branch,
+ "remote": x.remote,
+ }
+
+ return [to_json(x) for x in extensions.extensions if not x.is_builtin and x.enabled == enabled]
+ except Exception as e:
+ return str(e)
+
+
+def get_config():
+ try:
+ return shared.opts.data
+ except Exception as e:
+ return str(e)
diff --git a/stable-diffusion-webui/modules/textual_inversion/autocrop.py b/stable-diffusion-webui/modules/textual_inversion/autocrop.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a42d3503fd100a59759f18149c18d9618af440b
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/autocrop.py
@@ -0,0 +1,340 @@
+import cv2
+import requests
+import os
+import numpy as np
+from PIL import ImageDraw
+
+GREEN = "#0F0"
+BLUE = "#00F"
+RED = "#F00"
+
+
+def crop_image(im, settings):
+ """ Intelligently crop an image to the subject matter """
+
+ scale_by = 1
+ if is_landscape(im.width, im.height):
+ scale_by = settings.crop_height / im.height
+ elif is_portrait(im.width, im.height):
+ scale_by = settings.crop_width / im.width
+ elif is_square(im.width, im.height):
+ if is_square(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_width / im.width
+ elif is_landscape(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_width / im.width
+ elif is_portrait(settings.crop_width, settings.crop_height):
+ scale_by = settings.crop_height / im.height
+
+
+ im = im.resize((int(im.width * scale_by), int(im.height * scale_by)))
+ im_debug = im.copy()
+
+ focus = focal_point(im_debug, settings)
+
+ # take the focal point and turn it into crop coordinates that try to center over the focal
+ # point but then get adjusted back into the frame
+ y_half = int(settings.crop_height / 2)
+ x_half = int(settings.crop_width / 2)
+
+ x1 = focus.x - x_half
+ if x1 < 0:
+ x1 = 0
+ elif x1 + settings.crop_width > im.width:
+ x1 = im.width - settings.crop_width
+
+ y1 = focus.y - y_half
+ if y1 < 0:
+ y1 = 0
+ elif y1 + settings.crop_height > im.height:
+ y1 = im.height - settings.crop_height
+
+ x2 = x1 + settings.crop_width
+ y2 = y1 + settings.crop_height
+
+ crop = [x1, y1, x2, y2]
+
+ results = []
+
+ results.append(im.crop(tuple(crop)))
+
+ if settings.annotate_image:
+ d = ImageDraw.Draw(im_debug)
+ rect = list(crop)
+ rect[2] -= 1
+ rect[3] -= 1
+ d.rectangle(rect, outline=GREEN)
+ results.append(im_debug)
+ if settings.destop_view_image:
+ im_debug.show()
+
+ return results
+
+def focal_point(im, settings):
+ corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []
+ entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []
+ face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []
+
+ pois = []
+
+ weight_pref_total = 0
+ if corner_points:
+ weight_pref_total += settings.corner_points_weight
+ if entropy_points:
+ weight_pref_total += settings.entropy_points_weight
+ if face_points:
+ weight_pref_total += settings.face_points_weight
+
+ corner_centroid = None
+ if corner_points:
+ corner_centroid = centroid(corner_points)
+ corner_centroid.weight = settings.corner_points_weight / weight_pref_total
+ pois.append(corner_centroid)
+
+ entropy_centroid = None
+ if entropy_points:
+ entropy_centroid = centroid(entropy_points)
+ entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total
+ pois.append(entropy_centroid)
+
+ face_centroid = None
+ if face_points:
+ face_centroid = centroid(face_points)
+ face_centroid.weight = settings.face_points_weight / weight_pref_total
+ pois.append(face_centroid)
+
+ average_point = poi_average(pois, settings)
+
+ if settings.annotate_image:
+ d = ImageDraw.Draw(im)
+ max_size = min(im.width, im.height) * 0.07
+ if corner_centroid is not None:
+ color = BLUE
+ box = corner_centroid.bounding(max_size * corner_centroid.weight)
+ d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
+ d.ellipse(box, outline=color)
+ if len(corner_points) > 1:
+ for f in corner_points:
+ d.rectangle(f.bounding(4), outline=color)
+ if entropy_centroid is not None:
+ color = "#ff0"
+ box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
+ d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
+ d.ellipse(box, outline=color)
+ if len(entropy_points) > 1:
+ for f in entropy_points:
+ d.rectangle(f.bounding(4), outline=color)
+ if face_centroid is not None:
+ color = RED
+ box = face_centroid.bounding(max_size * face_centroid.weight)
+ d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
+ d.ellipse(box, outline=color)
+ if len(face_points) > 1:
+ for f in face_points:
+ d.rectangle(f.bounding(4), outline=color)
+
+ d.ellipse(average_point.bounding(max_size), outline=GREEN)
+
+ return average_point
+
+
+def image_face_points(im, settings):
+ if settings.dnn_model_path is not None:
+ detector = cv2.FaceDetectorYN.create(
+ settings.dnn_model_path,
+ "",
+ (im.width, im.height),
+ 0.9, # score threshold
+ 0.3, # nms threshold
+ 5000 # keep top k before nms
+ )
+ faces = detector.detect(np.array(im))
+ results = []
+ if faces[1] is not None:
+ for face in faces[1]:
+ x = face[0]
+ y = face[1]
+ w = face[2]
+ h = face[3]
+ results.append(
+ PointOfInterest(
+ int(x + (w * 0.5)), # face focus left/right is center
+ int(y + (h * 0.33)), # face focus up/down is close to the top of the head
+ size = w,
+ weight = 1/len(faces[1])
+ )
+ )
+ return results
+ else:
+ np_im = np.array(im)
+ gray = cv2.cvtColor(np_im, cv2.COLOR_BGR2GRAY)
+
+ tries = [
+ [ f'{cv2.data.haarcascades}haarcascade_eye.xml', 0.01 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_default.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_profileface.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml', 0.05 ],
+ [ f'{cv2.data.haarcascades}haarcascade_upperbody.xml', 0.05 ]
+ ]
+ for t in tries:
+ classifier = cv2.CascadeClassifier(t[0])
+ minsize = int(min(im.width, im.height) * t[1]) # at least N percent of the smallest side
+ try:
+ faces = classifier.detectMultiScale(gray, scaleFactor=1.1,
+ minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)
+ except Exception:
+ continue
+
+ if faces:
+ rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]
+ return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]
+ return []
+
+
+def image_corner_points(im, settings):
+ grayscale = im.convert("L")
+
+ # naive attempt at preventing focal points from collecting at watermarks near the bottom
+ gd = ImageDraw.Draw(grayscale)
+ gd.rectangle([0, im.height*.9, im.width, im.height], fill="#999")
+
+ np_im = np.array(grayscale)
+
+ points = cv2.goodFeaturesToTrack(
+ np_im,
+ maxCorners=100,
+ qualityLevel=0.04,
+ minDistance=min(grayscale.width, grayscale.height)*0.06,
+ useHarrisDetector=False,
+ )
+
+ if points is None:
+ return []
+
+ focal_points = []
+ for point in points:
+ x, y = point.ravel()
+ focal_points.append(PointOfInterest(x, y, size=4, weight=1/len(points)))
+
+ return focal_points
+
+
+def image_entropy_points(im, settings):
+ landscape = im.height < im.width
+ portrait = im.height > im.width
+ if landscape:
+ move_idx = [0, 2]
+ move_max = im.size[0]
+ elif portrait:
+ move_idx = [1, 3]
+ move_max = im.size[1]
+ else:
+ return []
+
+ e_max = 0
+ crop_current = [0, 0, settings.crop_width, settings.crop_height]
+ crop_best = crop_current
+ while crop_current[move_idx[1]] < move_max:
+ crop = im.crop(tuple(crop_current))
+ e = image_entropy(crop)
+
+ if (e > e_max):
+ e_max = e
+ crop_best = list(crop_current)
+
+ crop_current[move_idx[0]] += 4
+ crop_current[move_idx[1]] += 4
+
+ x_mid = int(crop_best[0] + settings.crop_width/2)
+ y_mid = int(crop_best[1] + settings.crop_height/2)
+
+ return [PointOfInterest(x_mid, y_mid, size=25, weight=1.0)]
+
+
+def image_entropy(im):
+ # greyscale image entropy
+ # band = np.asarray(im.convert("L"))
+ band = np.asarray(im.convert("1"), dtype=np.uint8)
+ hist, _ = np.histogram(band, bins=range(0, 256))
+ hist = hist[hist > 0]
+ return -np.log2(hist / hist.sum()).sum()
+
+
+def centroid(pois):
+ x = [poi.x for poi in pois]
+ y = [poi.y for poi in pois]
+ return PointOfInterest(sum(x) / len(pois), sum(y) / len(pois))
+
+
+def poi_average(pois, settings):
+ weight = 0.0
+ x = 0.0
+ y = 0.0
+ for poi in pois:
+ weight += poi.weight
+ x += poi.x * poi.weight
+ y += poi.y * poi.weight
+ avg_x = round(weight and x / weight)
+ avg_y = round(weight and y / weight)
+
+ return PointOfInterest(avg_x, avg_y)
+
+
+def is_landscape(w, h):
+ return w > h
+
+
+def is_portrait(w, h):
+ return h > w
+
+
+def is_square(w, h):
+ return w == h
+
+
+def download_and_cache_models(dirname):
+ download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'
+ model_file_name = 'face_detection_yunet.onnx'
+
+ os.makedirs(dirname, exist_ok=True)
+
+ cache_file = os.path.join(dirname, model_file_name)
+ if not os.path.exists(cache_file):
+ print(f"downloading face detection model from '{download_url}' to '{cache_file}'")
+ response = requests.get(download_url)
+ with open(cache_file, "wb") as f:
+ f.write(response.content)
+
+ if os.path.exists(cache_file):
+ return cache_file
+ return None
+
+
+class PointOfInterest:
+ def __init__(self, x, y, weight=1.0, size=10):
+ self.x = x
+ self.y = y
+ self.weight = weight
+ self.size = size
+
+ def bounding(self, size):
+ return [
+ self.x - size // 2,
+ self.y - size // 2,
+ self.x + size // 2,
+ self.y + size // 2
+ ]
+
+
+class Settings:
+ def __init__(self, crop_width=512, crop_height=512, corner_points_weight=0.5, entropy_points_weight=0.5, face_points_weight=0.5, annotate_image=False, dnn_model_path=None):
+ self.crop_width = crop_width
+ self.crop_height = crop_height
+ self.corner_points_weight = corner_points_weight
+ self.entropy_points_weight = entropy_points_weight
+ self.face_points_weight = face_points_weight
+ self.annotate_image = annotate_image
+ self.destop_view_image = False
+ self.dnn_model_path = dnn_model_path
diff --git a/stable-diffusion-webui/modules/textual_inversion/dataset.py b/stable-diffusion-webui/modules/textual_inversion/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..21a3437571dd2762beab2de811ef93be0e425fb3
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/dataset.py
@@ -0,0 +1,246 @@
+import os
+import numpy as np
+import PIL
+import torch
+from PIL import Image
+from torch.utils.data import Dataset, DataLoader, Sampler
+from torchvision import transforms
+from collections import defaultdict
+from random import shuffle, choices
+
+import random
+import tqdm
+from modules import devices, shared
+import re
+
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+re_numbers_at_start = re.compile(r"^[-\d]+\s*")
+
+
+class DatasetEntry:
+ def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
+ self.filename = filename
+ self.filename_text = filename_text
+ self.weight = weight
+ self.latent_dist = latent_dist
+ self.latent_sample = latent_sample
+ self.cond = cond
+ self.cond_text = cond_text
+ self.pixel_values = pixel_values
+
+
+class PersonalizedBase(Dataset):
+ def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
+ re_word = re.compile(shared.opts.dataset_filename_word_regex) if shared.opts.dataset_filename_word_regex else None
+
+ self.placeholder_token = placeholder_token
+
+ self.flip = transforms.RandomHorizontalFlip(p=flip_p)
+
+ self.dataset = []
+
+ with open(template_file, "r") as file:
+ lines = [x.strip() for x in file.readlines()]
+
+ self.lines = lines
+
+ assert data_root, 'dataset directory not specified'
+ assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+ assert os.listdir(data_root), "Dataset directory is empty"
+
+ self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
+
+ self.shuffle_tags = shuffle_tags
+ self.tag_drop_out = tag_drop_out
+ groups = defaultdict(list)
+
+ print("Preparing dataset...")
+ for path in tqdm.tqdm(self.image_paths):
+ alpha_channel = None
+ if shared.state.interrupted:
+ raise Exception("interrupted")
+ try:
+ image = Image.open(path)
+ #Currently does not work for single color transparency
+ #We would need to read image.info['transparency'] for that
+ if use_weight and 'A' in image.getbands():
+ alpha_channel = image.getchannel('A')
+ image = image.convert('RGB')
+ if not varsize:
+ image = image.resize((width, height), PIL.Image.BICUBIC)
+ except Exception:
+ continue
+
+ text_filename = f"{os.path.splitext(path)[0]}.txt"
+ filename = os.path.basename(path)
+
+ if os.path.exists(text_filename):
+ with open(text_filename, "r", encoding="utf8") as file:
+ filename_text = file.read()
+ else:
+ filename_text = os.path.splitext(filename)[0]
+ filename_text = re.sub(re_numbers_at_start, '', filename_text)
+ if re_word:
+ tokens = re_word.findall(filename_text)
+ filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
+
+ npimage = np.array(image).astype(np.uint8)
+ npimage = (npimage / 127.5 - 1.0).astype(np.float32)
+
+ torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
+ latent_sample = None
+
+ with devices.autocast():
+ latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
+
+ #Perform latent sampling, even for random sampling.
+ #We need the sample dimensions for the weights
+ if latent_sampling_method == "deterministic":
+ if isinstance(latent_dist, DiagonalGaussianDistribution):
+ # Works only for DiagonalGaussianDistribution
+ latent_dist.std = 0
+ else:
+ latent_sampling_method = "once"
+ latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+
+ if use_weight and alpha_channel is not None:
+ channels, *latent_size = latent_sample.shape
+ weight_img = alpha_channel.resize(latent_size)
+ npweight = np.array(weight_img).astype(np.float32)
+ #Repeat for every channel in the latent sample
+ weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
+ #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
+ weight -= weight.min()
+ weight /= weight.mean()
+ elif use_weight:
+ #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
+ weight = torch.ones(latent_sample.shape)
+ else:
+ weight = None
+
+ if latent_sampling_method == "random":
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
+ else:
+ entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)
+
+ if not (self.tag_drop_out != 0 or self.shuffle_tags):
+ entry.cond_text = self.create_text(filename_text)
+
+ if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
+ with devices.autocast():
+ entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
+ groups[image.size].append(len(self.dataset))
+ self.dataset.append(entry)
+ del torchdata
+ del latent_dist
+ del latent_sample
+ del weight
+
+ self.length = len(self.dataset)
+ self.groups = list(groups.values())
+ assert self.length > 0, "No images have been found in the dataset."
+ self.batch_size = min(batch_size, self.length)
+ self.gradient_step = min(gradient_step, self.length // self.batch_size)
+ self.latent_sampling_method = latent_sampling_method
+
+ if len(groups) > 1:
+ print("Buckets:")
+ for (w, h), ids in sorted(groups.items(), key=lambda x: x[0]):
+ print(f" {w}x{h}: {len(ids)}")
+ print()
+
+ def create_text(self, filename_text):
+ text = random.choice(self.lines)
+ tags = filename_text.split(',')
+ if self.tag_drop_out != 0:
+ tags = [t for t in tags if random.random() > self.tag_drop_out]
+ if self.shuffle_tags:
+ random.shuffle(tags)
+ text = text.replace("[filewords]", ','.join(tags))
+ text = text.replace("[name]", self.placeholder_token)
+ return text
+
+ def __len__(self):
+ return self.length
+
+ def __getitem__(self, i):
+ entry = self.dataset[i]
+ if self.tag_drop_out != 0 or self.shuffle_tags:
+ entry.cond_text = self.create_text(entry.filename_text)
+ if self.latent_sampling_method == "random":
+ entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
+ return entry
+
+
+class GroupedBatchSampler(Sampler):
+ def __init__(self, data_source: PersonalizedBase, batch_size: int):
+ super().__init__(data_source)
+
+ n = len(data_source)
+ self.groups = data_source.groups
+ self.len = n_batch = n // batch_size
+ expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
+ self.base = [int(e) // batch_size for e in expected]
+ self.n_rand_batches = nrb = n_batch - sum(self.base)
+ self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
+ self.batch_size = batch_size
+
+ def __len__(self):
+ return self.len
+
+ def __iter__(self):
+ b = self.batch_size
+
+ for g in self.groups:
+ shuffle(g)
+
+ batches = []
+ for g in self.groups:
+ batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
+ for _ in range(self.n_rand_batches):
+ rand_group = choices(self.groups, self.probs)[0]
+ batches.append(choices(rand_group, k=b))
+
+ shuffle(batches)
+
+ yield from batches
+
+
+class PersonalizedDataLoader(DataLoader):
+ def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
+ super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
+ if latent_sampling_method == "random":
+ self.collate_fn = collate_wrapper_random
+ else:
+ self.collate_fn = collate_wrapper
+
+
+class BatchLoader:
+ def __init__(self, data):
+ self.cond_text = [entry.cond_text for entry in data]
+ self.cond = [entry.cond for entry in data]
+ self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+ if all(entry.weight is not None for entry in data):
+ self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
+ else:
+ self.weight = None
+ #self.emb_index = [entry.emb_index for entry in data]
+ #print(self.latent_sample.device)
+
+ def pin_memory(self):
+ self.latent_sample = self.latent_sample.pin_memory()
+ return self
+
+def collate_wrapper(batch):
+ return BatchLoader(batch)
+
+class BatchLoaderRandom(BatchLoader):
+ def __init__(self, data):
+ super().__init__(data)
+
+ def pin_memory(self):
+ return self
+
+def collate_wrapper_random(batch):
+ return BatchLoaderRandom(batch)
diff --git a/stable-diffusion-webui/modules/textual_inversion/image_embedding.py b/stable-diffusion-webui/modules/textual_inversion/image_embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..09df97e9572ff10fb90cc00e8cbbcff2c8089538
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/image_embedding.py
@@ -0,0 +1,220 @@
+import base64
+import json
+import warnings
+
+import numpy as np
+import zlib
+from PIL import Image, ImageDraw
+import torch
+
+
+class EmbeddingEncoder(json.JSONEncoder):
+ def default(self, obj):
+ if isinstance(obj, torch.Tensor):
+ return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
+ return json.JSONEncoder.default(self, obj)
+
+
+class EmbeddingDecoder(json.JSONDecoder):
+ def __init__(self, *args, **kwargs):
+ json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)
+
+ def object_hook(self, d):
+ if 'TORCHTENSOR' in d:
+ return torch.from_numpy(np.array(d['TORCHTENSOR']))
+ return d
+
+
+def embedding_to_b64(data):
+ d = json.dumps(data, cls=EmbeddingEncoder)
+ return base64.b64encode(d.encode())
+
+
+def embedding_from_b64(data):
+ d = base64.b64decode(data)
+ return json.loads(d, cls=EmbeddingDecoder)
+
+
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+ while True:
+ seed = (a * seed + c) % m
+ yield seed % 255
+
+
+def xor_block(block):
+ g = lcg()
+ randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+ return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
+
+
+def style_block(block, sequence):
+ im = Image.new('RGB', (block.shape[1], block.shape[0]))
+ draw = ImageDraw.Draw(im)
+ i = 0
+ for x in range(-6, im.size[0], 8):
+ for yi, y in enumerate(range(-6, im.size[1], 8)):
+ offset = 0
+ if yi % 2 == 0:
+ offset = 4
+ shade = sequence[i % len(sequence)]
+ i += 1
+ draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
+
+ fg = np.array(im).astype(np.uint8) & 0xF0
+
+ return block ^ fg
+
+
+def insert_image_data_embed(image, data):
+ d = 3
+ data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
+ data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
+ data_np_high = data_np_ >> 4
+ data_np_low = data_np_ & 0x0F
+
+ h = image.size[1]
+ next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
+ next_size = next_size + ((h*d)-(next_size % (h*d)))
+
+ data_np_low = np.resize(data_np_low, next_size)
+ data_np_low = data_np_low.reshape((h, -1, d))
+
+ data_np_high = np.resize(data_np_high, next_size)
+ data_np_high = data_np_high.reshape((h, -1, d))
+
+ edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+ edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
+
+ data_np_low = style_block(data_np_low, sequence=edge_style)
+ data_np_low = xor_block(data_np_low)
+ data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
+ data_np_high = xor_block(data_np_high)
+
+ im_low = Image.fromarray(data_np_low, mode='RGB')
+ im_high = Image.fromarray(data_np_high, mode='RGB')
+
+ background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
+ background.paste(im_low, (0, 0))
+ background.paste(image, (im_low.size[0]+1, 0))
+ background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
+
+ return background
+
+
+def crop_black(img, tol=0):
+ mask = (img > tol).all(2)
+ mask0, mask1 = mask.any(0), mask.any(1)
+ col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
+ row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
+ return img[row_start:row_end, col_start:col_end]
+
+
+def extract_image_data_embed(image):
+ d = 3
+ outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
+ black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
+ if black_cols[0].shape[0] < 2:
+ print('No Image data blocks found.')
+ return None
+
+ data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
+ data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
+
+ data_block_lower = xor_block(data_block_lower)
+ data_block_upper = xor_block(data_block_upper)
+
+ data_block = (data_block_upper << 4) | (data_block_lower)
+ data_block = data_block.flatten().tobytes()
+
+ data = zlib.decompress(data_block)
+ return json.loads(data, cls=EmbeddingDecoder)
+
+
+def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
+ from modules.images import get_font
+ if textfont:
+ warnings.warn(
+ 'passing in a textfont to caption_image_overlay is deprecated and does nothing',
+ DeprecationWarning,
+ stacklevel=2,
+ )
+ from math import cos
+
+ image = srcimage.copy()
+ fontsize = 32
+ factor = 1.5
+ gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
+ for y in range(image.size[1]):
+ mag = 1-cos(y/image.size[1]*factor)
+ mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+ gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
+ image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+ draw = ImageDraw.Draw(image)
+
+ font = get_font(fontsize)
+ padding = 10
+
+ _, _, w, h = draw.textbbox((0, 0), title, font=font)
+ fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
+ font = get_font(fontsize)
+ _, _, w, h = draw.textbbox((0, 0), title, font=font)
+ draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
+
+ _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
+ fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+ _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
+ fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+ _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
+ fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+
+ font = get_font(min(fontsize_left, fontsize_mid, fontsize_right))
+
+ draw.text((padding, image.size[1]-padding), footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
+ draw.text((image.size[0]/2, image.size[1]-padding), footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
+ draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
+
+ return image
+
+
+if __name__ == '__main__':
+
+ testEmbed = Image.open('test_embedding.png')
+ data = extract_image_data_embed(testEmbed)
+ assert data is not None
+
+ data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
+ assert data is not None
+
+ image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
+ cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
+
+ test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
+
+ embedded_image = insert_image_data_embed(cap_image, test_embed)
+
+ retrived_embed = extract_image_data_embed(embedded_image)
+
+ assert str(retrived_embed) == str(test_embed)
+
+ embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
+
+ assert embedded_image == embedded_image2
+
+ g = lcg()
+ shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
+
+ reference_random = [253, 242, 127, 44, 157, 27, 239, 133, 38, 79, 167, 4, 177,
+ 95, 130, 79, 78, 14, 52, 215, 220, 194, 126, 28, 240, 179,
+ 160, 153, 149, 50, 105, 14, 21, 218, 199, 18, 54, 198, 193,
+ 38, 128, 19, 53, 195, 124, 75, 205, 12, 6, 145, 0, 28,
+ 30, 148, 8, 45, 218, 171, 55, 249, 97, 166, 12, 35, 0,
+ 41, 221, 122, 215, 170, 31, 113, 186, 97, 119, 31, 23, 185,
+ 66, 140, 30, 41, 37, 63, 137, 109, 216, 55, 159, 145, 82,
+ 204, 86, 73, 222, 44, 198, 118, 240, 97]
+
+ assert shared_random == reference_random
+
+ hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
+
+ assert 12731374 == hunna_kay_random_sum
diff --git a/stable-diffusion-webui/modules/textual_inversion/learn_schedule.py b/stable-diffusion-webui/modules/textual_inversion/learn_schedule.py
new file mode 100644
index 0000000000000000000000000000000000000000..252368a66bcc98db86f6d49f99d14d2d75f5543a
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/learn_schedule.py
@@ -0,0 +1,81 @@
+import tqdm
+
+
+class LearnScheduleIterator:
+ def __init__(self, learn_rate, max_steps, cur_step=0):
+ """
+ specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000
+ """
+
+ pairs = learn_rate.split(',')
+ self.rates = []
+ self.it = 0
+ self.maxit = 0
+ try:
+ for pair in pairs:
+ if not pair.strip():
+ continue
+ tmp = pair.split(':')
+ if len(tmp) == 2:
+ step = int(tmp[1])
+ if step > cur_step:
+ self.rates.append((float(tmp[0]), min(step, max_steps)))
+ self.maxit += 1
+ if step > max_steps:
+ return
+ elif step == -1:
+ self.rates.append((float(tmp[0]), max_steps))
+ self.maxit += 1
+ return
+ else:
+ self.rates.append((float(tmp[0]), max_steps))
+ self.maxit += 1
+ return
+ assert self.rates
+ except (ValueError, AssertionError) as e:
+ raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e
+
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ if self.it < self.maxit:
+ self.it += 1
+ return self.rates[self.it - 1]
+ else:
+ raise StopIteration
+
+
+class LearnRateScheduler:
+ def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
+ self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
+ (self.learn_rate, self.end_step) = next(self.schedules)
+ self.verbose = verbose
+
+ if self.verbose:
+ print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+ self.finished = False
+
+ def step(self, step_number):
+ if step_number < self.end_step:
+ return False
+
+ try:
+ (self.learn_rate, self.end_step) = next(self.schedules)
+ except StopIteration:
+ self.finished = True
+ return False
+ return True
+
+ def apply(self, optimizer, step_number):
+ if not self.step(step_number):
+ return
+
+ if self.verbose:
+ tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
+
+ for pg in optimizer.param_groups:
+ pg['lr'] = self.learn_rate
+
diff --git a/stable-diffusion-webui/modules/textual_inversion/logging.py b/stable-diffusion-webui/modules/textual_inversion/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..953051409dd87a119ed54f60238336bd50396184
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/logging.py
@@ -0,0 +1,64 @@
+import datetime
+import json
+import os
+
+saved_params_shared = {
+ "batch_size",
+ "clip_grad_mode",
+ "clip_grad_value",
+ "create_image_every",
+ "data_root",
+ "gradient_step",
+ "initial_step",
+ "latent_sampling_method",
+ "learn_rate",
+ "log_directory",
+ "model_hash",
+ "model_name",
+ "num_of_dataset_images",
+ "steps",
+ "template_file",
+ "training_height",
+ "training_width",
+}
+saved_params_ti = {
+ "embedding_name",
+ "num_vectors_per_token",
+ "save_embedding_every",
+ "save_image_with_stored_embedding",
+}
+saved_params_hypernet = {
+ "activation_func",
+ "add_layer_norm",
+ "hypernetwork_name",
+ "layer_structure",
+ "save_hypernetwork_every",
+ "use_dropout",
+ "weight_init",
+}
+saved_params_all = saved_params_shared | saved_params_ti | saved_params_hypernet
+saved_params_previews = {
+ "preview_cfg_scale",
+ "preview_height",
+ "preview_negative_prompt",
+ "preview_prompt",
+ "preview_sampler_index",
+ "preview_seed",
+ "preview_steps",
+ "preview_width",
+}
+
+
+def save_settings_to_file(log_directory, all_params):
+ now = datetime.datetime.now()
+ params = {"datetime": now.strftime("%Y-%m-%d %H:%M:%S")}
+
+ keys = saved_params_all
+ if all_params.get('preview_from_txt2img'):
+ keys = keys | saved_params_previews
+
+ params.update({k: v for k, v in all_params.items() if k in keys})
+
+ filename = f'settings-{now.strftime("%Y-%m-%d-%H-%M-%S")}.json'
+ with open(os.path.join(log_directory, filename), "w") as file:
+ json.dump(params, file, indent=4)
diff --git a/stable-diffusion-webui/modules/textual_inversion/preprocess.py b/stable-diffusion-webui/modules/textual_inversion/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..914adebb8c869d65c89541e6d8196bbcb750ccee
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/preprocess.py
@@ -0,0 +1,232 @@
+import os
+from PIL import Image, ImageOps
+import math
+import tqdm
+
+from modules import paths, shared, images, deepbooru
+from modules.textual_inversion import autocrop
+
+
+def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.15, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+ try:
+ if process_caption:
+ shared.interrogator.load()
+
+ if process_caption_deepbooru:
+ deepbooru.model.start()
+
+ preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
+
+ finally:
+
+ if process_caption:
+ shared.interrogator.send_blip_to_ram()
+
+ if process_caption_deepbooru:
+ deepbooru.model.stop()
+
+
+def listfiles(dirname):
+ return os.listdir(dirname)
+
+
+class PreprocessParams:
+ src = None
+ dstdir = None
+ subindex = 0
+ flip = False
+ process_caption = False
+ process_caption_deepbooru = False
+ preprocess_txt_action = None
+
+
+def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):
+ caption = ""
+
+ if params.process_caption:
+ caption += shared.interrogator.generate_caption(image)
+
+ if params.process_caption_deepbooru:
+ if caption:
+ caption += ", "
+ caption += deepbooru.model.tag_multi(image)
+
+ filename_part = params.src
+ filename_part = os.path.splitext(filename_part)[0]
+ filename_part = os.path.basename(filename_part)
+
+ basename = f"{index:05}-{params.subindex}-{filename_part}"
+ image.save(os.path.join(params.dstdir, f"{basename}.png"))
+
+ if params.preprocess_txt_action == 'prepend' and existing_caption:
+ caption = f"{existing_caption} {caption}"
+ elif params.preprocess_txt_action == 'append' and existing_caption:
+ caption = f"{caption} {existing_caption}"
+ elif params.preprocess_txt_action == 'copy' and existing_caption:
+ caption = existing_caption
+
+ caption = caption.strip()
+
+ if caption:
+ with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:
+ file.write(caption)
+
+ params.subindex += 1
+
+
+def save_pic(image, index, params, existing_caption=None):
+ save_pic_with_caption(image, index, params, existing_caption=existing_caption)
+
+ if params.flip:
+ save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)
+
+
+def split_pic(image, inverse_xy, width, height, overlap_ratio):
+ if inverse_xy:
+ from_w, from_h = image.height, image.width
+ to_w, to_h = height, width
+ else:
+ from_w, from_h = image.width, image.height
+ to_w, to_h = width, height
+ h = from_h * to_w // from_w
+ if inverse_xy:
+ image = image.resize((h, to_w))
+ else:
+ image = image.resize((to_w, h))
+
+ split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))
+ y_step = (h - to_h) / (split_count - 1)
+ for i in range(split_count):
+ y = int(y_step * i)
+ if inverse_xy:
+ splitted = image.crop((y, 0, y + to_h, to_w))
+ else:
+ splitted = image.crop((0, y, to_w, y + to_h))
+ yield splitted
+
+# not using torchvision.transforms.CenterCrop because it doesn't allow float regions
+def center_crop(image: Image, w: int, h: int):
+ iw, ih = image.size
+ if ih / h < iw / w:
+ sw = w * ih / h
+ box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih
+ else:
+ sh = h * iw / w
+ box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2
+ return image.resize((w, h), Image.Resampling.LANCZOS, box)
+
+
+def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold):
+ iw, ih = image.size
+ err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h))
+ wh = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64)
+ if minarea <= w * h <= maxarea and err(w, h) <= threshold),
+ key= lambda wh: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1],
+ default=None
+ )
+ return wh and center_crop(image, *wh)
+
+
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+ width = process_width
+ height = process_height
+ src = os.path.abspath(process_src)
+ dst = os.path.abspath(process_dst)
+ split_threshold = max(0.0, min(1.0, split_threshold))
+ overlap_ratio = max(0.0, min(0.9, overlap_ratio))
+
+ assert src != dst, 'same directory specified as source and destination'
+
+ os.makedirs(dst, exist_ok=True)
+
+ files = listfiles(src)
+
+ shared.state.job = "preprocess"
+ shared.state.textinfo = "Preprocessing..."
+ shared.state.job_count = len(files)
+
+ params = PreprocessParams()
+ params.dstdir = dst
+ params.flip = process_flip
+ params.process_caption = process_caption
+ params.process_caption_deepbooru = process_caption_deepbooru
+ params.preprocess_txt_action = preprocess_txt_action
+
+ pbar = tqdm.tqdm(files)
+ for index, imagefile in enumerate(pbar):
+ params.subindex = 0
+ filename = os.path.join(src, imagefile)
+ try:
+ img = Image.open(filename)
+ img = ImageOps.exif_transpose(img)
+ img = img.convert("RGB")
+ except Exception:
+ continue
+
+ description = f"Preprocessing [Image {index}/{len(files)}]"
+ pbar.set_description(description)
+ shared.state.textinfo = description
+
+ params.src = filename
+
+ existing_caption = None
+ existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
+ if os.path.exists(existing_caption_filename):
+ with open(existing_caption_filename, 'r', encoding="utf8") as file:
+ existing_caption = file.read()
+
+ if shared.state.interrupted:
+ break
+
+ if img.height > img.width:
+ ratio = (img.width * height) / (img.height * width)
+ inverse_xy = False
+ else:
+ ratio = (img.height * width) / (img.width * height)
+ inverse_xy = True
+
+ process_default_resize = True
+
+ if process_split and ratio < 1.0 and ratio <= split_threshold:
+ for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):
+ save_pic(splitted, index, params, existing_caption=existing_caption)
+ process_default_resize = False
+
+ if process_focal_crop and img.height != img.width:
+
+ dnn_model_path = None
+ try:
+ dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
+ except Exception as e:
+ print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
+
+ autocrop_settings = autocrop.Settings(
+ crop_width = width,
+ crop_height = height,
+ face_points_weight = process_focal_crop_face_weight,
+ entropy_points_weight = process_focal_crop_entropy_weight,
+ corner_points_weight = process_focal_crop_edges_weight,
+ annotate_image = process_focal_crop_debug,
+ dnn_model_path = dnn_model_path,
+ )
+ for focal in autocrop.crop_image(img, autocrop_settings):
+ save_pic(focal, index, params, existing_caption=existing_caption)
+ process_default_resize = False
+
+ if process_multicrop:
+ cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
+ if cropped is not None:
+ save_pic(cropped, index, params, existing_caption=existing_caption)
+ else:
+ print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
+ process_default_resize = False
+
+ if process_keep_original_size:
+ save_pic(img, index, params, existing_caption=existing_caption)
+ process_default_resize = False
+
+ if process_default_resize:
+ img = images.resize_image(1, img, width, height)
+ save_pic(img, index, params, existing_caption=existing_caption)
+
+ shared.state.nextjob()
diff --git a/stable-diffusion-webui/modules/textual_inversion/test_embedding.png b/stable-diffusion-webui/modules/textual_inversion/test_embedding.png
new file mode 100644
index 0000000000000000000000000000000000000000..5a570c7be308aa1043c6b5e1d0c53df3227e001c
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/test_embedding.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ceb3de4098040013be6ce7169b0c0e67c2de86a8cfb43d02b16013f6af2d352e
+size 489220
diff --git a/stable-diffusion-webui/modules/textual_inversion/textual_inversion.py b/stable-diffusion-webui/modules/textual_inversion/textual_inversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..25b71b83d9c52af6608a981ff0677e440700547b
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/textual_inversion.py
@@ -0,0 +1,694 @@
+import os
+from collections import namedtuple
+from contextlib import closing
+
+import torch
+import tqdm
+import html
+import datetime
+import csv
+import safetensors.torch
+
+import numpy as np
+from PIL import Image, PngImagePlugin
+from torch.utils.tensorboard import SummaryWriter
+
+from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
+import modules.textual_inversion.dataset
+from modules.textual_inversion.learn_schedule import LearnRateScheduler
+
+from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
+from modules.textual_inversion.logging import save_settings_to_file
+
+
+TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"])
+textual_inversion_templates = {}
+
+
+def list_textual_inversion_templates():
+ textual_inversion_templates.clear()
+
+ for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
+ for fn in fns:
+ path = os.path.join(root, fn)
+
+ textual_inversion_templates[fn] = TextualInversionTemplate(fn, path)
+
+ return textual_inversion_templates
+
+
+class Embedding:
+ def __init__(self, vec, name, step=None):
+ self.vec = vec
+ self.name = name
+ self.step = step
+ self.shape = None
+ self.vectors = 0
+ self.cached_checksum = None
+ self.sd_checkpoint = None
+ self.sd_checkpoint_name = None
+ self.optimizer_state_dict = None
+ self.filename = None
+ self.hash = None
+ self.shorthash = None
+
+ def save(self, filename):
+ embedding_data = {
+ "string_to_token": {"*": 265},
+ "string_to_param": {"*": self.vec},
+ "name": self.name,
+ "step": self.step,
+ "sd_checkpoint": self.sd_checkpoint,
+ "sd_checkpoint_name": self.sd_checkpoint_name,
+ }
+
+ torch.save(embedding_data, filename)
+
+ if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
+ optimizer_saved_dict = {
+ 'hash': self.checksum(),
+ 'optimizer_state_dict': self.optimizer_state_dict,
+ }
+ torch.save(optimizer_saved_dict, f"{filename}.optim")
+
+ def checksum(self):
+ if self.cached_checksum is not None:
+ return self.cached_checksum
+
+ def const_hash(a):
+ r = 0
+ for v in a:
+ r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
+ return r
+
+ self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
+ return self.cached_checksum
+
+ def set_hash(self, v):
+ self.hash = v
+ self.shorthash = self.hash[0:12]
+
+
+class DirWithTextualInversionEmbeddings:
+ def __init__(self, path):
+ self.path = path
+ self.mtime = None
+
+ def has_changed(self):
+ if not os.path.isdir(self.path):
+ return False
+
+ mt = os.path.getmtime(self.path)
+ if self.mtime is None or mt > self.mtime:
+ return True
+
+ def update(self):
+ if not os.path.isdir(self.path):
+ return
+
+ self.mtime = os.path.getmtime(self.path)
+
+
+class EmbeddingDatabase:
+ def __init__(self):
+ self.ids_lookup = {}
+ self.word_embeddings = {}
+ self.skipped_embeddings = {}
+ self.expected_shape = -1
+ self.embedding_dirs = {}
+ self.previously_displayed_embeddings = ()
+
+ def add_embedding_dir(self, path):
+ self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
+
+ def clear_embedding_dirs(self):
+ self.embedding_dirs.clear()
+
+ def register_embedding(self, embedding, model):
+ return self.register_embedding_by_name(embedding, model, embedding.name)
+
+ def register_embedding_by_name(self, embedding, model, name):
+ ids = model.cond_stage_model.tokenize([name])[0]
+ first_id = ids[0]
+ if first_id not in self.ids_lookup:
+ self.ids_lookup[first_id] = []
+ if name in self.word_embeddings:
+ # remove old one from the lookup list
+ lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]
+ else:
+ lookup = self.ids_lookup[first_id]
+ if embedding is not None:
+ lookup += [(ids, embedding)]
+ self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)
+ if embedding is None:
+ # unregister embedding with specified name
+ if name in self.word_embeddings:
+ del self.word_embeddings[name]
+ if len(self.ids_lookup[first_id])==0:
+ del self.ids_lookup[first_id]
+ return None
+ self.word_embeddings[name] = embedding
+ return embedding
+
+ def get_expected_shape(self):
+ vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
+ return vec.shape[1]
+
+ def load_from_file(self, path, filename):
+ name, ext = os.path.splitext(filename)
+ ext = ext.upper()
+
+ if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
+ _, second_ext = os.path.splitext(name)
+ if second_ext.upper() == '.PREVIEW':
+ return
+
+ embed_image = Image.open(path)
+ if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
+ data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
+ name = data.get('name', name)
+ else:
+ data = extract_image_data_embed(embed_image)
+ if data:
+ name = data.get('name', name)
+ else:
+ # if data is None, means this is not an embeding, just a preview image
+ return
+ elif ext in ['.BIN', '.PT']:
+ data = torch.load(path, map_location="cpu")
+ elif ext in ['.SAFETENSORS']:
+ data = safetensors.torch.load_file(path, device="cpu")
+ else:
+ return
+
+
+ # textual inversion embeddings
+ if 'string_to_param' in data:
+ param_dict = data['string_to_param']
+ param_dict = getattr(param_dict, '_parameters', param_dict) # fix for torch 1.12.1 loading saved file from torch 1.11
+ assert len(param_dict) == 1, 'embedding file has multiple terms in it'
+ emb = next(iter(param_dict.items()))[1]
+ vec = emb.detach().to(devices.device, dtype=torch.float32)
+ shape = vec.shape[-1]
+ vectors = vec.shape[0]
+ elif type(data) == dict and 'clip_g' in data and 'clip_l' in data: # SDXL embedding
+ vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
+ shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
+ vectors = data['clip_g'].shape[0]
+ elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
+ assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
+
+ emb = next(iter(data.values()))
+ if len(emb.shape) == 1:
+ emb = emb.unsqueeze(0)
+ vec = emb.detach().to(devices.device, dtype=torch.float32)
+ shape = vec.shape[-1]
+ vectors = vec.shape[0]
+ else:
+ raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
+
+ embedding = Embedding(vec, name)
+ embedding.step = data.get('step', None)
+ embedding.sd_checkpoint = data.get('sd_checkpoint', None)
+ embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
+ embedding.vectors = vectors
+ embedding.shape = shape
+ embedding.filename = path
+ embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')
+
+ if self.expected_shape == -1 or self.expected_shape == embedding.shape:
+ self.register_embedding(embedding, shared.sd_model)
+ else:
+ self.skipped_embeddings[name] = embedding
+
+ def load_from_dir(self, embdir):
+ if not os.path.isdir(embdir.path):
+ return
+
+ for root, _, fns in os.walk(embdir.path, followlinks=True):
+ for fn in fns:
+ try:
+ fullfn = os.path.join(root, fn)
+
+ if os.stat(fullfn).st_size == 0:
+ continue
+
+ self.load_from_file(fullfn, fn)
+ except Exception:
+ errors.report(f"Error loading embedding {fn}", exc_info=True)
+ continue
+
+ def load_textual_inversion_embeddings(self, force_reload=False):
+ if not force_reload:
+ need_reload = False
+ for embdir in self.embedding_dirs.values():
+ if embdir.has_changed():
+ need_reload = True
+ break
+
+ if not need_reload:
+ return
+
+ self.ids_lookup.clear()
+ self.word_embeddings.clear()
+ self.skipped_embeddings.clear()
+ self.expected_shape = self.get_expected_shape()
+
+ for embdir in self.embedding_dirs.values():
+ self.load_from_dir(embdir)
+ embdir.update()
+
+ # re-sort word_embeddings because load_from_dir may not load in alphabetic order.
+ # using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
+ sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
+ self.word_embeddings.clear()
+ self.word_embeddings.update(sorted_word_embeddings)
+
+ displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
+ if shared.opts.textual_inversion_print_at_load and self.previously_displayed_embeddings != displayed_embeddings:
+ self.previously_displayed_embeddings = displayed_embeddings
+ print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
+ if self.skipped_embeddings:
+ print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
+
+ def find_embedding_at_position(self, tokens, offset):
+ token = tokens[offset]
+ possible_matches = self.ids_lookup.get(token, None)
+
+ if possible_matches is None:
+ return None, None
+
+ for ids, embedding in possible_matches:
+ if tokens[offset:offset + len(ids)] == ids:
+ return embedding, len(ids)
+
+ return None, None
+
+
+def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
+ cond_model = shared.sd_model.cond_stage_model
+
+ with devices.autocast():
+ cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
+
+ #cond_model expects at least some text, so we provide '*' as backup.
+ embedded = cond_model.encode_embedding_init_text(init_text or '*', num_vectors_per_token)
+ vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
+
+ #Only copy if we provided an init_text, otherwise keep vectors as zeros
+ if init_text:
+ for i in range(num_vectors_per_token):
+ vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
+
+ # Remove illegal characters from name.
+ name = "".join( x for x in name if (x.isalnum() or x in "._- "))
+ fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
+ if not overwrite_old:
+ assert not os.path.exists(fn), f"file {fn} already exists"
+
+ embedding = Embedding(vec, name)
+ embedding.step = 0
+ embedding.save(fn)
+
+ return fn
+
+
+def write_loss(log_directory, filename, step, epoch_len, values):
+ if shared.opts.training_write_csv_every == 0:
+ return
+
+ if step % shared.opts.training_write_csv_every != 0:
+ return
+ write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
+
+ with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
+ csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
+
+ if write_csv_header:
+ csv_writer.writeheader()
+
+ epoch = (step - 1) // epoch_len
+ epoch_step = (step - 1) % epoch_len
+
+ csv_writer.writerow({
+ "step": step,
+ "epoch": epoch,
+ "epoch_step": epoch_step,
+ **values,
+ })
+
+def tensorboard_setup(log_directory):
+ os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
+ return SummaryWriter(
+ log_dir=os.path.join(log_directory, "tensorboard"),
+ flush_secs=shared.opts.training_tensorboard_flush_every)
+
+def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num):
+ tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step)
+ tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step)
+ tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step)
+ tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)
+
+def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
+ tensorboard_writer.add_scalar(tag=tag,
+ scalar_value=value, global_step=step)
+
+def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
+ # Convert a pil image to a torch tensor
+ img_tensor = torch.as_tensor(np.array(pil_image, copy=True))
+ img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],
+ len(pil_image.getbands()))
+ img_tensor = img_tensor.permute((2, 0, 1))
+
+ tensorboard_writer.add_image(tag, img_tensor, global_step=step)
+
+def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"):
+ assert model_name, f"{name} not selected"
+ assert learn_rate, "Learning rate is empty or 0"
+ assert isinstance(batch_size, int), "Batch size must be integer"
+ assert batch_size > 0, "Batch size must be positive"
+ assert isinstance(gradient_step, int), "Gradient accumulation step must be integer"
+ assert gradient_step > 0, "Gradient accumulation step must be positive"
+ assert data_root, "Dataset directory is empty"
+ assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+ assert os.listdir(data_root), "Dataset directory is empty"
+ assert template_filename, "Prompt template file not selected"
+ assert template_file, f"Prompt template file {template_filename} not found"
+ assert os.path.isfile(template_file.path), f"Prompt template file {template_filename} doesn't exist"
+ assert steps, "Max steps is empty or 0"
+ assert isinstance(steps, int), "Max steps must be integer"
+ assert steps > 0, "Max steps must be positive"
+ assert isinstance(save_model_every, int), "Save {name} must be integer"
+ assert save_model_every >= 0, "Save {name} must be positive or 0"
+ assert isinstance(create_image_every, int), "Create image must be integer"
+ assert create_image_every >= 0, "Create image must be positive or 0"
+ if save_model_every or create_image_every:
+ assert log_directory, "Log directory is empty"
+
+
+def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+ from modules import processing
+
+ save_embedding_every = save_embedding_every or 0
+ create_image_every = create_image_every or 0
+ template_file = textual_inversion_templates.get(template_filename, None)
+ validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
+ template_file = template_file.path
+
+ shared.state.job = "train-embedding"
+ shared.state.textinfo = "Initializing textual inversion training..."
+ shared.state.job_count = steps
+
+ filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+
+ log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
+ unload = shared.opts.unload_models_when_training
+
+ if save_embedding_every > 0:
+ embedding_dir = os.path.join(log_directory, "embeddings")
+ os.makedirs(embedding_dir, exist_ok=True)
+ else:
+ embedding_dir = None
+
+ if create_image_every > 0:
+ images_dir = os.path.join(log_directory, "images")
+ os.makedirs(images_dir, exist_ok=True)
+ else:
+ images_dir = None
+
+ if create_image_every > 0 and save_image_with_stored_embedding:
+ images_embeds_dir = os.path.join(log_directory, "image_embeddings")
+ os.makedirs(images_embeds_dir, exist_ok=True)
+ else:
+ images_embeds_dir = None
+
+ hijack = sd_hijack.model_hijack
+
+ embedding = hijack.embedding_db.word_embeddings[embedding_name]
+ checkpoint = sd_models.select_checkpoint()
+
+ initial_step = embedding.step or 0
+ if initial_step >= steps:
+ shared.state.textinfo = "Model has already been trained beyond specified max steps"
+ return embedding, filename
+
+ scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
+ clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
+ torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
+ None
+ if clip_grad:
+ clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
+ # dataset loading may take a while, so input validations and early returns should be done before this
+ shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
+ old_parallel_processing_allowed = shared.parallel_processing_allowed
+
+ if shared.opts.training_enable_tensorboard:
+ tensorboard_writer = tensorboard_setup(log_directory)
+
+ pin_memory = shared.opts.pin_memory
+
+ ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)
+
+ if shared.opts.save_training_settings_to_txt:
+ save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
+
+ latent_sampling_method = ds.latent_sampling_method
+
+ dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)
+
+ if unload:
+ shared.parallel_processing_allowed = False
+ shared.sd_model.first_stage_model.to(devices.cpu)
+
+ embedding.vec.requires_grad = True
+ optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
+ if shared.opts.save_optimizer_state:
+ optimizer_state_dict = None
+ if os.path.exists(f"{filename}.optim"):
+ optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')
+ if embedding.checksum() == optimizer_saved_dict.get('hash', None):
+ optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+
+ if optimizer_state_dict is not None:
+ optimizer.load_state_dict(optimizer_state_dict)
+ print("Loaded existing optimizer from checkpoint")
+ else:
+ print("No saved optimizer exists in checkpoint")
+
+ scaler = torch.cuda.amp.GradScaler()
+
+ batch_size = ds.batch_size
+ gradient_step = ds.gradient_step
+ # n steps = batch_size * gradient_step * n image processed
+ steps_per_epoch = len(ds) // batch_size // gradient_step
+ max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
+ loss_step = 0
+ _loss_step = 0 #internal
+
+ last_saved_file = "<none>"
+ last_saved_image = "<none>"
+ forced_filename = "<none>"
+ embedding_yet_to_be_embedded = False
+
+ is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}
+ img_c = None
+
+ pbar = tqdm.tqdm(total=steps - initial_step)
+ try:
+ sd_hijack_checkpoint.add()
+
+ for _ in range((steps-initial_step) * gradient_step):
+ if scheduler.finished:
+ break
+ if shared.state.interrupted:
+ break
+ for j, batch in enumerate(dl):
+ # works as a drop_last=True for gradient accumulation
+ if j == max_steps_per_epoch:
+ break
+ scheduler.apply(optimizer, embedding.step)
+ if scheduler.finished:
+ break
+ if shared.state.interrupted:
+ break
+
+ if clip_grad:
+ clip_grad_sched.step(embedding.step)
+
+ with devices.autocast():
+ x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+ if use_weight:
+ w = batch.weight.to(devices.device, non_blocking=pin_memory)
+ c = shared.sd_model.cond_stage_model(batch.cond_text)
+
+ if is_training_inpainting_model:
+ if img_c is None:
+ img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)
+
+ cond = {"c_concat": [img_c], "c_crossattn": [c]}
+ else:
+ cond = c
+
+ if use_weight:
+ loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
+ del w
+ else:
+ loss = shared.sd_model.forward(x, cond)[0] / gradient_step
+ del x
+
+ _loss_step += loss.item()
+ scaler.scale(loss).backward()
+
+ # go back until we reach gradient accumulation steps
+ if (j + 1) % gradient_step != 0:
+ continue
+
+ if clip_grad:
+ clip_grad(embedding.vec, clip_grad_sched.learn_rate)
+
+ scaler.step(optimizer)
+ scaler.update()
+ embedding.step += 1
+ pbar.update()
+ optimizer.zero_grad(set_to_none=True)
+ loss_step = _loss_step
+ _loss_step = 0
+
+ steps_done = embedding.step + 1
+
+ epoch_num = embedding.step // steps_per_epoch
+ epoch_step = embedding.step % steps_per_epoch
+
+ description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}] loss: {loss_step:.7f}"
+ pbar.set_description(description)
+ if embedding_dir is not None and steps_done % save_embedding_every == 0:
+ # Before saving, change name to match current checkpoint.
+ embedding_name_every = f'{embedding_name}-{steps_done}'
+ last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+ save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
+ embedding_yet_to_be_embedded = True
+
+ write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
+ "loss": f"{loss_step:.7f}",
+ "learn_rate": scheduler.learn_rate
+ })
+
+ if images_dir is not None and steps_done % create_image_every == 0:
+ forced_filename = f'{embedding_name}-{steps_done}'
+ last_saved_image = os.path.join(images_dir, forced_filename)
+
+ shared.sd_model.first_stage_model.to(devices.device)
+
+ p = processing.StableDiffusionProcessingTxt2Img(
+ sd_model=shared.sd_model,
+ do_not_save_grid=True,
+ do_not_save_samples=True,
+ do_not_reload_embeddings=True,
+ )
+
+ if preview_from_txt2img:
+ p.prompt = preview_prompt
+ p.negative_prompt = preview_negative_prompt
+ p.steps = preview_steps
+ p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
+ p.cfg_scale = preview_cfg_scale
+ p.seed = preview_seed
+ p.width = preview_width
+ p.height = preview_height
+ else:
+ p.prompt = batch.cond_text[0]
+ p.steps = 20
+ p.width = training_width
+ p.height = training_height
+
+ preview_text = p.prompt
+
+ with closing(p):
+ processed = processing.process_images(p)
+ image = processed.images[0] if len(processed.images) > 0 else None
+
+ if unload:
+ shared.sd_model.first_stage_model.to(devices.cpu)
+
+ if image is not None:
+ shared.state.assign_current_image(image)
+
+ last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+ last_saved_image += f", prompt: {preview_text}"
+
+ if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+ tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
+
+ if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
+
+ last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
+
+ info = PngImagePlugin.PngInfo()
+ data = torch.load(last_saved_file)
+ info.add_text("sd-ti-embedding", embedding_to_b64(data))
+
+ title = f"<{data.get('name', '???')}>"
+
+ try:
+ vectorSize = list(data['string_to_param'].values())[0].shape[0]
+ except Exception:
+ vectorSize = '?'
+
+ checkpoint = sd_models.select_checkpoint()
+ footer_left = checkpoint.model_name
+ footer_mid = f'[{checkpoint.shorthash}]'
+ footer_right = f'{vectorSize}v {steps_done}s'
+
+ captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+ captioned_image = insert_image_data_embed(captioned_image, data)
+
+ captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+ embedding_yet_to_be_embedded = False
+
+ last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
+ last_saved_image += f", prompt: {preview_text}"
+
+ shared.state.job_no = embedding.step
+
+ shared.state.textinfo = f"""
+<p>
+Loss: {loss_step:.7f}<br/>
+Step: {steps_done}<br/>
+Last prompt: {html.escape(batch.cond_text[0])}<br/>
+Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved image: {html.escape(last_saved_image)}<br/>
+</p>
+"""
+ filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+ save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+ except Exception:
+ errors.report("Error training embedding", exc_info=True)
+ finally:
+ pbar.leave = False
+ pbar.close()
+ shared.sd_model.first_stage_model.to(devices.device)
+ shared.parallel_processing_allowed = old_parallel_processing_allowed
+ sd_hijack_checkpoint.remove()
+
+ return embedding, filename
+
+
+def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+ old_embedding_name = embedding.name
+ old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
+ old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
+ old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
+ try:
+ embedding.sd_checkpoint = checkpoint.shorthash
+ embedding.sd_checkpoint_name = checkpoint.model_name
+ if remove_cached_checksum:
+ embedding.cached_checksum = None
+ embedding.name = embedding_name
+ embedding.optimizer_state_dict = optimizer.state_dict()
+ embedding.save(filename)
+ except:
+ embedding.sd_checkpoint = old_sd_checkpoint
+ embedding.sd_checkpoint_name = old_sd_checkpoint_name
+ embedding.name = old_embedding_name
+ embedding.cached_checksum = old_cached_checksum
+ raise
diff --git a/stable-diffusion-webui/modules/textual_inversion/ui.py b/stable-diffusion-webui/modules/textual_inversion/ui.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b75f799e745fa693cda06763af80069324a964f
--- /dev/null
+++ b/stable-diffusion-webui/modules/textual_inversion/ui.py
@@ -0,0 +1,45 @@
+import html
+
+import gradio as gr
+
+import modules.textual_inversion.textual_inversion
+import modules.textual_inversion.preprocess
+from modules import sd_hijack, shared
+
+
+def create_embedding(name, initialization_text, nvpt, overwrite_old):
+ filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
+
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
+
+ return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
+
+
+def preprocess(*args):
+ modules.textual_inversion.preprocess.preprocess(*args)
+
+ return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
+
+
+def train_embedding(*args):
+
+ assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
+
+ apply_optimizations = shared.opts.training_xattention_optimizations
+ try:
+ if not apply_optimizations:
+ sd_hijack.undo_optimizations()
+
+ embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
+
+ res = f"""
+Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
+Embedding saved to {html.escape(filename)}
+"""
+ return res, ""
+ except Exception:
+ raise
+ finally:
+ if not apply_optimizations:
+ sd_hijack.apply_optimizations()
+
diff --git a/stable-diffusion-webui/modules/timer.py b/stable-diffusion-webui/modules/timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..22d1272dddfb5710525c04126a8b17e449c7a8d9
--- /dev/null
+++ b/stable-diffusion-webui/modules/timer.py
@@ -0,0 +1,91 @@
+import time
+import argparse
+
+
+class TimerSubcategory:
+ def __init__(self, timer, category):
+ self.timer = timer
+ self.category = category
+ self.start = None
+ self.original_base_category = timer.base_category
+
+ def __enter__(self):
+ self.start = time.time()
+ self.timer.base_category = self.original_base_category + self.category + "/"
+ self.timer.subcategory_level += 1
+
+ if self.timer.print_log:
+ print(f"{' ' * self.timer.subcategory_level}{self.category}:")
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ elapsed_for_subcategroy = time.time() - self.start
+ self.timer.base_category = self.original_base_category
+ self.timer.add_time_to_record(self.original_base_category + self.category, elapsed_for_subcategroy)
+ self.timer.subcategory_level -= 1
+ self.timer.record(self.category, disable_log=True)
+
+
+class Timer:
+ def __init__(self, print_log=False):
+ self.start = time.time()
+ self.records = {}
+ self.total = 0
+ self.base_category = ''
+ self.print_log = print_log
+ self.subcategory_level = 0
+
+ def elapsed(self):
+ end = time.time()
+ res = end - self.start
+ self.start = end
+ return res
+
+ def add_time_to_record(self, category, amount):
+ if category not in self.records:
+ self.records[category] = 0
+
+ self.records[category] += amount
+
+ def record(self, category, extra_time=0, disable_log=False):
+ e = self.elapsed()
+
+ self.add_time_to_record(self.base_category + category, e + extra_time)
+
+ self.total += e + extra_time
+
+ if self.print_log and not disable_log:
+ print(f"{' ' * self.subcategory_level}{category}: done in {e + extra_time:.3f}s")
+
+ def subcategory(self, name):
+ self.elapsed()
+
+ subcat = TimerSubcategory(self, name)
+ return subcat
+
+ def summary(self):
+ res = f"{self.total:.1f}s"
+
+ additions = [(category, time_taken) for category, time_taken in self.records.items() if time_taken >= 0.1 and '/' not in category]
+ if not additions:
+ return res
+
+ res += " ("
+ res += ", ".join([f"{category}: {time_taken:.1f}s" for category, time_taken in additions])
+ res += ")"
+
+ return res
+
+ def dump(self):
+ return {'total': self.total, 'records': self.records}
+
+ def reset(self):
+ self.__init__()
+
+
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument("--log-startup", action='store_true', help="print a detailed log of what's happening at startup")
+args = parser.parse_known_args()[0]
+
+startup_timer = Timer(print_log=args.log_startup)
+
+startup_record = None
diff --git a/stable-diffusion-webui/modules/txt2img.py b/stable-diffusion-webui/modules/txt2img.py
new file mode 100644
index 0000000000000000000000000000000000000000..94391ce6d84942b908a1a6e5bf92b26e4621991b
--- /dev/null
+++ b/stable-diffusion-webui/modules/txt2img.py
@@ -0,0 +1,66 @@
+from contextlib import closing
+
+import modules.scripts
+from modules import processing
+from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules.shared import opts, cmd_opts
+import modules.shared as shared
+from modules.ui import plaintext_to_html
+import gradio as gr
+
+
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
+ override_settings = create_override_settings_dict(override_settings_texts)
+
+ p = processing.StableDiffusionProcessingTxt2Img(
+ sd_model=shared.sd_model,
+ outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
+ outpath_grids=opts.outdir_grids or opts.outdir_txt2img_grids,
+ prompt=prompt,
+ styles=prompt_styles,
+ negative_prompt=negative_prompt,
+ sampler_name=sampler_name,
+ batch_size=batch_size,
+ n_iter=n_iter,
+ steps=steps,
+ cfg_scale=cfg_scale,
+ width=width,
+ height=height,
+ enable_hr=enable_hr,
+ denoising_strength=denoising_strength if enable_hr else None,
+ hr_scale=hr_scale,
+ hr_upscaler=hr_upscaler,
+ hr_second_pass_steps=hr_second_pass_steps,
+ hr_resize_x=hr_resize_x,
+ hr_resize_y=hr_resize_y,
+ hr_checkpoint_name=None if hr_checkpoint_name == 'Use same checkpoint' else hr_checkpoint_name,
+ hr_sampler_name=None if hr_sampler_name == 'Use same sampler' else hr_sampler_name,
+ hr_prompt=hr_prompt,
+ hr_negative_prompt=hr_negative_prompt,
+ override_settings=override_settings,
+ )
+
+ p.scripts = modules.scripts.scripts_txt2img
+ p.script_args = args
+
+ p.user = request.username
+
+ if cmd_opts.enable_console_prompts:
+ print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
+
+ with closing(p):
+ processed = modules.scripts.scripts_txt2img.run(p, *args)
+
+ if processed is None:
+ processed = processing.process_images(p)
+
+ shared.total_tqdm.clear()
+
+ generation_info_js = processed.js()
+ if opts.samples_log_stdout:
+ print(generation_info_js)
+
+ if opts.do_not_show_images:
+ processed.images = []
+
+ return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
diff --git a/stable-diffusion-webui/modules/ui.py b/stable-diffusion-webui/modules/ui.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ed47eb2d1adcadd63fa7feaf5aac95b331bc5f7
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui.py
@@ -0,0 +1,1366 @@
+import datetime
+import mimetypes
+import os
+import sys
+from functools import reduce
+import warnings
+
+import gradio as gr
+import gradio.utils
+import numpy as np
+from PIL import Image, PngImagePlugin # noqa: F401
+from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
+
+from modules import gradio_extensons # noqa: F401
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
+from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
+from modules.paths import script_path
+from modules.ui_common import create_refresh_button
+from modules.ui_gradio_extensions import reload_javascript
+
+from modules.shared import opts, cmd_opts
+
+import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.hypernetworks.ui as hypernetworks_ui
+import modules.textual_inversion.ui as textual_inversion_ui
+import modules.textual_inversion.textual_inversion as textual_inversion
+import modules.shared as shared
+import modules.images
+from modules import prompt_parser
+from modules.sd_hijack import model_hijack
+from modules.generation_parameters_copypaste import image_from_url_text
+
+create_setting_component = ui_settings.create_setting_component
+
+warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
+warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gr.deprecation.GradioDeprecationWarning)
+
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
+mimetypes.init()
+mimetypes.add_type('application/javascript', '.js')
+
+# Likewise, add explicit content-type header for certain missing image types
+mimetypes.add_type('image/webp', '.webp')
+
+if not cmd_opts.share and not cmd_opts.listen:
+ # fix gradio phoning home
+ gradio.utils.version_check = lambda: None
+ gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
+
+if cmd_opts.ngrok is not None:
+ import modules.ngrok as ngrok
+ print('ngrok authtoken detected, trying to connect...')
+ ngrok.connect(
+ cmd_opts.ngrok,
+ cmd_opts.port if cmd_opts.port is not None else 7860,
+ cmd_opts.ngrok_options
+ )
+
+
+def gr_show(visible=True):
+ return {"visible": visible, "__type__": "update"}
+
+
+sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
+sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
+
+# Using constants for these since the variation selector isn't visible.
+# Important that they exactly match script.js for tooltip to work.
+random_symbol = '\U0001f3b2\ufe0f' # 🎲️
+reuse_symbol = '\u267b\ufe0f' # ♻️
+paste_symbol = '\u2199\ufe0f' # ↙
+refresh_symbol = '\U0001f504' # 🔄
+save_style_symbol = '\U0001f4be' # 💾
+apply_style_symbol = '\U0001f4cb' # 📋
+clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️
+extra_networks_symbol = '\U0001F3B4' # 🎴
+switch_values_symbol = '\U000021C5' # ⇅
+restore_progress_symbol = '\U0001F300' # 🌀
+detect_image_size_symbol = '\U0001F4D0' # 📐
+
+
+plaintext_to_html = ui_common.plaintext_to_html
+
+
+def send_gradio_gallery_to_image(x):
+ if len(x) == 0:
+ return None
+ return image_from_url_text(x[0])
+
+
+def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
+ if not enable:
+ return ""
+
+ p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
+ p.calculate_target_resolution()
+
+ return f"from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
+
+
+def resize_from_to_html(width, height, scale_by):
+ target_width = int(width * scale_by)
+ target_height = int(height * scale_by)
+
+ if not target_width or not target_height:
+ return "no image selected"
+
+ return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
+
+
+def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
+ if mode in {0, 1, 3, 4}:
+ return [interrogation_function(ii_singles[mode]), None]
+ elif mode == 2:
+ return [interrogation_function(ii_singles[mode]["image"]), None]
+ elif mode == 5:
+ assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
+ images = shared.listfiles(ii_input_dir)
+ print(f"Will process {len(images)} images.")
+ if ii_output_dir != "":
+ os.makedirs(ii_output_dir, exist_ok=True)
+ else:
+ ii_output_dir = ii_input_dir
+
+ for image in images:
+ img = Image.open(image)
+ filename = os.path.basename(image)
+ left, _ = os.path.splitext(filename)
+ print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
+
+ return [gr.update(), None]
+
+
+def interrogate(image):
+ prompt = shared.interrogator.interrogate(image.convert("RGB"))
+ return gr.update() if prompt is None else prompt
+
+
+def interrogate_deepbooru(image):
+ prompt = deepbooru.model.tag(image)
+ return gr.update() if prompt is None else prompt
+
+
+def connect_clear_prompt(button):
+ """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
+ button.click(
+ _js="clear_prompt",
+ fn=None,
+ inputs=[],
+ outputs=[],
+ )
+
+
+def update_token_counter(text, steps):
+ try:
+ text, _ = extra_networks.parse_prompt(text)
+
+ _, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
+ prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
+
+ except Exception:
+ # a parsing error can happen here during typing, and we don't want to bother the user with
+ # messages related to it in console
+ prompt_schedules = [[[steps, text]]]
+
+ flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
+ prompts = [prompt_text for step, prompt_text in flat_prompts]
+ token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
+ return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
+
+
+class Toprow:
+ """Creates a top row UI with prompts, generate button, styles, extra little buttons for things, and enables some functionality related to their operation"""
+
+ def __init__(self, is_img2img):
+ id_part = "img2img" if is_img2img else "txt2img"
+ self.id_part = id_part
+
+ with gr.Row(elem_id=f"{id_part}_toprow", variant="compact"):
+ with gr.Column(elem_id=f"{id_part}_prompt_container", scale=6):
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ self.prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+ self.prompt_img = gr.File(label="", elem_id=f"{id_part}_prompt_image", file_count="single", type="binary", visible=False)
+
+ with gr.Row():
+ with gr.Column(scale=80):
+ with gr.Row():
+ self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+
+ self.button_interrogate = None
+ self.button_deepbooru = None
+ if is_img2img:
+ with gr.Column(scale=1, elem_classes="interrogate-col"):
+ self.button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+ self.button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+
+ with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
+ with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+ self.interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+ self.skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
+ self.submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+
+ self.skip.click(
+ fn=lambda: shared.state.skip(),
+ inputs=[],
+ outputs=[],
+ )
+
+ self.interrupt.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ with gr.Row(elem_id=f"{id_part}_tools"):
+ self.paste = ToolButton(value=paste_symbol, elem_id="paste")
+ self.clear_prompt_button = ToolButton(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
+ self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)
+
+ self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
+ self.token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+ self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
+ self.negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
+
+ self.clear_prompt_button.click(
+ fn=lambda *x: x,
+ _js="confirm_clear_prompt",
+ inputs=[self.prompt, self.negative_prompt],
+ outputs=[self.prompt, self.negative_prompt],
+ )
+
+ self.ui_styles = ui_prompt_styles.UiPromptStyles(id_part, self.prompt, self.negative_prompt)
+
+ self.prompt_img.change(
+ fn=modules.images.image_data,
+ inputs=[self.prompt_img],
+ outputs=[self.prompt, self.prompt_img],
+ show_progress=False,
+ )
+
+
+def setup_progressbar(*args, **kwargs):
+ pass
+
+
+def apply_setting(key, value):
+ if value is None:
+ return gr.update()
+
+ if shared.cmd_opts.freeze_settings:
+ return gr.update()
+
+ # dont allow model to be swapped when model hash exists in prompt
+ if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
+ return gr.update()
+
+ if key == "sd_model_checkpoint":
+ ckpt_info = sd_models.get_closet_checkpoint_match(value)
+
+ if ckpt_info is not None:
+ value = ckpt_info.title
+ else:
+ return gr.update()
+
+ comp_args = opts.data_labels[key].component_args
+ if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
+ return
+
+ valtype = type(opts.data_labels[key].default)
+ oldval = opts.data.get(key, None)
+ opts.data[key] = valtype(value) if valtype != type(None) else value
+ if oldval != value and opts.data_labels[key].onchange is not None:
+ opts.data_labels[key].onchange()
+
+ opts.save(shared.config_filename)
+ return getattr(opts, key)
+
+
+def create_output_panel(tabname, outdir):
+ return ui_common.create_output_panel(tabname, outdir)
+
+
+def create_sampler_and_steps_selection(choices, tabname):
+ if opts.samplers_in_dropdown:
+ with FormRow(elem_id=f"sampler_selection_{tabname}"):
+ sampler_name = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0])
+ steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+ else:
+ with FormGroup(elem_id=f"sampler_selection_{tabname}"):
+ steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
+ sampler_name = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=choices, value=choices[0])
+
+ return steps, sampler_name
+
+
+def ordered_ui_categories():
+ user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
+
+ for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+ yield category
+
+
+def create_override_settings_dropdown(tabname, row):
+ dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+ dropdown.change(
+ fn=lambda x: gr.Dropdown.update(visible=bool(x)),
+ inputs=[dropdown],
+ outputs=[dropdown],
+ )
+
+ return dropdown
+
+
+def create_ui():
+ import modules.img2img
+ import modules.txt2img
+
+ reload_javascript()
+
+ parameters_copypaste.reset()
+
+ scripts.scripts_current = scripts.scripts_txt2img
+ scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
+
+ with gr.Blocks(analytics_enabled=False) as txt2img_interface:
+ toprow = Toprow(is_img2img=False)
+
+ dummy_component = gr.Label(visible=False)
+
+ extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
+ extra_tabs.__enter__()
+
+ with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
+ with gr.Column(variant='compact', elem_id="txt2img_settings"):
+ scripts.scripts_txt2img.prepare_ui()
+
+ for category in ordered_ui_categories():
+ if category == "sampler":
+ steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "txt2img")
+
+ elif category == "dimensions":
+ with FormRow():
+ with gr.Column(elem_id="txt2img_column_size", scale=4):
+ width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
+
+ with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
+
+ if opts.dimensions_and_batch_together:
+ with gr.Column(elem_id="txt2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+ elif category == "cfg":
+ with gr.Row():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
+
+ elif category == "checkboxes":
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
+ pass
+
+ elif category == "accordions":
+ with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"):
+ with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr:
+ with enable_hr.extra():
+ hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
+
+ with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+ hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+ hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+
+ with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+ hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+ hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
+ hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+
+ with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+
+ hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
+ create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
+
+ hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
+
+ with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+ with gr.Column(scale=80):
+ with gr.Row():
+ hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
+ scripts.scripts_txt2img.setup_ui_for_section(category)
+
+ elif category == "batch":
+ if not opts.dimensions_and_batch_together:
+ with FormRow(elem_id="txt2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
+
+ elif category == "override_settings":
+ with FormRow(elem_id="txt2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('txt2img', row)
+
+ elif category == "scripts":
+ with FormGroup(elem_id="txt2img_script_container"):
+ custom_inputs = scripts.scripts_txt2img.setup_ui()
+
+ if category not in {"accordions"}:
+ scripts.scripts_txt2img.setup_ui_for_section(category)
+
+ hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
+
+ for component in hr_resolution_preview_inputs:
+ event = component.release if isinstance(component, gr.Slider) else component.change
+
+ event(
+ fn=calc_resolution_hires,
+ inputs=hr_resolution_preview_inputs,
+ outputs=[hr_final_resolution],
+ show_progress=False,
+ )
+ event(
+ None,
+ _js="onCalcResolutionHires",
+ inputs=hr_resolution_preview_inputs,
+ outputs=[],
+ show_progress=False,
+ )
+
+ txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
+
+ txt2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
+ _js="submit",
+ inputs=[
+ dummy_component,
+ toprow.prompt,
+ toprow.negative_prompt,
+ toprow.ui_styles.dropdown,
+ steps,
+ sampler_name,
+ batch_count,
+ batch_size,
+ cfg_scale,
+ height,
+ width,
+ enable_hr,
+ denoising_strength,
+ hr_scale,
+ hr_upscaler,
+ hr_second_pass_steps,
+ hr_resize_x,
+ hr_resize_y,
+ hr_checkpoint_name,
+ hr_sampler_name,
+ hr_prompt,
+ hr_negative_prompt,
+ override_settings,
+
+ ] + custom_inputs,
+
+ outputs=[
+ txt2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ toprow.prompt.submit(**txt2img_args)
+ toprow.submit.click(**txt2img_args)
+
+ res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
+
+ toprow.restore_progress_button.click(
+ fn=progress.restore_progress,
+ _js="restoreProgressTxt2img",
+ inputs=[dummy_component],
+ outputs=[
+ txt2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ txt2img_paste_fields = [
+ (toprow.prompt, "Prompt"),
+ (toprow.negative_prompt, "Negative prompt"),
+ (steps, "Steps"),
+ (sampler_name, "Sampler"),
+ (cfg_scale, "CFG scale"),
+ (width, "Size-1"),
+ (height, "Size-2"),
+ (batch_size, "Batch size"),
+ (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+ (denoising_strength, "Denoising strength"),
+ (enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
+ (hr_scale, "Hires upscale"),
+ (hr_upscaler, "Hires upscaler"),
+ (hr_second_pass_steps, "Hires steps"),
+ (hr_resize_x, "Hires resize-1"),
+ (hr_resize_y, "Hires resize-2"),
+ (hr_checkpoint_name, "Hires checkpoint"),
+ (hr_sampler_name, "Hires sampler"),
+ (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()),
+ (hr_prompt, "Hires prompt"),
+ (hr_negative_prompt, "Hires negative prompt"),
+ (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
+ *scripts.scripts_txt2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
+ ))
+
+ txt2img_preview_params = [
+ toprow.prompt,
+ toprow.negative_prompt,
+ steps,
+ sampler_name,
+ cfg_scale,
+ scripts.scripts_txt2img.script('Seed').seed,
+ width,
+ height,
+ ]
+
+ toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
+
+ extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
+ ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+
+ extra_tabs.__exit__()
+
+ scripts.scripts_current = scripts.scripts_img2img
+ scripts.scripts_img2img.initialize_scripts(is_img2img=True)
+
+ with gr.Blocks(analytics_enabled=False) as img2img_interface:
+ toprow = Toprow(is_img2img=True)
+
+ extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
+ extra_tabs.__enter__()
+
+ with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
+ with gr.Column(variant='compact', elem_id="img2img_settings"):
+ copy_image_buttons = []
+ copy_image_destinations = {}
+
+ def add_copy_image_controls(tab_name, elem):
+ with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
+ gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
+
+ for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
+ if name == tab_name:
+ gr.Button(title, interactive=False)
+ copy_image_destinations[name] = elem
+ continue
+
+ button = gr.Button(title)
+ copy_image_buttons.append((button, name, elem))
+
+ with gr.Tabs(elem_id="mode_img2img"):
+ img2img_selected_tab = gr.State(0)
+
+ with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+ init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
+ add_copy_image_controls('img2img', init_img)
+
+ with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+ sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_sketch_default_brush_color)
+ add_copy_image_controls('sketch', sketch)
+
+ with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+ init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_mask_brush_color)
+ add_copy_image_controls('inpaint', init_img_with_mask)
+
+ with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+ inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGB", height=opts.img2img_editor_height, brush_color=opts.img2img_inpaint_sketch_default_brush_color)
+ inpaint_color_sketch_orig = gr.State(None)
+ add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
+
+ def update_orig(image, state):
+ if image is not None:
+ same_size = state is not None and state.size == image.size
+ has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+ edited = same_size and has_exact_match
+ return image if not edited or state is None else state
+
+ inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
+
+ with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+ init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+ init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
+
+ with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
+ hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
+ gr.HTML(
+ "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+ "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+ f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
+ f"{hidden}</p>"
+ )
+ img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
+ img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+ img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+ with gr.Accordion("PNG info", open=False):
+ img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
+ img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
+ img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
+
+ img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+
+ for i, tab in enumerate(img2img_tabs):
+ tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
+ def copy_image(img):
+ if isinstance(img, dict) and 'image' in img:
+ return img['image']
+
+ return img
+
+ for button, name, elem in copy_image_buttons:
+ button.click(
+ fn=copy_image,
+ inputs=[elem],
+ outputs=[copy_image_destinations[name]],
+ )
+ button.click(
+ fn=lambda: None,
+ _js=f"switch_to_{name.replace(' ', '_')}",
+ inputs=[],
+ outputs=[],
+ )
+
+ with FormRow():
+ resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
+
+ scripts.scripts_img2img.prepare_ui()
+
+ for category in ordered_ui_categories():
+ if category == "sampler":
+ steps, sampler_name = create_sampler_and_steps_selection(sd_samplers.visible_sampler_names(), "img2img")
+
+ elif category == "dimensions":
+ with FormRow():
+ with gr.Column(elem_id="img2img_column_size", scale=4):
+ selected_scale_tab = gr.State(value=0)
+
+ with gr.Tabs():
+ with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
+ with FormRow():
+ with gr.Column(elem_id="img2img_column_size", scale=4):
+ width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+ height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+ with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+ detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn")
+
+ with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
+ scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
+
+ with FormRow():
+ scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+ gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+ button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+
+ on_change_args = dict(
+ fn=resize_from_to_html,
+ _js="currentImg2imgSourceResolution",
+ inputs=[dummy_component, dummy_component, scale_by],
+ outputs=scale_by_html,
+ show_progress=False,
+ )
+
+ scale_by.release(**on_change_args)
+ button_update_resize_to.click(**on_change_args)
+
+ # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+ # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+ # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+ for component in [init_img, sketch]:
+ component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+ tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+ tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
+
+ if opts.dimensions_and_batch_together:
+ with gr.Column(elem_id="img2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ elif category == "denoising":
+ denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+
+ elif category == "cfg":
+ with gr.Row():
+ cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+ image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
+
+ elif category == "checkboxes":
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
+ pass
+
+ elif category == "accordions":
+ with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
+ scripts.scripts_img2img.setup_ui_for_section(category)
+
+ elif category == "batch":
+ if not opts.dimensions_and_batch_together:
+ with FormRow(elem_id="img2img_column_batch"):
+ batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
+ batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
+
+ elif category == "override_settings":
+ with FormRow(elem_id="img2img_override_settings_row") as row:
+ override_settings = create_override_settings_dropdown('img2img', row)
+
+ elif category == "scripts":
+ with FormGroup(elem_id="img2img_script_container"):
+ custom_inputs = scripts.scripts_img2img.setup_ui()
+
+ elif category == "inpaint":
+ with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+ with FormRow():
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+ mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+ with FormRow():
+ inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+ with FormRow():
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+ with FormRow():
+ with gr.Column():
+ inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+ with gr.Column(scale=4):
+ inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+ def select_img2img_tab(tab):
+ return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+ for i, elem in enumerate(img2img_tabs):
+ elem.select(
+ fn=lambda tab=i: select_img2img_tab(tab),
+ inputs=[],
+ outputs=[inpaint_controls, mask_alpha],
+ )
+
+ if category not in {"accordions"}:
+ scripts.scripts_img2img.setup_ui_for_section(category)
+
+ img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
+
+ img2img_args = dict(
+ fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
+ _js="submit_img2img",
+ inputs=[
+ dummy_component,
+ dummy_component,
+ toprow.prompt,
+ toprow.negative_prompt,
+ toprow.ui_styles.dropdown,
+ init_img,
+ sketch,
+ init_img_with_mask,
+ inpaint_color_sketch,
+ inpaint_color_sketch_orig,
+ init_img_inpaint,
+ init_mask_inpaint,
+ steps,
+ sampler_name,
+ mask_blur,
+ mask_alpha,
+ inpainting_fill,
+ batch_count,
+ batch_size,
+ cfg_scale,
+ image_cfg_scale,
+ denoising_strength,
+ selected_scale_tab,
+ height,
+ width,
+ scale_by,
+ resize_mode,
+ inpaint_full_res,
+ inpaint_full_res_padding,
+ inpainting_mask_invert,
+ img2img_batch_input_dir,
+ img2img_batch_output_dir,
+ img2img_batch_inpaint_mask_dir,
+ override_settings,
+ img2img_batch_use_png_info,
+ img2img_batch_png_info_props,
+ img2img_batch_png_info_dir,
+ ] + custom_inputs,
+ outputs=[
+ img2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ interrogate_args = dict(
+ _js="get_img2img_tab_index",
+ inputs=[
+ dummy_component,
+ img2img_batch_input_dir,
+ img2img_batch_output_dir,
+ init_img,
+ sketch,
+ init_img_with_mask,
+ inpaint_color_sketch,
+ init_img_inpaint,
+ ],
+ outputs=[toprow.prompt, dummy_component],
+ )
+
+ toprow.prompt.submit(**img2img_args)
+ toprow.submit.click(**img2img_args)
+
+ res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('img2img')}", inputs=None, outputs=None, show_progress=False)
+
+ detect_image_size_btn.click(
+ fn=lambda w, h, _: (w or gr.update(), h or gr.update()),
+ _js="currentImg2imgSourceResolution",
+ inputs=[dummy_component, dummy_component, dummy_component],
+ outputs=[width, height],
+ show_progress=False,
+ )
+
+ toprow.restore_progress_button.click(
+ fn=progress.restore_progress,
+ _js="restoreProgressImg2img",
+ inputs=[dummy_component],
+ outputs=[
+ img2img_gallery,
+ generation_info,
+ html_info,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ toprow.button_interrogate.click(
+ fn=lambda *args: process_interrogate(interrogate, *args),
+ **interrogate_args,
+ )
+
+ toprow.button_deepbooru.click(
+ fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
+ **interrogate_args,
+ )
+
+ toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
+
+ img2img_paste_fields = [
+ (toprow.prompt, "Prompt"),
+ (toprow.negative_prompt, "Negative prompt"),
+ (steps, "Steps"),
+ (sampler_name, "Sampler"),
+ (cfg_scale, "CFG scale"),
+ (image_cfg_scale, "Image CFG scale"),
+ (width, "Size-1"),
+ (height, "Size-2"),
+ (batch_size, "Batch size"),
+ (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
+ (denoising_strength, "Denoising strength"),
+ (mask_blur, "Mask blur"),
+ *scripts.scripts_img2img.infotext_fields
+ ]
+ parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+ parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
+ ))
+
+ extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
+ ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+
+ extra_tabs.__exit__()
+
+ scripts.scripts_current = None
+
+ with gr.Blocks(analytics_enabled=False) as extras_interface:
+ ui_postprocessing.create_ui()
+
+ with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
+ with gr.Row(equal_height=False):
+ with gr.Column(variant='panel'):
+ image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
+
+ with gr.Column(variant='panel'):
+ html = gr.HTML()
+ generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
+ html2 = gr.HTML()
+ with gr.Row():
+ buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+ for tabname, button in buttons.items():
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+ ))
+
+ image.change(
+ fn=wrap_gradio_call(modules.extras.run_pnginfo),
+ inputs=[image],
+ outputs=[html, generation_info, html2],
+ )
+
+ modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
+
+ with gr.Blocks(analytics_enabled=False) as train_interface:
+ with gr.Row(equal_height=False):
+ gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+
+ with gr.Row(variant="compact", equal_height=False):
+ with gr.Tabs(elem_id="train_tabs"):
+
+ with gr.Tab(label="Create embedding", id="create_embedding"):
+ new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
+ initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
+ nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
+ overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
+
+ with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
+ new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
+ new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
+ new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=hypernetworks_ui.keys, elem_id="train_new_hypernetwork_activation_func")
+ new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
+ new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
+ new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
+ new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
+ overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
+
+ with gr.Tab(label="Preprocess images", id="preprocess_images"):
+ process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
+ process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
+ process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
+ process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
+ preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
+
+ with gr.Row():
+ process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
+ process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
+ process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
+ process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
+ process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
+ process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
+ process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
+
+ with gr.Row(visible=False) as process_split_extra_row:
+ process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
+ process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
+
+ with gr.Row(visible=False) as process_focal_crop_row:
+ process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
+ process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
+ process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
+ process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
+
+ with gr.Column(visible=False) as process_multicrop_col:
+ gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
+ with gr.Row():
+ process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
+ process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
+ with gr.Row():
+ process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
+ process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
+ with gr.Row():
+ process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
+ process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
+
+ with gr.Row():
+ with gr.Column(scale=3):
+ gr.HTML(value="")
+
+ with gr.Column():
+ with gr.Row():
+ interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
+ run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
+
+ process_split.change(
+ fn=lambda show: gr_show(show),
+ inputs=[process_split],
+ outputs=[process_split_extra_row],
+ )
+
+ process_focal_crop.change(
+ fn=lambda show: gr_show(show),
+ inputs=[process_focal_crop],
+ outputs=[process_focal_crop_row],
+ )
+
+ process_multicrop.change(
+ fn=lambda show: gr_show(show),
+ inputs=[process_multicrop],
+ outputs=[process_multicrop_col],
+ )
+
+ def get_textual_inversion_template_names():
+ return sorted(textual_inversion.textual_inversion_templates)
+
+ with gr.Tab(label="Train", id="train"):
+ gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
+ with FormRow():
+ train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+ create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+
+ train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=sorted(shared.hypernetworks))
+ create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted(shared.hypernetworks)}, "refresh_train_hypernetwork_name")
+
+ with FormRow():
+ embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
+ hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
+
+ with FormRow():
+ clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
+ clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
+
+ with FormRow():
+ batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
+ gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
+
+ dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
+ log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
+
+ with FormRow():
+ template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
+ create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
+
+ training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
+ training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
+ varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
+ steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
+
+ with FormRow():
+ create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
+ save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
+
+ use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
+ save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
+ preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
+
+ shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
+ tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
+
+ latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
+
+ with gr.Row():
+ train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
+ interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
+ train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
+
+ params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
+
+ script_callbacks.ui_train_tabs_callback(params)
+
+ with gr.Column(elem_id='ti_gallery_container'):
+ ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
+ gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery', columns=4)
+ gr.HTML(elem_id="ti_progress", value="")
+ ti_outcome = gr.HTML(elem_id="ti_error", value="")
+
+ create_embedding.click(
+ fn=textual_inversion_ui.create_embedding,
+ inputs=[
+ new_embedding_name,
+ initialization_text,
+ nvpt,
+ overwrite_old_embedding,
+ ],
+ outputs=[
+ train_embedding_name,
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ create_hypernetwork.click(
+ fn=hypernetworks_ui.create_hypernetwork,
+ inputs=[
+ new_hypernetwork_name,
+ new_hypernetwork_sizes,
+ overwrite_old_hypernetwork,
+ new_hypernetwork_layer_structure,
+ new_hypernetwork_activation_func,
+ new_hypernetwork_initialization_option,
+ new_hypernetwork_add_layer_norm,
+ new_hypernetwork_use_dropout,
+ new_hypernetwork_dropout_structure
+ ],
+ outputs=[
+ train_hypernetwork_name,
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ run_preprocess.click(
+ fn=wrap_gradio_gpu_call(textual_inversion_ui.preprocess, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ process_src,
+ process_dst,
+ process_width,
+ process_height,
+ preprocess_txt_action,
+ process_keep_original_size,
+ process_flip,
+ process_split,
+ process_caption,
+ process_caption_deepbooru,
+ process_split_threshold,
+ process_overlap_ratio,
+ process_focal_crop,
+ process_focal_crop_face_weight,
+ process_focal_crop_entropy_weight,
+ process_focal_crop_edges_weight,
+ process_focal_crop_debug,
+ process_multicrop,
+ process_multicrop_mindim,
+ process_multicrop_maxdim,
+ process_multicrop_minarea,
+ process_multicrop_maxarea,
+ process_multicrop_objective,
+ process_multicrop_threshold,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ],
+ )
+
+ train_embedding.click(
+ fn=wrap_gradio_gpu_call(textual_inversion_ui.train_embedding, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ train_embedding_name,
+ embedding_learn_rate,
+ batch_size,
+ gradient_step,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ varsize,
+ steps,
+ clip_grad_mode,
+ clip_grad_value,
+ shuffle_tags,
+ tag_drop_out,
+ latent_sampling_method,
+ use_weight,
+ create_image_every,
+ save_embedding_every,
+ template_file,
+ save_image_with_stored_embedding,
+ preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ train_hypernetwork.click(
+ fn=wrap_gradio_gpu_call(hypernetworks_ui.train_hypernetwork, extra_outputs=[gr.update()]),
+ _js="start_training_textual_inversion",
+ inputs=[
+ dummy_component,
+ train_hypernetwork_name,
+ hypernetwork_learn_rate,
+ batch_size,
+ gradient_step,
+ dataset_directory,
+ log_directory,
+ training_width,
+ training_height,
+ varsize,
+ steps,
+ clip_grad_mode,
+ clip_grad_value,
+ shuffle_tags,
+ tag_drop_out,
+ latent_sampling_method,
+ use_weight,
+ create_image_every,
+ save_embedding_every,
+ template_file,
+ preview_from_txt2img,
+ *txt2img_preview_params,
+ ],
+ outputs=[
+ ti_output,
+ ti_outcome,
+ ]
+ )
+
+ interrupt_training.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ interrupt_preprocessing.click(
+ fn=lambda: shared.state.interrupt(),
+ inputs=[],
+ outputs=[],
+ )
+
+ loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
+
+ settings = ui_settings.UiSettings()
+ settings.create_ui(loadsave, dummy_component)
+
+ interfaces = [
+ (txt2img_interface, "txt2img", "txt2img"),
+ (img2img_interface, "img2img", "img2img"),
+ (extras_interface, "Extras", "extras"),
+ (pnginfo_interface, "PNG Info", "pnginfo"),
+ (modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
+ (train_interface, "Train", "train"),
+ ]
+
+ interfaces += script_callbacks.ui_tabs_callback()
+ interfaces += [(settings.interface, "Settings", "settings")]
+
+ extensions_interface = ui_extensions.create_ui()
+ interfaces += [(extensions_interface, "Extensions", "extensions")]
+
+ shared.tab_names = []
+ for _interface, label, _ifid in interfaces:
+ shared.tab_names.append(label)
+
+ with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
+ settings.add_quicksettings()
+
+ parameters_copypaste.connect_paste_params_buttons()
+
+ with gr.Tabs(elem_id="tabs") as tabs:
+ tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
+ sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
+
+ for interface, label, ifid in sorted_interfaces:
+ if label in shared.opts.hidden_tabs:
+ continue
+ with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
+ interface.render()
+
+ if ifid not in ["extensions", "settings"]:
+ loadsave.add_block(interface, ifid)
+
+ loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
+
+ loadsave.setup_ui()
+
+ if os.path.exists(os.path.join(script_path, "notification.mp3")):
+ gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+
+ footer = shared.html("footer.html")
+ footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
+ gr.HTML(footer, elem_id="footer")
+
+ settings.add_functionality(demo)
+
+ update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
+ settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+ demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
+
+ modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
+
+ loadsave.dump_defaults()
+ demo.ui_loadsave = loadsave
+
+ return demo
+
+
+def versions_html():
+ import torch
+ import launch
+
+ python_version = ".".join([str(x) for x in sys.version_info[0:3]])
+ commit = launch.commit_hash()
+ tag = launch.git_tag()
+
+ if shared.xformers_available:
+ import xformers
+ xformers_version = xformers.__version__
+ else:
+ xformers_version = "N/A"
+
+ return f"""
+version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
+ • 
+python: <span title="{sys.version}">{python_version}</span>
+ • 
+torch: {getattr(torch, '__long_version__',torch.__version__)}
+ • 
+xformers: {xformers_version}
+ • 
+gradio: {gr.__version__}
+ • 
+checkpoint: <a id="sd_checkpoint_hash">N/A</a>
+"""
+
+
+def setup_ui_api(app):
+ from pydantic import BaseModel, Field
+ from typing import List
+
+ class QuicksettingsHint(BaseModel):
+ name: str = Field(title="Name of the quicksettings field")
+ label: str = Field(title="Label of the quicksettings field")
+
+ def quicksettings_hint():
+ return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
+
+ app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
+
+ app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
+
+ app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
+
+ def download_sysinfo(attachment=False):
+ from fastapi.responses import PlainTextResponse
+
+ text = sysinfo.get()
+ filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+
+ return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
+
+ app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
+ app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+
diff --git a/stable-diffusion-webui/modules/ui_checkpoint_merger.py b/stable-diffusion-webui/modules/ui_checkpoint_merger.py
new file mode 100644
index 0000000000000000000000000000000000000000..d74fafa43f1f9b5c66b52f03fefb938488e8935b
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_checkpoint_merger.py
@@ -0,0 +1,124 @@
+
+import gradio as gr
+
+from modules import sd_models, sd_vae, errors, extras, call_queue
+from modules.ui_components import FormRow
+from modules.ui_common import create_refresh_button
+
+
+def update_interp_description(value):
+ interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
+ interp_descriptions = {
+ "No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
+ "Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
+ "Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
+ }
+ return interp_descriptions[value]
+
+
+def modelmerger(*args):
+ try:
+ results = extras.run_modelmerger(*args)
+ except Exception as e:
+ errors.report("Error loading/saving model file", exc_info=True)
+ sd_models.list_models() # to remove the potentially missing models from the list
+ return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
+ return results
+
+
+class UiCheckpointMerger:
+ def __init__(self):
+ with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
+ with gr.Row(equal_height=False):
+ with gr.Column(variant='compact'):
+ self.interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
+
+ with FormRow(elem_id="modelmerger_models"):
+ self.primary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
+ create_refresh_button(self.primary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
+
+ self.secondary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
+ create_refresh_button(self.secondary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
+
+ self.tertiary_model_name = gr.Dropdown(sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
+ create_refresh_button(self.tertiary_model_name, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
+
+ self.custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
+ self.interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
+ self.interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
+ self.interp_method.change(fn=update_interp_description, inputs=[self.interp_method], outputs=[self.interp_description])
+
+ with FormRow():
+ self.checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+ self.save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+
+ with FormRow():
+ with gr.Column():
+ self.config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
+
+ with gr.Column():
+ with FormRow():
+ self.bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
+ create_refresh_button(self.bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
+
+ with FormRow():
+ self.discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
+
+ with gr.Accordion("Metadata", open=False) as metadata_editor:
+ with FormRow():
+ self.save_metadata = gr.Checkbox(value=True, label="Save metadata", elem_id="modelmerger_save_metadata")
+ self.add_merge_recipe = gr.Checkbox(value=True, label="Add merge recipe metadata", elem_id="modelmerger_add_recipe")
+ self.copy_metadata_fields = gr.Checkbox(value=True, label="Copy metadata from merged models", elem_id="modelmerger_copy_metadata")
+
+ self.metadata_json = gr.TextArea('{}', label="Metadata in JSON format")
+ self.read_metadata = gr.Button("Read metadata from selected checkpoints")
+
+ with FormRow():
+ self.modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+
+ with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
+ with gr.Group(elem_id="modelmerger_results_panel"):
+ self.modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
+
+ self.metadata_editor = metadata_editor
+ self.blocks = modelmerger_interface
+
+ def setup_ui(self, dummy_component, sd_model_checkpoint_component):
+ self.checkpoint_format.change(lambda fmt: gr.update(visible=fmt == 'safetensors'), inputs=[self.checkpoint_format], outputs=[self.metadata_editor], show_progress=False)
+
+ self.read_metadata.click(extras.read_metadata, inputs=[self.primary_model_name, self.secondary_model_name, self.tertiary_model_name], outputs=[self.metadata_json])
+
+ self.modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[self.modelmerger_result])
+ self.modelmerger_merge.click(
+ fn=call_queue.wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
+ _js='modelmerger',
+ inputs=[
+ dummy_component,
+ self.primary_model_name,
+ self.secondary_model_name,
+ self.tertiary_model_name,
+ self.interp_method,
+ self.interp_amount,
+ self.save_as_half,
+ self.custom_name,
+ self.checkpoint_format,
+ self.config_source,
+ self.bake_in_vae,
+ self.discard_weights,
+ self.save_metadata,
+ self.add_merge_recipe,
+ self.copy_metadata_fields,
+ self.metadata_json,
+ ],
+ outputs=[
+ self.primary_model_name,
+ self.secondary_model_name,
+ self.tertiary_model_name,
+ sd_model_checkpoint_component,
+ self.modelmerger_result,
+ ]
+ )
+
+ # Required as a workaround for change() event not triggering when loading values from ui-config.json
+ self.interp_description.value = update_interp_description(self.interp_method.value)
+
diff --git a/stable-diffusion-webui/modules/ui_common.py b/stable-diffusion-webui/modules/ui_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e3b0f0801c2c1334c1e3909ce7da9bb637daec0
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_common.py
@@ -0,0 +1,268 @@
+import json
+import html
+import os
+import platform
+import sys
+
+import gradio as gr
+import subprocess as sp
+
+from modules import call_queue, shared
+from modules.generation_parameters_copypaste import image_from_url_text
+import modules.images
+from modules.ui_components import ToolButton
+import modules.generation_parameters_copypaste as parameters_copypaste
+
+folder_symbol = '\U0001f4c2' # 📂
+refresh_symbol = '\U0001f504' # 🔄
+
+
+def update_generation_info(generation_info, html_info, img_index):
+ try:
+ generation_info = json.loads(generation_info)
+ if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+ return html_info, gr.update()
+ return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
+ except Exception:
+ pass
+ # if the json parse or anything else fails, just return the old html_info
+ return html_info, gr.update()
+
+
+def plaintext_to_html(text, classname=None):
+ content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
+
+ return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
+
+
+def save_files(js_data, images, do_make_zip, index):
+ import csv
+ filenames = []
+ fullfns = []
+
+ #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+ class MyObject:
+ def __init__(self, d=None):
+ if d is not None:
+ for key, value in d.items():
+ setattr(self, key, value)
+
+ data = json.loads(js_data)
+
+ p = MyObject(data)
+ path = shared.opts.outdir_save
+ save_to_dirs = shared.opts.use_save_to_dirs_for_ui
+ extension: str = shared.opts.samples_format
+ start_index = 0
+ only_one = False
+
+ if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+ only_one = True
+ images = [images[index]]
+ start_index = index
+
+ os.makedirs(shared.opts.outdir_save, exist_ok=True)
+
+ with open(os.path.join(shared.opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+ at_start = file.tell() == 0
+ writer = csv.writer(file)
+ if at_start:
+ writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+
+ for image_index, filedata in enumerate(images, start_index):
+ image = image_from_url_text(filedata)
+
+ is_grid = image_index < p.index_of_first_image
+ i = 0 if is_grid else (image_index - p.index_of_first_image)
+
+ p.batch_index = image_index-1
+ fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+ filename = os.path.relpath(fullfn, path)
+ filenames.append(filename)
+ fullfns.append(fullfn)
+ if txt_fullfn:
+ filenames.append(os.path.basename(txt_fullfn))
+ fullfns.append(txt_fullfn)
+
+ writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+
+ # Make Zip
+ if do_make_zip:
+ zip_fileseed = p.all_seeds[index-1] if only_one else p.all_seeds[0]
+ namegen = modules.images.FilenameGenerator(p, zip_fileseed, p.all_prompts[0], image, True)
+ zip_filename = namegen.apply(shared.opts.grid_zip_filename_pattern or "[datetime]_[[model_name]]_[seed]-[seed_last]")
+ zip_filepath = os.path.join(path, f"{zip_filename}.zip")
+
+ from zipfile import ZipFile
+ with ZipFile(zip_filepath, "w") as zip_file:
+ for i in range(len(fullfns)):
+ with open(fullfns[i], mode="rb") as f:
+ zip_file.writestr(filenames[i], f.read())
+ fullfns.insert(0, zip_filepath)
+
+ return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
+
+
+def create_output_panel(tabname, outdir):
+
+ def open_folder(f):
+ if not os.path.exists(f):
+ print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+ return
+ elif not os.path.isdir(f):
+ print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+ return
+
+ if not shared.cmd_opts.hide_ui_dir_config:
+ path = os.path.normpath(f)
+ if platform.system() == "Windows":
+ os.startfile(path)
+ elif platform.system() == "Darwin":
+ sp.Popen(["open", path])
+ elif "microsoft-standard-WSL2" in platform.uname().release:
+ sp.Popen(["wsl-open", path])
+ else:
+ sp.Popen(["xdg-open", path])
+
+ with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
+ with gr.Group(elem_id=f"{tabname}_gallery_container"):
+ result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
+
+ generation_info = None
+ with gr.Column():
+ with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+ open_folder_button = ToolButton(folder_symbol, elem_id=f'{tabname}_open_folder', visible=not shared.cmd_opts.hide_ui_dir_config, tooltip="Open images output directory.")
+
+ if tabname != "extras":
+ save = ToolButton('💾', elem_id=f'save_{tabname}', tooltip=f"Save the image to a dedicated directory ({shared.opts.outdir_save}).")
+ save_zip = ToolButton('🗃️', elem_id=f'save_zip_{tabname}', tooltip=f"Save zip archive with images to a dedicated directory ({shared.opts.outdir_save})")
+
+ buttons = {
+ 'img2img': ToolButton('🖼️', elem_id=f'{tabname}_send_to_img2img', tooltip="Send image and generation parameters to img2img tab."),
+ 'inpaint': ToolButton('🎨️', elem_id=f'{tabname}_send_to_inpaint', tooltip="Send image and generation parameters to img2img inpaint tab."),
+ 'extras': ToolButton('📐', elem_id=f'{tabname}_send_to_extras', tooltip="Send image and generation parameters to extras tab.")
+ }
+
+ open_folder_button.click(
+ fn=lambda: open_folder(shared.opts.outdir_samples or outdir),
+ inputs=[],
+ outputs=[],
+ )
+
+ if tabname != "extras":
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
+
+ with gr.Group():
+ html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
+ html_log = gr.HTML(elem_id=f'html_log_{tabname}', elem_classes="html-log")
+
+ generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
+ if tabname == 'txt2img' or tabname == 'img2img':
+ generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+ generation_info_button.click(
+ fn=update_generation_info,
+ _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
+ inputs=[generation_info, html_info, html_info],
+ outputs=[html_info, html_info],
+ show_progress=False,
+ )
+
+ save.click(
+ fn=call_queue.wrap_gradio_call(save_files),
+ _js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
+ inputs=[
+ generation_info,
+ result_gallery,
+ html_info,
+ html_info,
+ ],
+ outputs=[
+ download_files,
+ html_log,
+ ],
+ show_progress=False,
+ )
+
+ save_zip.click(
+ fn=call_queue.wrap_gradio_call(save_files),
+ _js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
+ inputs=[
+ generation_info,
+ result_gallery,
+ html_info,
+ html_info,
+ ],
+ outputs=[
+ download_files,
+ html_log,
+ ]
+ )
+
+ else:
+ html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
+ html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
+ html_log = gr.HTML(elem_id=f'html_log_{tabname}')
+
+ paste_field_names = []
+ if tabname == "txt2img":
+ paste_field_names = modules.scripts.scripts_txt2img.paste_field_names
+ elif tabname == "img2img":
+ paste_field_names = modules.scripts.scripts_img2img.paste_field_names
+
+ for paste_tabname, paste_button in buttons.items():
+ parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+ paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery,
+ paste_field_names=paste_field_names
+ ))
+
+ return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
+
+
+def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+ refresh_components = refresh_component if isinstance(refresh_component, list) else [refresh_component]
+
+ label = None
+ for comp in refresh_components:
+ label = getattr(comp, 'label', None)
+ if label is not None:
+ break
+
+ def refresh():
+ refresh_method()
+ args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+ for k, v in args.items():
+ for comp in refresh_components:
+ setattr(comp, k, v)
+
+ return [gr.update(**(args or {})) for _ in refresh_components] if len(refresh_components) > 1 else gr.update(**(args or {}))
+
+ refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id, tooltip=f"{label}: refresh" if label else "Refresh")
+ refresh_button.click(
+ fn=refresh,
+ inputs=[],
+ outputs=refresh_components
+ )
+ return refresh_button
+
+
+def setup_dialog(button_show, dialog, *, button_close=None):
+ """Sets up the UI so that the dialog (gr.Box) is invisible, and is only shown when buttons_show is clicked, in a fullscreen modal window."""
+
+ dialog.visible = False
+
+ button_show.click(
+ fn=lambda: gr.update(visible=True),
+ inputs=[],
+ outputs=[dialog],
+ ).then(fn=None, _js="function(){ popupId('" + dialog.elem_id + "'); }")
+
+ if button_close:
+ button_close.click(fn=None, _js="closePopup")
+
diff --git a/stable-diffusion-webui/modules/ui_components.py b/stable-diffusion-webui/modules/ui_components.py
new file mode 100644
index 0000000000000000000000000000000000000000..86b26ff436cb62c7a8c5ccef5c36d1f5b437cbaf
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_components.py
@@ -0,0 +1,145 @@
+import gradio as gr
+
+
+class FormComponent:
+ def get_expected_parent(self):
+ return gr.components.Form
+
+
+gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
+
+
+class ToolButton(FormComponent, gr.Button):
+ """Small button with single emoji as text, fits inside gradio forms"""
+
+ def __init__(self, *args, **kwargs):
+ classes = kwargs.pop("elem_classes", [])
+ super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
+
+ def get_block_name(self):
+ return "button"
+
+
+class ResizeHandleRow(gr.Row):
+ """Same as gr.Row but fits inside gradio forms"""
+
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ self.elem_classes.append("resize-handle-row")
+
+ def get_block_name(self):
+ return "row"
+
+
+class FormRow(FormComponent, gr.Row):
+ """Same as gr.Row but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "row"
+
+
+class FormColumn(FormComponent, gr.Column):
+ """Same as gr.Column but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "column"
+
+
+class FormGroup(FormComponent, gr.Group):
+ """Same as gr.Group but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "group"
+
+
+class FormHTML(FormComponent, gr.HTML):
+ """Same as gr.HTML but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "html"
+
+
+class FormColorPicker(FormComponent, gr.ColorPicker):
+ """Same as gr.ColorPicker but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "colorpicker"
+
+
+class DropdownMulti(FormComponent, gr.Dropdown):
+ """Same as gr.Dropdown but always multiselect"""
+ def __init__(self, **kwargs):
+ super().__init__(multiselect=True, **kwargs)
+
+ def get_block_name(self):
+ return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+ """Same as gr.Dropdown but allows editing value"""
+ def __init__(self, **kwargs):
+ super().__init__(allow_custom_value=True, **kwargs)
+
+ def get_block_name(self):
+ return "dropdown"
+
+
+class InputAccordion(gr.Checkbox):
+ """A gr.Accordion that can be used as an input - returns True if open, False if closed.
+
+ Actaully just a hidden checkbox, but creates an accordion that follows and is followed by the state of the checkbox.
+ """
+
+ global_index = 0
+
+ def __init__(self, value, **kwargs):
+ self.accordion_id = kwargs.get('elem_id')
+ if self.accordion_id is None:
+ self.accordion_id = f"input-accordion-{InputAccordion.global_index}"
+ InputAccordion.global_index += 1
+
+ kwargs_checkbox = {
+ **kwargs,
+ "elem_id": f"{self.accordion_id}-checkbox",
+ "visible": False,
+ }
+ super().__init__(value, **kwargs_checkbox)
+
+ self.change(fn=None, _js='function(checked){ inputAccordionChecked("' + self.accordion_id + '", checked); }', inputs=[self])
+
+ kwargs_accordion = {
+ **kwargs,
+ "elem_id": self.accordion_id,
+ "label": kwargs.get('label', 'Accordion'),
+ "elem_classes": ['input-accordion'],
+ "open": value,
+ }
+ self.accordion = gr.Accordion(**kwargs_accordion)
+
+ def extra(self):
+ """Allows you to put something into the label of the accordion.
+
+ Use it like this:
+
+ ```
+ with InputAccordion(False, label="Accordion") as acc:
+ with acc.extra():
+ FormHTML(value="hello", min_width=0)
+
+ ...
+ ```
+ """
+
+ return gr.Column(elem_id=self.accordion_id + '-extra', elem_classes='input-accordion-extra', min_width=0)
+
+ def __enter__(self):
+ self.accordion.__enter__()
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ self.accordion.__exit__(exc_type, exc_val, exc_tb)
+
+ def get_block_name(self):
+ return "checkbox"
+
diff --git a/stable-diffusion-webui/modules/ui_extensions.py b/stable-diffusion-webui/modules/ui_extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4b98a0ea6af5314faafd232deaf834994a5b242
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_extensions.py
@@ -0,0 +1,669 @@
+import json
+import os
+import threading
+import time
+from datetime import datetime, timezone
+
+import git
+
+import gradio as gr
+import html
+import shutil
+import errno
+
+from modules import extensions, shared, paths, config_states, errors, restart
+from modules.paths_internal import config_states_dir
+from modules.call_queue import wrap_gradio_gpu_call
+
+available_extensions = {"extensions": []}
+STYLE_PRIMARY = ' style="color: var(--primary-400)"'
+
+
+def check_access():
+ assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
+
+
+def apply_and_restart(disable_list, update_list, disable_all):
+ check_access()
+
+ disabled = json.loads(disable_list)
+ assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+
+ update = json.loads(update_list)
+ assert type(update) == list, f"wrong update_list data for apply_and_restart: {update_list}"
+
+ if update:
+ save_config_state("Backup (pre-update)")
+
+ update = set(update)
+
+ for ext in extensions.extensions:
+ if ext.name not in update:
+ continue
+
+ try:
+ ext.fetch_and_reset_hard()
+ except Exception:
+ errors.report(f"Error getting updates for {ext.name}", exc_info=True)
+
+ shared.opts.disabled_extensions = disabled
+ shared.opts.disable_all_extensions = disable_all
+ shared.opts.save(shared.config_filename)
+
+ if restart.is_restartable():
+ restart.restart_program()
+ else:
+ restart.stop_program()
+
+
+def save_config_state(name):
+ current_config_state = config_states.get_config()
+ if not name:
+ name = "Config"
+ current_config_state["name"] = name
+ timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S')
+ filename = os.path.join(config_states_dir, f"{timestamp}_{name}.json")
+ print(f"Saving backup of webui/extension state to {filename}.")
+ with open(filename, "w", encoding="utf-8") as f:
+ json.dump(current_config_state, f, indent=4)
+ config_states.list_config_states()
+ new_value = next(iter(config_states.all_config_states.keys()), "Current")
+ new_choices = ["Current"] + list(config_states.all_config_states.keys())
+ return gr.Dropdown.update(value=new_value, choices=new_choices), f"<span>Saved current webui/extension state to \"{filename}\"</span>"
+
+
+def restore_config_state(confirmed, config_state_name, restore_type):
+ if config_state_name == "Current":
+ return "<span>Select a config to restore from.</span>"
+ if not confirmed:
+ return "<span>Cancelled.</span>"
+
+ check_access()
+
+ config_state = config_states.all_config_states[config_state_name]
+
+ print(f"*** Restoring webui state from backup: {restore_type} ***")
+
+ if restore_type == "extensions" or restore_type == "both":
+ shared.opts.restore_config_state_file = config_state["filepath"]
+ shared.opts.save(shared.config_filename)
+
+ if restore_type == "webui" or restore_type == "both":
+ config_states.restore_webui_config(config_state)
+
+ shared.state.request_restart()
+
+ return ""
+
+
+def check_updates(id_task, disable_list):
+ check_access()
+
+ disabled = json.loads(disable_list)
+ assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+
+ exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
+ shared.state.job_count = len(exts)
+
+ for ext in exts:
+ shared.state.textinfo = ext.name
+
+ try:
+ ext.check_updates()
+ except FileNotFoundError as e:
+ if 'FETCH_HEAD' not in str(e):
+ raise
+ except Exception:
+ errors.report(f"Error checking updates for {ext.name}", exc_info=True)
+
+ shared.state.nextjob()
+
+ return extension_table(), ""
+
+
+def make_commit_link(commit_hash, remote, text=None):
+ if text is None:
+ text = commit_hash[:8]
+ if remote.startswith("https://github.com/"):
+ if remote.endswith(".git"):
+ remote = remote[:-4]
+ href = remote + "/commit/" + commit_hash
+ return f'<a href="{href}" target="_blank">{text}</a>'
+ else:
+ return text
+
+
+def extension_table():
+ code = f"""<!-- {time.time()} -->
+ <table id="extensions">
+ <thead>
+ <tr>
+ <th>
+ <input class="gr-check-radio gr-checkbox all_extensions_toggle" type="checkbox" {'checked="checked"' if all(ext.enabled for ext in extensions.extensions) else ''} onchange="toggle_all_extensions(event)" />
+ <abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr>
+ </th>
+ <th>URL</th>
+ <th>Branch</th>
+ <th>Version</th>
+ <th>Date</th>
+ <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
+ </tr>
+ </thead>
+ <tbody>
+ """
+
+ for ext in extensions.extensions:
+ ext: extensions.Extension
+ ext.read_info_from_repo()
+
+ remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
+
+ if ext.can_update:
+ ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
+ else:
+ ext_status = ext.status
+
+ style = ""
+ if shared.cmd_opts.disable_extra_extensions and not ext.is_builtin or shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all":
+ style = STYLE_PRIMARY
+
+ version_link = ext.version
+ if ext.commit_hash and ext.remote:
+ version_link = make_commit_link(ext.commit_hash, ext.remote, ext.version)
+
+ code += f"""
+ <tr>
+ <td><label{style}><input class="gr-check-radio gr-checkbox extension_toggle" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''} onchange="toggle_extension(event)" />{html.escape(ext.name)}</label></td>
+ <td>{remote}</td>
+ <td>{ext.branch}</td>
+ <td>{version_link}</td>
+ <td>{datetime.fromtimestamp(ext.commit_date) if ext.commit_date else ""}</td>
+ <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
+ </tr>
+ """
+
+ code += """
+ </tbody>
+ </table>
+ """
+
+ return code
+
+
+def update_config_states_table(state_name):
+ if state_name == "Current":
+ config_state = config_states.get_config()
+ else:
+ config_state = config_states.all_config_states[state_name]
+
+ config_name = config_state.get("name", "Config")
+ created_date = time.asctime(time.gmtime(config_state["created_at"]))
+ filepath = config_state.get("filepath", "<unknown>")
+
+ try:
+ webui_remote = config_state["webui"]["remote"] or ""
+ webui_branch = config_state["webui"]["branch"]
+ webui_commit_hash = config_state["webui"]["commit_hash"] or "<unknown>"
+ webui_commit_date = config_state["webui"]["commit_date"]
+ if webui_commit_date:
+ webui_commit_date = time.asctime(time.gmtime(webui_commit_date))
+ else:
+ webui_commit_date = "<unknown>"
+
+ remote = f"""<a href="{html.escape(webui_remote)}" target="_blank">{html.escape(webui_remote or '')}</a>"""
+ commit_link = make_commit_link(webui_commit_hash, webui_remote)
+ date_link = make_commit_link(webui_commit_hash, webui_remote, webui_commit_date)
+
+ current_webui = config_states.get_webui_config()
+
+ style_remote = ""
+ style_branch = ""
+ style_commit = ""
+ if current_webui["remote"] != webui_remote:
+ style_remote = STYLE_PRIMARY
+ if current_webui["branch"] != webui_branch:
+ style_branch = STYLE_PRIMARY
+ if current_webui["commit_hash"] != webui_commit_hash:
+ style_commit = STYLE_PRIMARY
+
+ code = f"""<!-- {time.time()} -->
+<h2>Config Backup: {config_name}</h2>
+<div><b>Filepath:</b> {filepath}</div>
+<div><b>Created at:</b> {created_date}</div>
+<h2>WebUI State</h2>
+<table id="config_state_webui">
+ <thead>
+ <tr>
+ <th>URL</th>
+ <th>Branch</th>
+ <th>Commit</th>
+ <th>Date</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td>
+ <label{style_remote}>{remote}</label>
+ </td>
+ <td>
+ <label{style_branch}>{webui_branch}</label>
+ </td>
+ <td>
+ <label{style_commit}>{commit_link}</label>
+ </td>
+ <td>
+ <label{style_commit}>{date_link}</label>
+ </td>
+ </tr>
+ </tbody>
+</table>
+<h2>Extension State</h2>
+<table id="config_state_extensions">
+ <thead>
+ <tr>
+ <th>Extension</th>
+ <th>URL</th>
+ <th>Branch</th>
+ <th>Commit</th>
+ <th>Date</th>
+ </tr>
+ </thead>
+ <tbody>
+"""
+
+ ext_map = {ext.name: ext for ext in extensions.extensions}
+
+ for ext_name, ext_conf in config_state["extensions"].items():
+ ext_remote = ext_conf["remote"] or ""
+ ext_branch = ext_conf["branch"] or "<unknown>"
+ ext_enabled = ext_conf["enabled"]
+ ext_commit_hash = ext_conf["commit_hash"] or "<unknown>"
+ ext_commit_date = ext_conf["commit_date"]
+ if ext_commit_date:
+ ext_commit_date = time.asctime(time.gmtime(ext_commit_date))
+ else:
+ ext_commit_date = "<unknown>"
+
+ remote = f"""<a href="{html.escape(ext_remote)}" target="_blank">{html.escape(ext_remote or '')}</a>"""
+ commit_link = make_commit_link(ext_commit_hash, ext_remote)
+ date_link = make_commit_link(ext_commit_hash, ext_remote, ext_commit_date)
+
+ style_enabled = ""
+ style_remote = ""
+ style_branch = ""
+ style_commit = ""
+ if ext_name in ext_map:
+ current_ext = ext_map[ext_name]
+ current_ext.read_info_from_repo()
+ if current_ext.enabled != ext_enabled:
+ style_enabled = STYLE_PRIMARY
+ if current_ext.remote != ext_remote:
+ style_remote = STYLE_PRIMARY
+ if current_ext.branch != ext_branch:
+ style_branch = STYLE_PRIMARY
+ if current_ext.commit_hash != ext_commit_hash:
+ style_commit = STYLE_PRIMARY
+
+ code += f""" <tr>
+ <td><label{style_enabled}><input class="gr-check-radio gr-checkbox" type="checkbox" disabled="true" {'checked="checked"' if ext_enabled else ''}>{html.escape(ext_name)}</label></td>
+ <td><label{style_remote}>{remote}</label></td>
+ <td><label{style_branch}>{ext_branch}</label></td>
+ <td><label{style_commit}>{commit_link}</label></td>
+ <td><label{style_commit}>{date_link}</label></td>
+ </tr>
+"""
+
+ code += """ </tbody>
+</table>"""
+
+ except Exception as e:
+ print(f"[ERROR]: Config states {filepath}, {e}")
+ code = f"""<!-- {time.time()} -->
+<h2>Config Backup: {config_name}</h2>
+<div><b>Filepath:</b> {filepath}</div>
+<div><b>Created at:</b> {created_date}</div>
+<h2>This file is corrupted</h2>"""
+
+ return code
+
+
+def normalize_git_url(url):
+ if url is None:
+ return ""
+
+ url = url.replace(".git", "")
+ return url
+
+
+def install_extension_from_url(dirname, url, branch_name=None):
+ check_access()
+
+ if isinstance(dirname, str):
+ dirname = dirname.strip()
+ if isinstance(url, str):
+ url = url.strip()
+
+ assert url, 'No URL specified'
+
+ if dirname is None or dirname == "":
+ *parts, last_part = url.split('/')
+ last_part = normalize_git_url(last_part)
+
+ dirname = last_part
+
+ target_dir = os.path.join(extensions.extensions_dir, dirname)
+ assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
+
+ normalized_url = normalize_git_url(url)
+ if any(x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url):
+ raise Exception(f'Extension with this URL is already installed: {url}')
+
+ tmpdir = os.path.join(paths.data_path, "tmp", dirname)
+
+ try:
+ shutil.rmtree(tmpdir, True)
+ if not branch_name:
+ # if no branch is specified, use the default branch
+ with git.Repo.clone_from(url, tmpdir, filter=['blob:none']) as repo:
+ repo.remote().fetch()
+ for submodule in repo.submodules:
+ submodule.update()
+ else:
+ with git.Repo.clone_from(url, tmpdir, filter=['blob:none'], branch=branch_name) as repo:
+ repo.remote().fetch()
+ for submodule in repo.submodules:
+ submodule.update()
+ try:
+ os.rename(tmpdir, target_dir)
+ except OSError as err:
+ if err.errno == errno.EXDEV:
+ # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
+ # Since we can't use a rename, do the slower but more versitile shutil.move()
+ shutil.move(tmpdir, target_dir)
+ else:
+ # Something else, not enough free space, permissions, etc. rethrow it so that it gets handled.
+ raise err
+
+ import launch
+ launch.run_extension_installer(target_dir)
+
+ extensions.list_extensions()
+ return [extension_table(), html.escape(f"Installed into {target_dir}. Use Installed tab to restart.")]
+ finally:
+ shutil.rmtree(tmpdir, True)
+
+
+def install_extension_from_index(url, hide_tags, sort_column, filter_text):
+ ext_table, message = install_extension_from_url(None, url)
+
+ code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
+
+ return code, ext_table, message, ''
+
+
+def refresh_available_extensions(url, hide_tags, sort_column):
+ global available_extensions
+
+ import urllib.request
+ with urllib.request.urlopen(url) as response:
+ text = response.read()
+
+ available_extensions = json.loads(text)
+
+ code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)
+
+ return url, code, gr.CheckboxGroup.update(choices=tags), '', ''
+
+
+def refresh_available_extensions_for_tags(hide_tags, sort_column, filter_text):
+ code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
+
+ return code, ''
+
+
+def search_extensions(filter_text, hide_tags, sort_column):
+ code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
+
+ return code, ''
+
+
+sort_ordering = [
+ # (reverse, order_by_function)
+ (True, lambda x: x.get('added', 'z')),
+ (False, lambda x: x.get('added', 'z')),
+ (False, lambda x: x.get('name', 'z')),
+ (True, lambda x: x.get('name', 'z')),
+ (False, lambda x: 'z'),
+ (True, lambda x: x.get('commit_time', '')),
+ (True, lambda x: x.get('created_at', '')),
+ (True, lambda x: x.get('stars', 0)),
+]
+
+
+def get_date(info: dict, key):
+ try:
+ return datetime.strptime(info.get(key), "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc).astimezone().strftime("%Y-%m-%d")
+ except (ValueError, TypeError):
+ return ''
+
+
+def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
+ extlist = available_extensions["extensions"]
+ installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
+
+ tags = available_extensions.get("tags", {})
+ tags_to_hide = set(hide_tags)
+ hidden = 0
+
+ code = f"""<!-- {time.time()} -->
+ <table id="available_extensions">
+ <thead>
+ <tr>
+ <th>Extension</th>
+ <th>Description</th>
+ <th>Action</th>
+ </tr>
+ </thead>
+ <tbody>
+ """
+
+ sort_reverse, sort_function = sort_ordering[sort_column if 0 <= sort_column < len(sort_ordering) else 0]
+
+ for ext in sorted(extlist, key=sort_function, reverse=sort_reverse):
+ name = ext.get("name", "noname")
+ stars = int(ext.get("stars", 0))
+ added = ext.get('added', 'unknown')
+ update_time = get_date(ext, 'commit_time')
+ create_time = get_date(ext, 'created_at')
+ url = ext.get("url", None)
+ description = ext.get("description", "")
+ extension_tags = ext.get("tags", [])
+
+ if url is None:
+ continue
+
+ existing = installed_extension_urls.get(normalize_git_url(url), None)
+ extension_tags = extension_tags + ["installed"] if existing else extension_tags
+
+ if any(x for x in extension_tags if x in tags_to_hide):
+ hidden += 1
+ continue
+
+ if filter_text and filter_text.strip():
+ if filter_text.lower() not in html.escape(name).lower() and filter_text.lower() not in html.escape(description).lower():
+ hidden += 1
+ continue
+
+ install_code = f"""<button onclick="install_extension_from_index(this, '{html.escape(url)}')" {"disabled=disabled" if existing else ""} class="lg secondary gradio-button custom-button">{"Install" if not existing else "Installed"}</button>"""
+
+ tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
+
+ code += f"""
+ <tr>
+ <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
+ <td>{html.escape(description)}<p class="info">
+ <span class="date_added">Update: {html.escape(update_time)} Added: {html.escape(added)} Created: {html.escape(create_time)}</span><span class="star_count">stars: <b>{stars}</b></a></p></td>
+ <td>{install_code}</td>
+ </tr>
+
+ """
+
+ for tag in [x for x in extension_tags if x not in tags]:
+ tags[tag] = tag
+
+ code += """
+ </tbody>
+ </table>
+ """
+
+ if hidden > 0:
+ code += f"<p>Extension hidden: {hidden}</p>"
+
+ return code, list(tags)
+
+
+def preload_extensions_git_metadata():
+ for extension in extensions.extensions:
+ extension.read_info_from_repo()
+
+
+def create_ui():
+ import modules.ui
+
+ config_states.list_config_states()
+
+ threading.Thread(target=preload_extensions_git_metadata).start()
+
+ with gr.Blocks(analytics_enabled=False) as ui:
+ with gr.Tabs(elem_id="tabs_extensions"):
+ with gr.TabItem("Installed", id="installed"):
+
+ with gr.Row(elem_id="extensions_installed_top"):
+ apply_label = ("Apply and restart UI" if restart.is_restartable() else "Apply and quit")
+ apply = gr.Button(value=apply_label, variant="primary")
+ check = gr.Button(value="Check for updates")
+ extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
+ extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False, container=False)
+ extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False, container=False)
+
+ html = ""
+
+ if shared.cmd_opts.disable_all_extensions or shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions != "none":
+ if shared.cmd_opts.disable_all_extensions:
+ msg = '"--disable-all-extensions" was used, remove it to load all extensions again'
+ elif shared.opts.disable_all_extensions != "none":
+ msg = '"Disable all extensions" was set, change it to "none" to load all extensions again'
+ elif shared.cmd_opts.disable_extra_extensions:
+ msg = '"--disable-extra-extensions" was used, remove it to load all extensions again'
+ html = f'<span style="color: var(--primary-400);">{msg}</span>'
+
+ with gr.Row():
+ info = gr.HTML(html)
+
+ with gr.Row(elem_classes="progress-container"):
+ extensions_table = gr.HTML('Loading...', elem_id="extensions_installed_html")
+
+ ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
+
+ apply.click(
+ fn=apply_and_restart,
+ _js="extensions_apply",
+ inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all],
+ outputs=[],
+ )
+
+ check.click(
+ fn=wrap_gradio_gpu_call(check_updates, extra_outputs=[gr.update()]),
+ _js="extensions_check",
+ inputs=[info, extensions_disabled_list],
+ outputs=[extensions_table, info],
+ )
+
+ with gr.TabItem("Available", id="available"):
+ with gr.Row():
+ refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
+ extensions_index_url = os.environ.get('WEBUI_EXTENSIONS_INDEX', "https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json")
+ available_extensions_index = gr.Text(value=extensions_index_url, label="Extension index URL", container=False)
+ extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
+ install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
+
+ with gr.Row():
+ hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
+ sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
+
+ with gr.Row():
+ search_extensions_text = gr.Text(label="Search", container=False)
+
+ install_result = gr.HTML()
+ available_extensions_table = gr.HTML()
+
+ refresh_available_extensions_button.click(
+ fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]),
+ inputs=[available_extensions_index, hide_tags, sort_column],
+ outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result],
+ )
+
+ install_extension_button.click(
+ fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
+ inputs=[extension_to_install, hide_tags, sort_column, search_extensions_text],
+ outputs=[available_extensions_table, extensions_table, install_result],
+ )
+
+ search_extensions_text.change(
+ fn=modules.ui.wrap_gradio_call(search_extensions, extra_outputs=[gr.update()]),
+ inputs=[search_extensions_text, hide_tags, sort_column],
+ outputs=[available_extensions_table, install_result],
+ )
+
+ hide_tags.change(
+ fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+ inputs=[hide_tags, sort_column, search_extensions_text],
+ outputs=[available_extensions_table, install_result]
+ )
+
+ sort_column.change(
+ fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+ inputs=[hide_tags, sort_column, search_extensions_text],
+ outputs=[available_extensions_table, install_result]
+ )
+
+ with gr.TabItem("Install from URL", id="install_from_url"):
+ install_url = gr.Text(label="URL for extension's git repository")
+ install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch")
+ install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
+ install_button = gr.Button(value="Install", variant="primary")
+ install_result = gr.HTML(elem_id="extension_install_result")
+
+ install_button.click(
+ fn=modules.ui.wrap_gradio_call(lambda *args: [gr.update(), *install_extension_from_url(*args)], extra_outputs=[gr.update(), gr.update()]),
+ inputs=[install_dirname, install_url, install_branch],
+ outputs=[install_url, extensions_table, install_result],
+ )
+
+ with gr.TabItem("Backup/Restore"):
+ with gr.Row(elem_id="extensions_backup_top_row"):
+ config_states_list = gr.Dropdown(label="Saved Configs", elem_id="extension_backup_saved_configs", value="Current", choices=["Current"] + list(config_states.all_config_states.keys()))
+ modules.ui.create_refresh_button(config_states_list, config_states.list_config_states, lambda: {"choices": ["Current"] + list(config_states.all_config_states.keys())}, "refresh_config_states")
+ config_restore_type = gr.Radio(label="State to restore", choices=["extensions", "webui", "both"], value="extensions", elem_id="extension_backup_restore_type")
+ config_restore_button = gr.Button(value="Restore Selected Config", variant="primary", elem_id="extension_backup_restore")
+ with gr.Row(elem_id="extensions_backup_top_row2"):
+ config_save_name = gr.Textbox("", placeholder="Config Name", show_label=False)
+ config_save_button = gr.Button(value="Save Current Config")
+
+ config_states_info = gr.HTML("")
+ config_states_table = gr.HTML("Loading...")
+ ui.load(fn=update_config_states_table, inputs=[config_states_list], outputs=[config_states_table])
+
+ config_save_button.click(fn=save_config_state, inputs=[config_save_name], outputs=[config_states_list, config_states_info])
+
+ dummy_component = gr.Label(visible=False)
+ config_restore_button.click(fn=restore_config_state, _js="config_state_confirm_restore", inputs=[dummy_component, config_states_list, config_restore_type], outputs=[config_states_info])
+
+ config_states_list.change(
+ fn=update_config_states_table,
+ inputs=[config_states_list],
+ outputs=[config_states_table],
+ )
+
+
+ return ui
diff --git a/stable-diffusion-webui/modules/ui_extra_networks.py b/stable-diffusion-webui/modules/ui_extra_networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..e919d105b18fe2c4c19d20f9e26a69dd173c9b94
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_extra_networks.py
@@ -0,0 +1,455 @@
+import os.path
+import urllib.parse
+from pathlib import Path
+
+from modules import shared, ui_extra_networks_user_metadata, errors, extra_networks
+from modules.images import read_info_from_image, save_image_with_geninfo
+import gradio as gr
+import json
+import html
+from fastapi.exceptions import HTTPException
+
+from modules.generation_parameters_copypaste import image_from_url_text
+from modules.ui_components import ToolButton
+
+extra_pages = []
+allowed_dirs = set()
+
+
+def register_page(page):
+ """registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
+
+ extra_pages.append(page)
+ allowed_dirs.clear()
+ allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
+
+
+def fetch_file(filename: str = ""):
+ from starlette.responses import FileResponse
+
+ if not os.path.isfile(filename):
+ raise HTTPException(status_code=404, detail="File not found")
+
+ if not any(Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs):
+ raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+
+ ext = os.path.splitext(filename)[1].lower()
+ if ext not in (".png", ".jpg", ".jpeg", ".webp", ".gif"):
+ raise ValueError(f"File cannot be fetched: {filename}. Only png, jpg, webp, and gif.")
+
+ # would profit from returning 304
+ return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+
+
+def get_metadata(page: str = "", item: str = ""):
+ from starlette.responses import JSONResponse
+
+ page = next(iter([x for x in extra_pages if x.name == page]), None)
+ if page is None:
+ return JSONResponse({})
+
+ metadata = page.metadata.get(item)
+ if metadata is None:
+ return JSONResponse({})
+
+ return JSONResponse({"metadata": json.dumps(metadata, indent=4, ensure_ascii=False)})
+
+
+def get_single_card(page: str = "", tabname: str = "", name: str = ""):
+ from starlette.responses import JSONResponse
+
+ page = next(iter([x for x in extra_pages if x.name == page]), None)
+
+ try:
+ item = page.create_item(name, enable_filter=False)
+ page.items[name] = item
+ except Exception as e:
+ errors.display(e, "creating item for extra network")
+ item = page.items.get(name)
+
+ page.read_user_metadata(item)
+ item_html = page.create_html_for_item(item, tabname)
+
+ return JSONResponse({"html": item_html})
+
+
+def add_pages_to_demo(app):
+ app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
+ app.add_api_route("/sd_extra_networks/metadata", get_metadata, methods=["GET"])
+ app.add_api_route("/sd_extra_networks/get-single-card", get_single_card, methods=["GET"])
+
+
+def quote_js(s):
+ s = s.replace('\\', '\\\\')
+ s = s.replace('"', '\\"')
+ return f'"{s}"'
+
+
+class ExtraNetworksPage:
+ def __init__(self, title):
+ self.title = title
+ self.name = title.lower()
+ self.id_page = self.name.replace(" ", "_")
+ self.card_page = shared.html("extra-networks-card.html")
+ self.allow_negative_prompt = False
+ self.metadata = {}
+ self.items = {}
+
+ def refresh(self):
+ pass
+
+ def read_user_metadata(self, item):
+ filename = item.get("filename", None)
+ metadata = extra_networks.get_user_metadata(filename)
+
+ desc = metadata.get("description", None)
+ if desc is not None:
+ item["description"] = desc
+
+ item["user_metadata"] = metadata
+
+ def link_preview(self, filename):
+ quoted_filename = urllib.parse.quote(filename.replace('\\', '/'))
+ mtime = os.path.getmtime(filename)
+ return f"./sd_extra_networks/thumb?filename={quoted_filename}&mtime={mtime}"
+
+ def search_terms_from_path(self, filename, possible_directories=None):
+ abspath = os.path.abspath(filename)
+
+ for parentdir in (possible_directories if possible_directories is not None else self.allowed_directories_for_previews()):
+ parentdir = os.path.abspath(parentdir)
+ if abspath.startswith(parentdir):
+ return abspath[len(parentdir):].replace('\\', '/')
+
+ return ""
+
+ def create_html(self, tabname):
+ items_html = ''
+
+ self.metadata = {}
+
+ subdirs = {}
+ for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
+ for root, dirs, _ in sorted(os.walk(parentdir, followlinks=True), key=lambda x: shared.natural_sort_key(x[0])):
+ for dirname in sorted(dirs, key=shared.natural_sort_key):
+ x = os.path.join(root, dirname)
+
+ if not os.path.isdir(x):
+ continue
+
+ subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
+ while subdir.startswith("/"):
+ subdir = subdir[1:]
+
+ is_empty = len(os.listdir(x)) == 0
+ if not is_empty and not subdir.endswith("/"):
+ subdir = subdir + "/"
+
+ if ("/." in subdir or subdir.startswith(".")) and not shared.opts.extra_networks_show_hidden_directories:
+ continue
+
+ subdirs[subdir] = 1
+
+ if subdirs:
+ subdirs = {"": 1, **subdirs}
+
+ subdirs_html = "".join([f"""
+<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_search", event)'>
+{html.escape(subdir if subdir!="" else "all")}
+</button>
+""" for subdir in subdirs])
+
+ self.items = {x["name"]: x for x in self.list_items()}
+ for item in self.items.values():
+ metadata = item.get("metadata")
+ if metadata:
+ self.metadata[item["name"]] = metadata
+
+ if "user_metadata" not in item:
+ self.read_user_metadata(item)
+
+ items_html += self.create_html_for_item(item, tabname)
+
+ if items_html == '':
+ dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
+ items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
+
+ self_name_id = self.name.replace(" ", "_")
+
+ res = f"""
+<div id='{tabname}_{self_name_id}_subdirs' class='extra-network-subdirs extra-network-subdirs-cards'>
+{subdirs_html}
+</div>
+<div id='{tabname}_{self_name_id}_cards' class='extra-network-cards'>
+{items_html}
+</div>
+"""
+
+ return res
+
+ def create_item(self, name, index=None):
+ raise NotImplementedError()
+
+ def list_items(self):
+ raise NotImplementedError()
+
+ def allowed_directories_for_previews(self):
+ return []
+
+ def create_html_for_item(self, item, tabname):
+ """
+ Create HTML for card item in tab tabname; can return empty string if the item is not meant to be shown.
+ """
+
+ preview = item.get("preview", None)
+
+ onclick = item.get("onclick", None)
+ if onclick is None:
+ onclick = '"' + html.escape(f"""return cardClicked({quote_js(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
+
+ height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
+ width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
+ background_image = f'<img src="{html.escape(preview)}" class="preview" loading="lazy">' if preview else ''
+ metadata_button = ""
+ metadata = item.get("metadata")
+ if metadata:
+ metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(item['name'])})'></div>"
+
+ edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(item['name'])})'></div>"
+
+ local_path = ""
+ filename = item.get("filename", "")
+ for reldir in self.allowed_directories_for_previews():
+ absdir = os.path.abspath(reldir)
+
+ if filename.startswith(absdir):
+ local_path = filename[len(absdir):]
+
+ # if this is true, the item must not be shown in the default view, and must instead only be
+ # shown when searching for it
+ if shared.opts.extra_networks_hidden_models == "Always":
+ search_only = False
+ else:
+ search_only = "/." in local_path or "\\." in local_path
+
+ if search_only and shared.opts.extra_networks_hidden_models == "Never":
+ return ""
+
+ sort_keys = " ".join([html.escape(f'data-sort-{k}={v}') for k, v in item.get("sort_keys", {}).items()]).strip()
+
+ args = {
+ "background_image": background_image,
+ "style": f"'display: none; {height}{width}; font-size: {shared.opts.extra_networks_card_text_scale*100}%'",
+ "prompt": item.get("prompt", None),
+ "tabname": quote_js(tabname),
+ "local_preview": quote_js(item["local_preview"]),
+ "name": html.escape(item["name"]),
+ "description": (item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
+ "card_clicked": onclick,
+ "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {quote_js(tabname)}, {quote_js(item["local_preview"])})""") + '"',
+ "search_term": item.get("search_term", ""),
+ "metadata_button": metadata_button,
+ "edit_button": edit_button,
+ "search_only": " search_only" if search_only else "",
+ "sort_keys": sort_keys,
+ }
+
+ return self.card_page.format(**args)
+
+ def get_sort_keys(self, path):
+ """
+ List of default keys used for sorting in the UI.
+ """
+ pth = Path(path)
+ stat = pth.stat()
+ return {
+ "date_created": int(stat.st_ctime or 0),
+ "date_modified": int(stat.st_mtime or 0),
+ "name": pth.name.lower(),
+ }
+
+ def find_preview(self, path):
+ """
+ Find a preview PNG for a given path (without extension) and call link_preview on it.
+ """
+
+ preview_extensions = ["png", "jpg", "jpeg", "webp"]
+ if shared.opts.samples_format not in preview_extensions:
+ preview_extensions.append(shared.opts.samples_format)
+
+ potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in preview_extensions], [])
+
+ for file in potential_files:
+ if os.path.isfile(file):
+ return self.link_preview(file)
+
+ return None
+
+ def find_description(self, path):
+ """
+ Find and read a description file for a given path (without extension).
+ """
+ for file in [f"{path}.txt", f"{path}.description.txt"]:
+ try:
+ with open(file, "r", encoding="utf-8", errors="replace") as f:
+ return f.read()
+ except OSError:
+ pass
+ return None
+
+ def create_user_metadata_editor(self, ui, tabname):
+ return ui_extra_networks_user_metadata.UserMetadataEditor(ui, tabname, self)
+
+
+def initialize():
+ extra_pages.clear()
+
+
+def register_default_pages():
+ from modules.ui_extra_networks_textual_inversion import ExtraNetworksPageTextualInversion
+ from modules.ui_extra_networks_hypernets import ExtraNetworksPageHypernetworks
+ from modules.ui_extra_networks_checkpoints import ExtraNetworksPageCheckpoints
+ register_page(ExtraNetworksPageTextualInversion())
+ register_page(ExtraNetworksPageHypernetworks())
+ register_page(ExtraNetworksPageCheckpoints())
+
+
+class ExtraNetworksUi:
+ def __init__(self):
+ self.pages = None
+ """gradio HTML components related to extra networks' pages"""
+
+ self.page_contents = None
+ """HTML content of the above; empty initially, filled when extra pages have to be shown"""
+
+ self.stored_extra_pages = None
+
+ self.button_save_preview = None
+ self.preview_target_filename = None
+
+ self.tabname = None
+
+
+def pages_in_preferred_order(pages):
+ tab_order = [x.lower().strip() for x in shared.opts.ui_extra_networks_tab_reorder.split(",")]
+
+ def tab_name_score(name):
+ name = name.lower()
+ for i, possible_match in enumerate(tab_order):
+ if possible_match in name:
+ return i
+
+ return len(pages)
+
+ tab_scores = {page.name: (tab_name_score(page.name), original_index) for original_index, page in enumerate(pages)}
+
+ return sorted(pages, key=lambda x: tab_scores[x.name])
+
+
+def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
+ from modules.ui import switch_values_symbol
+
+ ui = ExtraNetworksUi()
+ ui.pages = []
+ ui.pages_contents = []
+ ui.user_metadata_editors = []
+ ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
+ ui.tabname = tabname
+
+ related_tabs = []
+
+ for page in ui.stored_extra_pages:
+ with gr.Tab(page.title, id=page.id_page) as tab:
+ elem_id = f"{tabname}_{page.id_page}_cards_html"
+ page_elem = gr.HTML('Loading...', elem_id=elem_id)
+ ui.pages.append(page_elem)
+
+ page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + quote_js(tabname) + '); return []}', inputs=[], outputs=[])
+
+ editor = page.create_user_metadata_editor(ui, tabname)
+ editor.create_ui()
+ ui.user_metadata_editors.append(editor)
+
+ related_tabs.append(tab)
+
+ edit_search = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", elem_classes="search", placeholder="Search...", visible=False, interactive=True)
+ dropdown_sort = gr.Dropdown(choices=['Default Sort', 'Date Created', 'Date Modified', 'Name'], value='Default Sort', elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
+ button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes="sortorder", visible=False)
+ button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
+ checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
+
+ ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
+ ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
+
+ for tab in unrelated_tabs:
+ tab.select(fn=lambda: [gr.update(visible=False) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
+
+ for tab in related_tabs:
+ tab.select(fn=lambda: [gr.update(visible=True) for _ in range(5)], inputs=[], outputs=[edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs], show_progress=False)
+
+ def pages_html():
+ if not ui.pages_contents:
+ return refresh()
+
+ return ui.pages_contents
+
+ def refresh():
+ for pg in ui.stored_extra_pages:
+ pg.refresh()
+
+ ui.pages_contents = [pg.create_html(ui.tabname) for pg in ui.stored_extra_pages]
+
+ return ui.pages_contents
+
+ interface.load(fn=pages_html, inputs=[], outputs=[*ui.pages])
+ button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
+
+ return ui
+
+
+def path_is_parent(parent_path, child_path):
+ parent_path = os.path.abspath(parent_path)
+ child_path = os.path.abspath(child_path)
+
+ return child_path.startswith(parent_path)
+
+
+def setup_ui(ui, gallery):
+ def save_preview(index, images, filename):
+ # this function is here for backwards compatibility and likely will be removed soon
+
+ if len(images) == 0:
+ print("There is no image in gallery to save as a preview.")
+ return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
+
+ index = int(index)
+ index = 0 if index < 0 else index
+ index = len(images) - 1 if index >= len(images) else index
+
+ img_info = images[index if index >= 0 else 0]
+ image = image_from_url_text(img_info)
+ geninfo, items = read_info_from_image(image)
+
+ is_allowed = False
+ for extra_page in ui.stored_extra_pages:
+ if any(path_is_parent(x, filename) for x in extra_page.allowed_directories_for_previews()):
+ is_allowed = True
+ break
+
+ assert is_allowed, f'writing to {filename} is not allowed'
+
+ save_image_with_geninfo(image, geninfo, filename)
+
+ return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
+
+ ui.button_save_preview.click(
+ fn=save_preview,
+ _js="function(x, y, z){return [selected_gallery_index(), y, z]}",
+ inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename],
+ outputs=[*ui.pages]
+ )
+
+ for editor in ui.user_metadata_editors:
+ editor.setup_ui(gallery)
+
+
diff --git a/stable-diffusion-webui/modules/ui_extra_networks_checkpoints.py b/stable-diffusion-webui/modules/ui_extra_networks_checkpoints.py
new file mode 100644
index 0000000000000000000000000000000000000000..418e359f30ad76c053ee66ae655c725a55fb01e5
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_extra_networks_checkpoints.py
@@ -0,0 +1,41 @@
+import html
+import os
+
+from modules import shared, ui_extra_networks, sd_models
+from modules.ui_extra_networks import quote_js
+from modules.ui_extra_networks_checkpoints_user_metadata import CheckpointUserMetadataEditor
+
+
+class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Checkpoints')
+
+ def refresh(self):
+ shared.refresh_checkpoints()
+
+ def create_item(self, name, index=None, enable_filter=True):
+ checkpoint: sd_models.CheckpointInfo = sd_models.checkpoint_aliases.get(name)
+ path, ext = os.path.splitext(checkpoint.filename)
+ return {
+ "name": checkpoint.name_for_extra,
+ "filename": checkpoint.filename,
+ "shorthash": checkpoint.shorthash,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
+ "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
+ "onclick": '"' + html.escape(f"""return selectCheckpoint({quote_js(name)})""") + '"',
+ "local_preview": f"{path}.{shared.opts.samples_format}",
+ "metadata": checkpoint.metadata,
+ "sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
+ }
+
+ def list_items(self):
+ names = list(sd_models.checkpoints_list)
+ for index, name in enumerate(names):
+ yield self.create_item(name, index)
+
+ def allowed_directories_for_previews(self):
+ return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None]
+
+ def create_user_metadata_editor(self, ui, tabname):
+ return CheckpointUserMetadataEditor(ui, tabname, self)
diff --git a/stable-diffusion-webui/modules/ui_extra_networks_checkpoints_user_metadata.py b/stable-diffusion-webui/modules/ui_extra_networks_checkpoints_user_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..aae7ce7f4144dd6ca01edb7c688a8c1db96618fd
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_extra_networks_checkpoints_user_metadata.py
@@ -0,0 +1,66 @@
+import gradio as gr
+
+from modules import ui_extra_networks_user_metadata, sd_vae, shared
+from modules.ui_common import create_refresh_button
+
+
+class CheckpointUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
+ def __init__(self, ui, tabname, page):
+ super().__init__(ui, tabname, page)
+
+ self.select_vae = None
+
+ def save_user_metadata(self, name, desc, notes, vae):
+ user_metadata = self.get_user_metadata(name)
+ user_metadata["description"] = desc
+ user_metadata["notes"] = notes
+ user_metadata["vae"] = vae
+
+ self.write_user_metadata(name, user_metadata)
+
+ def update_vae(self, name):
+ if name == shared.sd_model.sd_checkpoint_info.name_for_extra:
+ sd_vae.reload_vae_weights()
+
+ def put_values_into_components(self, name):
+ user_metadata = self.get_user_metadata(name)
+ values = super().put_values_into_components(name)
+
+ return [
+ *values[0:5],
+ user_metadata.get('vae', ''),
+ ]
+
+ def create_editor(self):
+ self.create_default_editor_elems()
+
+ with gr.Row():
+ self.select_vae = gr.Dropdown(choices=["Automatic", "None"] + list(sd_vae.vae_dict), value="None", label="Preferred VAE", elem_id="checpoint_edit_user_metadata_preferred_vae")
+ create_refresh_button(self.select_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, "checpoint_edit_user_metadata_refresh_preferred_vae")
+
+ self.edit_notes = gr.TextArea(label='Notes', lines=4)
+
+ self.create_default_buttons()
+
+ viewed_components = [
+ self.edit_name,
+ self.edit_description,
+ self.html_filedata,
+ self.html_preview,
+ self.edit_notes,
+ self.select_vae,
+ ]
+
+ self.button_edit\
+ .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
+ .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
+
+ edited_components = [
+ self.edit_description,
+ self.edit_notes,
+ self.select_vae,
+ ]
+
+ self.setup_save_handler(self.button_save, self.save_user_metadata, edited_components)
+ self.button_save.click(fn=self.update_vae, inputs=[self.edit_name_input])
+
diff --git a/stable-diffusion-webui/modules/ui_extra_networks_hypernets.py b/stable-diffusion-webui/modules/ui_extra_networks_hypernets.py
new file mode 100644
index 0000000000000000000000000000000000000000..a591d42f5eb86ce9ecee9766452c860d280e5533
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_extra_networks_hypernets.py
@@ -0,0 +1,39 @@
+import os
+
+from modules import shared, ui_extra_networks
+from modules.ui_extra_networks import quote_js
+from modules.hashes import sha256_from_cache
+
+
+class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Hypernetworks')
+
+ def refresh(self):
+ shared.reload_hypernetworks()
+
+ def create_item(self, name, index=None, enable_filter=True):
+ full_path = shared.hypernetworks[name]
+ path, ext = os.path.splitext(full_path)
+ sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
+ shorthash = sha256[0:10] if sha256 else None
+
+ return {
+ "name": name,
+ "filename": full_path,
+ "shorthash": shorthash,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
+ "search_term": self.search_terms_from_path(path) + " " + (sha256 or ""),
+ "prompt": quote_js(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + quote_js(">"),
+ "local_preview": f"{path}.preview.{shared.opts.samples_format}",
+ "sort_keys": {'default': index, **self.get_sort_keys(path + ext)},
+ }
+
+ def list_items(self):
+ for index, name in enumerate(shared.hypernetworks):
+ yield self.create_item(name, index)
+
+ def allowed_directories_for_previews(self):
+ return [shared.cmd_opts.hypernetwork_dir]
+
diff --git a/stable-diffusion-webui/modules/ui_extra_networks_textual_inversion.py b/stable-diffusion-webui/modules/ui_extra_networks_textual_inversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcdd6981be68d75ac583c0a617f68dffae393c52
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_extra_networks_textual_inversion.py
@@ -0,0 +1,36 @@
+import os
+
+from modules import ui_extra_networks, sd_hijack, shared
+from modules.ui_extra_networks import quote_js
+
+
+class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
+ def __init__(self):
+ super().__init__('Textual Inversion')
+ self.allow_negative_prompt = True
+
+ def refresh(self):
+ sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
+
+ def create_item(self, name, index=None, enable_filter=True):
+ embedding = sd_hijack.model_hijack.embedding_db.word_embeddings.get(name)
+
+ path, ext = os.path.splitext(embedding.filename)
+ return {
+ "name": name,
+ "filename": embedding.filename,
+ "shorthash": embedding.shorthash,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
+ "search_term": self.search_terms_from_path(embedding.filename) + " " + (embedding.hash or ""),
+ "prompt": quote_js(embedding.name),
+ "local_preview": f"{path}.preview.{shared.opts.samples_format}",
+ "sort_keys": {'default': index, **self.get_sort_keys(embedding.filename)},
+ }
+
+ def list_items(self):
+ for index, name in enumerate(sd_hijack.model_hijack.embedding_db.word_embeddings):
+ yield self.create_item(name, index)
+
+ def allowed_directories_for_previews(self):
+ return list(sd_hijack.model_hijack.embedding_db.embedding_dirs)
diff --git a/stable-diffusion-webui/modules/ui_extra_networks_user_metadata.py b/stable-diffusion-webui/modules/ui_extra_networks_user_metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7c3f472a9534074c25028ea3263948f63d6afc5
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_extra_networks_user_metadata.py
@@ -0,0 +1,205 @@
+import datetime
+import html
+import json
+import os.path
+
+import gradio as gr
+
+from modules import generation_parameters_copypaste, images, sysinfo, errors, ui_extra_networks
+
+
+class UserMetadataEditor:
+
+ def __init__(self, ui, tabname, page):
+ self.ui = ui
+ self.tabname = tabname
+ self.page = page
+ self.id_part = f"{self.tabname}_{self.page.id_page}_edit_user_metadata"
+
+ self.box = None
+
+ self.edit_name_input = None
+ self.button_edit = None
+
+ self.edit_name = None
+ self.edit_description = None
+ self.edit_notes = None
+ self.html_filedata = None
+ self.html_preview = None
+ self.html_status = None
+
+ self.button_cancel = None
+ self.button_replace_preview = None
+ self.button_save = None
+
+ def get_user_metadata(self, name):
+ item = self.page.items.get(name, {})
+
+ user_metadata = item.get('user_metadata', None)
+ if not user_metadata:
+ user_metadata = {'description': item.get('description', '')}
+ item['user_metadata'] = user_metadata
+
+ return user_metadata
+
+ def create_extra_default_items_in_left_column(self):
+ pass
+
+ def create_default_editor_elems(self):
+ with gr.Row():
+ with gr.Column(scale=2):
+ self.edit_name = gr.HTML(elem_classes="extra-network-name")
+ self.edit_description = gr.Textbox(label="Description", lines=4)
+ self.html_filedata = gr.HTML()
+
+ self.create_extra_default_items_in_left_column()
+
+ with gr.Column(scale=1, min_width=0):
+ self.html_preview = gr.HTML()
+
+ def create_default_buttons(self):
+
+ with gr.Row(elem_classes="edit-user-metadata-buttons"):
+ self.button_cancel = gr.Button('Cancel')
+ self.button_replace_preview = gr.Button('Replace preview', variant='primary')
+ self.button_save = gr.Button('Save', variant='primary')
+
+ self.html_status = gr.HTML(elem_classes="edit-user-metadata-status")
+
+ self.button_cancel.click(fn=None, _js="closePopup")
+
+ def get_card_html(self, name):
+ item = self.page.items.get(name, {})
+
+ preview_url = item.get("preview", None)
+
+ if not preview_url:
+ filename, _ = os.path.splitext(item["filename"])
+ preview_url = self.page.find_preview(filename)
+ item["preview"] = preview_url
+
+ if preview_url:
+ preview = f'''
+ <div class='card standalone-card-preview'>
+ <img src="{html.escape(preview_url)}" class="preview">
+ </div>
+ '''
+ else:
+ preview = "<div class='card standalone-card-preview'></div>"
+
+ return preview
+
+ def relative_path(self, path):
+ for parent_path in self.page.allowed_directories_for_previews():
+ if ui_extra_networks.path_is_parent(parent_path, path):
+ return os.path.relpath(path, parent_path)
+
+ return os.path.basename(path)
+
+ def get_metadata_table(self, name):
+ item = self.page.items.get(name, {})
+ try:
+ filename = item["filename"]
+ shorthash = item.get("shorthash", None)
+
+ stats = os.stat(filename)
+ params = [
+ ('Filename: ', self.relative_path(filename)),
+ ('File size: ', sysinfo.pretty_bytes(stats.st_size)),
+ ('Hash: ', shorthash),
+ ('Modified: ', datetime.datetime.fromtimestamp(stats.st_mtime).strftime('%Y-%m-%d %H:%M')),
+ ]
+
+ return params
+ except Exception as e:
+ errors.display(e, f"reading info for {name}")
+ return []
+
+ def put_values_into_components(self, name):
+ user_metadata = self.get_user_metadata(name)
+
+ try:
+ params = self.get_metadata_table(name)
+ except Exception as e:
+ errors.display(e, f"reading metadata info for {name}")
+ params = []
+
+ table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params if value is not None) + '</table>'
+
+ return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', '')
+
+ def write_user_metadata(self, name, metadata):
+ item = self.page.items.get(name, {})
+ filename = item.get("filename", None)
+ basename, ext = os.path.splitext(filename)
+
+ with open(basename + '.json', "w", encoding="utf8") as file:
+ json.dump(metadata, file, indent=4)
+
+ def save_user_metadata(self, name, desc, notes):
+ user_metadata = self.get_user_metadata(name)
+ user_metadata["description"] = desc
+ user_metadata["notes"] = notes
+
+ self.write_user_metadata(name, user_metadata)
+
+ def setup_save_handler(self, button, func, components):
+ button\
+ .click(fn=func, inputs=[self.edit_name_input, *components], outputs=[])\
+ .then(fn=None, _js="function(name){closePopup(); extraNetworksRefreshSingleCard(" + json.dumps(self.page.name) + "," + json.dumps(self.tabname) + ", name);}", inputs=[self.edit_name_input], outputs=[])
+
+ def create_editor(self):
+ self.create_default_editor_elems()
+
+ self.edit_notes = gr.TextArea(label='Notes', lines=4)
+
+ self.create_default_buttons()
+
+ self.button_edit\
+ .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=[self.edit_name, self.edit_description, self.html_filedata, self.html_preview, self.edit_notes])\
+ .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
+
+ self.setup_save_handler(self.button_save, self.save_user_metadata, [self.edit_description, self.edit_notes])
+
+ def create_ui(self):
+ with gr.Box(visible=False, elem_id=self.id_part, elem_classes="edit-user-metadata") as box:
+ self.box = box
+
+ self.edit_name_input = gr.Textbox("Edit user metadata card id", visible=False, elem_id=f"{self.id_part}_name")
+ self.button_edit = gr.Button("Edit user metadata", visible=False, elem_id=f"{self.id_part}_button")
+
+ self.create_editor()
+
+ def save_preview(self, index, gallery, name):
+ if len(gallery) == 0:
+ return self.get_card_html(name), "There is no image in gallery to save as a preview."
+
+ item = self.page.items.get(name, {})
+
+ index = int(index)
+ index = 0 if index < 0 else index
+ index = len(gallery) - 1 if index >= len(gallery) else index
+
+ img_info = gallery[index if index >= 0 else 0]
+ image = generation_parameters_copypaste.image_from_url_text(img_info)
+ geninfo, items = images.read_info_from_image(image)
+
+ images.save_image_with_geninfo(image, geninfo, item["local_preview"])
+
+ return self.get_card_html(name), ''
+
+ def setup_ui(self, gallery):
+ self.button_replace_preview.click(
+ fn=self.save_preview,
+ _js="function(x, y, z){return [selected_gallery_index(), y, z]}",
+ inputs=[self.edit_name_input, gallery, self.edit_name_input],
+ outputs=[self.html_preview, self.html_status]
+ ).then(
+ fn=None,
+ _js="function(name){extraNetworksRefreshSingleCard(" + json.dumps(self.page.name) + "," + json.dumps(self.tabname) + ", name);}",
+ inputs=[self.edit_name_input],
+ outputs=[]
+ )
+
+
+
diff --git a/stable-diffusion-webui/modules/ui_gradio_extensions.py b/stable-diffusion-webui/modules/ui_gradio_extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e761aa54828d44d9479fa626fa835c225c66b1c
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_gradio_extensions.py
@@ -0,0 +1,69 @@
+import os
+import gradio as gr
+
+from modules import localization, shared, scripts
+from modules.paths import script_path, data_path
+
+
+def webpath(fn):
+ if fn.startswith(script_path):
+ web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+ else:
+ web_path = os.path.abspath(fn)
+
+ return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
+ # Ensure localization is in `window` before scripts
+ head = f'<script type="text/javascript">{localization.localization_js(shared.opts.localization)}</script>\n'
+
+ script_js = os.path.join(script_path, "script.js")
+ head += f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
+
+ for script in scripts.list_scripts("javascript", ".js"):
+ head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
+
+ for script in scripts.list_scripts("javascript", ".mjs"):
+ head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
+
+ if shared.cmd_opts.theme:
+ head += f'<script type="text/javascript">set_theme(\"{shared.cmd_opts.theme}\");</script>\n'
+
+ return head
+
+
+def css_html():
+ head = ""
+
+ def stylesheet(fn):
+ return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
+
+ for cssfile in scripts.list_files_with_name("style.css"):
+ if not os.path.isfile(cssfile):
+ continue
+
+ head += stylesheet(cssfile)
+
+ if os.path.exists(os.path.join(data_path, "user.css")):
+ head += stylesheet(os.path.join(data_path, "user.css"))
+
+ return head
+
+
+def reload_javascript():
+ js = javascript_html()
+ css = css_html()
+
+ def template_response(*args, **kwargs):
+ res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
+ res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
+ res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
+ res.init_headers()
+ return res
+
+ gr.routes.templates.TemplateResponse = template_response
+
+
+if not hasattr(shared, 'GradioTemplateResponseOriginal'):
+ shared.GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse
diff --git a/stable-diffusion-webui/modules/ui_loadsave.py b/stable-diffusion-webui/modules/ui_loadsave.py
new file mode 100644
index 0000000000000000000000000000000000000000..42b1bdceb07401bd575d017f768e3e7be66ff274
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_loadsave.py
@@ -0,0 +1,227 @@
+import json
+import os
+
+import gradio as gr
+
+from modules import errors
+from modules.ui_components import ToolButton
+
+
+def radio_choices(comp): # gradio 3.41 changes choices from list of values to list of pairs
+ return [x[0] if isinstance(x, tuple) else x for x in getattr(comp, 'choices', [])]
+
+
+class UiLoadsave:
+ """allows saving and restoring default values for gradio components"""
+
+ def __init__(self, filename):
+ self.filename = filename
+ self.ui_settings = {}
+ self.component_mapping = {}
+ self.error_loading = False
+ self.finalized_ui = False
+
+ self.ui_defaults_view = None
+ self.ui_defaults_apply = None
+ self.ui_defaults_review = None
+
+ try:
+ if os.path.exists(self.filename):
+ self.ui_settings = self.read_from_file()
+ except Exception as e:
+ self.error_loading = True
+ errors.display(e, "loading settings")
+
+
+
+ def add_component(self, path, x):
+ """adds component to the registry of tracked components"""
+
+ assert not self.finalized_ui
+
+ def apply_field(obj, field, condition=None, init_field=None):
+ key = f"{path}/{field}"
+
+ if getattr(obj, 'custom_script_source', None) is not None:
+ key = f"customscript/{obj.custom_script_source}/{key}"
+
+ if getattr(obj, 'do_not_save_to_config', False):
+ return
+
+ saved_value = self.ui_settings.get(key, None)
+ if saved_value is None:
+ self.ui_settings[key] = getattr(obj, field)
+ elif condition and not condition(saved_value):
+ pass
+ else:
+ if isinstance(x, gr.Textbox) and field == 'value': # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
+ saved_value = str(saved_value)
+ elif isinstance(x, gr.Number) and field == 'value':
+ try:
+ saved_value = float(saved_value)
+ except ValueError:
+ return
+
+ setattr(obj, field, saved_value)
+ if init_field is not None:
+ init_field(saved_value)
+
+ if field == 'value' and key not in self.component_mapping:
+ self.component_mapping[key] = x
+
+ if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton, gr.Button] and x.visible:
+ apply_field(x, 'visible')
+
+ if type(x) == gr.Slider:
+ apply_field(x, 'value')
+ apply_field(x, 'minimum')
+ apply_field(x, 'maximum')
+ apply_field(x, 'step')
+
+ if type(x) == gr.Radio:
+ apply_field(x, 'value', lambda val: val in radio_choices(x))
+
+ if type(x) == gr.Checkbox:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Textbox:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Number:
+ apply_field(x, 'value')
+
+ if type(x) == gr.Dropdown:
+ def check_dropdown(val):
+ choices = radio_choices(x)
+ if getattr(x, 'multiselect', False):
+ return all(value in choices for value in val)
+ else:
+ return val in choices
+
+ apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
+
+ def check_tab_id(tab_id):
+ tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
+ if type(tab_id) == str:
+ tab_ids = [t.id for t in tab_items]
+ return tab_id in tab_ids
+ elif type(tab_id) == int:
+ return 0 <= tab_id < len(tab_items)
+ else:
+ return False
+
+ if type(x) == gr.Tabs:
+ apply_field(x, 'selected', check_tab_id)
+
+ def add_block(self, x, path=""):
+ """adds all components inside a gradio block x to the registry of tracked components"""
+
+ if hasattr(x, 'children'):
+ if isinstance(x, gr.Tabs) and x.elem_id is not None:
+ # Tabs element can't have a label, have to use elem_id instead
+ self.add_component(f"{path}/Tabs@{x.elem_id}", x)
+ for c in x.children:
+ self.add_block(c, path)
+ elif x.label is not None:
+ self.add_component(f"{path}/{x.label}", x)
+ elif isinstance(x, gr.Button) and x.value is not None:
+ self.add_component(f"{path}/{x.value}", x)
+
+ def read_from_file(self):
+ with open(self.filename, "r", encoding="utf8") as file:
+ return json.load(file)
+
+ def write_to_file(self, current_ui_settings):
+ with open(self.filename, "w", encoding="utf8") as file:
+ json.dump(current_ui_settings, file, indent=4)
+
+ def dump_defaults(self):
+ """saves default values to a file unless tjhe file is present and there was an error loading default values at start"""
+
+ if self.error_loading and os.path.exists(self.filename):
+ return
+
+ self.write_to_file(self.ui_settings)
+
+ def iter_changes(self, current_ui_settings, values):
+ """
+ given a dictionary with defaults from a file and current values from gradio elements, returns
+ an iterator over tuples of values that are not the same between the file and the current;
+ tuple contents are: path, old value, new value
+ """
+
+ for (path, component), new_value in zip(self.component_mapping.items(), values):
+ old_value = current_ui_settings.get(path)
+
+ choices = radio_choices(component)
+ if isinstance(new_value, int) and choices:
+ if new_value >= len(choices):
+ continue
+
+ new_value = choices[new_value]
+ if isinstance(new_value, tuple):
+ new_value = new_value[0]
+
+ if new_value == old_value:
+ continue
+
+ if old_value is None and new_value == '' or new_value == []:
+ continue
+
+ yield path, old_value, new_value
+
+ def ui_view(self, *values):
+ text = ["<table><thead><tr><th>Path</th><th>Old value</th><th>New value</th></thead><tbody>"]
+
+ for path, old_value, new_value in self.iter_changes(self.read_from_file(), values):
+ if old_value is None:
+ old_value = "<span class='ui-defaults-none'>None</span>"
+
+ text.append(f"<tr><td>{path}</td><td>{old_value}</td><td>{new_value}</td></tr>")
+
+ if len(text) == 1:
+ text.append("<tr><td colspan=3>No changes</td></tr>")
+
+ text.append("</tbody>")
+ return "".join(text)
+
+ def ui_apply(self, *values):
+ num_changed = 0
+
+ current_ui_settings = self.read_from_file()
+
+ for path, _, new_value in self.iter_changes(current_ui_settings.copy(), values):
+ num_changed += 1
+ current_ui_settings[path] = new_value
+
+ if num_changed == 0:
+ return "No changes."
+
+ self.write_to_file(current_ui_settings)
+
+ return f"Wrote {num_changed} changes."
+
+ def create_ui(self):
+ """creates ui elements for editing defaults UI, without adding any logic to them"""
+
+ gr.HTML(
+ f"This page allows you to change default values in UI elements on other tabs.<br />"
+ f"Make your changes, press 'View changes' to review the changed default values,<br />"
+ f"then press 'Apply' to write them to {self.filename}.<br />"
+ f"New defaults will apply after you restart the UI.<br />"
+ )
+
+ with gr.Row():
+ self.ui_defaults_view = gr.Button(value='View changes', elem_id="ui_defaults_view", variant="secondary")
+ self.ui_defaults_apply = gr.Button(value='Apply', elem_id="ui_defaults_apply", variant="primary")
+
+ self.ui_defaults_review = gr.HTML("")
+
+ def setup_ui(self):
+ """adds logic to elements created with create_ui; all add_block class must be made before this"""
+
+ assert not self.finalized_ui
+ self.finalized_ui = True
+
+ self.ui_defaults_view.click(fn=self.ui_view, inputs=list(self.component_mapping.values()), outputs=[self.ui_defaults_review])
+ self.ui_defaults_apply.click(fn=self.ui_apply, inputs=list(self.component_mapping.values()), outputs=[self.ui_defaults_review])
diff --git a/stable-diffusion-webui/modules/ui_postprocessing.py b/stable-diffusion-webui/modules/ui_postprocessing.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa7576253967ac6bf2e3186c8db288f6b5d3c828
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_postprocessing.py
@@ -0,0 +1,57 @@
+import gradio as gr
+from modules import scripts, shared, ui_common, postprocessing, call_queue
+import modules.generation_parameters_copypaste as parameters_copypaste
+
+
+def create_ui():
+ tab_index = gr.State(value=0)
+
+ with gr.Row(equal_height=False, variant='compact'):
+ with gr.Column(variant='compact'):
+ with gr.Tabs(elem_id="mode_extras"):
+ with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:
+ extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
+
+ with gr.TabItem('Batch Process', id="batch_process", elem_id="extras_batch_process_tab") as tab_batch:
+ image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
+
+ with gr.TabItem('Batch from Directory', id="batch_from_directory", elem_id="extras_batch_directory_tab") as tab_batch_dir:
+ extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
+ extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
+ show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
+
+ submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
+ script_inputs = scripts.scripts_postproc.setup_ui()
+
+ with gr.Column():
+ result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
+
+ tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
+ tab_batch.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
+ tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
+
+ submit.click(
+ fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing, extra_outputs=[None, '']),
+ inputs=[
+ tab_index,
+ extras_image,
+ image_batch,
+ extras_batch_input_dir,
+ extras_batch_output_dir,
+ show_extras_results,
+ *script_inputs
+ ],
+ outputs=[
+ result_images,
+ html_info_x,
+ html_info,
+ ]
+ )
+
+ parameters_copypaste.add_paste_fields("extras", extras_image, None)
+
+ extras_image.change(
+ fn=scripts.scripts_postproc.image_changed,
+ inputs=[], outputs=[]
+ )
diff --git a/stable-diffusion-webui/modules/ui_prompt_styles.py b/stable-diffusion-webui/modules/ui_prompt_styles.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2a88a411938b77167fde0896c24fa0decb1a83a
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_prompt_styles.py
@@ -0,0 +1,110 @@
+import gradio as gr
+
+from modules import shared, ui_common, ui_components, styles
+
+styles_edit_symbol = '\U0001f58c\uFE0F' # 🖌️
+styles_materialize_symbol = '\U0001f4cb' # 📋
+
+
+def select_style(name):
+ style = shared.prompt_styles.styles.get(name)
+ existing = style is not None
+ empty = not name
+
+ prompt = style.prompt if style else gr.update()
+ negative_prompt = style.negative_prompt if style else gr.update()
+
+ return prompt, negative_prompt, gr.update(visible=existing), gr.update(visible=not empty)
+
+
+def save_style(name, prompt, negative_prompt):
+ if not name:
+ return gr.update(visible=False)
+
+ style = styles.PromptStyle(name, prompt, negative_prompt)
+ shared.prompt_styles.styles[style.name] = style
+ shared.prompt_styles.save_styles(shared.styles_filename)
+
+ return gr.update(visible=True)
+
+
+def delete_style(name):
+ if name == "":
+ return
+
+ shared.prompt_styles.styles.pop(name, None)
+ shared.prompt_styles.save_styles(shared.styles_filename)
+
+ return '', '', ''
+
+
+def materialize_styles(prompt, negative_prompt, styles):
+ prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
+ negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(negative_prompt, styles)
+
+ return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=negative_prompt), gr.Dropdown.update(value=[])]
+
+
+def refresh_styles():
+ return gr.update(choices=list(shared.prompt_styles.styles)), gr.update(choices=list(shared.prompt_styles.styles))
+
+
+class UiPromptStyles:
+ def __init__(self, tabname, main_ui_prompt, main_ui_negative_prompt):
+ self.tabname = tabname
+
+ with gr.Row(elem_id=f"{tabname}_styles_row"):
+ self.dropdown = gr.Dropdown(label="Styles", show_label=False, elem_id=f"{tabname}_styles", choices=list(shared.prompt_styles.styles), value=[], multiselect=True, tooltip="Styles")
+ edit_button = ui_components.ToolButton(value=styles_edit_symbol, elem_id=f"{tabname}_styles_edit_button", tooltip="Edit styles")
+
+ with gr.Box(elem_id=f"{tabname}_styles_dialog", elem_classes="popup-dialog") as styles_dialog:
+ with gr.Row():
+ self.selection = gr.Dropdown(label="Styles", elem_id=f"{tabname}_styles_edit_select", choices=list(shared.prompt_styles.styles), value=[], allow_custom_value=True, info="Styles allow you to add custom text to prompt. Use the {prompt} token in style text, and it will be replaced with user's prompt when applying style. Otherwise, style's text will be added to the end of the prompt.")
+ ui_common.create_refresh_button([self.dropdown, self.selection], shared.prompt_styles.reload, lambda: {"choices": list(shared.prompt_styles.styles)}, f"refresh_{tabname}_styles")
+ self.materialize = ui_components.ToolButton(value=styles_materialize_symbol, elem_id=f"{tabname}_style_apply", tooltip="Apply all selected styles from the style selction dropdown in main UI to the prompt.")
+
+ with gr.Row():
+ self.prompt = gr.Textbox(label="Prompt", show_label=True, elem_id=f"{tabname}_edit_style_prompt", lines=3)
+
+ with gr.Row():
+ self.neg_prompt = gr.Textbox(label="Negative prompt", show_label=True, elem_id=f"{tabname}_edit_style_neg_prompt", lines=3)
+
+ with gr.Row():
+ self.save = gr.Button('Save', variant='primary', elem_id=f'{tabname}_edit_style_save', visible=False)
+ self.delete = gr.Button('Delete', variant='primary', elem_id=f'{tabname}_edit_style_delete', visible=False)
+ self.close = gr.Button('Close', variant='secondary', elem_id=f'{tabname}_edit_style_close')
+
+ self.selection.change(
+ fn=select_style,
+ inputs=[self.selection],
+ outputs=[self.prompt, self.neg_prompt, self.delete, self.save],
+ show_progress=False,
+ )
+
+ self.save.click(
+ fn=save_style,
+ inputs=[self.selection, self.prompt, self.neg_prompt],
+ outputs=[self.delete],
+ show_progress=False,
+ ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+ self.delete.click(
+ fn=delete_style,
+ _js='function(name){ if(name == "") return ""; return confirm("Delete style " + name + "?") ? name : ""; }',
+ inputs=[self.selection],
+ outputs=[self.selection, self.prompt, self.neg_prompt],
+ show_progress=False,
+ ).then(refresh_styles, outputs=[self.dropdown, self.selection], show_progress=False)
+
+ self.materialize.click(
+ fn=materialize_styles,
+ inputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+ outputs=[main_ui_prompt, main_ui_negative_prompt, self.dropdown],
+ show_progress=False,
+ ).then(fn=None, _js="function(){update_"+tabname+"_tokens(); closePopup();}", show_progress=False)
+
+ ui_common.setup_dialog(button_show=edit_button, dialog=styles_dialog, button_close=self.close)
+
+
+
+
diff --git a/stable-diffusion-webui/modules/ui_settings.py b/stable-diffusion-webui/modules/ui_settings.py
new file mode 100644
index 0000000000000000000000000000000000000000..568e9d6ffe2cbb85a50e3819e1d7c7506edad37c
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_settings.py
@@ -0,0 +1,296 @@
+import gradio as gr
+
+from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo
+from modules.call_queue import wrap_gradio_call
+from modules.shared import opts
+from modules.ui_components import FormRow
+from modules.ui_gradio_extensions import reload_javascript
+
+
+def get_value_for_setting(key):
+ value = getattr(opts, key)
+
+ info = opts.data_labels[key]
+ args = info.component_args() if callable(info.component_args) else info.component_args or {}
+ args = {k: v for k, v in args.items() if k not in {'precision'}}
+
+ return gr.update(value=value, **args)
+
+
+def create_setting_component(key, is_quicksettings=False):
+ def fun():
+ return opts.data[key] if key in opts.data else opts.data_labels[key].default
+
+ info = opts.data_labels[key]
+ t = type(info.default)
+
+ args = info.component_args() if callable(info.component_args) else info.component_args
+
+ if info.component is not None:
+ comp = info.component
+ elif t == str:
+ comp = gr.Textbox
+ elif t == int:
+ comp = gr.Number
+ elif t == bool:
+ comp = gr.Checkbox
+ else:
+ raise Exception(f'bad options item type: {t} for key {key}')
+
+ elem_id = f"setting_{key}"
+
+ if info.refresh is not None:
+ if is_quicksettings:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+ else:
+ with FormRow():
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+ ui_common.create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
+ else:
+ res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
+
+ return res
+
+
+class UiSettings:
+ submit = None
+ result = None
+ interface = None
+ components = None
+ component_dict = None
+ dummy_component = None
+ quicksettings_list = None
+ quicksettings_names = None
+ text_settings = None
+
+ def run_settings(self, *args):
+ changed = []
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
+ assert comp == self.dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+
+ for key, value, comp in zip(opts.data_labels.keys(), args, self.components):
+ if comp == self.dummy_component:
+ continue
+
+ if opts.set(key, value):
+ changed.append(key)
+
+ try:
+ opts.save(shared.config_filename)
+ except RuntimeError:
+ return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+ return opts.dumpjson(), f'{len(changed)} settings changed{": " if changed else ""}{", ".join(changed)}.'
+
+ def run_settings_single(self, value, key):
+ if not opts.same_type(value, opts.data_labels[key].default):
+ return gr.update(visible=True), opts.dumpjson()
+
+ if value is None or not opts.set(key, value):
+ return gr.update(value=getattr(opts, key)), opts.dumpjson()
+
+ opts.save(shared.config_filename)
+
+ return get_value_for_setting(key), opts.dumpjson()
+
+ def create_ui(self, loadsave, dummy_component):
+ self.components = []
+ self.component_dict = {}
+ self.dummy_component = dummy_component
+
+ shared.settings_components = self.component_dict
+
+ script_callbacks.ui_settings_callback()
+ opts.reorder()
+
+ with gr.Blocks(analytics_enabled=False) as settings_interface:
+ with gr.Row():
+ with gr.Column(scale=6):
+ self.submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
+ with gr.Column():
+ restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
+
+ self.result = gr.HTML(elem_id="settings_result")
+
+ self.quicksettings_names = opts.quicksettings_list
+ self.quicksettings_names = {x: i for i, x in enumerate(self.quicksettings_names) if x != 'quicksettings'}
+
+ self.quicksettings_list = []
+
+ previous_section = None
+ current_tab = None
+ current_row = None
+ with gr.Tabs(elem_id="settings"):
+ for i, (k, item) in enumerate(opts.data_labels.items()):
+ section_must_be_skipped = item.section[0] is None
+
+ if previous_section != item.section and not section_must_be_skipped:
+ elem_id, text = item.section
+
+ if current_tab is not None:
+ current_row.__exit__()
+ current_tab.__exit__()
+
+ gr.Group()
+ current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
+ current_tab.__enter__()
+ current_row = gr.Column(variant='compact')
+ current_row.__enter__()
+
+ previous_section = item.section
+
+ if k in self.quicksettings_names and not shared.cmd_opts.freeze_settings:
+ self.quicksettings_list.append((i, k, item))
+ self.components.append(dummy_component)
+ elif section_must_be_skipped:
+ self.components.append(dummy_component)
+ else:
+ component = create_setting_component(k)
+ self.component_dict[k] = component
+ self.components.append(component)
+
+ if current_tab is not None:
+ current_row.__exit__()
+ current_tab.__exit__()
+
+ with gr.TabItem("Defaults", id="defaults", elem_id="settings_tab_defaults"):
+ loadsave.create_ui()
+
+ with gr.TabItem("Sysinfo", id="sysinfo", elem_id="settings_tab_sysinfo"):
+ gr.HTML('<a href="./internal/sysinfo-download" class="sysinfo_big_link" download>Download system info</a><br /><a href="./internal/sysinfo" target="_blank">(or open as text in a new page)</a>', elem_id="sysinfo_download")
+
+ with gr.Row():
+ with gr.Column(scale=1):
+ sysinfo_check_file = gr.File(label="Check system info for validity", type='binary')
+ with gr.Column(scale=1):
+ sysinfo_check_output = gr.HTML("", elem_id="sysinfo_validity")
+ with gr.Column(scale=100):
+ pass
+
+ with gr.TabItem("Actions", id="actions", elem_id="settings_tab_actions"):
+ request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+ download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
+ reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+ with gr.Row():
+ unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+ reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
+
+ with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
+ gr.HTML(shared.html("licenses.html"), elem_id="licenses")
+
+ gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
+ self.text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
+
+ unload_sd_model.click(
+ fn=sd_models.unload_model_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ reload_sd_model.click(
+ fn=sd_models.reload_model_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ request_notifications.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='function(){}'
+ )
+
+ download_localization.click(
+ fn=lambda: None,
+ inputs=[],
+ outputs=[],
+ _js='download_localization'
+ )
+
+ def reload_scripts():
+ scripts.reload_script_body_only()
+ reload_javascript() # need to refresh the html page
+
+ reload_script_bodies.click(
+ fn=reload_scripts,
+ inputs=[],
+ outputs=[]
+ )
+
+ restart_gradio.click(
+ fn=shared.state.request_restart,
+ _js='restart_reload',
+ inputs=[],
+ outputs=[],
+ )
+
+ def check_file(x):
+ if x is None:
+ return ''
+
+ if sysinfo.check(x.decode('utf8', errors='ignore')):
+ return 'Valid'
+
+ return 'Invalid'
+
+ sysinfo_check_file.change(
+ fn=check_file,
+ inputs=[sysinfo_check_file],
+ outputs=[sysinfo_check_output],
+ )
+
+ self.interface = settings_interface
+
+ def add_quicksettings(self):
+ with gr.Row(elem_id="quicksettings", variant="compact"):
+ for _i, k, _item in sorted(self.quicksettings_list, key=lambda x: self.quicksettings_names.get(x[1], x[0])):
+ component = create_setting_component(k, is_quicksettings=True)
+ self.component_dict[k] = component
+
+ def add_functionality(self, demo):
+ self.submit.click(
+ fn=wrap_gradio_call(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
+ inputs=self.components,
+ outputs=[self.text_settings, self.result],
+ )
+
+ for _i, k, _item in self.quicksettings_list:
+ component = self.component_dict[k]
+ info = opts.data_labels[k]
+
+ if isinstance(component, gr.Textbox):
+ methods = [component.submit, component.blur]
+ elif hasattr(component, 'release'):
+ methods = [component.release]
+ else:
+ methods = [component.change]
+
+ for method in methods:
+ method(
+ fn=lambda value, k=k: self.run_settings_single(value, key=k),
+ inputs=[component],
+ outputs=[component, self.text_settings],
+ show_progress=info.refresh is not None,
+ )
+
+ button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
+ button_set_checkpoint.click(
+ fn=lambda value, _: self.run_settings_single(value, key='sd_model_checkpoint'),
+ _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+ inputs=[self.component_dict['sd_model_checkpoint'], self.dummy_component],
+ outputs=[self.component_dict['sd_model_checkpoint'], self.text_settings],
+ )
+
+ component_keys = [k for k in opts.data_labels.keys() if k in self.component_dict]
+
+ def get_settings_values():
+ return [get_value_for_setting(key) for key in component_keys]
+
+ demo.load(
+ fn=get_settings_values,
+ inputs=[],
+ outputs=[self.component_dict[k] for k in component_keys],
+ queue=False,
+ )
diff --git a/stable-diffusion-webui/modules/ui_tempdir.py b/stable-diffusion-webui/modules/ui_tempdir.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ec0949d6aa9666cbe60d43062b2b247ba1706bd
--- /dev/null
+++ b/stable-diffusion-webui/modules/ui_tempdir.py
@@ -0,0 +1,88 @@
+import os
+import tempfile
+from collections import namedtuple
+from pathlib import Path
+
+import gradio.components
+
+from PIL import PngImagePlugin
+
+from modules import shared
+
+
+Savedfile = namedtuple("Savedfile", ["name"])
+
+
+def register_tmp_file(gradio, filename):
+ if hasattr(gradio, 'temp_file_sets'): # gradio 3.15
+ gradio.temp_file_sets[0] = gradio.temp_file_sets[0] | {os.path.abspath(filename)}
+
+ if hasattr(gradio, 'temp_dirs'): # gradio 3.9
+ gradio.temp_dirs = gradio.temp_dirs | {os.path.abspath(os.path.dirname(filename))}
+
+
+def check_tmp_file(gradio, filename):
+ if hasattr(gradio, 'temp_file_sets'):
+ return any(filename in fileset for fileset in gradio.temp_file_sets)
+
+ if hasattr(gradio, 'temp_dirs'):
+ return any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in gradio.temp_dirs)
+
+ return False
+
+
+def save_pil_to_file(self, pil_image, dir=None, format="png"):
+ already_saved_as = getattr(pil_image, 'already_saved_as', None)
+ if already_saved_as and os.path.isfile(already_saved_as):
+ register_tmp_file(shared.demo, already_saved_as)
+ filename = already_saved_as
+
+ if not shared.opts.save_images_add_number:
+ filename += f'?{os.path.getmtime(already_saved_as)}'
+
+ return filename
+
+ if shared.opts.temp_dir != "":
+ dir = shared.opts.temp_dir
+ else:
+ os.makedirs(dir, exist_ok=True)
+
+ use_metadata = False
+ metadata = PngImagePlugin.PngInfo()
+ for key, value in pil_image.info.items():
+ if isinstance(key, str) and isinstance(value, str):
+ metadata.add_text(key, value)
+ use_metadata = True
+
+ file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+ pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+ return file_obj.name
+
+
+def install_ui_tempdir_override():
+ """override save to file function so that it also writes PNG info"""
+ gradio.components.IOComponent.pil_to_temp_file = save_pil_to_file
+
+
+def on_tmpdir_changed():
+ if shared.opts.temp_dir == "" or shared.demo is None:
+ return
+
+ os.makedirs(shared.opts.temp_dir, exist_ok=True)
+
+ register_tmp_file(shared.demo, os.path.join(shared.opts.temp_dir, "x"))
+
+
+def cleanup_tmpdr():
+ temp_dir = shared.opts.temp_dir
+ if temp_dir == "" or not os.path.isdir(temp_dir):
+ return
+
+ for root, _, files in os.walk(temp_dir, topdown=False):
+ for name in files:
+ _, extension = os.path.splitext(name)
+ if extension != ".png":
+ continue
+
+ filename = os.path.join(root, name)
+ os.remove(filename)
diff --git a/stable-diffusion-webui/modules/upscaler.py b/stable-diffusion-webui/modules/upscaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..e682bbaa26cd05fa8f00f6e6ca438a8c53f7d47b
--- /dev/null
+++ b/stable-diffusion-webui/modules/upscaler.py
@@ -0,0 +1,144 @@
+import os
+from abc import abstractmethod
+
+import PIL
+from PIL import Image
+
+import modules.shared
+from modules import modelloader, shared
+
+LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
+NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
+
+
+class Upscaler:
+ name = None
+ model_path = None
+ model_name = None
+ model_url = None
+ enable = True
+ filter = None
+ model = None
+ user_path = None
+ scalers: []
+ tile = True
+
+ def __init__(self, create_dirs=False):
+ self.mod_pad_h = None
+ self.tile_size = modules.shared.opts.ESRGAN_tile
+ self.tile_pad = modules.shared.opts.ESRGAN_tile_overlap
+ self.device = modules.shared.device
+ self.img = None
+ self.output = None
+ self.scale = 1
+ self.half = not modules.shared.cmd_opts.no_half
+ self.pre_pad = 0
+ self.mod_scale = None
+ self.model_download_path = None
+
+ if self.model_path is None and self.name:
+ self.model_path = os.path.join(shared.models_path, self.name)
+ if self.model_path and create_dirs:
+ os.makedirs(self.model_path, exist_ok=True)
+
+ try:
+ import cv2 # noqa: F401
+ self.can_tile = True
+ except Exception:
+ pass
+
+ @abstractmethod
+ def do_upscale(self, img: PIL.Image, selected_model: str):
+ return img
+
+ def upscale(self, img: PIL.Image, scale, selected_model: str = None):
+ self.scale = scale
+ dest_w = int((img.width * scale) // 8 * 8)
+ dest_h = int((img.height * scale) // 8 * 8)
+
+ for _ in range(3):
+ shape = (img.width, img.height)
+
+ img = self.do_upscale(img, selected_model)
+
+ if shape == (img.width, img.height):
+ break
+
+ if img.width >= dest_w and img.height >= dest_h:
+ break
+
+ if img.width != dest_w or img.height != dest_h:
+ img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
+
+ return img
+
+ @abstractmethod
+ def load_model(self, path: str):
+ pass
+
+ def find_models(self, ext_filter=None) -> list:
+ return modelloader.load_models(model_path=self.model_path, model_url=self.model_url, command_path=self.user_path, ext_filter=ext_filter)
+
+ def update_status(self, prompt):
+ print(f"\nextras: {prompt}", file=shared.progress_print_out)
+
+
+class UpscalerData:
+ name = None
+ data_path = None
+ scale: int = 4
+ scaler: Upscaler = None
+ model: None
+
+ def __init__(self, name: str, path: str, upscaler: Upscaler = None, scale: int = 4, model=None):
+ self.name = name
+ self.data_path = path
+ self.local_data_path = path
+ self.scaler = upscaler
+ self.scale = scale
+ self.model = model
+
+
+class UpscalerNone(Upscaler):
+ name = "None"
+ scalers = []
+
+ def load_model(self, path):
+ pass
+
+ def do_upscale(self, img, selected_model=None):
+ return img
+
+ def __init__(self, dirname=None):
+ super().__init__(False)
+ self.scalers = [UpscalerData("None", None, self)]
+
+
+class UpscalerLanczos(Upscaler):
+ scalers = []
+
+ def do_upscale(self, img, selected_model=None):
+ return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=LANCZOS)
+
+ def load_model(self, _):
+ pass
+
+ def __init__(self, dirname=None):
+ super().__init__(False)
+ self.name = "Lanczos"
+ self.scalers = [UpscalerData("Lanczos", None, self)]
+
+
+class UpscalerNearest(Upscaler):
+ scalers = []
+
+ def do_upscale(self, img, selected_model=None):
+ return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST)
+
+ def load_model(self, _):
+ pass
+
+ def __init__(self, dirname=None):
+ super().__init__(False)
+ self.name = "Nearest"
+ self.scalers = [UpscalerData("Nearest", None, self)]
diff --git a/stable-diffusion-webui/modules/util.py b/stable-diffusion-webui/modules/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e25ce9a39b344f5ecff45443eaf08ce79193a2bd
--- /dev/null
+++ b/stable-diffusion-webui/modules/util.py
@@ -0,0 +1,58 @@
+import os
+import re
+
+from modules import shared
+from modules.paths_internal import script_path
+
+
+def natural_sort_key(s, regex=re.compile('([0-9]+)')):
+ return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]
+
+
+def listfiles(dirname):
+ filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
+ return [file for file in filenames if os.path.isfile(file)]
+
+
+def html_path(filename):
+ return os.path.join(script_path, "html", filename)
+
+
+def html(filename):
+ path = html_path(filename)
+
+ if os.path.exists(path):
+ with open(path, encoding="utf8") as file:
+ return file.read()
+
+ return ""
+
+
+def walk_files(path, allowed_extensions=None):
+ if not os.path.exists(path):
+ return
+
+ if allowed_extensions is not None:
+ allowed_extensions = set(allowed_extensions)
+
+ items = list(os.walk(path, followlinks=True))
+ items = sorted(items, key=lambda x: natural_sort_key(x[0]))
+
+ for root, _, files in items:
+ for filename in sorted(files, key=natural_sort_key):
+ if allowed_extensions is not None:
+ _, ext = os.path.splitext(filename)
+ if ext not in allowed_extensions:
+ continue
+
+ if not shared.opts.list_hidden_files and ("/." in root or "\\." in root):
+ continue
+
+ yield os.path.join(root, filename)
+
+
+def ldm_print(*args, **kwargs):
+ if shared.opts.hide_ldm_prints:
+ return
+
+ print(*args, **kwargs)
diff --git a/stable-diffusion-webui/modules/xlmr.py b/stable-diffusion-webui/modules/xlmr.py
new file mode 100644
index 0000000000000000000000000000000000000000..a407a3cade8198bd8600bc7c9bbf8d778520a28c
--- /dev/null
+++ b/stable-diffusion-webui/modules/xlmr.py
@@ -0,0 +1,137 @@
+from transformers import BertPreTrainedModel, BertConfig
+import torch.nn as nn
+import torch
+from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
+from transformers import XLMRobertaModel,XLMRobertaTokenizer
+from typing import Optional
+
+class BertSeriesConfig(BertConfig):
+ def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
+
+ super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
+ self.project_dim = project_dim
+ self.pooler_fn = pooler_fn
+ self.learn_encoder = learn_encoder
+
+class RobertaSeriesConfig(XLMRobertaConfig):
+ def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
+ super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+ self.project_dim = project_dim
+ self.pooler_fn = pooler_fn
+ self.learn_encoder = learn_encoder
+
+
+class BertSeriesModelWithTransformation(BertPreTrainedModel):
+
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+ _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+ config_class = BertSeriesConfig
+
+ def __init__(self, config=None, **kargs):
+ # modify initialization for autoloading
+ if config is None:
+ config = XLMRobertaConfig()
+ config.attention_probs_dropout_prob= 0.1
+ config.bos_token_id=0
+ config.eos_token_id=2
+ config.hidden_act='gelu'
+ config.hidden_dropout_prob=0.1
+ config.hidden_size=1024
+ config.initializer_range=0.02
+ config.intermediate_size=4096
+ config.layer_norm_eps=1e-05
+ config.max_position_embeddings=514
+
+ config.num_attention_heads=16
+ config.num_hidden_layers=24
+ config.output_past=True
+ config.pad_token_id=1
+ config.position_embedding_type= "absolute"
+
+ config.type_vocab_size= 1
+ config.use_cache=True
+ config.vocab_size= 250002
+ config.project_dim = 768
+ config.learn_encoder = False
+ super().__init__(config)
+ self.roberta = XLMRobertaModel(config)
+ self.transformation = nn.Linear(config.hidden_size,config.project_dim)
+ self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+ self.pooler = lambda x: x[:,0]
+ self.post_init()
+
+ def encode(self,c):
+ device = next(self.parameters()).device
+ text = self.tokenizer(c,
+ truncation=True,
+ max_length=77,
+ return_length=False,
+ return_overflowing_tokens=False,
+ padding="max_length",
+ return_tensors="pt")
+ text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
+ text["attention_mask"] = torch.tensor(
+ text['attention_mask']).to(device)
+ features = self(**text)
+ return features['projection_state']
+
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) :
+ r"""
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+ outputs = self.roberta(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=True,
+ return_dict=return_dict,
+ )
+
+ # last module outputs
+ sequence_output = outputs[0]
+
+
+ # project every module
+ sequence_output_ln = self.pre_LN(sequence_output)
+
+ # pooler
+ pooler_output = self.pooler(sequence_output_ln)
+ pooler_output = self.transformation(pooler_output)
+ projection_state = self.transformation(outputs.last_hidden_state)
+
+ return {
+ 'pooler_output':pooler_output,
+ 'last_hidden_state':outputs.last_hidden_state,
+ 'hidden_states':outputs.hidden_states,
+ 'attentions':outputs.attentions,
+ 'projection_state':projection_state,
+ 'sequence_out': sequence_output
+ }
+
+
+class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
+ base_model_prefix = 'roberta'
+ config_class= RobertaSeriesConfig
diff --git a/stable-diffusion-webui/package.json b/stable-diffusion-webui/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0ba406787db88b636d72767866274554f77381b
--- /dev/null
+++ b/stable-diffusion-webui/package.json
@@ -0,0 +1,11 @@
+{
+ "name": "stable-diffusion-webui",
+ "version": "0.0.0",
+ "devDependencies": {
+ "eslint": "^8.40.0"
+ },
+ "scripts": {
+ "lint": "eslint .",
+ "fix": "eslint --fix ."
+ }
+}
diff --git a/stable-diffusion-webui/pyproject.toml b/stable-diffusion-webui/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..80541a8f35319e15d837ea8bdd3ffc4de25776ea
--- /dev/null
+++ b/stable-diffusion-webui/pyproject.toml
@@ -0,0 +1,35 @@
+[tool.ruff]
+
+target-version = "py39"
+
+extend-select = [
+ "B",
+ "C",
+ "I",
+ "W",
+]
+
+exclude = [
+ "extensions",
+ "extensions-disabled",
+]
+
+ignore = [
+ "E501", # Line too long
+ "E731", # Do not assign a `lambda` expression, use a `def`
+
+ "I001", # Import block is un-sorted or un-formatted
+ "C901", # Function is too complex
+ "C408", # Rewrite as a literal
+ "W605", # invalid escape sequence, messes with some docstrings
+]
+
+[tool.ruff.per-file-ignores]
+"webui.py" = ["E402"] # Module level import not at top of file
+
+[tool.ruff.flake8-bugbear]
+# Allow default arguments like, e.g., `data: List[str] = fastapi.Query(None)`.
+extend-immutable-calls = ["fastapi.Depends", "fastapi.security.HTTPBasic"]
+
+[tool.pytest.ini_options]
+base_url = "http://127.0.0.1:7860"
diff --git a/stable-diffusion-webui/requirements-test.txt b/stable-diffusion-webui/requirements-test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..37838ca25e87551365de00a940f82654b0f7762b
--- /dev/null
+++ b/stable-diffusion-webui/requirements-test.txt
@@ -0,0 +1,3 @@
+pytest-base-url~=2.0
+pytest-cov~=4.0
+pytest~=7.3
diff --git a/stable-diffusion-webui/requirements.txt b/stable-diffusion-webui/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa1f929280503c7494f992aa29a76608ff52d5e5
--- /dev/null
+++ b/stable-diffusion-webui/requirements.txt
@@ -0,0 +1,34 @@
+GitPython
+Pillow
+accelerate
+
+basicsr
+blendmodes
+clean-fid
+einops
+fastapi>=0.90.1
+gfpgan
+gradio==3.41.2
+inflection
+jsonmerge
+kornia
+lark
+numpy
+omegaconf
+open-clip-torch
+
+piexif
+psutil
+pytorch_lightning
+realesrgan
+requests
+resize-right
+
+safetensors
+scikit-image>=0.19
+timm
+tomesd
+torch
+torchdiffeq
+torchsde
+transformers==4.30.2
diff --git a/stable-diffusion-webui/requirements_versions.txt b/stable-diffusion-webui/requirements_versions.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca49d6cb2302f2da3c675fe0308908310be80f68
--- /dev/null
+++ b/stable-diffusion-webui/requirements_versions.txt
@@ -0,0 +1,31 @@
+GitPython==3.1.32
+Pillow==9.5.0
+accelerate==0.21.0
+basicsr==1.4.2
+blendmodes==2022
+clean-fid==0.1.35
+einops==0.4.1
+fastapi==0.94.0
+gfpgan==1.3.8
+gradio==3.41.2
+httpcore==0.15
+inflection==0.5.1
+jsonmerge==1.8.0
+kornia==0.6.7
+lark==1.1.2
+numpy==1.23.5
+omegaconf==2.2.3
+open-clip-torch==2.20.0
+piexif==1.1.3
+psutil==5.9.5
+pytorch_lightning==1.9.4
+realesrgan==0.3.0
+resize-right==0.0.2
+safetensors==0.3.1
+scikit-image==0.21.0
+timm==0.9.2
+tomesd==0.1.3
+torch
+torchdiffeq==0.2.3
+torchsde==0.2.5
+transformers==4.30.2
diff --git a/stable-diffusion-webui/screenshot.png b/stable-diffusion-webui/screenshot.png
new file mode 100644
index 0000000000000000000000000000000000000000..c9b754b5e18cad82ef2042579194ac5965176ec4
--- /dev/null
+++ b/stable-diffusion-webui/screenshot.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b1e371a6d937b5504ceb69f0577086d562d94f42288e314821094ce7c79b2c09
+size 420577
diff --git a/stable-diffusion-webui/script.js b/stable-diffusion-webui/script.js
new file mode 100644
index 0000000000000000000000000000000000000000..34cca7651dd31a79fe68b27cb0febb58d3e4a237
--- /dev/null
+++ b/stable-diffusion-webui/script.js
@@ -0,0 +1,163 @@
+function gradioApp() {
+ const elems = document.getElementsByTagName('gradio-app');
+ const elem = elems.length == 0 ? document : elems[0];
+
+ if (elem !== document) {
+ elem.getElementById = function(id) {
+ return document.getElementById(id);
+ };
+ }
+ return elem.shadowRoot ? elem.shadowRoot : elem;
+}
+
+/**
+ * Get the currently selected top-level UI tab button (e.g. the button that says "Extras").
+ */
+function get_uiCurrentTab() {
+ return gradioApp().querySelector('#tabs > .tab-nav > button.selected');
+}
+
+/**
+ * Get the first currently visible top-level UI tab content (e.g. the div hosting the "txt2img" UI).
+ */
+function get_uiCurrentTabContent() {
+ return gradioApp().querySelector('#tabs > .tabitem[id^=tab_]:not([style*="display: none"])');
+}
+
+var uiUpdateCallbacks = [];
+var uiAfterUpdateCallbacks = [];
+var uiLoadedCallbacks = [];
+var uiTabChangeCallbacks = [];
+var optionsChangedCallbacks = [];
+var uiAfterUpdateTimeout = null;
+var uiCurrentTab = null;
+
+/**
+ * Register callback to be called at each UI update.
+ * The callback receives an array of MutationRecords as an argument.
+ */
+function onUiUpdate(callback) {
+ uiUpdateCallbacks.push(callback);
+}
+
+/**
+ * Register callback to be called soon after UI updates.
+ * The callback receives no arguments.
+ *
+ * This is preferred over `onUiUpdate` if you don't need
+ * access to the MutationRecords, as your function will
+ * not be called quite as often.
+ */
+function onAfterUiUpdate(callback) {
+ uiAfterUpdateCallbacks.push(callback);
+}
+
+/**
+ * Register callback to be called when the UI is loaded.
+ * The callback receives no arguments.
+ */
+function onUiLoaded(callback) {
+ uiLoadedCallbacks.push(callback);
+}
+
+/**
+ * Register callback to be called when the UI tab is changed.
+ * The callback receives no arguments.
+ */
+function onUiTabChange(callback) {
+ uiTabChangeCallbacks.push(callback);
+}
+
+/**
+ * Register callback to be called when the options are changed.
+ * The callback receives no arguments.
+ * @param callback
+ */
+function onOptionsChanged(callback) {
+ optionsChangedCallbacks.push(callback);
+}
+
+function executeCallbacks(queue, arg) {
+ for (const callback of queue) {
+ try {
+ callback(arg);
+ } catch (e) {
+ console.error("error running callback", callback, ":", e);
+ }
+ }
+}
+
+/**
+ * Schedule the execution of the callbacks registered with onAfterUiUpdate.
+ * The callbacks are executed after a short while, unless another call to this function
+ * is made before that time. IOW, the callbacks are executed only once, even
+ * when there are multiple mutations observed.
+ */
+function scheduleAfterUiUpdateCallbacks() {
+ clearTimeout(uiAfterUpdateTimeout);
+ uiAfterUpdateTimeout = setTimeout(function() {
+ executeCallbacks(uiAfterUpdateCallbacks);
+ }, 200);
+}
+
+var executedOnLoaded = false;
+
+document.addEventListener("DOMContentLoaded", function() {
+ var mutationObserver = new MutationObserver(function(m) {
+ if (!executedOnLoaded && gradioApp().querySelector('#txt2img_prompt')) {
+ executedOnLoaded = true;
+ executeCallbacks(uiLoadedCallbacks);
+ }
+
+ executeCallbacks(uiUpdateCallbacks, m);
+ scheduleAfterUiUpdateCallbacks();
+ const newTab = get_uiCurrentTab();
+ if (newTab && (newTab !== uiCurrentTab)) {
+ uiCurrentTab = newTab;
+ executeCallbacks(uiTabChangeCallbacks);
+ }
+ });
+ mutationObserver.observe(gradioApp(), {childList: true, subtree: true});
+});
+
+/**
+ * Add a ctrl+enter as a shortcut to start a generation
+ */
+document.addEventListener('keydown', function(e) {
+ var handled = false;
+ if (e.key !== undefined) {
+ if ((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
+ } else if (e.keyCode !== undefined) {
+ if ((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
+ }
+ if (handled) {
+ var button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
+ if (button) {
+ button.click();
+ }
+ e.preventDefault();
+ }
+});
+
+/**
+ * checks that a UI element is not in another hidden element or tab content
+ */
+function uiElementIsVisible(el) {
+ if (el === document) {
+ return true;
+ }
+
+ const computedStyle = getComputedStyle(el);
+ const isVisible = computedStyle.display !== 'none';
+
+ if (!isVisible) return false;
+ return uiElementIsVisible(el.parentNode);
+}
+
+function uiElementInSight(el) {
+ const clRect = el.getBoundingClientRect();
+ const windowHeight = window.innerHeight;
+ const isOnScreen = clRect.bottom > 0 && clRect.top < windowHeight;
+
+ return isOnScreen;
+}
diff --git a/stable-diffusion-webui/scripts/custom_code.py b/stable-diffusion-webui/scripts/custom_code.py
new file mode 100644
index 0000000000000000000000000000000000000000..b163b376c993da3512401586d2d8a2a265e0d4b1
--- /dev/null
+++ b/stable-diffusion-webui/scripts/custom_code.py
@@ -0,0 +1,90 @@
+import modules.scripts as scripts
+import gradio as gr
+import ast
+import copy
+
+from modules.processing import Processed
+from modules.shared import cmd_opts
+
+
+def convertExpr2Expression(expr):
+ expr.lineno = 0
+ expr.col_offset = 0
+ result = ast.Expression(expr.value, lineno=0, col_offset = 0)
+
+ return result
+
+
+def exec_with_return(code, module):
+ """
+ like exec() but can return values
+ https://stackoverflow.com/a/52361938/5862977
+ """
+ code_ast = ast.parse(code)
+
+ init_ast = copy.deepcopy(code_ast)
+ init_ast.body = code_ast.body[:-1]
+
+ last_ast = copy.deepcopy(code_ast)
+ last_ast.body = code_ast.body[-1:]
+
+ exec(compile(init_ast, "<ast>", "exec"), module.__dict__)
+ if type(last_ast.body[0]) == ast.Expr:
+ return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
+ else:
+ exec(compile(last_ast, "<ast>", "exec"), module.__dict__)
+
+
+class Script(scripts.Script):
+
+ def title(self):
+ return "Custom code"
+
+ def show(self, is_img2img):
+ return cmd_opts.allow_code
+
+ def ui(self, is_img2img):
+ example = """from modules.processing import process_images
+
+p.width = 768
+p.height = 768
+p.batch_size = 2
+p.steps = 10
+
+return process_images(p)
+"""
+
+
+ code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code"))
+ indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level"))
+
+ return [code, indent_level]
+
+ def run(self, p, code, indent_level):
+ assert cmd_opts.allow_code, '--allow-code option must be enabled'
+
+ display_result_data = [[], -1, ""]
+
+ def display(imgs, s=display_result_data[1], i=display_result_data[2]):
+ display_result_data[0] = imgs
+ display_result_data[1] = s
+ display_result_data[2] = i
+
+ from types import ModuleType
+ module = ModuleType("testmodule")
+ module.__dict__.update(globals())
+ module.p = p
+ module.display = display
+
+ indent = " " * indent_level
+ indented = code.replace('\n', f"\n{indent}")
+ body = f"""def __webuitemp__():
+{indent}{indented}
+__webuitemp__()"""
+
+ result = exec_with_return(body, module)
+
+ if isinstance(result, Processed):
+ return result
+
+ return Processed(p, *display_result_data)
diff --git a/stable-diffusion-webui/scripts/img2imgalt.py b/stable-diffusion-webui/scripts/img2imgalt.py
new file mode 100644
index 0000000000000000000000000000000000000000..85fcbe6c5c2680e29bc47b65a02fb07a71eb338f
--- /dev/null
+++ b/stable-diffusion-webui/scripts/img2imgalt.py
@@ -0,0 +1,218 @@
+from collections import namedtuple
+
+import numpy as np
+from tqdm import trange
+
+import modules.scripts as scripts
+import gradio as gr
+
+from modules import processing, shared, sd_samplers, sd_samplers_common
+
+import torch
+import k_diffusion as K
+
+def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
+ x = p.init_latent
+
+ s_in = x.new_ones([x.shape[0]])
+ if shared.sd_model.parameterization == "v":
+ dnw = K.external.CompVisVDenoiser(shared.sd_model)
+ skip = 1
+ else:
+ dnw = K.external.CompVisDenoiser(shared.sd_model)
+ skip = 0
+ sigmas = dnw.get_sigmas(steps).flip(0)
+
+ shared.state.sampling_steps = steps
+
+ for i in trange(1, len(sigmas)):
+ shared.state.sampling_step += 1
+
+ x_in = torch.cat([x] * 2)
+ sigma_in = torch.cat([sigmas[i] * s_in] * 2)
+ cond_in = torch.cat([uncond, cond])
+
+ image_conditioning = torch.cat([p.image_conditioning] * 2)
+ cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
+
+ c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
+ t = dnw.sigma_to_t(sigma_in)
+
+ eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
+ denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
+
+ denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
+
+ d = (x - denoised) / sigmas[i]
+ dt = sigmas[i] - sigmas[i - 1]
+
+ x = x + d * dt
+
+ sd_samplers_common.store_latent(x)
+
+ # This shouldn't be necessary, but solved some VRAM issues
+ del x_in, sigma_in, cond_in, c_out, c_in, t,
+ del eps, denoised_uncond, denoised_cond, denoised, d, dt
+
+ shared.state.nextjob()
+
+ return x / x.std()
+
+
+Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
+
+
+# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
+def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
+ x = p.init_latent
+
+ s_in = x.new_ones([x.shape[0]])
+ if shared.sd_model.parameterization == "v":
+ dnw = K.external.CompVisVDenoiser(shared.sd_model)
+ skip = 1
+ else:
+ dnw = K.external.CompVisDenoiser(shared.sd_model)
+ skip = 0
+ sigmas = dnw.get_sigmas(steps).flip(0)
+
+ shared.state.sampling_steps = steps
+
+ for i in trange(1, len(sigmas)):
+ shared.state.sampling_step += 1
+
+ x_in = torch.cat([x] * 2)
+ sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
+ cond_in = torch.cat([uncond, cond])
+
+ image_conditioning = torch.cat([p.image_conditioning] * 2)
+ cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
+
+ c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
+
+ if i == 1:
+ t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
+ else:
+ t = dnw.sigma_to_t(sigma_in)
+
+ eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
+ denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
+
+ denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
+
+ if i == 1:
+ d = (x - denoised) / (2 * sigmas[i])
+ else:
+ d = (x - denoised) / sigmas[i - 1]
+
+ dt = sigmas[i] - sigmas[i - 1]
+ x = x + d * dt
+
+ sd_samplers_common.store_latent(x)
+
+ # This shouldn't be necessary, but solved some VRAM issues
+ del x_in, sigma_in, cond_in, c_out, c_in, t,
+ del eps, denoised_uncond, denoised_cond, denoised, d, dt
+
+ shared.state.nextjob()
+
+ return x / sigmas[-1]
+
+
+class Script(scripts.Script):
+ def __init__(self):
+ self.cache = None
+
+ def title(self):
+ return "img2img alternative test"
+
+ def show(self, is_img2img):
+ return is_img2img
+
+ def ui(self, is_img2img):
+ info = gr.Markdown('''
+ * `CFG Scale` should be 2 or lower.
+ ''')
+
+ override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True, elem_id=self.elem_id("override_sampler"))
+
+ override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True, elem_id=self.elem_id("override_prompt"))
+ original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt"))
+ original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt"))
+
+ override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps"))
+ st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st"))
+
+ override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength"))
+
+ cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg"))
+ randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness"))
+ sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))
+
+ return [
+ info,
+ override_sampler,
+ override_prompt, original_prompt, original_negative_prompt,
+ override_steps, st,
+ override_strength,
+ cfg, randomness, sigma_adjustment,
+ ]
+
+ def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
+ # Override
+ if override_sampler:
+ p.sampler_name = "Euler"
+ if override_prompt:
+ p.prompt = original_prompt
+ p.negative_prompt = original_negative_prompt
+ if override_steps:
+ p.steps = st
+ if override_strength:
+ p.denoising_strength = 1.0
+
+ def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
+ lat = (p.init_latent.cpu().numpy() * 10).astype(int)
+
+ same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
+ and self.cache.original_prompt == original_prompt \
+ and self.cache.original_negative_prompt == original_negative_prompt \
+ and self.cache.sigma_adjustment == sigma_adjustment
+ same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
+
+ if same_everything:
+ rec_noise = self.cache.noise
+ else:
+ shared.state.job_count += 1
+ cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
+ uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
+ if sigma_adjustment:
+ rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
+ else:
+ rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
+ self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
+
+ rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
+
+ combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
+
+ sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
+
+ sigmas = sampler.model_wrap.get_sigmas(p.steps)
+
+ noise_dt = combined_noise - (p.init_latent / sigmas[0])
+
+ p.seed = p.seed + 1
+
+ return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
+
+ p.sample = sample_extra
+
+ p.extra_generation_params["Decode prompt"] = original_prompt
+ p.extra_generation_params["Decode negative prompt"] = original_negative_prompt
+ p.extra_generation_params["Decode CFG scale"] = cfg
+ p.extra_generation_params["Decode steps"] = st
+ p.extra_generation_params["Randomness"] = randomness
+ p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
+
+ processed = processing.process_images(p)
+
+ return processed
diff --git a/stable-diffusion-webui/scripts/loopback.py b/stable-diffusion-webui/scripts/loopback.py
new file mode 100644
index 0000000000000000000000000000000000000000..80fba9f1b2913634067968208e49cd4b5b714d1c
--- /dev/null
+++ b/stable-diffusion-webui/scripts/loopback.py
@@ -0,0 +1,140 @@
+import math
+
+import gradio as gr
+import modules.scripts as scripts
+from modules import deepbooru, images, processing, shared
+from modules.processing import Processed
+from modules.shared import opts, state
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "Loopback"
+
+ def show(self, is_img2img):
+ return is_img2img
+
+ def ui(self, is_img2img):
+ loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
+ final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
+ denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
+ append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
+
+ return [loops, final_denoising_strength, denoising_curve, append_interrogation]
+
+ def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
+ processing.fix_seed(p)
+ batch_count = p.n_iter
+ p.extra_generation_params = {
+ "Final denoising strength": final_denoising_strength,
+ "Denoising curve": denoising_curve
+ }
+
+ p.batch_size = 1
+ p.n_iter = 1
+
+ info = None
+ initial_seed = None
+ initial_info = None
+ initial_denoising_strength = p.denoising_strength
+
+ grids = []
+ all_images = []
+ original_init_image = p.init_images
+ original_prompt = p.prompt
+ original_inpainting_fill = p.inpainting_fill
+ state.job_count = loops * batch_count
+
+ initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
+
+ def calculate_denoising_strength(loop):
+ strength = initial_denoising_strength
+
+ if loops == 1:
+ return strength
+
+ progress = loop / (loops - 1)
+ if denoising_curve == "Aggressive":
+ strength = math.sin((progress) * math.pi * 0.5)
+ elif denoising_curve == "Lazy":
+ strength = 1 - math.cos((progress) * math.pi * 0.5)
+ else:
+ strength = progress
+
+ change = (final_denoising_strength - initial_denoising_strength) * strength
+ return initial_denoising_strength + change
+
+ history = []
+
+ for n in range(batch_count):
+ # Reset to original init image at the start of each batch
+ p.init_images = original_init_image
+
+ # Reset to original denoising strength
+ p.denoising_strength = initial_denoising_strength
+
+ last_image = None
+
+ for i in range(loops):
+ p.n_iter = 1
+ p.batch_size = 1
+ p.do_not_save_grid = True
+
+ if opts.img2img_color_correction:
+ p.color_corrections = initial_color_corrections
+
+ if append_interrogation != "None":
+ p.prompt = f"{original_prompt}, " if original_prompt else ""
+ if append_interrogation == "CLIP":
+ p.prompt += shared.interrogator.interrogate(p.init_images[0])
+ elif append_interrogation == "DeepBooru":
+ p.prompt += deepbooru.model.tag(p.init_images[0])
+
+ state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
+
+ processed = processing.process_images(p)
+
+ # Generation cancelled.
+ if state.interrupted:
+ break
+
+ if initial_seed is None:
+ initial_seed = processed.seed
+ initial_info = processed.info
+
+ p.seed = processed.seed + 1
+ p.denoising_strength = calculate_denoising_strength(i + 1)
+
+ if state.skipped:
+ break
+
+ last_image = processed.images[0]
+ p.init_images = [last_image]
+ p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
+
+ if batch_count == 1:
+ history.append(last_image)
+ all_images.append(last_image)
+
+ if batch_count > 1 and not state.skipped and not state.interrupted:
+ history.append(last_image)
+ all_images.append(last_image)
+
+ p.inpainting_fill = original_inpainting_fill
+
+ if state.interrupted:
+ break
+
+ if len(history) > 1:
+ grid = images.image_grid(history, rows=1)
+ if opts.grid_save:
+ images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
+
+ if opts.return_grid:
+ grids.append(grid)
+
+ all_images = grids + all_images
+
+ processed = Processed(p, all_images, initial_seed, initial_info)
+
+ return processed
diff --git a/stable-diffusion-webui/scripts/outpainting_mk_2.py b/stable-diffusion-webui/scripts/outpainting_mk_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..7034e3bb967e96afea21e212850febe0e4fc249b
--- /dev/null
+++ b/stable-diffusion-webui/scripts/outpainting_mk_2.py
@@ -0,0 +1,295 @@
+import math
+
+import numpy as np
+import skimage
+
+import modules.scripts as scripts
+import gradio as gr
+from PIL import Image, ImageDraw
+
+from modules import images
+from modules.processing import Processed, process_images
+from modules.shared import opts, state
+
+
+# this function is taken from https://github.com/parlance-zz/g-diffuser-bot
+def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
+ # helper fft routines that keep ortho normalization and auto-shift before and after fft
+ def _fft2(data):
+ if data.ndim > 2: # has channels
+ out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+ for c in range(data.shape[2]):
+ c_data = data[:, :, c]
+ out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
+ out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
+ else: # one channel
+ out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+ out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
+ out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
+
+ return out_fft
+
+ def _ifft2(data):
+ if data.ndim > 2: # has channels
+ out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
+ for c in range(data.shape[2]):
+ c_data = data[:, :, c]
+ out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
+ out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
+ else: # one channel
+ out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
+ out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
+ out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
+
+ return out_ifft
+
+ def _get_gaussian_window(width, height, std=3.14, mode=0):
+ window_scale_x = float(width / min(width, height))
+ window_scale_y = float(height / min(width, height))
+
+ window = np.zeros((width, height))
+ x = (np.arange(width) / width * 2. - 1.) * window_scale_x
+ for y in range(height):
+ fy = (y / height * 2. - 1.) * window_scale_y
+ if mode == 0:
+ window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
+ else:
+ window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian
+
+ return window
+
+ def _get_masked_window_rgb(np_mask_grey, hardness=1.):
+ np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
+ if hardness != 1.:
+ hardened = np_mask_grey[:] ** hardness
+ else:
+ hardened = np_mask_grey[:]
+ for c in range(3):
+ np_mask_rgb[:, :, c] = hardened[:]
+ return np_mask_rgb
+
+ width = _np_src_image.shape[0]
+ height = _np_src_image.shape[1]
+ num_channels = _np_src_image.shape[2]
+
+ _np_src_image[:] * (1. - np_mask_rgb)
+ np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
+ img_mask = np_mask_grey > 1e-6
+ ref_mask = np_mask_grey < 1e-3
+
+ windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey))
+ windowed_image /= np.max(windowed_image)
+ windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
+
+ src_fft = _fft2(windowed_image) # get feature statistics from masked src img
+ src_dist = np.absolute(src_fft)
+ src_phase = src_fft / src_dist
+
+ # create a generator with a static seed to make outpainting deterministic / only follow global seed
+ rng = np.random.default_rng(0)
+
+ noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
+ noise_rgb = rng.random((width, height, num_channels))
+ noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
+ noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
+ for c in range(num_channels):
+ noise_rgb[:, :, c] += (1. - color_variation) * noise_grey
+
+ noise_fft = _fft2(noise_rgb)
+ for c in range(num_channels):
+ noise_fft[:, :, c] *= noise_window
+ noise_rgb = np.real(_ifft2(noise_fft))
+ shaped_noise_fft = _fft2(noise_rgb)
+ shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
+
+ brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now
+ contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
+
+ # scikit-image is used for histogram matching, very convenient!
+ shaped_noise = np.real(_ifft2(shaped_noise_fft))
+ shaped_noise -= np.min(shaped_noise)
+ shaped_noise /= np.max(shaped_noise)
+ shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
+ shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
+
+ matched_noise = shaped_noise[:]
+
+ return np.clip(matched_noise, 0., 1.)
+
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "Outpainting mk2"
+
+ def show(self, is_img2img):
+ return is_img2img
+
+ def ui(self, is_img2img):
+ if not is_img2img:
+ return None
+
+ info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
+
+ pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id("mask_blur"))
+ direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
+ noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id("noise_q"))
+ color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id("color_variation"))
+
+ return [info, pixels, mask_blur, direction, noise_q, color_variation]
+
+ def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
+ initial_seed_and_info = [None, None]
+
+ process_width = p.width
+ process_height = p.height
+
+ p.inpaint_full_res = False
+ p.inpainting_fill = 1
+ p.do_not_save_samples = True
+ p.do_not_save_grid = True
+
+ left = pixels if "left" in direction else 0
+ right = pixels if "right" in direction else 0
+ up = pixels if "up" in direction else 0
+ down = pixels if "down" in direction else 0
+
+ if left > 0 or right > 0:
+ mask_blur_x = mask_blur
+ else:
+ mask_blur_x = 0
+
+ if up > 0 or down > 0:
+ mask_blur_y = mask_blur
+ else:
+ mask_blur_y = 0
+
+ p.mask_blur_x = mask_blur_x*4
+ p.mask_blur_y = mask_blur_y*4
+
+ init_img = p.init_images[0]
+ target_w = math.ceil((init_img.width + left + right) / 64) * 64
+ target_h = math.ceil((init_img.height + up + down) / 64) * 64
+
+ if left > 0:
+ left = left * (target_w - init_img.width) // (left + right)
+
+ if right > 0:
+ right = target_w - init_img.width - left
+
+ if up > 0:
+ up = up * (target_h - init_img.height) // (up + down)
+
+ if down > 0:
+ down = target_h - init_img.height - up
+
+ def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
+ is_horiz = is_left or is_right
+ is_vert = is_top or is_bottom
+ pixels_horiz = expand_pixels if is_horiz else 0
+ pixels_vert = expand_pixels if is_vert else 0
+
+ images_to_process = []
+ output_images = []
+ for n in range(count):
+ res_w = init[n].width + pixels_horiz
+ res_h = init[n].height + pixels_vert
+ process_res_w = math.ceil(res_w / 64) * 64
+ process_res_h = math.ceil(res_h / 64) * 64
+
+ img = Image.new("RGB", (process_res_w, process_res_h))
+ img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
+ mask = Image.new("RGB", (process_res_w, process_res_h), "white")
+ draw = ImageDraw.Draw(mask)
+ draw.rectangle((
+ expand_pixels + mask_blur_x if is_left else 0,
+ expand_pixels + mask_blur_y if is_top else 0,
+ mask.width - expand_pixels - mask_blur_x if is_right else res_w,
+ mask.height - expand_pixels - mask_blur_y if is_bottom else res_h,
+ ), fill="black")
+
+ np_image = (np.asarray(img) / 255.0).astype(np.float64)
+ np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
+ noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
+ output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
+
+ target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
+ target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
+ p.width = target_width if is_horiz else img.width
+ p.height = target_height if is_vert else img.height
+
+ crop_region = (
+ 0 if is_left else output_images[n].width - target_width,
+ 0 if is_top else output_images[n].height - target_height,
+ target_width if is_left else output_images[n].width,
+ target_height if is_top else output_images[n].height,
+ )
+ mask = mask.crop(crop_region)
+ p.image_mask = mask
+
+ image_to_process = output_images[n].crop(crop_region)
+ images_to_process.append(image_to_process)
+
+ p.init_images = images_to_process
+
+ latent_mask = Image.new("RGB", (p.width, p.height), "white")
+ draw = ImageDraw.Draw(latent_mask)
+ draw.rectangle((
+ expand_pixels + mask_blur_x * 2 if is_left else 0,
+ expand_pixels + mask_blur_y * 2 if is_top else 0,
+ mask.width - expand_pixels - mask_blur_x * 2 if is_right else res_w,
+ mask.height - expand_pixels - mask_blur_y * 2 if is_bottom else res_h,
+ ), fill="black")
+ p.latent_mask = latent_mask
+
+ proc = process_images(p)
+
+ if initial_seed_and_info[0] is None:
+ initial_seed_and_info[0] = proc.seed
+ initial_seed_and_info[1] = proc.info
+
+ for n in range(count):
+ output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))
+ output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
+
+ return output_images
+
+ batch_count = p.n_iter
+ batch_size = p.batch_size
+ p.n_iter = 1
+ state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
+ all_processed_images = []
+
+ for i in range(batch_count):
+ imgs = [init_img] * batch_size
+ state.job = f"Batch {i + 1} out of {batch_count}"
+
+ if left > 0:
+ imgs = expand(imgs, batch_size, left, is_left=True)
+ if right > 0:
+ imgs = expand(imgs, batch_size, right, is_right=True)
+ if up > 0:
+ imgs = expand(imgs, batch_size, up, is_top=True)
+ if down > 0:
+ imgs = expand(imgs, batch_size, down, is_bottom=True)
+
+ all_processed_images += imgs
+
+ all_images = all_processed_images
+
+ combined_grid_image = images.image_grid(all_processed_images)
+ unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
+ if opts.return_grid and not unwanted_grid_because_of_img_count:
+ all_images = [combined_grid_image] + all_processed_images
+
+ res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
+
+ if opts.samples_save:
+ for img in all_processed_images:
+ images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.samples_format, info=res.info, p=p)
+
+ if opts.grid_save and not unwanted_grid_because_of_img_count:
+ images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
+
+ return res
diff --git a/stable-diffusion-webui/scripts/poor_mans_outpainting.py b/stable-diffusion-webui/scripts/poor_mans_outpainting.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e5b1eecc637eaf3f188ad1fc40c93fb2d18c71a
--- /dev/null
+++ b/stable-diffusion-webui/scripts/poor_mans_outpainting.py
@@ -0,0 +1,146 @@
+import math
+
+import modules.scripts as scripts
+import gradio as gr
+from PIL import Image, ImageDraw
+
+from modules import images, devices
+from modules.processing import Processed, process_images
+from modules.shared import opts, state
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "Poor man's outpainting"
+
+ def show(self, is_img2img):
+ return is_img2img
+
+ def ui(self, is_img2img):
+ if not is_img2img:
+ return None
+
+ pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
+ mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id("mask_blur"))
+ inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", elem_id=self.elem_id("inpainting_fill"))
+ direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
+
+ return [pixels, mask_blur, inpainting_fill, direction]
+
+ def run(self, p, pixels, mask_blur, inpainting_fill, direction):
+ initial_seed = None
+ initial_info = None
+
+ p.mask_blur = mask_blur * 2
+ p.inpainting_fill = inpainting_fill
+ p.inpaint_full_res = False
+
+ left = pixels if "left" in direction else 0
+ right = pixels if "right" in direction else 0
+ up = pixels if "up" in direction else 0
+ down = pixels if "down" in direction else 0
+
+ init_img = p.init_images[0]
+ target_w = math.ceil((init_img.width + left + right) / 64) * 64
+ target_h = math.ceil((init_img.height + up + down) / 64) * 64
+
+ if left > 0:
+ left = left * (target_w - init_img.width) // (left + right)
+ if right > 0:
+ right = target_w - init_img.width - left
+
+ if up > 0:
+ up = up * (target_h - init_img.height) // (up + down)
+
+ if down > 0:
+ down = target_h - init_img.height - up
+
+ img = Image.new("RGB", (target_w, target_h))
+ img.paste(init_img, (left, up))
+
+ mask = Image.new("L", (img.width, img.height), "white")
+ draw = ImageDraw.Draw(mask)
+ draw.rectangle((
+ left + (mask_blur * 2 if left > 0 else 0),
+ up + (mask_blur * 2 if up > 0 else 0),
+ mask.width - right - (mask_blur * 2 if right > 0 else 0),
+ mask.height - down - (mask_blur * 2 if down > 0 else 0)
+ ), fill="black")
+
+ latent_mask = Image.new("L", (img.width, img.height), "white")
+ latent_draw = ImageDraw.Draw(latent_mask)
+ latent_draw.rectangle((
+ left + (mask_blur//2 if left > 0 else 0),
+ up + (mask_blur//2 if up > 0 else 0),
+ mask.width - right - (mask_blur//2 if right > 0 else 0),
+ mask.height - down - (mask_blur//2 if down > 0 else 0)
+ ), fill="black")
+
+ devices.torch_gc()
+
+ grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
+ grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
+ grid_latent_mask = images.split_grid(latent_mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
+
+ p.n_iter = 1
+ p.batch_size = 1
+ p.do_not_save_grid = True
+ p.do_not_save_samples = True
+
+ work = []
+ work_mask = []
+ work_latent_mask = []
+ work_results = []
+
+ for (y, h, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):
+ for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):
+ x, w = tiledata[0:2]
+
+ if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
+ continue
+
+ work.append(tiledata[2])
+ work_mask.append(tiledata_mask[2])
+ work_latent_mask.append(tiledata_latent_mask[2])
+
+ batch_count = len(work)
+ print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
+
+ state.job_count = batch_count
+
+ for i in range(batch_count):
+ p.init_images = [work[i]]
+ p.image_mask = work_mask[i]
+ p.latent_mask = work_latent_mask[i]
+
+ state.job = f"Batch {i + 1} out of {batch_count}"
+ processed = process_images(p)
+
+ if initial_seed is None:
+ initial_seed = processed.seed
+ initial_info = processed.info
+
+ p.seed = processed.seed + 1
+ work_results += processed.images
+
+
+ image_index = 0
+ for y, h, row in grid.tiles:
+ for tiledata in row:
+ x, w = tiledata[0:2]
+
+ if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
+ continue
+
+ tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
+ image_index += 1
+
+ combined_image = images.combine_grid(grid)
+
+ if opts.samples_save:
+ images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
+
+ processed = Processed(p, [combined_image], initial_seed, initial_info)
+
+ return processed
+
diff --git a/stable-diffusion-webui/scripts/postprocessing_codeformer.py b/stable-diffusion-webui/scripts/postprocessing_codeformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e337ec41ffffe11fd88fced0ff4f6338d959571
--- /dev/null
+++ b/stable-diffusion-webui/scripts/postprocessing_codeformer.py
@@ -0,0 +1,36 @@
+from PIL import Image
+import numpy as np
+
+from modules import scripts_postprocessing, codeformer_model
+import gradio as gr
+
+from modules.ui_components import FormRow
+
+
+class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):
+ name = "CodeFormer"
+ order = 3000
+
+ def ui(self):
+ with FormRow():
+ codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, elem_id="extras_codeformer_visibility")
+ codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
+
+ return {
+ "codeformer_visibility": codeformer_visibility,
+ "codeformer_weight": codeformer_weight,
+ }
+
+ def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):
+ if codeformer_visibility == 0:
+ return
+
+ restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)
+ res = Image.fromarray(restored_img)
+
+ if codeformer_visibility < 1.0:
+ res = Image.blend(pp.image, res, codeformer_visibility)
+
+ pp.image = res
+ pp.info["CodeFormer visibility"] = round(codeformer_visibility, 3)
+ pp.info["CodeFormer weight"] = round(codeformer_weight, 3)
diff --git a/stable-diffusion-webui/scripts/postprocessing_gfpgan.py b/stable-diffusion-webui/scripts/postprocessing_gfpgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f7c2baaa28333958818d332324b34bcb8bce3ca
--- /dev/null
+++ b/stable-diffusion-webui/scripts/postprocessing_gfpgan.py
@@ -0,0 +1,33 @@
+from PIL import Image
+import numpy as np
+
+from modules import scripts_postprocessing, gfpgan_model
+import gradio as gr
+
+from modules.ui_components import FormRow
+
+
+class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
+ name = "GFPGAN"
+ order = 2000
+
+ def ui(self):
+ with FormRow():
+ gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, elem_id="extras_gfpgan_visibility")
+
+ return {
+ "gfpgan_visibility": gfpgan_visibility,
+ }
+
+ def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):
+ if gfpgan_visibility == 0:
+ return
+
+ restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
+ res = Image.fromarray(restored_img)
+
+ if gfpgan_visibility < 1.0:
+ res = Image.blend(pp.image, res, gfpgan_visibility)
+
+ pp.image = res
+ pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)
diff --git a/stable-diffusion-webui/scripts/postprocessing_upscale.py b/stable-diffusion-webui/scripts/postprocessing_upscale.py
new file mode 100644
index 0000000000000000000000000000000000000000..972e68b899264a28301c67d72f19185caad5ce27
--- /dev/null
+++ b/stable-diffusion-webui/scripts/postprocessing_upscale.py
@@ -0,0 +1,137 @@
+from PIL import Image
+import numpy as np
+
+from modules import scripts_postprocessing, shared
+import gradio as gr
+
+from modules.ui_components import FormRow, ToolButton
+from modules.ui import switch_values_symbol
+
+upscale_cache = {}
+
+
+class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
+ name = "Upscale"
+ order = 1000
+
+ def ui(self):
+ selected_tab = gr.State(value=0)
+
+ with gr.Column():
+ with FormRow():
+ with gr.Tabs(elem_id="extras_resize_mode"):
+ with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+ upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+
+ with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
+ with FormRow():
+ with gr.Column(elem_id="upscaling_column_size", scale=4):
+ upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
+ upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
+ with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
+ upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+
+ with FormRow():
+ extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+
+ with FormRow():
+ extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+ extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+
+ upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
+ tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
+ tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
+
+ return {
+ "upscale_mode": selected_tab,
+ "upscale_by": upscaling_resize,
+ "upscale_to_width": upscaling_resize_w,
+ "upscale_to_height": upscaling_resize_h,
+ "upscale_crop": upscaling_crop,
+ "upscaler_1_name": extras_upscaler_1,
+ "upscaler_2_name": extras_upscaler_2,
+ "upscaler_2_visibility": extras_upscaler_2_visibility,
+ }
+
+ def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop):
+ if upscale_mode == 1:
+ upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
+ info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}"
+ else:
+ info["Postprocess upscale by"] = upscale_by
+
+ cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
+ cached_image = upscale_cache.pop(cache_key, None)
+
+ if cached_image is not None:
+ image = cached_image
+ else:
+ image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path)
+
+ upscale_cache[cache_key] = image
+ if len(upscale_cache) > shared.opts.upscaling_max_images_in_cache:
+ upscale_cache.pop(next(iter(upscale_cache), None), None)
+
+ if upscale_mode == 1 and upscale_crop:
+ cropped = Image.new("RGB", (upscale_to_width, upscale_to_height))
+ cropped.paste(image, box=(upscale_to_width // 2 - image.width // 2, upscale_to_height // 2 - image.height // 2))
+ image = cropped
+ info["Postprocess crop to"] = f"{image.width}x{image.height}"
+
+ return image
+
+ def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
+ if upscaler_1_name == "None":
+ upscaler_1_name = None
+
+ upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_1_name]), None)
+ assert upscaler1 or (upscaler_1_name is None), f'could not find upscaler named {upscaler_1_name}'
+
+ if not upscaler1:
+ return
+
+ if upscaler_2_name == "None":
+ upscaler_2_name = None
+
+ upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None)
+ assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
+
+ upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
+ pp.info["Postprocess upscaler"] = upscaler1.name
+
+ if upscaler2 and upscaler_2_visibility > 0:
+ second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
+ upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
+
+ pp.info["Postprocess upscaler 2"] = upscaler2.name
+
+ pp.image = upscaled_image
+
+ def image_changed(self):
+ upscale_cache.clear()
+
+
+class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
+ name = "Simple Upscale"
+ order = 900
+
+ def ui(self):
+ with FormRow():
+ upscaler_name = gr.Dropdown(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+ upscale_by = gr.Slider(minimum=0.05, maximum=8.0, step=0.05, label="Upscale by", value=2)
+
+ return {
+ "upscale_by": upscale_by,
+ "upscaler_name": upscaler_name,
+ }
+
+ def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):
+ if upscaler_name is None or upscaler_name == "None":
+ return
+
+ upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_name]), None)
+ assert upscaler1, f'could not find upscaler named {upscaler_name}'
+
+ pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False)
+ pp.info["Postprocess upscaler"] = upscaler1.name
diff --git a/stable-diffusion-webui/scripts/prompt_matrix.py b/stable-diffusion-webui/scripts/prompt_matrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..409c3047aedcb2f9d7d55d39dc23a1b7c20c4c09
--- /dev/null
+++ b/stable-diffusion-webui/scripts/prompt_matrix.py
@@ -0,0 +1,108 @@
+import math
+
+import modules.scripts as scripts
+import gradio as gr
+
+from modules import images
+from modules.processing import process_images
+from modules.shared import opts, state
+import modules.sd_samplers
+
+
+def draw_xy_grid(xs, ys, x_label, y_label, cell):
+ res = []
+
+ ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
+ hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
+
+ first_processed = None
+
+ state.job_count = len(xs) * len(ys)
+
+ for iy, y in enumerate(ys):
+ for ix, x in enumerate(xs):
+ state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
+
+ processed = cell(x, y)
+ if first_processed is None:
+ first_processed = processed
+
+ res.append(processed.images[0])
+
+ grid = images.image_grid(res, rows=len(ys))
+ grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
+
+ first_processed.images = [grid]
+
+ return first_processed
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "Prompt matrix"
+
+ def ui(self, is_img2img):
+ gr.HTML('<br />')
+ with gr.Row():
+ with gr.Column():
+ put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
+ different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
+ with gr.Column():
+ prompt_type = gr.Radio(["positive", "negative"], label="Select prompt", elem_id=self.elem_id("prompt_type"), value="positive")
+ variations_delimiter = gr.Radio(["comma", "space"], label="Select joining char", elem_id=self.elem_id("variations_delimiter"), value="comma")
+ with gr.Column():
+ margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
+
+ return [put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size]
+
+ def run(self, p, put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size):
+ modules.processing.fix_seed(p)
+ # Raise error if promp type is not positive or negative
+ if prompt_type not in ["positive", "negative"]:
+ raise ValueError(f"Unknown prompt type {prompt_type}")
+ # Raise error if variations delimiter is not comma or space
+ if variations_delimiter not in ["comma", "space"]:
+ raise ValueError(f"Unknown variations delimiter {variations_delimiter}")
+
+ prompt = p.prompt if prompt_type == "positive" else p.negative_prompt
+ original_prompt = prompt[0] if type(prompt) == list else prompt
+ positive_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
+
+ delimiter = ", " if variations_delimiter == "comma" else " "
+
+ all_prompts = []
+ prompt_matrix_parts = original_prompt.split("|")
+ combination_count = 2 ** (len(prompt_matrix_parts) - 1)
+ for combination_num in range(combination_count):
+ selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
+
+ if put_at_start:
+ selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
+ else:
+ selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
+
+ all_prompts.append(delimiter.join(selected_prompts))
+
+ p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
+ p.do_not_save_grid = True
+
+ print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
+
+ if prompt_type == "positive":
+ p.prompt = all_prompts
+ else:
+ p.negative_prompt = all_prompts
+ p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
+ p.prompt_for_display = positive_prompt
+ processed = process_images(p)
+
+ grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
+ grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[0].height, prompt_matrix_parts, margin_size)
+ processed.images.insert(0, grid)
+ processed.index_of_first_image = 1
+ processed.infotexts.insert(0, processed.infotexts[0])
+
+ if opts.grid_save:
+ images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", extension=opts.grid_format, prompt=original_prompt, seed=processed.seed, grid=True, p=p)
+
+ return processed
diff --git a/stable-diffusion-webui/scripts/prompts_from_file.py b/stable-diffusion-webui/scripts/prompts_from_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..b19fa94274b1070af61be9ec5e21b8e3c9cde1a7
--- /dev/null
+++ b/stable-diffusion-webui/scripts/prompts_from_file.py
@@ -0,0 +1,169 @@
+import copy
+import random
+import shlex
+
+import modules.scripts as scripts
+import gradio as gr
+
+from modules import sd_samplers, errors
+from modules.processing import Processed, process_images
+from modules.shared import state
+
+
+def process_string_tag(tag):
+ return tag
+
+
+def process_int_tag(tag):
+ return int(tag)
+
+
+def process_float_tag(tag):
+ return float(tag)
+
+
+def process_boolean_tag(tag):
+ return True if (tag == "true") else False
+
+
+prompt_tags = {
+ "sd_model": None,
+ "outpath_samples": process_string_tag,
+ "outpath_grids": process_string_tag,
+ "prompt_for_display": process_string_tag,
+ "prompt": process_string_tag,
+ "negative_prompt": process_string_tag,
+ "styles": process_string_tag,
+ "seed": process_int_tag,
+ "subseed_strength": process_float_tag,
+ "subseed": process_int_tag,
+ "seed_resize_from_h": process_int_tag,
+ "seed_resize_from_w": process_int_tag,
+ "sampler_index": process_int_tag,
+ "sampler_name": process_string_tag,
+ "batch_size": process_int_tag,
+ "n_iter": process_int_tag,
+ "steps": process_int_tag,
+ "cfg_scale": process_float_tag,
+ "width": process_int_tag,
+ "height": process_int_tag,
+ "restore_faces": process_boolean_tag,
+ "tiling": process_boolean_tag,
+ "do_not_save_samples": process_boolean_tag,
+ "do_not_save_grid": process_boolean_tag
+}
+
+
+def cmdargs(line):
+ args = shlex.split(line)
+ pos = 0
+ res = {}
+
+ while pos < len(args):
+ arg = args[pos]
+
+ assert arg.startswith("--"), f'must start with "--": {arg}'
+ assert pos+1 < len(args), f'missing argument for command line option {arg}'
+
+ tag = arg[2:]
+
+ if tag == "prompt" or tag == "negative_prompt":
+ pos += 1
+ prompt = args[pos]
+ pos += 1
+ while pos < len(args) and not args[pos].startswith("--"):
+ prompt += " "
+ prompt += args[pos]
+ pos += 1
+ res[tag] = prompt
+ continue
+
+
+ func = prompt_tags.get(tag, None)
+ assert func, f'unknown commandline option: {arg}'
+
+ val = args[pos+1]
+ if tag == "sampler_name":
+ val = sd_samplers.samplers_map.get(val.lower(), None)
+
+ res[tag] = func(val)
+
+ pos += 2
+
+ return res
+
+
+def load_prompt_file(file):
+ if file is None:
+ return None, gr.update(), gr.update(lines=7)
+ else:
+ lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
+ return None, "\n".join(lines), gr.update(lines=7)
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "Prompts from file or textbox"
+
+ def ui(self, is_img2img):
+ checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))
+ checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
+
+ prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt"))
+ file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file"))
+
+ file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt], show_progress=False)
+
+ # We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.
+ # We don't shrink back to 1, because that causes the control to ignore [enter], and it may
+ # be unclear to the user that shift-enter is needed.
+ prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt], show_progress=False)
+ return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
+
+ def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
+ lines = [x for x in (x.strip() for x in prompt_txt.splitlines()) if x]
+
+ p.do_not_save_grid = True
+
+ job_count = 0
+ jobs = []
+
+ for line in lines:
+ if "--" in line:
+ try:
+ args = cmdargs(line)
+ except Exception:
+ errors.report(f"Error parsing line {line} as commandline", exc_info=True)
+ args = {"prompt": line}
+ else:
+ args = {"prompt": line}
+
+ job_count += args.get("n_iter", p.n_iter)
+
+ jobs.append(args)
+
+ print(f"Will process {len(lines)} lines in {job_count} jobs.")
+ if (checkbox_iterate or checkbox_iterate_batch) and p.seed == -1:
+ p.seed = int(random.randrange(4294967294))
+
+ state.job_count = job_count
+
+ images = []
+ all_prompts = []
+ infotexts = []
+ for args in jobs:
+ state.job = f"{state.job_no + 1} out of {state.job_count}"
+
+ copy_p = copy.copy(p)
+ for k, v in args.items():
+ setattr(copy_p, k, v)
+
+ proc = process_images(copy_p)
+ images += proc.images
+
+ if checkbox_iterate:
+ p.seed = p.seed + (p.batch_size * p.n_iter)
+ all_prompts += proc.all_prompts
+ infotexts += proc.infotexts
+
+ return Processed(p, images, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)
diff --git a/stable-diffusion-webui/scripts/sd_upscale.py b/stable-diffusion-webui/scripts/sd_upscale.py
new file mode 100644
index 0000000000000000000000000000000000000000..a04afaafef4df53756b314e445d18ccc8b5b3374
--- /dev/null
+++ b/stable-diffusion-webui/scripts/sd_upscale.py
@@ -0,0 +1,101 @@
+import math
+
+import modules.scripts as scripts
+import gradio as gr
+from PIL import Image
+
+from modules import processing, shared, images, devices
+from modules.processing import Processed
+from modules.shared import opts, state
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "SD upscale"
+
+ def show(self, is_img2img):
+ return is_img2img
+
+ def ui(self, is_img2img):
+ info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
+ overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=self.elem_id("overlap"))
+ scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id("scale_factor"))
+ upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", elem_id=self.elem_id("upscaler_index"))
+
+ return [info, overlap, upscaler_index, scale_factor]
+
+ def run(self, p, _, overlap, upscaler_index, scale_factor):
+ if isinstance(upscaler_index, str):
+ upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
+ processing.fix_seed(p)
+ upscaler = shared.sd_upscalers[upscaler_index]
+
+ p.extra_generation_params["SD upscale overlap"] = overlap
+ p.extra_generation_params["SD upscale upscaler"] = upscaler.name
+
+ initial_info = None
+ seed = p.seed
+
+ init_img = p.init_images[0]
+ init_img = images.flatten(init_img, opts.img2img_background_color)
+
+ if upscaler.name != "None":
+ img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
+ else:
+ img = init_img
+
+ devices.torch_gc()
+
+ grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)
+
+ batch_size = p.batch_size
+ upscale_count = p.n_iter
+ p.n_iter = 1
+ p.do_not_save_grid = True
+ p.do_not_save_samples = True
+
+ work = []
+
+ for _y, _h, row in grid.tiles:
+ for tiledata in row:
+ work.append(tiledata[2])
+
+ batch_count = math.ceil(len(work) / batch_size)
+ state.job_count = batch_count * upscale_count
+
+ print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
+
+ result_images = []
+ for n in range(upscale_count):
+ start_seed = seed + n
+ p.seed = start_seed
+
+ work_results = []
+ for i in range(batch_count):
+ p.batch_size = batch_size
+ p.init_images = work[i * batch_size:(i + 1) * batch_size]
+
+ state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
+ processed = processing.process_images(p)
+
+ if initial_info is None:
+ initial_info = processed.info
+
+ p.seed = processed.seed + 1
+ work_results += processed.images
+
+ image_index = 0
+ for _y, _h, row in grid.tiles:
+ for tiledata in row:
+ tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
+ image_index += 1
+
+ combined_image = images.combine_grid(grid)
+ result_images.append(combined_image)
+
+ if opts.samples_save:
+ images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
+
+ processed = Processed(p, result_images, seed, initial_info)
+
+ return processed
diff --git a/stable-diffusion-webui/scripts/xyz_grid.py b/stable-diffusion-webui/scripts/xyz_grid.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf1a1edaee4329f3e7ef231aec0331c59d8cb516
--- /dev/null
+++ b/stable-diffusion-webui/scripts/xyz_grid.py
@@ -0,0 +1,785 @@
+from collections import namedtuple
+from copy import copy
+from itertools import permutations, chain
+import random
+import csv
+import os.path
+from io import StringIO
+from PIL import Image
+import numpy as np
+
+import modules.scripts as scripts
+import gradio as gr
+
+from modules import images, sd_samplers, processing, sd_models, sd_vae, sd_samplers_kdiffusion, errors
+from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
+from modules.shared import opts, state
+import modules.shared as shared
+import modules.sd_samplers
+import modules.sd_models
+import modules.sd_vae
+import re
+
+from modules.ui_components import ToolButton
+
+fill_values_symbol = "\U0001f4d2" # 📒
+
+AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
+
+
+def apply_field(field):
+ def fun(p, x, xs):
+ setattr(p, field, x)
+
+ return fun
+
+
+def apply_prompt(p, x, xs):
+ if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
+ raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
+
+ p.prompt = p.prompt.replace(xs[0], x)
+ p.negative_prompt = p.negative_prompt.replace(xs[0], x)
+
+
+def apply_order(p, x, xs):
+ token_order = []
+
+ # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
+ for token in x:
+ token_order.append((p.prompt.find(token), token))
+
+ token_order.sort(key=lambda t: t[0])
+
+ prompt_parts = []
+
+ # Split the prompt up, taking out the tokens
+ for _, token in token_order:
+ n = p.prompt.find(token)
+ prompt_parts.append(p.prompt[0:n])
+ p.prompt = p.prompt[n + len(token):]
+
+ # Rebuild the prompt with the tokens in the order we want
+ prompt_tmp = ""
+ for idx, part in enumerate(prompt_parts):
+ prompt_tmp += part
+ prompt_tmp += x[idx]
+ p.prompt = prompt_tmp + p.prompt
+
+
+def confirm_samplers(p, xs):
+ for x in xs:
+ if x.lower() not in sd_samplers.samplers_map:
+ raise RuntimeError(f"Unknown sampler: {x}")
+
+
+def apply_checkpoint(p, x, xs):
+ info = modules.sd_models.get_closet_checkpoint_match(x)
+ if info is None:
+ raise RuntimeError(f"Unknown checkpoint: {x}")
+ p.override_settings['sd_model_checkpoint'] = info.name
+
+
+def confirm_checkpoints(p, xs):
+ for x in xs:
+ if modules.sd_models.get_closet_checkpoint_match(x) is None:
+ raise RuntimeError(f"Unknown checkpoint: {x}")
+
+
+def confirm_checkpoints_or_none(p, xs):
+ for x in xs:
+ if x in (None, "", "None", "none"):
+ continue
+
+ if modules.sd_models.get_closet_checkpoint_match(x) is None:
+ raise RuntimeError(f"Unknown checkpoint: {x}")
+
+
+def apply_clip_skip(p, x, xs):
+ opts.data["CLIP_stop_at_last_layers"] = x
+
+
+def apply_upscale_latent_space(p, x, xs):
+ if x.lower().strip() != '0':
+ opts.data["use_scale_latent_for_hires_fix"] = True
+ else:
+ opts.data["use_scale_latent_for_hires_fix"] = False
+
+
+def find_vae(name: str):
+ if name.lower() in ['auto', 'automatic']:
+ return modules.sd_vae.unspecified
+ if name.lower() == 'none':
+ return None
+ else:
+ choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
+ if len(choices) == 0:
+ print(f"No VAE found for {name}; using automatic")
+ return modules.sd_vae.unspecified
+ else:
+ return modules.sd_vae.vae_dict[choices[0]]
+
+
+def apply_vae(p, x, xs):
+ modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
+
+
+def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
+ p.styles.extend(x.split(','))
+
+
+def apply_uni_pc_order(p, x, xs):
+ opts.data["uni_pc_order"] = min(x, p.steps - 1)
+
+
+def apply_face_restore(p, opt, x):
+ opt = opt.lower()
+ if opt == 'codeformer':
+ is_active = True
+ p.face_restoration_model = 'CodeFormer'
+ elif opt == 'gfpgan':
+ is_active = True
+ p.face_restoration_model = 'GFPGAN'
+ else:
+ is_active = opt in ('true', 'yes', 'y', '1')
+
+ p.restore_faces = is_active
+
+
+def apply_override(field, boolean: bool = False):
+ def fun(p, x, xs):
+ if boolean:
+ x = True if x.lower() == "true" else False
+ p.override_settings[field] = x
+ return fun
+
+
+def boolean_choice(reverse: bool = False):
+ def choice():
+ return ["False", "True"] if reverse else ["True", "False"]
+ return choice
+
+
+def format_value_add_label(p, opt, x):
+ if type(x) == float:
+ x = round(x, 8)
+
+ return f"{opt.label}: {x}"
+
+
+def format_value(p, opt, x):
+ if type(x) == float:
+ x = round(x, 8)
+ return x
+
+
+def format_value_join_list(p, opt, x):
+ return ", ".join(x)
+
+
+def do_nothing(p, x, xs):
+ pass
+
+
+def format_nothing(p, opt, x):
+ return ""
+
+
+def format_remove_path(p, opt, x):
+ return os.path.basename(x)
+
+
+def str_permutations(x):
+ """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
+ return x
+
+
+def list_to_csv_string(data_list):
+ with StringIO() as o:
+ csv.writer(o).writerow(data_list)
+ return o.getvalue().strip()
+
+
+def csv_string_to_list_strip(data_str):
+ return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str)))))
+
+
+class AxisOption:
+ def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
+ self.label = label
+ self.type = type
+ self.apply = apply
+ self.format_value = format_value
+ self.confirm = confirm
+ self.cost = cost
+ self.choices = choices
+
+
+class AxisOptionImg2Img(AxisOption):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.is_img2img = True
+
+
+class AxisOptionTxt2Img(AxisOption):
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.is_img2img = False
+
+
+axis_options = [
+ AxisOption("Nothing", str, do_nothing, format_value=format_nothing),
+ AxisOption("Seed", int, apply_field("seed")),
+ AxisOption("Var. seed", int, apply_field("subseed")),
+ AxisOption("Var. strength", float, apply_field("subseed_strength")),
+ AxisOption("Steps", int, apply_field("steps")),
+ AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
+ AxisOption("CFG Scale", float, apply_field("cfg_scale")),
+ AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
+ AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
+ AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
+ AxisOptionTxt2Img("Sampler", str, apply_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers if x.name not in opts.hide_samplers]),
+ AxisOptionTxt2Img("Hires sampler", str, apply_field("hr_sampler_name"), confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img if x.name not in opts.hide_samplers]),
+ AxisOptionImg2Img("Sampler", str, apply_field("sampler_name"), format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img if x.name not in opts.hide_samplers]),
+ AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_remove_path, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
+ AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
+ AxisOption("Sigma Churn", float, apply_field("s_churn")),
+ AxisOption("Sigma min", float, apply_field("s_tmin")),
+ AxisOption("Sigma max", float, apply_field("s_tmax")),
+ AxisOption("Sigma noise", float, apply_field("s_noise")),
+ AxisOption("Schedule type", str, apply_override("k_sched_type"), choices=lambda: list(sd_samplers_kdiffusion.k_diffusion_scheduler)),
+ AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
+ AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
+ AxisOption("Schedule rho", float, apply_override("rho")),
+ AxisOption("Eta", float, apply_field("eta")),
+ AxisOption("Clip skip", int, apply_clip_skip),
+ AxisOption("Denoising", float, apply_field("denoising_strength")),
+ AxisOption("Initial noise multiplier", float, apply_field("initial_noise_multiplier")),
+ AxisOption("Extra noise", float, apply_override("img2img_extra_noise")),
+ AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
+ AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
+ AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
+ AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
+ AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
+ AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
+ AxisOption("Token merging ratio", float, apply_override('token_merging_ratio')),
+ AxisOption("Token merging ratio high-res", float, apply_override('token_merging_ratio_hr')),
+ AxisOption("Always discard next-to-last sigma", str, apply_override('always_discard_next_to_last_sigma', boolean=True), choices=boolean_choice(reverse=True)),
+ AxisOption("SGM noise multiplier", str, apply_override('sgm_noise_multiplier', boolean=True), choices=boolean_choice(reverse=True)),
+ AxisOption("Refiner checkpoint", str, apply_field('refiner_checkpoint'), format_value=format_remove_path, confirm=confirm_checkpoints_or_none, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list, key=str.casefold)),
+ AxisOption("Refiner switch at", float, apply_field('refiner_switch_at')),
+ AxisOption("RNG source", str, apply_override("randn_source"), choices=lambda: ["GPU", "CPU", "NV"]),
+]
+
+
+def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):
+ hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
+ ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
+ title_texts = [[images.GridAnnotation(z)] for z in z_labels]
+
+ list_size = (len(xs) * len(ys) * len(zs))
+
+ processed_result = None
+
+ state.job_count = list_size * p.n_iter
+
+ def process_cell(x, y, z, ix, iy, iz):
+ nonlocal processed_result
+
+ def index(ix, iy, iz):
+ return ix + iy * len(xs) + iz * len(xs) * len(ys)
+
+ state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
+
+ processed: Processed = cell(x, y, z, ix, iy, iz)
+
+ if processed_result is None:
+ # Use our first processed result object as a template container to hold our full results
+ processed_result = copy(processed)
+ processed_result.images = [None] * list_size
+ processed_result.all_prompts = [None] * list_size
+ processed_result.all_seeds = [None] * list_size
+ processed_result.infotexts = [None] * list_size
+ processed_result.index_of_first_image = 1
+
+ idx = index(ix, iy, iz)
+ if processed.images:
+ # Non-empty list indicates some degree of success.
+ processed_result.images[idx] = processed.images[0]
+ processed_result.all_prompts[idx] = processed.prompt
+ processed_result.all_seeds[idx] = processed.seed
+ processed_result.infotexts[idx] = processed.infotexts[0]
+ else:
+ cell_mode = "P"
+ cell_size = (processed_result.width, processed_result.height)
+ if processed_result.images[0] is not None:
+ cell_mode = processed_result.images[0].mode
+ # This corrects size in case of batches:
+ cell_size = processed_result.images[0].size
+ processed_result.images[idx] = Image.new(cell_mode, cell_size)
+
+ if first_axes_processed == 'x':
+ for ix, x in enumerate(xs):
+ if second_axes_processed == 'y':
+ for iy, y in enumerate(ys):
+ for iz, z in enumerate(zs):
+ process_cell(x, y, z, ix, iy, iz)
+ else:
+ for iz, z in enumerate(zs):
+ for iy, y in enumerate(ys):
+ process_cell(x, y, z, ix, iy, iz)
+ elif first_axes_processed == 'y':
+ for iy, y in enumerate(ys):
+ if second_axes_processed == 'x':
+ for ix, x in enumerate(xs):
+ for iz, z in enumerate(zs):
+ process_cell(x, y, z, ix, iy, iz)
+ else:
+ for iz, z in enumerate(zs):
+ for ix, x in enumerate(xs):
+ process_cell(x, y, z, ix, iy, iz)
+ elif first_axes_processed == 'z':
+ for iz, z in enumerate(zs):
+ if second_axes_processed == 'x':
+ for ix, x in enumerate(xs):
+ for iy, y in enumerate(ys):
+ process_cell(x, y, z, ix, iy, iz)
+ else:
+ for iy, y in enumerate(ys):
+ for ix, x in enumerate(xs):
+ process_cell(x, y, z, ix, iy, iz)
+
+ if not processed_result:
+ # Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
+ print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.")
+ return Processed(p, [])
+ elif not any(processed_result.images):
+ print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
+ return Processed(p, [])
+
+ z_count = len(zs)
+
+ for i in range(z_count):
+ start_index = (i * len(xs) * len(ys)) + i
+ end_index = start_index + len(xs) * len(ys)
+ grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
+ if draw_legend:
+ grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size)
+ processed_result.images.insert(i, grid)
+ processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
+ processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
+ processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
+
+ sub_grid_size = processed_result.images[0].size
+ z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
+ if draw_legend:
+ z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
+ processed_result.images.insert(0, z_grid)
+ # TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
+ # processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
+ # processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
+ processed_result.infotexts.insert(0, processed_result.infotexts[0])
+
+ return processed_result
+
+
+class SharedSettingsStackHelper(object):
+ def __enter__(self):
+ self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
+ self.vae = opts.sd_vae
+ self.uni_pc_order = opts.uni_pc_order
+
+ def __exit__(self, exc_type, exc_value, tb):
+ opts.data["sd_vae"] = self.vae
+ opts.data["uni_pc_order"] = self.uni_pc_order
+ modules.sd_models.reload_model_weights()
+ modules.sd_vae.reload_vae_weights()
+
+ opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
+
+
+re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
+re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
+
+re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*])?\s*")
+re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*])?\s*")
+
+
+class Script(scripts.Script):
+ def title(self):
+ return "X/Y/Z plot"
+
+ def ui(self, is_img2img):
+ self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
+
+ with gr.Row():
+ with gr.Column(scale=19):
+ with gr.Row():
+ x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
+ x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
+ x_values_dropdown = gr.Dropdown(label="X values", visible=False, multiselect=True, interactive=True)
+ fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
+
+ with gr.Row():
+ y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
+ y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
+ y_values_dropdown = gr.Dropdown(label="Y values", visible=False, multiselect=True, interactive=True)
+ fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
+
+ with gr.Row():
+ z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
+ z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
+ z_values_dropdown = gr.Dropdown(label="Z values", visible=False, multiselect=True, interactive=True)
+ fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
+
+ with gr.Row(variant="compact", elem_id="axis_options"):
+ with gr.Column():
+ draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
+ no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
+ with gr.Column():
+ include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
+ include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
+ with gr.Column():
+ margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
+ with gr.Column():
+ csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
+
+ with gr.Row(variant="compact", elem_id="swap_axes"):
+ swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
+ swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
+ swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
+
+ def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
+ return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
+
+ xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
+ swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
+ yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
+ swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
+ xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
+ swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
+
+ def fill(axis_type, csv_mode):
+ axis = self.current_axis_options[axis_type]
+ if axis.choices:
+ if csv_mode:
+ return list_to_csv_string(axis.choices()), gr.update()
+ else:
+ return gr.update(), axis.choices()
+ else:
+ return gr.update(), gr.update()
+
+ fill_x_button.click(fn=fill, inputs=[x_type, csv_mode], outputs=[x_values, x_values_dropdown])
+ fill_y_button.click(fn=fill, inputs=[y_type, csv_mode], outputs=[y_values, y_values_dropdown])
+ fill_z_button.click(fn=fill, inputs=[z_type, csv_mode], outputs=[z_values, z_values_dropdown])
+
+ def select_axis(axis_type, axis_values, axis_values_dropdown, csv_mode):
+ choices = self.current_axis_options[axis_type].choices
+ has_choices = choices is not None
+
+ if has_choices:
+ choices = choices()
+ if csv_mode:
+ if axis_values_dropdown:
+ axis_values = list_to_csv_string(list(filter(lambda x: x in choices, axis_values_dropdown)))
+ axis_values_dropdown = []
+ else:
+ if axis_values:
+ axis_values_dropdown = list(filter(lambda x: x in choices, csv_string_to_list_strip(axis_values)))
+ axis_values = ""
+
+ return (gr.Button.update(visible=has_choices), gr.Textbox.update(visible=not has_choices or csv_mode, value=axis_values),
+ gr.update(choices=choices if has_choices else None, visible=has_choices and not csv_mode, value=axis_values_dropdown))
+
+ x_type.change(fn=select_axis, inputs=[x_type, x_values, x_values_dropdown, csv_mode], outputs=[fill_x_button, x_values, x_values_dropdown])
+ y_type.change(fn=select_axis, inputs=[y_type, y_values, y_values_dropdown, csv_mode], outputs=[fill_y_button, y_values, y_values_dropdown])
+ z_type.change(fn=select_axis, inputs=[z_type, z_values, z_values_dropdown, csv_mode], outputs=[fill_z_button, z_values, z_values_dropdown])
+
+ def change_choice_mode(csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown):
+ _fill_x_button, _x_values, _x_values_dropdown = select_axis(x_type, x_values, x_values_dropdown, csv_mode)
+ _fill_y_button, _y_values, _y_values_dropdown = select_axis(y_type, y_values, y_values_dropdown, csv_mode)
+ _fill_z_button, _z_values, _z_values_dropdown = select_axis(z_type, z_values, z_values_dropdown, csv_mode)
+ return _fill_x_button, _x_values, _x_values_dropdown, _fill_y_button, _y_values, _y_values_dropdown, _fill_z_button, _z_values, _z_values_dropdown
+
+ csv_mode.change(fn=change_choice_mode, inputs=[csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown], outputs=[fill_x_button, x_values, x_values_dropdown, fill_y_button, y_values, y_values_dropdown, fill_z_button, z_values, z_values_dropdown])
+
+ def get_dropdown_update_from_params(axis, params):
+ val_key = f"{axis} Values"
+ vals = params.get(val_key, "")
+ valslist = csv_string_to_list_strip(vals)
+ return gr.update(value=valslist)
+
+ self.infotext_fields = (
+ (x_type, "X Type"),
+ (x_values, "X Values"),
+ (x_values_dropdown, lambda params: get_dropdown_update_from_params("X", params)),
+ (y_type, "Y Type"),
+ (y_values, "Y Values"),
+ (y_values_dropdown, lambda params: get_dropdown_update_from_params("Y", params)),
+ (z_type, "Z Type"),
+ (z_values, "Z Values"),
+ (z_values_dropdown, lambda params: get_dropdown_update_from_params("Z", params)),
+ )
+
+ return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode]
+
+ def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode):
+ if not no_fixed_seeds:
+ modules.processing.fix_seed(p)
+
+ if not opts.return_grid:
+ p.batch_size = 1
+
+ def process_axis(opt, vals, vals_dropdown):
+ if opt.label == 'Nothing':
+ return [0]
+
+ if opt.choices is not None and not csv_mode:
+ valslist = vals_dropdown
+ else:
+ valslist = csv_string_to_list_strip(vals)
+
+ if opt.type == int:
+ valslist_ext = []
+
+ for val in valslist:
+ m = re_range.fullmatch(val)
+ mc = re_range_count.fullmatch(val)
+ if m is not None:
+ start = int(m.group(1))
+ end = int(m.group(2))+1
+ step = int(m.group(3)) if m.group(3) is not None else 1
+
+ valslist_ext += list(range(start, end, step))
+ elif mc is not None:
+ start = int(mc.group(1))
+ end = int(mc.group(2))
+ num = int(mc.group(3)) if mc.group(3) is not None else 1
+
+ valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
+ else:
+ valslist_ext.append(val)
+
+ valslist = valslist_ext
+ elif opt.type == float:
+ valslist_ext = []
+
+ for val in valslist:
+ m = re_range_float.fullmatch(val)
+ mc = re_range_count_float.fullmatch(val)
+ if m is not None:
+ start = float(m.group(1))
+ end = float(m.group(2))
+ step = float(m.group(3)) if m.group(3) is not None else 1
+
+ valslist_ext += np.arange(start, end + step, step).tolist()
+ elif mc is not None:
+ start = float(mc.group(1))
+ end = float(mc.group(2))
+ num = int(mc.group(3)) if mc.group(3) is not None else 1
+
+ valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
+ else:
+ valslist_ext.append(val)
+
+ valslist = valslist_ext
+ elif opt.type == str_permutations:
+ valslist = list(permutations(valslist))
+
+ valslist = [opt.type(x) for x in valslist]
+
+ # Confirm options are valid before starting
+ if opt.confirm:
+ opt.confirm(p, valslist)
+
+ return valslist
+
+ x_opt = self.current_axis_options[x_type]
+ if x_opt.choices is not None and not csv_mode:
+ x_values = list_to_csv_string(x_values_dropdown)
+ xs = process_axis(x_opt, x_values, x_values_dropdown)
+
+ y_opt = self.current_axis_options[y_type]
+ if y_opt.choices is not None and not csv_mode:
+ y_values = list_to_csv_string(y_values_dropdown)
+ ys = process_axis(y_opt, y_values, y_values_dropdown)
+
+ z_opt = self.current_axis_options[z_type]
+ if z_opt.choices is not None and not csv_mode:
+ z_values = list_to_csv_string(z_values_dropdown)
+ zs = process_axis(z_opt, z_values, z_values_dropdown)
+
+ # this could be moved to common code, but unlikely to be ever triggered anywhere else
+ Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
+ grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
+ assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'
+
+ def fix_axis_seeds(axis_opt, axis_list):
+ if axis_opt.label in ['Seed', 'Var. seed']:
+ return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
+ else:
+ return axis_list
+
+ if not no_fixed_seeds:
+ xs = fix_axis_seeds(x_opt, xs)
+ ys = fix_axis_seeds(y_opt, ys)
+ zs = fix_axis_seeds(z_opt, zs)
+
+ if x_opt.label == 'Steps':
+ total_steps = sum(xs) * len(ys) * len(zs)
+ elif y_opt.label == 'Steps':
+ total_steps = sum(ys) * len(xs) * len(zs)
+ elif z_opt.label == 'Steps':
+ total_steps = sum(zs) * len(xs) * len(ys)
+ else:
+ total_steps = p.steps * len(xs) * len(ys) * len(zs)
+
+ if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
+ if x_opt.label == "Hires steps":
+ total_steps += sum(xs) * len(ys) * len(zs)
+ elif y_opt.label == "Hires steps":
+ total_steps += sum(ys) * len(xs) * len(zs)
+ elif z_opt.label == "Hires steps":
+ total_steps += sum(zs) * len(xs) * len(ys)
+ elif p.hr_second_pass_steps:
+ total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs)
+ else:
+ total_steps *= 2
+
+ total_steps *= p.n_iter
+
+ image_cell_count = p.n_iter * p.batch_size
+ cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
+ plural_s = 's' if len(zs) > 1 else ''
+ print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
+ shared.total_tqdm.updateTotal(total_steps)
+
+ state.xyz_plot_x = AxisInfo(x_opt, xs)
+ state.xyz_plot_y = AxisInfo(y_opt, ys)
+ state.xyz_plot_z = AxisInfo(z_opt, zs)
+
+ # If one of the axes is very slow to change between (like SD model
+ # checkpoint), then make sure it is in the outer iteration of the nested
+ # `for` loop.
+ first_axes_processed = 'z'
+ second_axes_processed = 'y'
+ if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
+ first_axes_processed = 'x'
+ if y_opt.cost > z_opt.cost:
+ second_axes_processed = 'y'
+ else:
+ second_axes_processed = 'z'
+ elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost:
+ first_axes_processed = 'y'
+ if x_opt.cost > z_opt.cost:
+ second_axes_processed = 'x'
+ else:
+ second_axes_processed = 'z'
+ elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost:
+ first_axes_processed = 'z'
+ if x_opt.cost > y_opt.cost:
+ second_axes_processed = 'x'
+ else:
+ second_axes_processed = 'y'
+
+ grid_infotext = [None] * (1 + len(zs))
+
+ def cell(x, y, z, ix, iy, iz):
+ if shared.state.interrupted:
+ return Processed(p, [], p.seed, "")
+
+ pc = copy(p)
+ pc.styles = pc.styles[:]
+ x_opt.apply(pc, x, xs)
+ y_opt.apply(pc, y, ys)
+ z_opt.apply(pc, z, zs)
+
+ try:
+ res = process_images(pc)
+ except Exception as e:
+ errors.display(e, "generating image for xyz plot")
+
+ res = Processed(p, [], p.seed, "")
+
+ # Sets subgrid infotexts
+ subgrid_index = 1 + iz
+ if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
+ pc.extra_generation_params = copy(pc.extra_generation_params)
+ pc.extra_generation_params['Script'] = self.title()
+
+ if x_opt.label != 'Nothing':
+ pc.extra_generation_params["X Type"] = x_opt.label
+ pc.extra_generation_params["X Values"] = x_values
+ if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
+ pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs])
+
+ if y_opt.label != 'Nothing':
+ pc.extra_generation_params["Y Type"] = y_opt.label
+ pc.extra_generation_params["Y Values"] = y_values
+ if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
+ pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
+
+ grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
+
+ # Sets main grid infotext
+ if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
+ pc.extra_generation_params = copy(pc.extra_generation_params)
+
+ if z_opt.label != 'Nothing':
+ pc.extra_generation_params["Z Type"] = z_opt.label
+ pc.extra_generation_params["Z Values"] = z_values
+ if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
+ pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs])
+
+ grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
+
+ return res
+
+ with SharedSettingsStackHelper():
+ processed = draw_xyz_grid(
+ p,
+ xs=xs,
+ ys=ys,
+ zs=zs,
+ x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
+ y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
+ z_labels=[z_opt.format_value(p, z_opt, z) for z in zs],
+ cell=cell,
+ draw_legend=draw_legend,
+ include_lone_images=include_lone_images,
+ include_sub_grids=include_sub_grids,
+ first_axes_processed=first_axes_processed,
+ second_axes_processed=second_axes_processed,
+ margin_size=margin_size
+ )
+
+ if not processed.images:
+ # It broke, no further handling needed.
+ return processed
+
+ z_count = len(zs)
+
+ # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
+ processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
+
+ if not include_lone_images:
+ # Don't need sub-images anymore, drop from list:
+ processed.images = processed.images[:z_count+1]
+
+ if opts.grid_save:
+ # Auto-save main and sub-grids:
+ grid_count = z_count + 1 if z_count > 1 else 1
+ for g in range(grid_count):
+ # TODO: See previous comment about intentional data misalignment.
+ adj_g = g-1 if g > 0 else g
+ images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
+
+ if not include_sub_grids:
+ # Done with sub-grids, drop all related information:
+ for _ in range(z_count):
+ del processed.images[1]
+ del processed.all_prompts[1]
+ del processed.all_seeds[1]
+ del processed.infotexts[1]
+
+ return processed
diff --git a/stable-diffusion-webui/style.css b/stable-diffusion-webui/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..e18660293dd87eb7977ebed55b209f97dee0c659
--- /dev/null
+++ b/stable-diffusion-webui/style.css
@@ -0,0 +1,1104 @@
+/* temporary fix to load default gradio font in frontend instead of backend */
+
+@import url('https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap');
+
+
+/* temporary fix to hide gradio crop tool until it's fixed https://github.com/gradio-app/gradio/issues/3810 */
+
+div.gradio-image button[aria-label="Edit"] {
+ display: none;
+}
+
+
+/* general gradio fixes */
+
+:root, .dark{
+ --checkbox-label-gap: 0.25em 0.1em;
+ --section-header-text-size: 12pt;
+ --block-background-fill: transparent;
+
+}
+
+.block.padded:not(.gradio-accordion) {
+ padding: 0 !important;
+}
+
+div.gradio-container{
+ max-width: unset !important;
+}
+
+.hidden{
+ display: none;
+}
+
+.compact{
+ background: transparent !important;
+ padding: 0 !important;
+}
+
+div.form{
+ border-width: 0;
+ box-shadow: none;
+ background: transparent;
+ overflow: visible;
+ gap: 0.5em;
+}
+
+.block.gradio-dropdown,
+.block.gradio-slider,
+.block.gradio-checkbox,
+.block.gradio-textbox,
+.block.gradio-radio,
+.block.gradio-checkboxgroup,
+.block.gradio-number,
+.block.gradio-colorpicker {
+ border-width: 0 !important;
+ box-shadow: none !important;
+}
+
+div.gradio-group, div.styler{
+ border-width: 0 !important;
+ background: none;
+}
+.gap.compact{
+ padding: 0;
+ gap: 0.2em 0;
+}
+
+div.compact{
+ gap: 1em;
+}
+
+.gradio-dropdown label span:not(.has-info),
+.gradio-textbox label span:not(.has-info),
+.gradio-number label span:not(.has-info)
+{
+ margin-bottom: 0;
+}
+
+.gradio-dropdown ul.options{
+ z-index: 3000;
+ min-width: fit-content;
+ max-width: inherit;
+ white-space: nowrap;
+}
+
+.gradio-dropdown ul.options li.item {
+ padding: 0.05em 0;
+}
+
+.gradio-dropdown ul.options li.item.selected {
+ background-color: var(--neutral-100);
+}
+
+.dark .gradio-dropdown ul.options li.item.selected {
+ background-color: var(--neutral-900);
+}
+
+.gradio-dropdown div.wrap.wrap.wrap.wrap{
+ box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
+}
+
+.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{
+ flex-wrap: unset;
+}
+
+.gradio-dropdown .single-select{
+ white-space: nowrap;
+ overflow: hidden;
+}
+
+.gradio-dropdown .token-remove.remove-all.remove-all{
+ display: none;
+}
+
+.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{
+ display: flex;
+}
+
+.gradio-slider input[type="number"]{
+ width: 6em;
+}
+
+.block.gradio-checkbox {
+ margin: 0.75em 1.5em 0 0;
+}
+
+.gradio-html div.wrap{
+ height: 100%;
+}
+div.gradio-html.min{
+ min-height: 0;
+}
+
+.block.gradio-gallery{
+ background: var(--input-background-fill);
+}
+
+.gradio-container .prose a, .gradio-container .prose a:visited{
+ color: unset;
+ text-decoration: none;
+}
+
+a{
+ font-weight: bold;
+ cursor: pointer;
+}
+
+/* gradio 3.39 puts a lot of overflow: hidden all over the place for an unknown reason. */
+div.gradio-container, .block.gradio-textbox, div.gradio-group, div.gradio-dropdown{
+ overflow: visible !important;
+}
+
+/* align-items isn't enough and elements may overflow in Safari. */
+.unequal-height {
+ align-content: flex-start;
+}
+
+
+/* general styled components */
+
+.gradio-button.tool{
+ max-width: 2.2em;
+ min-width: 2.2em !important;
+ height: 2.4em;
+ align-self: end;
+ line-height: 1em;
+ border-radius: 0.5em;
+}
+
+.gradio-button.secondary-down{
+ background: var(--button-secondary-background-fill);
+ color: var(--button-secondary-text-color);
+}
+.gradio-button.secondary-down, .gradio-button.secondary-down:hover{
+ box-shadow: 1px 1px 1px rgba(0,0,0,0.25) inset, 0px 0px 3px rgba(0,0,0,0.15) inset;
+}
+.gradio-button.secondary-down:hover{
+ background: var(--button-secondary-background-fill-hover);
+ color: var(--button-secondary-text-color-hover);
+}
+
+button.custom-button{
+ border-radius: var(--button-large-radius);
+ padding: var(--button-large-padding);
+ font-weight: var(--button-large-text-weight);
+ border: var(--button-border-width) solid var(--button-secondary-border-color);
+ background: var(--button-secondary-background-fill);
+ color: var(--button-secondary-text-color);
+ font-size: var(--button-large-text-size);
+ display: inline-flex;
+ justify-content: center;
+ align-items: center;
+ transition: var(--button-transition);
+ box-shadow: var(--button-shadow);
+ text-align: center;
+}
+
+div.block.gradio-accordion {
+ border: 1px solid var(--block-border-color) !important;
+ border-radius: 8px !important;
+ margin: 2px 0;
+ padding: 8px 8px;
+}
+
+
+/* txt2img/img2img specific */
+
+.block.token-counter{
+ position: absolute;
+ display: inline-block;
+ right: 1em;
+ min-width: 0 !important;
+ width: auto;
+ z-index: 100;
+ top: -0.75em;
+}
+
+.block.token-counter span{
+ background: var(--input-background-fill) !important;
+ box-shadow: 0 0 0.0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075);
+ border: 2px solid rgba(192,192,192,0.4) !important;
+ border-radius: 0.4em;
+}
+
+.block.token-counter.error span{
+ box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075);
+ border: 2px solid rgba(255,0,0,0.4) !important;
+}
+
+.block.token-counter div{
+ display: inline;
+}
+
+.block.token-counter span{
+ padding: 0.1em 0.75em;
+}
+
+[id$=_subseed_show]{
+ min-width: auto !important;
+ flex-grow: 0 !important;
+ display: flex;
+}
+
+[id$=_subseed_show] label{
+ margin-bottom: 0.65em;
+ align-self: end;
+}
+
+[id$=_seed_extras] > div{
+ gap: 0.5em;
+}
+
+.html-log .comments{
+ padding-top: 0.5em;
+}
+
+.html-log .comments:empty{
+ padding-top: 0;
+}
+
+.html-log .performance {
+ font-size: 0.85em;
+ color: #444;
+ display: flex;
+}
+
+.html-log .performance p{
+ display: inline-block;
+}
+
+.html-log .performance p.time, .performance p.vram, .performance p.time abbr, .performance p.vram abbr {
+ margin-bottom: 0;
+ color: var(--block-title-text-color);
+}
+
+.html-log .performance p.time {
+}
+
+.html-log .performance p.vram {
+ margin-left: auto;
+}
+
+.html-log .performance .measurement{
+ color: var(--body-text-color);
+ font-weight: bold;
+}
+
+#txt2img_generate, #img2img_generate {
+ min-height: 4.5em;
+}
+
+@media screen and (min-width: 2500px) {
+ #txt2img_gallery, #img2img_gallery {
+ min-height: 768px;
+ }
+}
+
+.gradio-gallery .thumbnails img {
+ object-fit: scale-down !important;
+}
+#txt2img_actions_column, #img2img_actions_column {
+ gap: 0.5em;
+}
+#txt2img_tools, #img2img_tools{
+ gap: 0.4em;
+}
+
+.interrogate-col{
+ min-width: 0 !important;
+ max-width: fit-content;
+ gap: 0.5em;
+}
+.interrogate-col > button{
+ flex: 1;
+}
+
+.generate-box{
+ position: relative;
+}
+.gradio-button.generate-box-skip, .gradio-button.generate-box-interrupt{
+ position: absolute;
+ width: 50%;
+ height: 100%;
+ display: none;
+ background: #b4c0cc;
+}
+.gradio-button.generate-box-skip:hover, .gradio-button.generate-box-interrupt:hover{
+ background: #c2cfdb;
+}
+.gradio-button.generate-box-interrupt{
+ left: 0;
+ border-radius: 0.5rem 0 0 0.5rem;
+}
+.gradio-button.generate-box-skip{
+ right: 0;
+ border-radius: 0 0.5rem 0.5rem 0;
+}
+
+#img2img_scale_resolution_preview.block{
+ display: flex;
+ align-items: end;
+}
+
+#txtimg_hr_finalres .resolution, #img2img_scale_resolution_preview .resolution{
+ font-weight: bold;
+}
+
+#txtimg_hr_finalres div.pending, #img2img_scale_resolution_preview div.pending {
+ opacity: 1;
+ transition: opacity 0s;
+}
+
+.inactive{
+ opacity: 0.5;
+}
+
+[id$=_column_batch]{
+ min-width: min(13.5em, 100%) !important;
+}
+
+div.dimensions-tools{
+ min-width: 1.6em !important;
+ max-width: fit-content;
+ flex-direction: column;
+ place-content: center;
+}
+
+div#extras_scale_to_tab div.form{
+ flex-direction: row;
+}
+
+#img2img_sketch, #img2maskimg, #inpaint_sketch {
+ overflow: overlay !important;
+ resize: auto;
+ background: var(--panel-background-fill);
+ z-index: 5;
+}
+
+.image-buttons > .form{
+ justify-content: center;
+}
+
+.infotext {
+ overflow-wrap: break-word;
+}
+
+#img2img_column_batch{
+ align-self: end;
+ margin-bottom: 0.9em;
+}
+
+#img2img_unused_scale_by_slider{
+ visibility: hidden;
+ width: 0.5em;
+ max-width: 0.5em;
+ min-width: 0.5em;
+}
+
+/* settings */
+#quicksettings {
+ align-items: end;
+}
+
+#quicksettings > div, #quicksettings > fieldset{
+ max-width: 36em;
+ width: fit-content;
+ flex: 0 1 fit-content;
+ padding: 0;
+ border: none;
+ box-shadow: none;
+ background: none;
+}
+#quicksettings > div.gradio-dropdown{
+ min-width: 24em !important;
+}
+
+#settings{
+ display: block;
+}
+
+#settings > div{
+ border: none;
+ margin-left: 10em;
+}
+
+#settings > div.tab-nav{
+ float: left;
+ display: block;
+ margin-left: 0;
+ width: 10em;
+}
+
+#settings > div.tab-nav button{
+ display: block;
+ border: none;
+ text-align: left;
+ white-space: initial;
+}
+
+#settings_result{
+ height: 1.4em;
+ margin: 0 1.2em;
+}
+
+table.popup-table{
+ background: var(--body-background-fill);
+ color: var(--body-text-color);
+ border-collapse: collapse;
+ margin: 1em;
+ border: 4px solid var(--body-background-fill);
+}
+
+table.popup-table td{
+ padding: 0.4em;
+ border: 1px solid rgba(128, 128, 128, 0.5);
+ max-width: 36em;
+}
+
+table.popup-table .muted{
+ color: #aaa;
+}
+
+table.popup-table .link{
+ text-decoration: underline;
+ cursor: pointer;
+ font-weight: bold;
+}
+
+.ui-defaults-none{
+ color: #aaa !important;
+}
+
+#settings span{
+ color: var(--body-text-color);
+}
+
+#settings .gradio-textbox, #settings .gradio-slider, #settings .gradio-number, #settings .gradio-dropdown, #settings .gradio-checkboxgroup, #settings .gradio-radio{
+ margin-top: 0.75em;
+}
+
+#settings span .settings-comment {
+ display: inline
+}
+
+.settings-comment a{
+ text-decoration: underline;
+}
+
+.settings-comment .info{
+ opacity: 0.75;
+}
+
+#sysinfo_download a.sysinfo_big_link{
+ font-size: 24pt;
+}
+
+#sysinfo_download a{
+ text-decoration: underline;
+}
+
+#sysinfo_validity{
+ font-size: 18pt;
+}
+
+#settings .settings-info{
+ max-width: 48em;
+ border: 1px dotted #777;
+ margin: 0;
+ padding: 1em;
+}
+
+
+/* live preview */
+.progressDiv{
+ position: absolute;
+ height: 20px;
+ background: #b4c0cc;
+ border-radius: 3px !important;
+ top: -20px;
+ width: 100%;
+}
+
+.progress-container{
+ position: relative;
+}
+
+[id$=_results].mobile{
+ margin-top: 28px;
+}
+
+.dark .progressDiv{
+ background: #424c5b;
+}
+
+.progressDiv .progress{
+ width: 0%;
+ height: 20px;
+ background: #0060df;
+ color: white;
+ font-weight: bold;
+ line-height: 20px;
+ padding: 0 8px 0 0;
+ text-align: right;
+ border-radius: 3px;
+ overflow: visible;
+ white-space: nowrap;
+ padding: 0 0.5em;
+}
+
+.livePreview{
+ position: absolute;
+ z-index: 300;
+ background: var(--background-fill-primary);
+ width: 100%;
+ height: 100%;
+}
+
+.livePreview img{
+ position: absolute;
+ object-fit: contain;
+ width: 100%;
+ height: calc(100% - 60px); /* to match gradio's height */
+}
+
+/* fullscreen popup (ie in Lora's (i) button) */
+
+.popup-metadata{
+ color: black;
+ background: white;
+ display: inline-block;
+ padding: 1em;
+ white-space: pre-wrap;
+}
+
+.global-popup{
+ display: flex;
+ position: fixed;
+ z-index: 1001;
+ left: 0;
+ top: 0;
+ width: 100%;
+ height: 100%;
+ overflow: auto;
+ background-color: rgba(20, 20, 20, 0.95);
+}
+
+.global-popup *{
+ box-sizing: border-box;
+}
+
+.global-popup-close:before {
+ content: "×";
+}
+
+.global-popup-close{
+ position: fixed;
+ right: 0.25em;
+ top: 0;
+ cursor: pointer;
+ color: white;
+ font-size: 32pt;
+}
+
+.global-popup-inner{
+ display: inline-block;
+ margin: auto;
+ padding: 2em;
+}
+
+/* fullpage image viewer */
+
+#lightboxModal{
+ display: none;
+ position: fixed;
+ z-index: 1001;
+ left: 0;
+ top: 0;
+ width: 100%;
+ height: 100%;
+ overflow: auto;
+ background-color: rgba(20, 20, 20, 0.95);
+ user-select: none;
+ -webkit-user-select: none;
+ flex-direction: column;
+}
+
+.modalControls {
+ display: flex;
+ position: absolute;
+ right: 0px;
+ left: 0px;
+ gap: 1em;
+ padding: 1em;
+ background-color:rgba(0,0,0,0);
+ z-index: 1;
+ transition: 0.2s ease background-color;
+}
+.modalControls:hover {
+ background-color:rgba(0,0,0,0.9);
+}
+.modalClose {
+ margin-left: auto;
+}
+.modalControls span{
+ color: white;
+ text-shadow: 0px 0px 0.25em black;
+ font-size: 35px;
+ font-weight: bold;
+ cursor: pointer;
+ width: 1em;
+}
+
+.modalControls span:hover, .modalControls span:focus{
+ color: #999;
+ text-decoration: none;
+}
+
+#lightboxModal > img {
+ display: block;
+ margin: auto;
+ width: auto;
+}
+
+#lightboxModal > img.modalImageFullscreen{
+ object-fit: contain;
+ height: 100%;
+ width: 100%;
+ min-height: 0;
+}
+
+.modalPrev,
+.modalNext {
+ cursor: pointer;
+ position: absolute;
+ top: 50%;
+ width: auto;
+ padding: 16px;
+ margin-top: -50px;
+ color: white;
+ font-weight: bold;
+ font-size: 20px;
+ transition: 0.6s ease;
+ border-radius: 0 3px 3px 0;
+ user-select: none;
+ -webkit-user-select: none;
+}
+
+.modalNext {
+ right: 0;
+ border-radius: 3px 0 0 3px;
+}
+
+.modalPrev:hover,
+.modalNext:hover {
+ background-color: rgba(0, 0, 0, 0.8);
+}
+
+#imageARPreview {
+ position: absolute;
+ top: 0px;
+ left: 0px;
+ border: 2px solid red;
+ background: rgba(255, 0, 0, 0.3);
+ z-index: 900;
+ pointer-events: none;
+ display: none;
+}
+
+/* context menu (ie for the generate button) */
+
+#context-menu{
+ z-index:9999;
+ position:absolute;
+ display:block;
+ padding:0px 0;
+ border:2px solid #a55000;
+ border-radius:8px;
+ box-shadow:1px 1px 2px #CE6400;
+ width: 200px;
+}
+
+.context-menu-items{
+ list-style: none;
+ margin: 0;
+ padding: 0;
+}
+
+.context-menu-items a{
+ display:block;
+ padding:5px;
+ cursor:pointer;
+}
+
+.context-menu-items a:hover{
+ background: #a55000;
+}
+
+
+/* extensions */
+
+#tab_extensions table{
+ border-collapse: collapse;
+}
+
+#tab_extensions table td, #tab_extensions table th{
+ border: 1px solid #ccc;
+ padding: 0.25em 0.5em;
+}
+
+#tab_extensions table input[type="checkbox"]{
+ margin-right: 0.5em;
+ appearance: checkbox;
+}
+
+#tab_extensions button{
+ max-width: 16em;
+}
+
+#tab_extensions input[disabled="disabled"]{
+ opacity: 0.5;
+}
+
+.extension-tag{
+ font-weight: bold;
+ font-size: 95%;
+}
+
+#available_extensions .info{
+ margin: 0;
+}
+
+#available_extensions .info{
+ margin: 0.5em 0;
+ display: flex;
+ margin-top: auto;
+ opacity: 0.80;
+ font-size: 90%;
+}
+
+#available_extensions .date_added{
+ margin-right: auto;
+ display: inline-block;
+}
+
+#available_extensions .star_count{
+ margin-left: auto;
+ display: inline-block;
+}
+
+/* replace original footer with ours */
+
+footer {
+ display: none !important;
+}
+
+#footer{
+ text-align: center;
+}
+
+#footer div{
+ display: inline-block;
+}
+
+#footer .versions{
+ font-size: 85%;
+ opacity: 0.85;
+}
+
+/* extra networks UI */
+
+.extra-network-cards{
+ height: calc(100vh - 24rem);
+ overflow: clip scroll;
+ resize: vertical;
+ min-height: 52rem;
+}
+
+.extra-networks > div.tab-nav{
+ min-height: 3.4rem;
+}
+
+.extra-networks > div > [id *= '_extra_']{
+ margin: 0.3em;
+}
+
+.extra-network-subdirs{
+ padding: 0.2em 0.35em;
+}
+
+.extra-network-subdirs button{
+ margin: 0 0.15em;
+}
+.extra-networks .tab-nav .search,
+.extra-networks .tab-nav .sort,
+.extra-networks .tab-nav .show-dirs
+{
+ margin: 0.3em;
+ align-self: center;
+ width: auto;
+}
+
+.extra-networks .tab-nav .search {
+ width: 16em;
+ max-width: 16em;
+}
+
+.extra-networks .tab-nav .sort {
+ width: 12em;
+ max-width: 12em;
+}
+
+#txt2img_extra_view, #img2img_extra_view {
+ width: auto;
+}
+
+.extra-network-cards .nocards{
+ margin: 1.25em 0.5em 0.5em 0.5em;
+}
+
+.extra-network-cards .nocards h1{
+ font-size: 1.5em;
+ margin-bottom: 1em;
+}
+
+.extra-network-cards .nocards li{
+ margin-left: 0.5em;
+}
+
+
+.extra-network-cards .card .button-row{
+ display: none;
+ position: absolute;
+ color: white;
+ right: 0;
+ z-index: 1
+}
+.extra-network-cards .card:hover .button-row{
+ display: flex;
+}
+
+.extra-network-cards .card .card-button{
+ color: white;
+}
+
+.extra-network-cards .card .metadata-button:before{
+ content: "🛈";
+}
+
+.extra-network-cards .card .edit-button:before{
+ content: "🛠";
+}
+
+.extra-network-cards .card .card-button {
+ text-shadow: 2px 2px 3px black;
+ padding: 0.25em 0.1em;
+ font-size: 200%;
+ width: 1.5em;
+}
+.extra-network-cards .card .card-button:hover{
+ color: red;
+}
+
+
+.standalone-card-preview.card .preview{
+ position: absolute;
+ object-fit: cover;
+ width: 100%;
+ height:100%;
+}
+
+.extra-network-cards .card, .standalone-card-preview.card{
+ display: inline-block;
+ margin: 0.5rem;
+ width: 16rem;
+ height: 24rem;
+ box-shadow: 0 0 5px rgba(128, 128, 128, 0.5);
+ border-radius: 0.2rem;
+ position: relative;
+
+ background-size: auto 100%;
+ background-position: center;
+ overflow: hidden;
+ cursor: pointer;
+
+ background-image: url('./file=html/card-no-preview.png')
+}
+
+.extra-network-cards .card:hover{
+ box-shadow: 0 0 2px 0.3em rgba(0, 128, 255, 0.35);
+}
+
+.extra-network-cards .card .actions .additional{
+ display: none;
+}
+
+.extra-network-cards .card .actions{
+ position: absolute;
+ bottom: 0;
+ left: 0;
+ right: 0;
+ padding: 0.5em;
+ background: rgba(0,0,0,0.5);
+ box-shadow: 0 0 0.25em 0.25em rgba(0,0,0,0.5);
+ text-shadow: 0 0 0.2em black;
+}
+
+.extra-network-cards .card .actions *{
+ color: white;
+}
+
+.extra-network-cards .card .actions .name{
+ font-size: 1.7em;
+ font-weight: bold;
+ line-break: anywhere;
+}
+
+.extra-network-cards .card .actions .description {
+ display: block;
+ max-height: 3em;
+ white-space: pre-wrap;
+ line-height: 1.1;
+}
+
+.extra-network-cards .card .actions .description:hover {
+ max-height: none;
+}
+
+.extra-network-cards .card .actions:hover .additional{
+ display: block;
+}
+
+.extra-network-cards .card ul{
+ margin: 0.25em 0 0.75em 0.25em;
+ cursor: unset;
+}
+
+.extra-network-cards .card ul a{
+ cursor: pointer;
+}
+
+.extra-network-cards .card ul a:hover{
+ color: red;
+}
+
+.extra-network-cards .card .preview{
+ position: absolute;
+ object-fit: cover;
+ width: 100%;
+ height:100%;
+}
+
+div.block.gradio-box.edit-user-metadata {
+ width: 56em;
+ background: var(--body-background-fill);
+ padding: 2em !important;
+}
+
+.edit-user-metadata .extra-network-name{
+ font-size: 18pt;
+ color: var(--body-text-color);
+}
+
+.edit-user-metadata .file-metadata{
+ color: var(--body-text-color);
+}
+
+.edit-user-metadata .file-metadata th{
+ text-align: left;
+}
+
+.edit-user-metadata .file-metadata th, .edit-user-metadata .file-metadata td{
+ padding: 0.3em 1em;
+ overflow-wrap: anywhere;
+ word-break: break-word;
+}
+
+.edit-user-metadata .wrap.translucent{
+ background: var(--body-background-fill);
+}
+.edit-user-metadata .gradio-highlightedtext span{
+ word-break: break-word;
+}
+
+.edit-user-metadata-buttons{
+ margin-top: 1.5em;
+}
+
+
+
+
+div.block.gradio-box.popup-dialog, .popup-dialog {
+ width: 56em;
+ background: var(--body-background-fill);
+ padding: 2em !important;
+}
+
+div.block.gradio-box.popup-dialog > div:last-child, .popup-dialog > div:last-child{
+ margin-top: 1em;
+}
+
+div.block.input-accordion{
+
+}
+
+.input-accordion-extra{
+ flex: 0 0 auto !important;
+ margin: 0 0.5em 0 auto;
+}
+
+div.accordions > div.input-accordion{
+ min-width: fit-content !important;
+}
+
+div.accordions > div.gradio-accordion .label-wrap span{
+ white-space: nowrap;
+ margin-right: 0.25em;
+}
+
+div.accordions{
+ gap: 0.5em;
+}
+
+div.accordions > div.input-accordion.input-accordion-open{
+ flex: 1 auto;
+ flex-flow: column;
+}
+
+
+/* sticky right hand columns */
+
+#img2img_results, #txt2img_results, #extras_results {
+ position: sticky;
+ top: 0.5em;
+}
+
+body.resizing {
+ cursor: col-resize !important;
+}
+
+body.resizing * {
+ pointer-events: none !important;
+}
+
+body.resizing .resize-handle {
+ pointer-events: initial !important;
+}
+
+.resize-handle {
+ position: relative;
+ cursor: col-resize;
+ grid-column: 2 / 3;
+ min-width: 16px !important;
+ max-width: 16px !important;
+ height: 100%;
+}
+
+.resize-handle::after {
+ content: '';
+ position: absolute;
+ top: 0;
+ bottom: 0;
+ left: 7.5px;
+ border-left: 1px dashed var(--border-color-primary);
+}
diff --git a/stable-diffusion-webui/test/__init__.py b/stable-diffusion-webui/test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/stable-diffusion-webui/test/conftest.py b/stable-diffusion-webui/test/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..31a5d9eafb8d76eaefd7be6f2f126211eb3b07d7
--- /dev/null
+++ b/stable-diffusion-webui/test/conftest.py
@@ -0,0 +1,25 @@
+import os
+
+import pytest
+import base64
+
+
+test_files_path = os.path.dirname(__file__) + "/test_files"
+
+
+def file_to_base64(filename):
+ with open(filename, "rb") as file:
+ data = file.read()
+
+ base64_str = str(base64.b64encode(data), "utf-8")
+ return "data:image/png;base64," + base64_str
+
+
+@pytest.fixture(scope="session") # session so we don't read this over and over
+def img2img_basic_image_base64() -> str:
+ return file_to_base64(os.path.join(test_files_path, "img2img_basic.png"))
+
+
+@pytest.fixture(scope="session") # session so we don't read this over and over
+def mask_basic_image_base64() -> str:
+ return file_to_base64(os.path.join(test_files_path, "mask_basic.png"))
diff --git a/stable-diffusion-webui/test/test_extras.py b/stable-diffusion-webui/test/test_extras.py
new file mode 100644
index 0000000000000000000000000000000000000000..799d9fadda106cb9feb7c49548a69eb90c228b98
--- /dev/null
+++ b/stable-diffusion-webui/test/test_extras.py
@@ -0,0 +1,35 @@
+import requests
+
+
+def test_simple_upscaling_performed(base_url, img2img_basic_image_base64):
+ payload = {
+ "resize_mode": 0,
+ "show_extras_results": True,
+ "gfpgan_visibility": 0,
+ "codeformer_visibility": 0,
+ "codeformer_weight": 0,
+ "upscaling_resize": 2,
+ "upscaling_resize_w": 128,
+ "upscaling_resize_h": 128,
+ "upscaling_crop": True,
+ "upscaler_1": "Lanczos",
+ "upscaler_2": "None",
+ "extras_upscaler_2_visibility": 0,
+ "image": img2img_basic_image_base64,
+ }
+ assert requests.post(f"{base_url}/sdapi/v1/extra-single-image", json=payload).status_code == 200
+
+
+def test_png_info_performed(base_url, img2img_basic_image_base64):
+ payload = {
+ "image": img2img_basic_image_base64,
+ }
+ assert requests.post(f"{base_url}/sdapi/v1/extra-single-image", json=payload).status_code == 200
+
+
+def test_interrogate_performed(base_url, img2img_basic_image_base64):
+ payload = {
+ "image": img2img_basic_image_base64,
+ "model": "clip",
+ }
+ assert requests.post(f"{base_url}/sdapi/v1/extra-single-image", json=payload).status_code == 200
diff --git a/stable-diffusion-webui/test/test_files/empty.pt b/stable-diffusion-webui/test/test_files/empty.pt
new file mode 100644
index 0000000000000000000000000000000000000000..72f13b63cc3353c1c7cdc27ee23a2ad242cfdfba
--- /dev/null
+++ b/stable-diffusion-webui/test/test_files/empty.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431
diff --git a/stable-diffusion-webui/test/test_files/img2img_basic.png b/stable-diffusion-webui/test/test_files/img2img_basic.png
new file mode 100644
index 0000000000000000000000000000000000000000..b5c1c9ad35162dbc6ebed5aa46682e1b065b2739
--- /dev/null
+++ b/stable-diffusion-webui/test/test_files/img2img_basic.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:401e359065bac7d28ec38bdb8e1c63667879941d0a46b67a3aaa07b0bd88fb7a
+size 9932
diff --git a/stable-diffusion-webui/test/test_files/mask_basic.png b/stable-diffusion-webui/test/test_files/mask_basic.png
new file mode 100644
index 0000000000000000000000000000000000000000..0500048c96868010a723fffe6f50673737991a8a
--- /dev/null
+++ b/stable-diffusion-webui/test/test_files/mask_basic.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f6f760130a1712dbf0312d9315ff9a07efd00f2816f1e9b69d3f0e85b68315d
+size 362
diff --git a/stable-diffusion-webui/test/test_img2img.py b/stable-diffusion-webui/test/test_img2img.py
new file mode 100644
index 0000000000000000000000000000000000000000..117d2d1eb4599fcd3019ed3e980b686d769b2f57
--- /dev/null
+++ b/stable-diffusion-webui/test/test_img2img.py
@@ -0,0 +1,68 @@
+
+import pytest
+import requests
+
+
+@pytest.fixture()
+def url_img2img(base_url):
+ return f"{base_url}/sdapi/v1/img2img"
+
+
+@pytest.fixture()
+def simple_img2img_request(img2img_basic_image_base64):
+ return {
+ "batch_size": 1,
+ "cfg_scale": 7,
+ "denoising_strength": 0.75,
+ "eta": 0,
+ "height": 64,
+ "include_init_images": False,
+ "init_images": [img2img_basic_image_base64],
+ "inpaint_full_res": False,
+ "inpaint_full_res_padding": 0,
+ "inpainting_fill": 0,
+ "inpainting_mask_invert": False,
+ "mask": None,
+ "mask_blur": 4,
+ "n_iter": 1,
+ "negative_prompt": "",
+ "override_settings": {},
+ "prompt": "example prompt",
+ "resize_mode": 0,
+ "restore_faces": False,
+ "s_churn": 0,
+ "s_noise": 1,
+ "s_tmax": 0,
+ "s_tmin": 0,
+ "sampler_index": "Euler a",
+ "seed": -1,
+ "seed_resize_from_h": -1,
+ "seed_resize_from_w": -1,
+ "steps": 3,
+ "styles": [],
+ "subseed": -1,
+ "subseed_strength": 0,
+ "tiling": False,
+ "width": 64,
+ }
+
+
+def test_img2img_simple_performed(url_img2img, simple_img2img_request):
+ assert requests.post(url_img2img, json=simple_img2img_request).status_code == 200
+
+
+def test_inpainting_masked_performed(url_img2img, simple_img2img_request, mask_basic_image_base64):
+ simple_img2img_request["mask"] = mask_basic_image_base64
+ assert requests.post(url_img2img, json=simple_img2img_request).status_code == 200
+
+
+def test_inpainting_with_inverted_masked_performed(url_img2img, simple_img2img_request, mask_basic_image_base64):
+ simple_img2img_request["mask"] = mask_basic_image_base64
+ simple_img2img_request["inpainting_mask_invert"] = True
+ assert requests.post(url_img2img, json=simple_img2img_request).status_code == 200
+
+
+def test_img2img_sd_upscale_performed(url_img2img, simple_img2img_request):
+ simple_img2img_request["script_name"] = "sd upscale"
+ simple_img2img_request["script_args"] = ["", 8, "Lanczos", 2.0]
+ assert requests.post(url_img2img, json=simple_img2img_request).status_code == 200
diff --git a/stable-diffusion-webui/test/test_txt2img.py b/stable-diffusion-webui/test/test_txt2img.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eb94f0a8596bb42dd2bed6bc51e1f4d51fee7a6
--- /dev/null
+++ b/stable-diffusion-webui/test/test_txt2img.py
@@ -0,0 +1,90 @@
+
+import pytest
+import requests
+
+
+@pytest.fixture()
+def url_txt2img(base_url):
+ return f"{base_url}/sdapi/v1/txt2img"
+
+
+@pytest.fixture()
+def simple_txt2img_request():
+ return {
+ "batch_size": 1,
+ "cfg_scale": 7,
+ "denoising_strength": 0,
+ "enable_hr": False,
+ "eta": 0,
+ "firstphase_height": 0,
+ "firstphase_width": 0,
+ "height": 64,
+ "n_iter": 1,
+ "negative_prompt": "",
+ "prompt": "example prompt",
+ "restore_faces": False,
+ "s_churn": 0,
+ "s_noise": 1,
+ "s_tmax": 0,
+ "s_tmin": 0,
+ "sampler_index": "Euler a",
+ "seed": -1,
+ "seed_resize_from_h": -1,
+ "seed_resize_from_w": -1,
+ "steps": 3,
+ "styles": [],
+ "subseed": -1,
+ "subseed_strength": 0,
+ "tiling": False,
+ "width": 64,
+ }
+
+
+def test_txt2img_simple_performed(url_txt2img, simple_txt2img_request):
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_with_negative_prompt_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["negative_prompt"] = "example negative prompt"
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_with_complex_prompt_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["prompt"] = "((emphasis)), (emphasis1:1.1), [to:1], [from::2], [from:to:0.3], [alt|alt1]"
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_not_square_image_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["height"] = 128
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_with_hrfix_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["enable_hr"] = True
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_with_tiling_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["tiling"] = True
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_with_restore_faces_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["restore_faces"] = True
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+@pytest.mark.parametrize("sampler", ["PLMS", "DDIM", "UniPC"])
+def test_txt2img_with_vanilla_sampler_performed(url_txt2img, simple_txt2img_request, sampler):
+ simple_txt2img_request["sampler_index"] = sampler
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_multiple_batches_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["n_iter"] = 2
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
+
+
+def test_txt2img_batch_performed(url_txt2img, simple_txt2img_request):
+ simple_txt2img_request["batch_size"] = 2
+ assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200
diff --git a/stable-diffusion-webui/test/test_utils.py b/stable-diffusion-webui/test/test_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..edba0b185345f5fa20193c7d8e8fc71fb069fd73
--- /dev/null
+++ b/stable-diffusion-webui/test/test_utils.py
@@ -0,0 +1,33 @@
+import pytest
+import requests
+
+
+def test_options_write(base_url):
+ url_options = f"{base_url}/sdapi/v1/options"
+ response = requests.get(url_options)
+ assert response.status_code == 200
+
+ pre_value = response.json()["send_seed"]
+
+ assert requests.post(url_options, json={'send_seed': (not pre_value)}).status_code == 200
+
+ response = requests.get(url_options)
+ assert response.status_code == 200
+ assert response.json()['send_seed'] == (not pre_value)
+
+ requests.post(url_options, json={"send_seed": pre_value})
+
+
+@pytest.mark.parametrize("url", [
+ "sdapi/v1/cmd-flags",
+ "sdapi/v1/samplers",
+ "sdapi/v1/upscalers",
+ "sdapi/v1/sd-models",
+ "sdapi/v1/hypernetworks",
+ "sdapi/v1/face-restorers",
+ "sdapi/v1/realesrgan-models",
+ "sdapi/v1/prompt-styles",
+ "sdapi/v1/embeddings",
+])
+def test_get_api_url(base_url, url):
+ assert requests.get(f"{base_url}/{url}").status_code == 200
diff --git a/stable-diffusion-webui/textual_inversion_templates/hypernetwork.txt b/stable-diffusion-webui/textual_inversion_templates/hypernetwork.txt
new file mode 100644
index 0000000000000000000000000000000000000000..91e06890571c7e4974d5a76c30fab62e8587c7d2
--- /dev/null
+++ b/stable-diffusion-webui/textual_inversion_templates/hypernetwork.txt
@@ -0,0 +1,27 @@
+a photo of a [filewords]
+a rendering of a [filewords]
+a cropped photo of the [filewords]
+the photo of a [filewords]
+a photo of a clean [filewords]
+a photo of a dirty [filewords]
+a dark photo of the [filewords]
+a photo of my [filewords]
+a photo of the cool [filewords]
+a close-up photo of a [filewords]
+a bright photo of the [filewords]
+a cropped photo of a [filewords]
+a photo of the [filewords]
+a good photo of the [filewords]
+a photo of one [filewords]
+a close-up photo of the [filewords]
+a rendition of the [filewords]
+a photo of the clean [filewords]
+a rendition of a [filewords]
+a photo of a nice [filewords]
+a good photo of a [filewords]
+a photo of the nice [filewords]
+a photo of the small [filewords]
+a photo of the weird [filewords]
+a photo of the large [filewords]
+a photo of a cool [filewords]
+a photo of a small [filewords]
diff --git a/stable-diffusion-webui/textual_inversion_templates/none.txt b/stable-diffusion-webui/textual_inversion_templates/none.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f77af4612b289a56b718c3bee62c66a6151f75be
--- /dev/null
+++ b/stable-diffusion-webui/textual_inversion_templates/none.txt
@@ -0,0 +1 @@
+picture
diff --git a/stable-diffusion-webui/textual_inversion_templates/style.txt b/stable-diffusion-webui/textual_inversion_templates/style.txt
new file mode 100644
index 0000000000000000000000000000000000000000..15af2d6b85f259d0bf41fbe0c8ca7a3340e1b259
--- /dev/null
+++ b/stable-diffusion-webui/textual_inversion_templates/style.txt
@@ -0,0 +1,19 @@
+a painting, art by [name]
+a rendering, art by [name]
+a cropped painting, art by [name]
+the painting, art by [name]
+a clean painting, art by [name]
+a dirty painting, art by [name]
+a dark painting, art by [name]
+a picture, art by [name]
+a cool painting, art by [name]
+a close-up painting, art by [name]
+a bright painting, art by [name]
+a cropped painting, art by [name]
+a good painting, art by [name]
+a close-up painting, art by [name]
+a rendition, art by [name]
+a nice painting, art by [name]
+a small painting, art by [name]
+a weird painting, art by [name]
+a large painting, art by [name]
diff --git a/stable-diffusion-webui/textual_inversion_templates/style_filewords.txt b/stable-diffusion-webui/textual_inversion_templates/style_filewords.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b3a8159a869a7890bdd42470664fadf015e0658d
--- /dev/null
+++ b/stable-diffusion-webui/textual_inversion_templates/style_filewords.txt
@@ -0,0 +1,19 @@
+a painting of [filewords], art by [name]
+a rendering of [filewords], art by [name]
+a cropped painting of [filewords], art by [name]
+the painting of [filewords], art by [name]
+a clean painting of [filewords], art by [name]
+a dirty painting of [filewords], art by [name]
+a dark painting of [filewords], art by [name]
+a picture of [filewords], art by [name]
+a cool painting of [filewords], art by [name]
+a close-up painting of [filewords], art by [name]
+a bright painting of [filewords], art by [name]
+a cropped painting of [filewords], art by [name]
+a good painting of [filewords], art by [name]
+a close-up painting of [filewords], art by [name]
+a rendition of [filewords], art by [name]
+a nice painting of [filewords], art by [name]
+a small painting of [filewords], art by [name]
+a weird painting of [filewords], art by [name]
+a large painting of [filewords], art by [name]
diff --git a/stable-diffusion-webui/textual_inversion_templates/subject.txt b/stable-diffusion-webui/textual_inversion_templates/subject.txt
new file mode 100644
index 0000000000000000000000000000000000000000..79f36aa0543fc2151b7f7e28725309c0c9a4912a
--- /dev/null
+++ b/stable-diffusion-webui/textual_inversion_templates/subject.txt
@@ -0,0 +1,27 @@
+a photo of a [name]
+a rendering of a [name]
+a cropped photo of the [name]
+the photo of a [name]
+a photo of a clean [name]
+a photo of a dirty [name]
+a dark photo of the [name]
+a photo of my [name]
+a photo of the cool [name]
+a close-up photo of a [name]
+a bright photo of the [name]
+a cropped photo of a [name]
+a photo of the [name]
+a good photo of the [name]
+a photo of one [name]
+a close-up photo of the [name]
+a rendition of the [name]
+a photo of the clean [name]
+a rendition of a [name]
+a photo of a nice [name]
+a good photo of a [name]
+a photo of the nice [name]
+a photo of the small [name]
+a photo of the weird [name]
+a photo of the large [name]
+a photo of a cool [name]
+a photo of a small [name]
diff --git a/stable-diffusion-webui/textual_inversion_templates/subject_filewords.txt b/stable-diffusion-webui/textual_inversion_templates/subject_filewords.txt
new file mode 100644
index 0000000000000000000000000000000000000000..008652a6bf4277f12a1759f5f3c815ae754dcfcf
--- /dev/null
+++ b/stable-diffusion-webui/textual_inversion_templates/subject_filewords.txt
@@ -0,0 +1,27 @@
+a photo of a [name], [filewords]
+a rendering of a [name], [filewords]
+a cropped photo of the [name], [filewords]
+the photo of a [name], [filewords]
+a photo of a clean [name], [filewords]
+a photo of a dirty [name], [filewords]
+a dark photo of the [name], [filewords]
+a photo of my [name], [filewords]
+a photo of the cool [name], [filewords]
+a close-up photo of a [name], [filewords]
+a bright photo of the [name], [filewords]
+a cropped photo of a [name], [filewords]
+a photo of the [name], [filewords]
+a good photo of the [name], [filewords]
+a photo of one [name], [filewords]
+a close-up photo of the [name], [filewords]
+a rendition of the [name], [filewords]
+a photo of the clean [name], [filewords]
+a rendition of a [name], [filewords]
+a photo of a nice [name], [filewords]
+a good photo of a [name], [filewords]
+a photo of the nice [name], [filewords]
+a photo of the small [name], [filewords]
+a photo of the weird [name], [filewords]
+a photo of the large [name], [filewords]
+a photo of a cool [name], [filewords]
+a photo of a small [name], [filewords]
diff --git a/stable-diffusion-webui/webui-macos-env.sh b/stable-diffusion-webui/webui-macos-env.sh
new file mode 100644
index 0000000000000000000000000000000000000000..24bc5c42615477b2cc6c16470f6f796bbde77ae7
--- /dev/null
+++ b/stable-diffusion-webui/webui-macos-env.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+####################################################################
+# macOS defaults #
+# Please modify webui-user.sh to change these instead of this file #
+####################################################################
+
+if [[ -x "$(command -v python3.10)" ]]
+then
+ python_cmd="python3.10"
+fi
+
+export install_dir="$HOME"
+export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
+export TORCH_COMMAND="pip install torch==2.0.1 torchvision==0.15.2"
+export PYTORCH_ENABLE_MPS_FALLBACK=1
+
+####################################################################
diff --git a/stable-diffusion-webui/webui-user.bat b/stable-diffusion-webui/webui-user.bat
new file mode 100644
index 0000000000000000000000000000000000000000..e5a257bef06f5bfcaff1c8b33c64a767eb8b3fe5
--- /dev/null
+++ b/stable-diffusion-webui/webui-user.bat
@@ -0,0 +1,8 @@
+@echo off
+
+set PYTHON=
+set GIT=
+set VENV_DIR=
+set COMMANDLINE_ARGS=
+
+call webui.bat
diff --git a/stable-diffusion-webui/webui-user.sh b/stable-diffusion-webui/webui-user.sh
new file mode 100644
index 0000000000000000000000000000000000000000..70306c60d5b495bebd87da8f06da58fb72706553
--- /dev/null
+++ b/stable-diffusion-webui/webui-user.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+#########################################################
+# Uncomment and change the variables below to your need:#
+#########################################################
+
+# Install directory without trailing slash
+#install_dir="/home/$(whoami)"
+
+# Name of the subdirectory
+#clone_dir="stable-diffusion-webui"
+
+# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
+#export COMMANDLINE_ARGS=""
+
+# python3 executable
+#python_cmd="python3"
+
+# git executable
+#export GIT="git"
+
+# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
+#venv_dir="venv"
+
+# script to launch to start the app
+#export LAUNCH_SCRIPT="launch.py"
+
+# install command for torch
+#export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113"
+
+# Requirements file to use for stable-diffusion-webui
+#export REQS_FILE="requirements_versions.txt"
+
+# Fixed git repos
+#export K_DIFFUSION_PACKAGE=""
+#export GFPGAN_PACKAGE=""
+
+# Fixed git commits
+#export STABLE_DIFFUSION_COMMIT_HASH=""
+#export CODEFORMER_COMMIT_HASH=""
+#export BLIP_COMMIT_HASH=""
+
+# Uncomment to enable accelerated launch
+#export ACCELERATE="True"
+
+# Uncomment to disable TCMalloc
+#export NO_TCMALLOC="True"
+
+###########################################
diff --git a/stable-diffusion-webui/webui.bat b/stable-diffusion-webui/webui.bat
new file mode 100644
index 0000000000000000000000000000000000000000..b0fee3e4ed58a4f7328afb4fc32610ba14c01ce3
--- /dev/null
+++ b/stable-diffusion-webui/webui.bat
@@ -0,0 +1,87 @@
+@echo off
+
+if not defined PYTHON (set PYTHON=python)
+if not defined VENV_DIR (set "VENV_DIR=%~dp0%venv")
+
+set SD_WEBUI_RESTART=tmp/restart
+set ERROR_REPORTING=FALSE
+
+mkdir tmp 2>NUL
+
+%PYTHON% -c "" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :check_pip
+echo Couldn't launch python
+goto :show_stdout_stderr
+
+:check_pip
+%PYTHON% -mpip --help >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :start_venv
+if "%PIP_INSTALLER_LOCATION%" == "" goto :show_stdout_stderr
+%PYTHON% "%PIP_INSTALLER_LOCATION%" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :start_venv
+echo Couldn't install pip
+goto :show_stdout_stderr
+
+:start_venv
+if ["%VENV_DIR%"] == ["-"] goto :skip_venv
+if ["%SKIP_VENV%"] == ["1"] goto :skip_venv
+
+dir "%VENV_DIR%\Scripts\Python.exe" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :activate_venv
+
+for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i"
+echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME%
+%PYTHON_FULLNAME% -m venv "%VENV_DIR%" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :activate_venv
+echo Unable to create venv in directory "%VENV_DIR%"
+goto :show_stdout_stderr
+
+:activate_venv
+set PYTHON="%VENV_DIR%\Scripts\Python.exe"
+echo venv %PYTHON%
+
+:skip_venv
+if [%ACCELERATE%] == ["True"] goto :accelerate
+goto :launch
+
+:accelerate
+echo Checking for accelerate
+set ACCELERATE="%VENV_DIR%\Scripts\accelerate.exe"
+if EXIST %ACCELERATE% goto :accelerate_launch
+
+:launch
+%PYTHON% launch.py %*
+if EXIST tmp/restart goto :skip_venv
+pause
+exit /b
+
+:accelerate_launch
+echo Accelerating
+%ACCELERATE% launch --num_cpu_threads_per_process=6 launch.py
+if EXIST tmp/restart goto :skip_venv
+pause
+exit /b
+
+:show_stdout_stderr
+
+echo.
+echo exit code: %errorlevel%
+
+for /f %%i in ("tmp\stdout.txt") do set size=%%~zi
+if %size% equ 0 goto :show_stderr
+echo.
+echo stdout:
+type tmp\stdout.txt
+
+:show_stderr
+for /f %%i in ("tmp\stderr.txt") do set size=%%~zi
+if %size% equ 0 goto :show_stderr
+echo.
+echo stderr:
+type tmp\stderr.txt
+
+:endofscript
+
+echo.
+echo Launch unsuccessful. Exiting.
+pause
diff --git a/stable-diffusion-webui/webui.py b/stable-diffusion-webui/webui.py
new file mode 100644
index 0000000000000000000000000000000000000000..f53912b94e72151cda55a92752b74e05fc874839
--- /dev/null
+++ b/stable-diffusion-webui/webui.py
@@ -0,0 +1,162 @@
+from __future__ import annotations
+
+import os
+import time
+
+from modules import timer
+from modules import initialize_util
+from modules import initialize
+
+startup_timer = timer.startup_timer
+startup_timer.record("launcher")
+
+initialize.imports()
+
+initialize.check_versions()
+
+
+def create_api(app):
+ from modules.api.api import Api
+ from modules.call_queue import queue_lock
+
+ api = Api(app, queue_lock)
+ return api
+
+
+def api_only():
+ from fastapi import FastAPI
+ from modules.shared_cmd_options import cmd_opts
+
+ initialize.initialize()
+
+ app = FastAPI()
+ initialize_util.setup_middleware(app)
+ api = create_api(app)
+
+ from modules import script_callbacks
+ script_callbacks.before_ui_callback()
+ script_callbacks.app_started_callback(None, app)
+
+ print(f"Startup time: {startup_timer.summary()}.")
+ api.launch(
+ server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
+ port=cmd_opts.port if cmd_opts.port else 7861,
+ root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else ""
+ )
+
+
+def webui():
+ from modules.shared_cmd_options import cmd_opts
+
+ launch_api = cmd_opts.api
+ initialize.initialize()
+
+ from modules import shared, ui_tempdir, script_callbacks, ui, progress, ui_extra_networks
+
+ while 1:
+ if shared.opts.clean_temp_dir_at_start:
+ ui_tempdir.cleanup_tmpdr()
+ startup_timer.record("cleanup temp dir")
+
+ script_callbacks.before_ui_callback()
+ startup_timer.record("scripts before_ui_callback")
+
+ shared.demo = ui.create_ui()
+ startup_timer.record("create ui")
+
+ if not cmd_opts.no_gradio_queue:
+ shared.demo.queue(64)
+
+ gradio_auth_creds = list(initialize_util.get_gradio_auth_creds()) or None
+
+ auto_launch_browser = False
+ if os.getenv('SD_WEBUI_RESTARTING') != '1':
+ if shared.opts.auto_launch_browser == "Remote" or cmd_opts.autolaunch:
+ auto_launch_browser = True
+ elif shared.opts.auto_launch_browser == "Local":
+ auto_launch_browser = not any([cmd_opts.listen, cmd_opts.share, cmd_opts.ngrok, cmd_opts.server_name])
+
+ app, local_url, share_url = shared.demo.launch(
+ share=cmd_opts.share,
+ server_name=initialize_util.gradio_server_name(),
+ server_port=cmd_opts.port,
+ ssl_keyfile=cmd_opts.tls_keyfile,
+ ssl_certfile=cmd_opts.tls_certfile,
+ ssl_verify=cmd_opts.disable_tls_verify,
+ debug=cmd_opts.gradio_debug,
+ auth=gradio_auth_creds,
+ inbrowser=auto_launch_browser,
+ prevent_thread_lock=True,
+ allowed_paths=cmd_opts.gradio_allowed_path,
+ app_kwargs={
+ "docs_url": "/docs",
+ "redoc_url": "/redoc",
+ },
+ root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else "",
+ )
+
+ startup_timer.record("gradio launch")
+
+ # gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
+ # an attacker to trick the user into opening a malicious HTML page, which makes a request to the
+ # running web ui and do whatever the attacker wants, including installing an extension and
+ # running its code. We disable this here. Suggested by RyotaK.
+ app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
+
+ initialize_util.setup_middleware(app)
+
+ progress.setup_progress_api(app)
+ ui.setup_ui_api(app)
+
+ if launch_api:
+ create_api(app)
+
+ ui_extra_networks.add_pages_to_demo(app)
+
+ startup_timer.record("add APIs")
+
+ with startup_timer.subcategory("app_started_callback"):
+ script_callbacks.app_started_callback(shared.demo, app)
+
+ timer.startup_record = startup_timer.dump()
+ print(f"Startup time: {startup_timer.summary()}.")
+
+ try:
+ while True:
+ server_command = shared.state.wait_for_server_command(timeout=5)
+ if server_command:
+ if server_command in ("stop", "restart"):
+ break
+ else:
+ print(f"Unknown server command: {server_command}")
+ except KeyboardInterrupt:
+ print('Caught KeyboardInterrupt, stopping...')
+ server_command = "stop"
+
+ if server_command == "stop":
+ print("Stopping server...")
+ # If we catch a keyboard interrupt, we want to stop the server and exit.
+ shared.demo.close()
+ break
+
+ # disable auto launch webui in browser for subsequent UI Reload
+ os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
+
+ print('Restarting UI...')
+ shared.demo.close()
+ time.sleep(0.5)
+ startup_timer.reset()
+ script_callbacks.app_reload_callback()
+ startup_timer.record("app reload callback")
+ script_callbacks.script_unloaded_callback()
+ startup_timer.record("scripts unloaded callback")
+ initialize.initialize_rest(reload_script_modules=True)
+
+
+if __name__ == "__main__":
+ from modules.shared_cmd_options import cmd_opts
+
+ if cmd_opts.nowebui:
+ api_only()
+ else:
+ webui()
diff --git a/stable-diffusion-webui/webui.sh b/stable-diffusion-webui/webui.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3d0f87eed741f82091175cce9ce4d644e9b1c130
--- /dev/null
+++ b/stable-diffusion-webui/webui.sh
@@ -0,0 +1,255 @@
+#!/usr/bin/env bash
+#################################################
+# Please do not make any changes to this file, #
+# change the variables in webui-user.sh instead #
+#################################################
+
+
+use_venv=1
+if [[ $venv_dir == "-" ]]; then
+ use_venv=0
+fi
+
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+
+
+# If run from macOS, load defaults from webui-macos-env.sh
+if [[ "$OSTYPE" == "darwin"* ]]; then
+ if [[ -f "$SCRIPT_DIR"/webui-macos-env.sh ]]
+ then
+ source "$SCRIPT_DIR"/webui-macos-env.sh
+ fi
+fi
+
+# Read variables from webui-user.sh
+# shellcheck source=/dev/null
+if [[ -f "$SCRIPT_DIR"/webui-user.sh ]]
+then
+ source "$SCRIPT_DIR"/webui-user.sh
+fi
+
+# Set defaults
+# Install directory without trailing slash
+if [[ -z "${install_dir}" ]]
+then
+ install_dir="$SCRIPT_DIR"
+fi
+
+# Name of the subdirectory (defaults to stable-diffusion-webui)
+if [[ -z "${clone_dir}" ]]
+then
+ clone_dir="stable-diffusion-webui"
+fi
+
+# python3 executable
+if [[ -z "${python_cmd}" ]]
+then
+ python_cmd="python3"
+fi
+
+# git executable
+if [[ -z "${GIT}" ]]
+then
+ export GIT="git"
+fi
+
+# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
+if [[ -z "${venv_dir}" ]] && [[ $use_venv -eq 1 ]]
+then
+ venv_dir="venv"
+fi
+
+if [[ -z "${LAUNCH_SCRIPT}" ]]
+then
+ LAUNCH_SCRIPT="launch.py"
+fi
+
+# this script cannot be run as root by default
+can_run_as_root=0
+
+# read any command line flags to the webui.sh script
+while getopts "f" flag > /dev/null 2>&1
+do
+ case ${flag} in
+ f) can_run_as_root=1;;
+ *) break;;
+ esac
+done
+
+# Disable sentry logging
+export ERROR_REPORTING=FALSE
+
+# Do not reinstall existing pip packages on Debian/Ubuntu
+export PIP_IGNORE_INSTALLED=0
+
+# Pretty print
+delimiter="################################################################"
+
+printf "\n%s\n" "${delimiter}"
+printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n"
+printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m"
+printf "\n%s\n" "${delimiter}"
+
+# Do not run as root
+if [[ $(id -u) -eq 0 && can_run_as_root -eq 0 ]]
+then
+ printf "\n%s\n" "${delimiter}"
+ printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m"
+ printf "\n%s\n" "${delimiter}"
+ exit 1
+else
+ printf "\n%s\n" "${delimiter}"
+ printf "Running on \e[1m\e[32m%s\e[0m user" "$(whoami)"
+ printf "\n%s\n" "${delimiter}"
+fi
+
+if [[ $(getconf LONG_BIT) = 32 ]]
+then
+ printf "\n%s\n" "${delimiter}"
+ printf "\e[1m\e[31mERROR: Unsupported Running on a 32bit OS\e[0m"
+ printf "\n%s\n" "${delimiter}"
+ exit 1
+fi
+
+if [[ -d .git ]]
+then
+ printf "\n%s\n" "${delimiter}"
+ printf "Repo already cloned, using it as install directory"
+ printf "\n%s\n" "${delimiter}"
+ install_dir="${PWD}/../"
+ clone_dir="${PWD##*/}"
+fi
+
+# Check prerequisites
+gpu_info=$(lspci 2>/dev/null | grep -E "VGA|Display")
+case "$gpu_info" in
+ *"Navi 1"*)
+ export HSA_OVERRIDE_GFX_VERSION=10.3.0
+ if [[ -z "${TORCH_COMMAND}" ]]
+ then
+ pyv="$(${python_cmd} -c 'import sys; print(".".join(map(str, sys.version_info[0:2])))')"
+ if [[ $(bc <<< "$pyv <= 3.10") -eq 1 ]]
+ then
+ # Navi users will still use torch 1.13 because 2.0 does not seem to work.
+ export TORCH_COMMAND="pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 --index-url https://download.pytorch.org/whl/rocm5.2"
+ else
+ printf "\e[1m\e[31mERROR: RX 5000 series GPUs must be using at max python 3.10, aborting...\e[0m"
+ exit 1
+ fi
+ fi
+ ;;
+ *"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
+ ;;
+ *"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \
+ export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.6"
+ # Navi 3 needs at least 5.5 which is only on the nightly chain, previous versions are no longer online (torch==2.1.0.dev-20230614+rocm5.5 torchvision==0.16.0.dev-20230614+rocm5.5 torchaudio==2.1.0.dev-20230614+rocm5.5)
+ # so switch to nightly rocm5.6 without explicit versions this time
+ ;;
+ *"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
+ printf "\n%s\n" "${delimiter}"
+ printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
+ printf "\n%s\n" "${delimiter}"
+ ;;
+ *)
+ ;;
+esac
+if ! echo "$gpu_info" | grep -q "NVIDIA";
+then
+ if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
+ then
+ export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2"
+ fi
+fi
+
+for preq in "${GIT}" "${python_cmd}"
+do
+ if ! hash "${preq}" &>/dev/null
+ then
+ printf "\n%s\n" "${delimiter}"
+ printf "\e[1m\e[31mERROR: %s is not installed, aborting...\e[0m" "${preq}"
+ printf "\n%s\n" "${delimiter}"
+ exit 1
+ fi
+done
+
+if [[ $use_venv -eq 1 ]] && ! "${python_cmd}" -c "import venv" &>/dev/null
+then
+ printf "\n%s\n" "${delimiter}"
+ printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m"
+ printf "\n%s\n" "${delimiter}"
+ exit 1
+fi
+
+cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; }
+if [[ -d "${clone_dir}" ]]
+then
+ cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
+else
+ printf "\n%s\n" "${delimiter}"
+ printf "Clone stable-diffusion-webui"
+ printf "\n%s\n" "${delimiter}"
+ "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
+ cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
+fi
+
+if [[ $use_venv -eq 1 ]] && [[ -z "${VIRTUAL_ENV}" ]];
+then
+ printf "\n%s\n" "${delimiter}"
+ printf "Create and activate python venv"
+ printf "\n%s\n" "${delimiter}"
+ cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
+ if [[ ! -d "${venv_dir}" ]]
+ then
+ "${python_cmd}" -m venv "${venv_dir}"
+ first_launch=1
+ fi
+ # shellcheck source=/dev/null
+ if [[ -f "${venv_dir}"/bin/activate ]]
+ then
+ source "${venv_dir}"/bin/activate
+ else
+ printf "\n%s\n" "${delimiter}"
+ printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
+ printf "\n%s\n" "${delimiter}"
+ exit 1
+ fi
+else
+ printf "\n%s\n" "${delimiter}"
+ printf "python venv already activate or run without venv: ${VIRTUAL_ENV}"
+ printf "\n%s\n" "${delimiter}"
+fi
+
+# Try using TCMalloc on Linux
+prepare_tcmalloc() {
+ if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
+ TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)"
+ if [[ ! -z "${TCMALLOC}" ]]; then
+ echo "Using TCMalloc: ${TCMALLOC}"
+ export LD_PRELOAD="${TCMALLOC}"
+ else
+ printf "\e[1m\e[31mCannot locate TCMalloc (improves CPU memory usage)\e[0m\n"
+ fi
+ fi
+}
+
+KEEP_GOING=1
+export SD_WEBUI_RESTART=tmp/restart
+while [[ "$KEEP_GOING" -eq "1" ]]; do
+ if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]; then
+ printf "\n%s\n" "${delimiter}"
+ printf "Accelerating launch.py..."
+ printf "\n%s\n" "${delimiter}"
+ prepare_tcmalloc
+ accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
+ else
+ printf "\n%s\n" "${delimiter}"
+ printf "Launching launch.py..."
+ printf "\n%s\n" "${delimiter}"
+ prepare_tcmalloc
+ "${python_cmd}" -u "${LAUNCH_SCRIPT}" "$@"
+ fi
+
+ if [[ ! -f tmp/restart ]]; then
+ KEEP_GOING=0
+ fi
+done