check

app.py

@@ -26,53 +26,53 @@ def setup():
     os.system("pip install tokenizers x-transformers==0.22.0 axial-positional-embedding")
     os.system("pip install einops==0.3.2")
     sys.path.append("improved-diffusion")
-
-    import improved_diffusion.pipeline
-    from transformer_utils.util.tfm_utils import get_local_path_from_huggingface_cdn
-
-    if not os.path.exists(model_path_diffusion):
-        model_tar_name = 'model.tar'
-        model_tar_path = get_local_path_from_huggingface_cdn(
-            HF_REPO_NAME_DIFFUSION, model_tar_name
-        )
-        subprocess.run(f"tar -xf {model_tar_path} && rm {model_tar_path}", shell=True)
-
-    checkpoint_path_sres1 = os.path.join(model_path_diffusion, "sres1.pt")
-    config_path_sres1 = os.path.join(model_path_diffusion, "config_sres1.json")
-
-    checkpoint_path_sres2 = os.path.join(model_path_diffusion, "sres2.pt")
-    config_path_sres2 = os.path.join(model_path_diffusion, "config_sres2.json")
-
-    # load
-    sampling_model_sres1 = improved_diffusion.pipeline.SamplingModel.from_config(
-        checkpoint_path=checkpoint_path_sres1,
-        config_path=config_path_sres1,
-        timestep_respacing=timestep_respacing_sres1
-    )
-
-    sampling_model_sres2 = improved_diffusion.pipeline.SamplingModel.from_config(
-        checkpoint_path=checkpoint_path_sres2,
-        config_path=config_path_sres2,
-        timestep_respacing=timestep_respacing_sres2
-    )
-
-    pipeline = improved_diffusion.pipeline.SamplingPipeline(sampling_model_sres1, sampling_model_sres2)
+    from improved_diffusion import pipeline
+    # import improved_diffusion.pipeline
+    # from transformer_utils.util.tfm_utils import get_local_path_from_huggingface_cdn
+    #
+    # if not os.path.exists(model_path_diffusion):
+    #     model_tar_name = 'model.tar'
+    #     model_tar_path = get_local_path_from_huggingface_cdn(
+    #         HF_REPO_NAME_DIFFUSION, model_tar_name
+    #     )
+    #     subprocess.run(f"tar -xf {model_tar_path} && rm {model_tar_path}", shell=True)
+    #
+    # checkpoint_path_sres1 = os.path.join(model_path_diffusion, "sres1.pt")
+    # config_path_sres1 = os.path.join(model_path_diffusion, "config_sres1.json")
+    #
+    # checkpoint_path_sres2 = os.path.join(model_path_diffusion, "sres2.pt")
+    # config_path_sres2 = os.path.join(model_path_diffusion, "config_sres2.json")
+    #
+    # # load
+    # sampling_model_sres1 = improved_diffusion.pipeline.SamplingModel.from_config(
+    #     checkpoint_path=checkpoint_path_sres1,
+    #     config_path=config_path_sres1,
+    #     timestep_respacing=timestep_respacing_sres1
+    # )
+    #
+    # sampling_model_sres2 = improved_diffusion.pipeline.SamplingModel.from_config(
+    #     checkpoint_path=checkpoint_path_sres2,
+    #     config_path=config_path_sres2,
+    #     timestep_respacing=timestep_respacing_sres2
+    # )
+    #
+    # pipeline = improved_diffusion.pipeline.SamplingPipeline(sampling_model_sres1, sampling_model_sres2)
     return pipeline
 
 pipeline = setup()
 
 def handler(text, ts1, ts2, gs1):
-    # a = np.random.randint(0, 255, (128, 128, 3)).astype(np.uint8)
-    data = {'text': text[:380], 'guidance_scale': gs1}
-    args = {k: v for k, v in DIFFUSION_DEFAULTS.items()}
-    args.update(data)
-
-    print(f"running: {args}")
-
-    pipeline.base_model.set_timestep_respacing(str(ts1))
-    pipeline.super_res_model.set_timestep_respacing(str(ts2))
-
-    return pipeline.sample(**args)
+    # # a = np.random.randint(0, 255, (128, 128, 3)).astype(np.uint8)
+    # data = {'text': text[:380], 'guidance_scale': gs1}
+    # args = {k: v for k, v in DIFFUSION_DEFAULTS.items()}
+    # args.update(data)
+    #
+    # print(f"running: {args}")
+    #
+    # pipeline.base_model.set_timestep_respacing(str(ts1))
+    # pipeline.super_res_model.set_timestep_respacing(str(ts2))
+    #
+    # return pipeline.sample(**args)
 
 
 text = st.text_area('asdf')