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import os
import gc
import re
import cv2
import numpy as np
import gradio as gr
import torch
import traceback
import math
import time
import ast
import argparse
from collections import defaultdict
from facexlib.utils.misc import download_from_url
from basicsr.utils.realesrganer import RealESRGANer
from utils.dataops import auto_split_upscale
input_images_limit = 5
# Define URLs and their corresponding local storage paths
face_models = {
"GFPGANv1.4.pth" : ["https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth",
"https://github.com/TencentARC/GFPGAN/",
"""GFPGAN: Towards Real-World Blind Face Restoration and Upscalling of the image with a Generative Facial Prior.
GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration.
It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration."""],
"RestoreFormer++.ckpt": ["https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer++.ckpt",
"https://github.com/wzhouxiff/RestoreFormerPlusPlus",
"""RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs.
RestoreFormer++ is an extension of RestoreFormer. It proposes to restore a degraded face image with both fidelity and \
realness by using the powerful fully-spacial attention mechanisms to model the abundant contextual information in the face and \
its interplay with reconstruction-oriented high-quality priors."""],
"CodeFormer.pth" : ["https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth",
"https://github.com/sczhou/CodeFormer",
"""CodeFormer: Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022).
CodeFormer is a Transformer-based model designed to tackle the challenging problem of blind face restoration, where inputs are often severely degraded.
By framing face restoration as a code prediction task, this approach ensures both improved mapping from degraded inputs to outputs and the generation of visually rich, high-quality faces.
"""],
"GPEN-BFR-512.pth" : ["https://huggingface.co/akhaliq/GPEN-BFR-512/resolve/main/GPEN-BFR-512.pth",
"https://github.com/yangxy/GPEN",
"""GPEN: GAN Prior Embedded Network for Blind Face Restoration in the Wild.
GPEN addresses blind face restoration (BFR) by embedding a GAN into a U-shaped DNN, combining GAN’s ability to generate high-quality images with DNN’s feature extraction.
This design reconstructs global structure, fine details, and backgrounds from degraded inputs.
Simple yet effective, GPEN outperforms state-of-the-art methods, delivering realistic results even for severely degraded images."""],
"GPEN-BFR-1024.pt" : ["https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/resolve/master/pytorch_model.pt",
"https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/files",
"""The same as GPEN but for 1024 resolution."""],
"GPEN-BFR-2048.pt" : ["https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/resolve/master/pytorch_model-2048.pt",
"https://www.modelscope.cn/models/iic/cv_gpen_image-portrait-enhancement-hires/files",
"""The same as GPEN but for 2048 resolution."""],
# legacy model
"GFPGANv1.3.pth" : ["https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth",
"https://github.com/TencentARC/GFPGAN/", "The same as GFPGAN but legacy model"],
"GFPGANv1.2.pth" : ["https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth",
"https://github.com/TencentARC/GFPGAN/", "The same as GFPGAN but legacy model"],
"RestoreFormer.ckpt": ["https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer.ckpt",
"https://github.com/wzhouxiff/RestoreFormerPlusPlus", "The same as RestoreFormer++ but legacy model"],
}
upscale_models = {
# SRVGGNet(Compact)
"realesr-general-x4v3.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.3.0",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: add realesr-general-x4v3 and realesr-general-wdn-x4v3. They are very tiny models for general scenes, and they may more robust. But as they are tiny models, their performance may be limited."""],
"realesr-animevideov3.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.5.0",
"""Anime, Cartoon, Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: update the RealESRGAN AnimeVideo-v3 model, which can achieve better results with a faster inference speed."""],
"4xLSDIRCompact.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact/4xLSDIRCompact.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact",
"""Realistic
Phhofm: Upscale small good quality photos to 4x their size. This is my first ever released self-trained sisr upscaling model."""],
"4xLSDIRCompactC.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompactC/4xLSDIRCompactC.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompactC",
"""Compression Removal, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler that handler jpg compression. Trying to extend my previous model to be able to handle compression (JPG 100-30) by manually altering the training dataset, since 4xLSDIRCompact cant handle compression. Use this instead of 4xLSDIRCompact if your photo has compression (like an image from the web)."""],
"4xLSDIRCompactR.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompactC/4xLSDIRCompactR.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompactC",
"""Compression Removal, Realistic, Restoration
Phhofm: 4x photo uspcaler that handles jpg compression, noise and slight. Extending my last 4xLSDIRCompact model to Real-ESRGAN, meaning trained on synthetic data instead to handle more kinds of degradations, it should be able to handle compression, noise, and slight blur."""],
"4xLSDIRCompactN.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactC3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Realistic
Phhofm: Upscale good quality input photos to x4 their size. The original 4xLSDIRCompact a bit more trained, cannot handle degradation.
I am releasing the Series 3 from my 4xLSDIRCompact models. In general my suggestion is, if you have good quality input images use 4xLSDIRCompactN3, otherwise try 4xLSDIRCompactC3 which will be able to handle jpg compression and a bit of blur, or then 4xLSDIRCompactCR3, which is an interpolation between C3 and R3 to be able to handle a bit of noise additionally."""],
"4xLSDIRCompactC3.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactC3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Compression Removal,
JPEG, Realistic, Restoration
Phhofm: Upscale compressed photos to x4 their size. Able to handle JPG compression (30-100).
I am releasing the Series 3 from my 4xLSDIRCompact models. In general my suggestion is, if you have good quality input images use 4xLSDIRCompactN3, otherwise try 4xLSDIRCompactC3 which will be able to handle jpg compression and a bit of blur, or then 4xLSDIRCompactCR3, which is an interpolation between C3 and R3 to be able to handle a bit of noise additionally."""],
"4xLSDIRCompactR3.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactR3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Realistic, Restoration
Phhofm: Upscale (degraded) photos to x4 their size. Trained on synthetic data, meant to handle more degradations.
I am releasing the Series 3 from my 4xLSDIRCompact models. In general my suggestion is, if you have good quality input images use 4xLSDIRCompactN3, otherwise try 4xLSDIRCompactC3 which will be able to handle jpg compression and a bit of blur, or then 4xLSDIRCompactCR3, which is an interpolation between C3 and R3 to be able to handle a bit of noise additionally."""],
"4xLSDIRCompactCR3.pth": ["https://github.com/Phhofm/models/releases/download/4xLSDIRCompact3/4xLSDIRCompactCR3.pth",
"https://github.com/Phhofm/models/releases/tag/4xLSDIRCompact3",
"""Phhofm: I am releasing the Series 3 from my 4xLSDIRCompact models. In general my suggestion is, if you have good quality input images use 4xLSDIRCompactN3, otherwise try 4xLSDIRCompactC3 which will be able to handle jpg compression and a bit of blur, or then 4xLSDIRCompactCR3, which is an interpolation between C3 and R3 to be able to handle a bit of noise additionally."""],
"2xParimgCompact.pth": ["https://github.com/Phhofm/models/releases/download/2xParimgCompact/2xParimgCompact.pth",
"https://github.com/Phhofm/models/releases/tag/2xParimgCompact",
"""Realistic
Phhofm: A 2x photo upscaling compact model based on Microsoft's ImagePairs. This was one of the earliest models I started training and finished it now for release. As can be seen in the examples, this model will affect colors."""],
"1xExposureCorrection_compact.pth": ["https://github.com/Phhofm/models/releases/download/1xExposureCorrection_compact/1xExposureCorrection_compact.pth",
"https://github.com/Phhofm/models/releases/tag/1xExposureCorrection_compact",
"""Restoration
Phhofm: This model is meant as an experiment to see if compact can be used to train on photos to exposure correct those using the pixel, perceptual, color, color and ldl losses. There is no brightness loss. Still it seems to kinda work."""],
"1xUnderExposureCorrection_compact.pth": ["https://github.com/Phhofm/models/releases/download/1xExposureCorrection_compact/1xUnderExposureCorrection_compact.pth",
"https://github.com/Phhofm/models/releases/tag/1xExposureCorrection_compact",
"""Restoration
Phhofm: This model is meant as an experiment to see if compact can be used to train on underexposed images to exposure correct those using the pixel, perceptual, color, color and ldl losses. There is no brightness loss. Still it seems to kinda work."""],
"1xOverExposureCorrection_compact.pth": ["https://github.com/Phhofm/models/releases/download/1xExposureCorrection_compact/1xOverExposureCorrection_compact.pth",
"https://github.com/Phhofm/models/releases/tag/1xExposureCorrection_compact",
"""Restoration
Phhofm: This model is meant as an experiment to see if compact can be used to train on overexposed images to exposure correct those using the pixel, perceptual, color, color and ldl losses. There is no brightness loss. Still it seems to kinda work."""],
"2x-sudo-UltraCompact.pth": ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/2x-sudo-UltraCompact.pth",
"https://openmodeldb.info/models/2x-sudo-UltraCompact",
"""Anime, Cartoon, Restoration
sudo: Realtime animation restauration and doing stuff like deblur and compression artefact removal.
My first attempt to make a REALTIME 2x upscaling model while also applying teacher student learning.
(Teacher: RealESRGANv2-animevideo-xsx2.pth)"""],
"2x_AnimeJaNai_HD_V3_SuperUltraCompact.pth": ["https://github.com/the-database/mpv-upscale-2x_animejanai/releases/download/3.0.0/2x_AnimeJaNai_HD_V3_ModelsOnly.zip",
"https://openmodeldb.info/models/2x-AnimeJaNai-HD-V3-SuperUltraCompact",
"""Anime, Compression Removal, Restoration
the-database: Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K.
The aim of these models is to address scaling, blur, oversharpening, and compression artifacts while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution."""],
"2x_AnimeJaNai_HD_V3_UltraCompact.pth": ["https://github.com/the-database/mpv-upscale-2x_animejanai/releases/download/3.0.0/2x_AnimeJaNai_HD_V3_ModelsOnly.zip",
"https://openmodeldb.info/models/2x-AnimeJaNai-HD-V3-UltraCompact",
"""Anime, Compression Removal, Restoration
the-database: Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K.
The aim of these models is to address scaling, blur, oversharpening, and compression artifacts while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution."""],
"2x_AnimeJaNai_HD_V3_Compact.pth": ["https://github.com/the-database/mpv-upscale-2x_animejanai/releases/download/3.0.0/2x_AnimeJaNai_HD_V3_ModelsOnly.zip",
"https://openmodeldb.info/models/2x-AnimeJaNai-HD-V3-Compact",
"""Anime, Compression Removal, Restoration
the-database: Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K.
The aim of these models is to address scaling, blur, oversharpening, and compression artifacts while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution."""],
# RRDBNet
"RealESRGAN_x4plus_anime_6B.pth": ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.2.4",
"""Anime, Cartoon, Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: We add RealESRGAN_x4plus_anime_6B.pth, which is optimized for anime images with much smaller model size. More details and comparisons with waifu2x are in anime_model.md"""],
"RealESRGAN_x2plus.pth" : ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.1",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: Add RealESRGAN_x2plus.pth model"""],
"RealESRNet_x4plus.pth" : ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.1.1",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: This release is mainly for storing pre-trained models and executable files."""],
"RealESRGAN_x4plus.pth" : ["https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
"https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.1.0",
"""Compression Removal, General Upscaler, JPEG, Realistic, Research, Restoration
xinntao: This release is mainly for storing pre-trained models and executable files."""],
# ESRGAN(oldRRDB)
"4x-AnimeSharp.pth": ["https://huggingface.co/utnah/esrgan/resolve/main/4x-AnimeSharp.pth?download=true",
"https://openmodeldb.info/models/4x-AnimeSharp",
"""Anime, Cartoon, Text
Kim2091: Interpolation between 4x-UltraSharp and 4x-TextSharp-v0.5. Works amazingly on anime. It also upscales text, but it's far better with anime content."""],
"4x_IllustrationJaNai_V1_ESRGAN_135k.pth": ["https://drive.google.com/uc?export=download&confirm=1&id=1qpioSqBkB_IkSBhEAewSSNFt6qgkBimP",
"https://openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2",
"""Anime, Cartoon, Compression Removal, Dehalftone, General Upscaler, JPEG, Manga, Restoration
the-database: Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more.
DAT2 version is the highest quality version but also the slowest. See the ESRGAN version for faster performance."""],
"2x-sudo-RealESRGAN.pth": ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/2x-sudo-RealESRGAN.pth",
"https://openmodeldb.info/models/2x-sudo-RealESRGAN",
"""Anime, Cartoon
sudo: Tried to make the best 2x model there is for drawings. I think i archived that.
And yes, it is nearly 3.8 million iterations (probably a record nobody will beat here), took me nearly half a year to train.
It can happen that in one edge is a noisy pattern in edges. You can use padding/crop for that.
I aimed for perceptual quality without zooming in like 400%. Since RealESRGAN is 4x, I downscaled these images with bicubic.
Pretrained: Pretrained_Model_G: RealESRGAN_x4plus_anime_6B.pth / RealESRGAN_x4plus_anime_6B.pth (sudo_RealESRGAN2x_3.332.758_G.pth)"""],
"2x-sudo-RealESRGAN-Dropout.pth": ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/2x-sudo-RealESRGAN-Dropout.pth",
"https://openmodeldb.info/models/2x-sudo-RealESRGAN-Dropout",
"""Anime, Cartoon
sudo: Tried to make the best 2x model there is for drawings. I think i archived that.
And yes, it is nearly 3.8 million iterations (probably a record nobody will beat here), took me nearly half a year to train.
It can happen that in one edge is a noisy pattern in edges. You can use padding/crop for that.
I aimed for perceptual quality without zooming in like 400%. Since RealESRGAN is 4x, I downscaled these images with bicubic.
Pretrained: Pretrained_Model_G: RealESRGAN_x4plus_anime_6B.pth / RealESRGAN_x4plus_anime_6B.pth (sudo_RealESRGAN2x_3.332.758_G.pth)"""],
"4xNomos2_otf_esrgan.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos2_otf_esrgan/4xNomos2_otf_esrgan.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_otf_esrgan",
"""Compression Removal, JPEG, Realistic, Restoration
Phhofm: Restoration, 4x ESRGAN model for photography, trained using the Real-ESRGAN otf degradation pipeline."""],
"4xNomosWebPhoto_esrgan.pth": ["https://github.com/Phhofm/models/releases/download/4xNomosWebPhoto_esrgan/4xNomosWebPhoto_esrgan.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomosWebPhoto_esrgan",
"""Realistic, Restoration
Phhofm: Restoration, 4x ESRGAN model for photography, trained with realistic noise, lens blur, jpg and webp re-compression.
ESRGAN version of 4xNomosWebPhoto_RealPLKSR, trained on the same dataset and in the same way."""],
# DATNet
"4xNomos8kDAT.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos8kDAT/4xNomos8kDAT.pth",
"https://openmodeldb.info/models/4x-Nomos8kDAT",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: A 4x photo upscaler with otf jpg compression, blur and resize, trained on musl's Nomos8k_sfw dataset for realisic sr, this time based on the DAT arch, as a finetune on the official 4x DAT model."""],
"4x-DWTP-DS-dat2-v3.pth" : ["https://objectstorage.us-phoenix-1.oraclecloud.com/n/ax6ygfvpvzka/b/open-modeldb-files/o/4x-DWTP-DS-dat2-v3.pth",
"https://openmodeldb.info/models/4x-DWTP-DS-dat2-v3",
"""Dehalftone, Restoration
umzi.x.dead: DAT descreenton model, designed to reduce discrepancies on tiles due to too much loss of the first version, while getting rid of the removal of paper texture"""],
"4xBHI_dat2_real.pth" : ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_real/4xBHI_dat2_real.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_real",
"""Compression Removal, JPEG, Realistic
Phhofm: 4x dat2 upscaling model for web and realistic images. It handles realistic noise, some realistic blur, and webp and jpg (re)compression. Trained on my BHI dataset (390'035 training tiles) with degraded LR subset."""],
"4xBHI_dat2_otf.pth" : ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_otf/4xBHI_dat2_otf.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_otf",
"""Compression Removal, JPEG
Phhofm: 4x dat2 upscaling model, trained with the real-esrgan otf pipeline on my bhi dataset. Handles noise and compression."""],
"4xBHI_dat2_multiblur.pth" : ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_multiblurjpg/4xBHI_dat2_multiblur.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_multiblurjpg",
"""Phhofm: the 4xBHI_dat2_multiblur checkpoint (trained to 250000 iters), which cannot handle compression but might give just slightly better output on non-degraded input."""],
"4xBHI_dat2_multiblurjpg.pth" : ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_multiblurjpg/4xBHI_dat2_multiblurjpg.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_multiblurjpg",
"""Compression Removal, JPEG
Phhofm: 4x dat2 upscaling model, trained with down_up,linear, cubic_mitchell, lanczos, gauss and box scaling algos, some average, gaussian and anisotropic blurs and jpg compression. Trained on my BHI sisr dataset."""],
"4x_IllustrationJaNai_V1_DAT2_190k.pth": ["https://drive.google.com/uc?export=download&confirm=1&id=1qpioSqBkB_IkSBhEAewSSNFt6qgkBimP",
"https://openmodeldb.info/models/4x-IllustrationJaNai-V1-DAT2",
"""Anime, Cartoon, Compression Removal, Dehalftone, General Upscaler, JPEG, Manga, Restoration
the-database: Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more.
DAT2 version is the highest quality version but also the slowest. See the ESRGAN version for faster performance."""],
"4x-PBRify_UpscalerDAT2_V1.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/4x-PBRify_UpscalerDAT2_V1/4x-PBRify_UpscalerDAT2_V1.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/4x-PBRify_UpscalerDAT2_V1",
"""Compression Removal, DDS, Game Textures, Restoration
Kim2091: Yet another model in the PBRify_Remix series. This is a new upscaler to replace the previous 4x-PBRify_UpscalerSIR-M_V2 model.
This model far exceeds the quality of the previous, with far more natural detail generation and better reconstruction of lines and edges."""],
"4xBHI_dat2_otf_nn.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_dat2_otf_nn/4xBHI_dat2_otf_nn.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_dat2_otf_nn",
"""Compression Removal, JPEG
Phhofm: 4x dat2 upscaling model, trained with the real-esrgan otf pipeline but without noise, on my bhi dataset. Handles resizes, and jpg compression."""],
# HAT
"4xNomos8kSCHAT-L.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos8kSCHAT/4xNomos8kSCHAT-L.pth",
"https://openmodeldb.info/models/4x-Nomos8kSCHAT-L",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler with otf jpg compression and blur, trained on musl's Nomos8k_sfw dataset for realisic sr. Since this is a big model, upscaling might take a while."""],
"4xNomos8kSCHAT-S.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos8kSCHAT/4xNomos8kSCHAT-S.pth",
"https://openmodeldb.info/models/4x-Nomos8kSCHAT-S",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler with otf jpg compression and blur, trained on musl's Nomos8k_sfw dataset for realisic sr. HAT-S version/model."""],
"4xNomos8kHAT-L_otf.pth": ["https://github.com/Phhofm/models/releases/download/4xNomos8kHAT-L_otf/4xNomos8kHAT-L_otf.pth",
"https://openmodeldb.info/models/4x-Nomos8kHAT-L-otf",
"""Faces, General Upscaler, Realistic, Restoration
Phhofm: 4x photo upscaler trained with otf, handles some jpg compression, some blur and some noise."""],
"4xBHI_small_hat-l.pth": ["https://github.com/Phhofm/models/releases/download/4xBHI_small_hat-l/4xBHI_small_hat-l.pth",
"https://github.com/Phhofm/models/releases/tag/4xBHI_small_hat-l",
"""Phhofm: 4x hat-l upscaling model for good quality input. This model does not handle any degradations.
This model is rather soft, I tried to balance sharpness and faithfulness/non-artifacts.
For a bit sharper output, but can generate a bit of artifacts, you can try the 4xBHI_small_hat-l_sharp version,
also included in this release, which might still feel soft if you are used to sharper outputs."""],
# RealPLKSR_dysample
"4xHFA2k_ludvae_realplksr_dysample.pth": ["https://github.com/Phhofm/models/releases/download/4xHFA2k_ludvae_realplksr_dysample/4xHFA2k_ludvae_realplksr_dysample.pth",
"https://openmodeldb.info/models/4x-HFA2k-ludvae-realplksr-dysample",
"""Anime, Compression Removal, Restoration
Phhofm: A Dysample RealPLKSR 4x upscaling model for anime single-image resolution."""],
"4xArtFaces_realplksr_dysample.pth" : ["https://github.com/Phhofm/models/releases/download/4xArtFaces_realplksr_dysample/4xArtFaces_realplksr_dysample.pth",
"https://openmodeldb.info/models/4x-ArtFaces-realplksr-dysample",
"""ArtFaces
Phhofm: A Dysample RealPLKSR 4x upscaling model for art / painted faces."""],
"4x-PBRify_RPLKSRd_V3.pth" : ["https://github.com/Kim2091/Kim2091-Models/releases/download/4x-PBRify_RPLKSRd_V3/4x-PBRify_RPLKSRd_V3.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/4x-PBRify_RPLKSRd_V3",
"""Compression Removal, DDS, Debanding, Dedither, Dehalo, Game Textures, Restoration
Kim2091: This update brings a new upscaling model, 4x-PBRify_RPLKSRd_V3. This model is roughly 8x faster than the current DAT2 model, while being higher quality.
It produces far more natural detail, resolves lines and edges more smoothly, and cleans up compression artifacts better.
As a result of those improvements, PBR is also much improved. It tends to be clearer with less defined artifacts."""],
"4xNomos2_realplksr_dysample.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos2_realplksr_dysample/4xNomos2_realplksr_dysample.pth",
"https://openmodeldb.info/models/4x-Nomos2-realplksr-dysample",
"""Compression Removal, JPEG, Realistic, Restoration
Phhofm: A Dysample RealPLKSR 4x upscaling model that was trained with / handles jpg compression down to 70 on the Nomosv2 dataset, preserves DoF.
This model affects / saturate colors, which can be counteracted a bit by using wavelet color fix, as used in these examples."""],
# RealPLKSR
"2x-AnimeSharpV2_RPLKSR_Sharp.pth": ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_RPLKSR_Sharp.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Hopefully you guys can find a good use-case for it.
RealPLKSR (Higher quality, slower) Sharp: For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts
"""],
"2x-AnimeSharpV2_RPLKSR_Soft.pth" : ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_RPLKSR_Soft.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Hopefully you guys can find a good use-case for it.
RealPLKSR (Higher quality, slower) Soft: For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well"""],
"4xPurePhoto-RealPLSKR.pth" : ["https://github.com/starinspace/StarinspaceUpscale/releases/download/Models/4xPurePhoto-RealPLSKR.pth",
"https://openmodeldb.info/models/4x-PurePhoto-RealPLSKR",
"""AI Generated, Compression Removal, JPEG, Realistic, Restoration
asterixcool: Skilled in working with cats, hair, parties, and creating clear images.
Also proficient in resizing photos and enlarging large, sharp images.
Can effectively improve images from small sizes as well (300px at smallest on one side, depending on the subject).
Experienced in experimenting with techniques like upscaling with this model twice and
then reducing it by 50% to enhance details, especially in features like hair or animals."""],
"2x_Text2HD_v.1-RealPLKSR.pth" : ["https://github.com/starinspace/StarinspaceUpscale/releases/download/Models/2x_Text2HD_v.1-RealPLKSR.pth",
"https://openmodeldb.info/models/2x-Text2HD-v-1",
"""Compression Removal, Denoise, General Upscaler, JPEG, Restoration, Text
asterixcool: The upscale model is specifically designed to enhance lower-quality text images,
improving their clarity and readability by upscaling them by 2x.
It excels at processing moderately sized text, effectively transforming it into high-quality, legible scans.
However, the model may encounter challenges when dealing with very small text,
as its performance is optimized for text of a certain minimum size. For best results,
input images should contain text that is not excessively small."""],
"2xVHS2HD-RealPLKSR.pth" : ["https://github.com/starinspace/StarinspaceUpscale/releases/download/Models/2xVHS2HD-RealPLKSR.pth",
"https://openmodeldb.info/models/2x-VHS2HD",
"""Compression Removal, Dehalo, Realistic, Restoration, Video Frame
asterixcool: An advanced VHS recording model designed to enhance video quality by reducing artifacts such as haloing, ghosting, and noise patterns.
Optimized primarily for PAL resolution (NTSC might work good as well)."""],
"4xNomosWebPhoto_RealPLKSR.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomosWebPhoto_RealPLKSR/4xNomosWebPhoto_RealPLKSR.pth",
"https://openmodeldb.info/models/4x-NomosWebPhoto-RealPLKSR",
"""Realistic, Restoration
Phhofm: 4x RealPLKSR model for photography, trained with realistic noise, lens blur, jpg and webp re-compression."""],
# DRCT
"4xNomos2_hq_drct-l.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos2_hq_drct-l/4xNomos2_hq_drct-l.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_drct-l",
"""General Upscaler, Realistic
Phhofm: An drct-l 4x upscaling model, similiar to the 4xNomos2_hq_atd, 4xNomos2_hq_dat2 and 4xNomos2_hq_mosr models, trained and for usage on non-degraded input to give good quality output.
"""],
# ATD
"4xNomos2_hq_atd.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos2_hq_atd/4xNomos2_hq_atd.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_atd",
"""General Upscaler, Realistic
Phhofm: An atd 4x upscaling model, similiar to the 4xNomos2_hq_dat2 or 4xNomos2_hq_mosr models, trained and for usage on non-degraded input to give good quality output.
"""],
# MoSR
"4xNomos2_hq_mosr.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos2_hq_mosr/4xNomos2_hq_mosr.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr",
"""General Upscaler, Realistic
Phhofm: A 4x MoSR upscaling model, meant for non-degraded input, since this model was trained on non-degraded input to give good quality output.
"""],
"2x-AnimeSharpV2_MoSR_Sharp.pth" : ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_MoSR_Sharp.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Hopefully you guys can find a good use-case for it.
MoSR (Lower quality, faster), Sharp: For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts
"""],
"2x-AnimeSharpV2_MoSR_Soft.pth" : ["https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV2_Set/2x-AnimeSharpV2_MoSR_Soft.pth",
"https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set",
"""Anime, Compression Removal, Restoration
Kim2091: This is my first anime model in years. Hopefully you guys can find a good use-case for it.
MoSR (Lower quality, faster), Soft: For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well
"""],
# SRFormer
"4xNomos8kSCSRFormer.pth" : ["https://github.com/Phhofm/models/releases/download/4xNomos8kSCSRFormer/4xNomos8kSCSRFormer.pth",
"https://github.com/Phhofm/models/releases/tag/4xNomos8kSCSRFormer",
"""Anime, Compression Removal, General Upscaler, JPEG, Realistic, Restoration
Phhofm: 4x photo upscaler with otf jpg compression and blur, trained on musl's Nomos8k_sfw dataset for realisic sr.
"""],
"4xFrankendataFullDegradation_SRFormer460K_g.pth" : ["https://drive.google.com/uc?export=download&confirm=1&id=1PZrj-8ofxhORv_OgTVSoRt3dYi-BtiDj",
"https://openmodeldb.info/models/4x-Frankendata-FullDegradation-SRFormer",
"""Compression Removal, Denoise, Realistic, Restoration
Crustaceous D: 4x realistic upscaler that may also work for general purpose usage.
It was trained with OTF random degradation with a very low to very high range of degradations, including blur, noise, and compression.
Trained with the same Frankendata dataset that I used for the pretrain model.
"""],
"4xFrankendataPretrainer_SRFormer400K_g.pth" : ["https://drive.google.com/uc?export=download&confirm=1&id=1SaKvpYYIm2Vj2m9GifUMlNCbmkE6JZmr",
"https://openmodeldb.info/models/4x-FrankendataPretainer-SRFormer",
"""Realistic, Restoration
Crustaceous D: 4x realistic upscaler that may also work for general purpose usage.
It was trained with OTF random degradation with a very low to very high range of degradations, including blur, noise, and compression.
Trained with the same Frankendata dataset that I used for the pretrain model.
"""],
"1xFrankenfixer_SRFormerLight_g.pth" : ["https://drive.google.com/uc?export=download&confirm=1&id=1UJ0iyFn4IGNhPIgNgrQrBxYsdDloFc9I",
"https://openmodeldb.info/models/1x-Frankenfixer-SRFormerLight",
"""Realistic, Restoration
Crustaceous D: A 1x model designed to reduce artifacts and restore detail to images upscaled by 4xFrankendata_FullDegradation_SRFormer. It could possibly work with other upscaling models too.
"""],
}
example_list = ["images/a01.jpg", "images/a02.jpg", "images/a03.jpg", "images/a04.jpg", "images/bus.jpg", "images/zidane.jpg",
"images/b01.jpg", "images/b02.jpg", "images/b03.jpg", "images/b04.jpg", "images/b05.jpg", "images/b06.jpg",
"images/b07.jpg", "images/b08.jpg", "images/b09.jpg", "images/b10.jpg", "images/b11.jpg", "images/c01.jpg",
"images/c02.jpg", "images/c03.jpg", "images/c04.jpg", "images/c05.jpg", "images/c06.jpg", "images/c07.jpg",
"images/c08.jpg", "images/c09.jpg", "images/c10.jpg"]
def get_model_type(model_name):
# Define model type mappings based on key parts of the model names
model_type = "other"
if any(value in model_name.lower() for value in ("4x-animesharp.pth", "sudo-realesrgan")):
model_type = "ESRGAN"
elif "srformer" in model_name.lower():
model_type = "SRFormer"
elif ("realplksr" in model_name.lower() and "dysample" in model_name.lower()) or "rplksrd" in model_name.lower():
model_type = "RealPLKSR_dysample"
elif any(value in model_name.lower() for value in ("realplksr", "rplksr", "realplskr")):
model_type = "RealPLKSR"
elif any(value in model_name.lower() for value in ("realesrgan", "realesrnet")):
model_type = "RRDB"
elif any(value in model_name.lower() for value in ("realesr", "compact")):
model_type = "SRVGG"
elif "esrgan" in model_name.lower():
model_type = "ESRGAN"
elif "dat" in model_name.lower():
model_type = "DAT"
elif "hat" in model_name.lower():
model_type = "HAT"
elif "drct" in model_name.lower():
model_type = "DRCT"
elif "atd" in model_name.lower():
model_type = "ATD"
elif "mosr" in model_name.lower():
model_type = "MoSR"
return f"{model_type}, {model_name}"
typed_upscale_models = {get_model_type(key): value[0] for key, value in upscale_models.items()}
class Upscale:
def __init__(self,):
self.scale = 4
self.modelInUse = ""
self.realesrganer = None
self.face_enhancer = None
def initBGUpscaleModel(self, upscale_model):
upscale_type, upscale_model = upscale_model.split(", ", 1)
download_from_url(upscale_models[upscale_model][0], upscale_model, os.path.join("weights", "upscale"))
self.modelInUse = f"_{os.path.splitext(upscale_model)[0]}"
netscale = 1 if any(sub in upscale_model.lower() for sub in ("x1", "1x")) else (2 if any(sub in upscale_model.lower() for sub in ("x2", "2x")) else 4)
model = None
half = True if torch.cuda.is_available() else False
if upscale_type:
# The values of the following hyperparameters are based on the research findings of the Spandrel project.
# https://github.com/chaiNNer-org/spandrel/tree/main/libs/spandrel/spandrel/architectures
from basicsr.archs.rrdbnet_arch import RRDBNet
loadnet = torch.load(os.path.join("weights", "upscale", upscale_model), map_location=torch.device('cpu'), weights_only=True)
if 'params_ema' in loadnet or 'params' in loadnet:
loadnet = loadnet['params_ema'] if 'params_ema' in loadnet else loadnet['params']
if upscale_type == "SRVGG":
from basicsr.archs.srvgg_arch import SRVGGNetCompact
body_max_num = self.find_max_numbers(loadnet, "body")
num_feat = loadnet["body.0.weight"].shape[0]
num_in_ch = loadnet["body.0.weight"].shape[1]
num_conv = body_max_num // 2 - 1
model = SRVGGNetCompact(num_in_ch=num_in_ch, num_out_ch=3, num_feat=num_feat, num_conv=num_conv, upscale=netscale, act_type='prelu')
elif upscale_type == "RRDB" or upscale_type == "ESRGAN":
if upscale_type == "RRDB":
num_block = self.find_max_numbers(loadnet, "body") + 1
num_feat = loadnet["conv_first.weight"].shape[0]
else:
num_block = self.find_max_numbers(loadnet, "model.1.sub")
num_feat = loadnet["model.0.weight"].shape[0]
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=num_feat, num_block=num_block, num_grow_ch=32, scale=netscale, is_real_esrgan=upscale_type == "RRDB")
elif upscale_type == "DAT":
from basicsr.archs.dat_arch import DAT
half = False
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depth = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depth[i] = self.find_max_numbers(loadnet, f"layers.{i}.blocks") + 1
num_heads[i] = loadnet[f"layers.{i}.blocks.1.attn.temperature"].shape[0] if depth[i] >= 2 else \
loadnet[f"layers.{i}.blocks.0.attn.attns.0.pos.pos3.2.weight"].shape[0] * 2
upsampler = "pixelshuffle" if "conv_last.weight" in loadnet else "pixelshuffledirect"
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else "3conv"
qkv_bias = "layers.0.blocks.0.attn.qkv.bias" in loadnet
expansion_factor = float(loadnet["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim)
img_size = 64
if "layers.0.blocks.2.attn.attn_mask_0" in loadnet:
attn_mask_0_x, attn_mask_0_y, _attn_mask_0_z = loadnet["layers.0.blocks.2.attn.attn_mask_0"].shape
img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y))
split_size = [2, 4]
if "layers.0.blocks.0.attn.attns.0.rpe_biases" in loadnet:
split_sizes = loadnet["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1
split_size = [int(x) for x in split_sizes]
model = DAT(img_size=img_size, in_chans=in_chans, embed_dim=embed_dim, split_size=split_size, depth=depth, num_heads=num_heads, expansion_factor=expansion_factor,
qkv_bias=qkv_bias, resi_connection=resi_connection, upsampler=upsampler, upscale=netscale)
elif upscale_type == "HAT":
half = False
from basicsr.archs.hat_arch import HAT
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
window_size = int(math.sqrt(loadnet["relative_position_index_SA"].shape[0]))
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depths[i] = self.find_max_numbers(loadnet, f"layers.{i}.residual_group.blocks") + 1
num_heads[i] = loadnet[f"layers.{i}.residual_group.overlap_attn.relative_position_bias_table"].shape[1]
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else "identity"
compress_ratio = self.find_divisor_for_quotient(embed_dim, loadnet["layers.0.residual_group.blocks.0.conv_block.cab.0.weight"].shape[0],)
squeeze_factor = self.find_divisor_for_quotient(embed_dim, loadnet["layers.0.residual_group.blocks.0.conv_block.cab.3.attention.1.weight"].shape[0],)
qkv_bias = "layers.0.residual_group.blocks.0.attn.qkv.bias" in loadnet
patch_norm = "patch_embed.norm.weight" in loadnet
ape = "absolute_pos_embed" in loadnet
mlp_hidden_dim = int(loadnet["layers.0.residual_group.blocks.0.mlp.fc1.weight"].shape[0])
mlp_ratio = mlp_hidden_dim / embed_dim
upsampler = "pixelshuffle"
model = HAT(img_size=64, patch_size=1, in_chans=in_chans, embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=window_size, compress_ratio=compress_ratio,
squeeze_factor=squeeze_factor, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ape=ape, patch_norm=patch_norm,
upsampler=upsampler, resi_connection=resi_connection, upscale=netscale,)
elif "RealPLKSR" in upscale_type:
from basicsr.archs.realplksr_arch import realplksr
half = False if "RealPLSKR" in upscale_model else half
use_ea = "feats.1.attn.f.0.weight" in loadnet
dim = loadnet["feats.0.weight"].shape[0]
num_feats = self.find_max_numbers(loadnet, "feats") + 1
n_blocks = num_feats - 3
kernel_size = loadnet["feats.1.lk.conv.weight"].shape[2]
split_ratio = loadnet["feats.1.lk.conv.weight"].shape[0] / dim
use_dysample = "to_img.init_pos" in loadnet
model = realplksr(upscaling_factor=netscale, dim=dim, n_blocks=n_blocks, kernel_size=kernel_size, split_ratio=split_ratio, use_ea=use_ea, dysample=use_dysample)
elif upscale_type == "DRCT":
half = False
from basicsr.archs.DRCT_arch import DRCT
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = (6,) * num_layers
num_heads = []
for i in range(num_layers):
num_heads.append(loadnet[f"layers.{i}.swin1.attn.relative_position_bias_table"].shape[1])
mlp_ratio = loadnet["layers.0.swin1.mlp.fc1.weight"].shape[0] / embed_dim
window_square = loadnet["layers.0.swin1.attn.relative_position_bias_table"].shape[0]
window_size = (math.isqrt(window_square) + 1) // 2
upsampler = "pixelshuffle" if "conv_last.weight" in loadnet else ""
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else ""
qkv_bias = "layers.0.swin1.attn.qkv.bias" in loadnet
gc_adjust1 = loadnet["layers.0.adjust1.weight"].shape[0]
patch_norm = "patch_embed.norm.weight" in loadnet
ape = "absolute_pos_embed" in loadnet
model = DRCT(in_chans=in_chans, img_size= 64, window_size=window_size, compress_ratio=3,squeeze_factor=30,
conv_scale= 0.01, overlap_ratio= 0.5, img_range= 1., depths=depths, embed_dim=embed_dim, num_heads=num_heads,
mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ape=ape, patch_norm=patch_norm, use_checkpoint=False,
upscale=netscale, upsampler=upsampler, resi_connection=resi_connection, gc =gc_adjust1,)
elif upscale_type == "ATD":
half = False
from basicsr.archs.atd_arch import ATD
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
window_size = math.isqrt(loadnet["relative_position_index_SA"].shape[0])
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depths[i] = self.find_max_numbers(loadnet, f"layers.{i}.residual_group.layers") + 1
num_heads[i] = loadnet[f"layers.{i}.residual_group.layers.0.attn_win.relative_position_bias_table"].shape[1]
num_tokens = loadnet["layers.0.residual_group.layers.0.attn_atd.scale"].shape[0]
reducted_dim = loadnet["layers.0.residual_group.layers.0.attn_atd.wq.weight"].shape[0]
convffn_kernel_size = loadnet["layers.0.residual_group.layers.0.convffn.dwconv.depthwise_conv.0.weight"].shape[2]
mlp_ratio = (loadnet["layers.0.residual_group.layers.0.convffn.fc1.weight"].shape[0] / embed_dim)
qkv_bias = "layers.0.residual_group.layers.0.wqkv.bias" in loadnet
ape = "absolute_pos_embed" in loadnet
patch_norm = "patch_embed.norm.weight" in loadnet
resi_connection = "1conv" if "layers.0.conv.weight" in loadnet else "3conv"
if "conv_up1.weight" in loadnet:
upsampler = "nearest+conv"
elif "conv_before_upsample.0.weight" in loadnet:
upsampler = "pixelshuffle"
elif "conv_last.weight" in loadnet:
upsampler = ""
else:
upsampler = "pixelshuffledirect"
is_light = upsampler == "pixelshuffledirect" and embed_dim == 48
category_size = 128 if is_light else 256
model = ATD(in_chans=in_chans, embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=window_size, category_size=category_size,
num_tokens=num_tokens, reducted_dim=reducted_dim, convffn_kernel_size=convffn_kernel_size, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ape=ape,
patch_norm=patch_norm, use_checkpoint=False, upscale=netscale, upsampler=upsampler, resi_connection='1conv',)
elif upscale_type == "MoSR":
from basicsr.archs.mosr_arch import mosr
n_block = self.find_max_numbers(loadnet, "gblocks") - 5
in_ch = loadnet["gblocks.0.weight"].shape[1]
out_ch = loadnet["upsampler.end_conv.weight"].shape[0] if "upsampler.init_pos" in loadnet else in_ch
dim = loadnet["gblocks.0.weight"].shape[0]
expansion_ratio = (loadnet["gblocks.1.fc1.weight"].shape[0] / loadnet["gblocks.1.fc1.weight"].shape[1]) / 2
conv_ratio = loadnet["gblocks.1.conv.weight"].shape[0] / dim
kernel_size = loadnet["gblocks.1.conv.weight"].shape[2]
upsampler = "dys" if "upsampler.init_pos" in loadnet else ("gps" if "upsampler.in_to_k.weight" in loadnet else "ps")
model = mosr(in_ch = in_ch, out_ch = out_ch, upscale = netscale, n_block = n_block, dim = dim,
upsampler = upsampler, kernel_size = kernel_size, expansion_ratio = expansion_ratio, conv_ratio = conv_ratio,)
elif upscale_type == "SRFormer":
half = False
from basicsr.archs.srformer_arch import SRFormer
in_chans = loadnet["conv_first.weight"].shape[1]
embed_dim = loadnet["conv_first.weight"].shape[0]
ape = "absolute_pos_embed" in loadnet
patch_norm = "patch_embed.norm.weight" in loadnet
qkv_bias = "layers.0.residual_group.blocks.0.attn.q.bias" in loadnet
mlp_ratio = float(loadnet["layers.0.residual_group.blocks.0.mlp.fc1.weight"].shape[0] / embed_dim)
num_layers = self.find_max_numbers(loadnet, "layers") + 1
depths = [6] * num_layers
num_heads = [6] * num_layers
for i in range(num_layers):
depths[i] = self.find_max_numbers(loadnet, f"layers.{i}.residual_group.blocks") + 1
num_heads[i] = loadnet[f"layers.{i}.residual_group.blocks.0.attn.relative_position_bias_table"].shape[1]
if "conv_hr.weight" in loadnet:
upsampler = "nearest+conv"
elif "conv_before_upsample.0.weight" in loadnet:
upsampler = "pixelshuffle"
elif "upsample.0.weight" in loadnet:
upsampler = "pixelshuffledirect"
resi_connection = "1conv" if "conv_after_body.weight" in loadnet else "3conv"
window_size = int(math.sqrt(loadnet["layers.0.residual_group.blocks.0.attn.relative_position_bias_table"].shape[0])) + 1
if "layers.0.residual_group.blocks.1.attn_mask" in loadnet:
attn_mask_0 = loadnet["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
patches_resolution = int(math.sqrt(attn_mask_0) * window_size)
else:
patches_resolution = window_size
if ape:
pos_embed_value = loadnet.get("absolute_pos_embed", [None, None])[1]
if pos_embed_value:
patches_resolution = int(math.sqrt(pos_embed_value))
img_size = patches_resolution
if img_size % window_size != 0:
for nice_number in [512, 256, 128, 96, 64, 48, 32, 24, 16]:
if nice_number % window_size != 0:
nice_number += window_size - (nice_number % window_size)
if nice_number == patches_resolution:
img_size = nice_number
break
model = SRFormer(img_size=img_size, in_chans=in_chans, embed_dim=embed_dim, depths=depths, num_heads=num_heads, window_size=window_size, mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=None, ape=ape, patch_norm=patch_norm, upscale=netscale, upsampler=upsampler, resi_connection=resi_connection,)
if model:
self.realesrganer = RealESRGANer(scale=netscale, model_path=os.path.join("weights", "upscale", upscale_model), model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
elif upscale_model:
import PIL
from image_gen_aux import UpscaleWithModel
class UpscaleWithModel_Gfpgan(UpscaleWithModel):
def cv2pil(self, image):
''' OpenCV type -> PIL type
https://qiita.com/derodero24/items/f22c22b22451609908ee
'''
new_image = image.copy()
if new_image.ndim == 2: # Grayscale
pass
elif new_image.shape[2] == 3: # Color
new_image = cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB)
elif new_image.shape[2] == 4: # Transparency
new_image = cv2.cvtColor(new_image, cv2.COLOR_BGRA2RGBA)
new_image = PIL.Image.fromarray(new_image)
return new_image
def pil2cv(self, image):
''' PIL type -> OpenCV type
https://qiita.com/derodero24/items/f22c22b22451609908ee
'''
new_image = np.array(image, dtype=np.uint8)
if new_image.ndim == 2: # Grayscale
pass
elif new_image.shape[2] == 3: # Color
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
elif new_image.shape[2] == 4: # Transparency
new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA)
return new_image
def enhance(self, img, outscale=None):
# img: numpy
h_input, w_input = img.shape[0:2]
pil_img = self.cv2pil(img)
pil_img = self.__call__(pil_img)
cv_image = self.pil2cv(pil_img)
if outscale is not None and outscale != float(netscale):
interpolation = cv2.INTER_AREA if outscale < float(netscale) else cv2.INTER_LANCZOS4
cv_image = cv2.resize(
cv_image, (
int(w_input * outscale),
int(h_input * outscale),
), interpolation=interpolation)
return cv_image, None
device = "cuda" if torch.cuda.is_available() else "cpu"
upscaler = UpscaleWithModel.from_pretrained(os.path.join("weights", "upscale", upscale_model)).to(device)
upscaler.__class__ = UpscaleWithModel_Gfpgan
self.realesrganer = upscaler
def initFaceEnhancerModel(self, face_restoration, face_detection):
model_rootpath = os.path.join("weights", "face")
model_path = os.path.join(model_rootpath, face_restoration)
download_from_url(face_models[face_restoration][0], face_restoration, model_rootpath)
self.modelInUse = f"_{os.path.splitext(face_restoration)[0]}" + self.modelInUse
from gfpgan.utils import GFPGANer
resolution = 512
channel_multiplier = None
if face_restoration and face_restoration.startswith("GFPGANv1."):
arch = "clean"
channel_multiplier = 2
elif face_restoration and face_restoration.startswith("RestoreFormer"):
arch = "RestoreFormer++" if face_restoration.startswith("RestoreFormer++") else "RestoreFormer"
elif face_restoration == 'CodeFormer.pth':
arch = "CodeFormer"
elif face_restoration.startswith("GPEN-BFR-"):
arch = "GPEN"
channel_multiplier = 2
if "1024" in face_restoration:
arch = "GPEN-1024"
resolution = 1024
elif "2048" in face_restoration:
arch = "GPEN-2048"
resolution = 2048
self.face_enhancer = GFPGANer(model_path=model_path, upscale=self.scale, arch=arch, channel_multiplier=channel_multiplier, model_rootpath=model_rootpath, det_model=face_detection, resolution=resolution)
def inference(self, gallery, face_restoration, upscale_model, scale: float, face_detection, face_detection_threshold: any, face_detection_only_center: bool, outputWithModelName: bool, progress=gr.Progress()):
try:
if not gallery or (not face_restoration and not upscale_model):
raise ValueError("Invalid parameter setting")
gallery_len = len(gallery)
print(face_restoration, upscale_model, scale, f"gallery length: {gallery_len}")
timer = Timer() # Create a timer
self.scale = scale
progressTotal = gallery_len + 1
progressRatio = 0.5 if upscale_model and face_restoration else 1
print(f"progressRatio: {progressRatio}")
current_progress = 0
progress(0, desc="Initialize model start")
if upscale_model:
self.initBGUpscaleModel(upscale_model)
current_progress += progressRatio/progressTotal;
progress(current_progress, desc="Initialize BG upscale model finished")
timer.checkpoint(f"Initialize BG upscale model")
if face_restoration:
self.initFaceEnhancerModel(face_restoration, face_detection)
current_progress += progressRatio/progressTotal;
progress(current_progress, desc="Initialize face enhancer model finished")
timer.checkpoint(f"Initialize face enhancer model")
timer.report()
if not outputWithModelName:
self.modelInUse = ""
files = []
is_auto_split_upscale = True
# Dictionary to track counters for each filename
name_counters = defaultdict(int)
for gallery_idx, value in enumerate(gallery):
try:
img_path = str(value[0])
img_name = os.path.basename(img_path)
# Increment the counter for the current name
name_counters[img_name] += 1
if name_counters[img_name] > 1:
img_name = f"{img_name}_{name_counters[img_name]:02d}"
basename, extension = os.path.splitext(img_name)
img_cv2 = cv2.imdecode(np.fromfile(img_path, np.uint8), cv2.IMREAD_UNCHANGED) # numpy.ndarray
img_mode = "RGBA" if len(img_cv2.shape) == 3 and img_cv2.shape[2] == 4 else None
if len(img_cv2.shape) == 2: # for gray inputs
img_cv2 = cv2.cvtColor(img_cv2, cv2.COLOR_GRAY2BGR)
print(f"> image{gallery_idx:02d}, {img_cv2.shape}:")
bg_upsample_img = None
if self.realesrganer and hasattr(self.realesrganer, "enhance"):
bg_upsample_img, _ = auto_split_upscale(img_cv2, self.realesrganer.enhance, self.scale) if is_auto_split_upscale else self.realesrganer.enhance(img_cv2, outscale=self.scale)
current_progress += progressRatio/progressTotal;
progress(current_progress, desc=f"image{gallery_idx:02d}, Background upscale Section")
timer.checkpoint(f"image{gallery_idx:02d}, Background upscale Section")
if self.face_enhancer:
cropped_faces, restored_aligned, bg_upsample_img = self.face_enhancer.enhance(img_cv2, has_aligned=False, only_center_face=face_detection_only_center, paste_back=True, bg_upsample_img=bg_upsample_img, eye_dist_threshold=face_detection_threshold)
# save faces
if cropped_faces and restored_aligned:
for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_aligned)):
# save cropped face
save_crop_path = f"output/{basename}{idx:02d}_cropped_faces{self.modelInUse}.png"
self.imwriteUTF8(save_crop_path, cropped_face)
# save restored face
save_restore_path = f"output/{basename}{idx:02d}_restored_faces{self.modelInUse}.png"
self.imwriteUTF8(save_restore_path, restored_face)
# save comparison image
save_cmp_path = f"output/{basename}{idx:02d}_cmp{self.modelInUse}.png"
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
self.imwriteUTF8(save_cmp_path, cmp_img)
files.append(save_crop_path)
files.append(save_restore_path)
files.append(save_cmp_path)
current_progress += progressRatio/progressTotal;
progress(current_progress, desc=f"image{gallery_idx:02d}, Face enhancer Section")
timer.checkpoint(f"image{gallery_idx:02d}, Face enhancer Section")
restored_img = bg_upsample_img
timer.report()
if not extension:
extension = ".png" if img_mode == "RGBA" else ".jpg" # RGBA images should be saved in png format
save_path = f"output/{basename}{self.modelInUse}{extension}"
self.imwriteUTF8(save_path, restored_img)
restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
files.append(save_path)
except RuntimeError as error:
print(traceback.format_exc())
print('Error', error)
progress(1, desc=f"Execution completed")
timer.report_all() # Print all recorded times
except Exception as error:
print(traceback.format_exc())
print("global exception: ", error)
return None, None
finally:
if self.face_enhancer:
self.face_enhancer._cleanup()
else:
# Free GPU memory and clean up resources
torch.cuda.empty_cache()
gc.collect()
return files, files
def find_max_numbers(self, state_dict, findkeys):
if isinstance(findkeys, str):
findkeys = [findkeys]
max_values = defaultdict(lambda: None)
patterns = {findkey: re.compile(rf"^{re.escape(findkey)}\.(\d+)\.") for findkey in findkeys}
for key in state_dict:
for findkey, pattern in patterns.items():
if match := pattern.match(key):
num = int(match.group(1))
max_values[findkey] = max(num, max_values[findkey] if max_values[findkey] is not None else num)
return tuple(max_values[findkey] for findkey in findkeys) if len(findkeys) > 1 else max_values[findkeys[0]]
def find_divisor_for_quotient(self, a: int, c: int):
"""
Returns a number `b` such that `a // b == c`.
If `b` is an integer, return it as an `int`, otherwise return a `float`.
"""
if c == 0:
raise ValueError("c cannot be zero to avoid division by zero.")
b_float = a / c
# Check if b is an integer
if b_float.is_integer():
return int(b_float)
# Try using ceil and floor
ceil_b = math.ceil(b_float)
floor_b = math.floor(b_float)
if a // ceil_b == c:
return ceil_b if ceil_b == b_float else float(ceil_b)
if a // floor_b == c:
return floor_b if floor_b == b_float else float(floor_b)
# account for rounding errors
if c == a // b_float:
return b_float
if c == a // (b_float - 0.01):
return b_float - 0.01
if c == a // (b_float + 0.01):
return b_float + 0.01
raise ValueError(f"Could not find a number b such that a // b == c. a={a}, c={c}")
def imwriteUTF8(self, save_path, image): # `cv2.imwrite` does not support writing files to UTF-8 file paths.
img_name = os.path.basename(save_path)
_, extension = os.path.splitext(img_name)
is_success, im_buf_arr = cv2.imencode(extension, image)
if (is_success): im_buf_arr.tofile(save_path)
class Timer:
def __init__(self):
self.start_time = time.perf_counter() # Record the start time
self.checkpoints = [("Start", self.start_time)] # Store checkpoints
def checkpoint(self, label="Checkpoint"):
"""Record a checkpoint with a given label."""
now = time.perf_counter()
self.checkpoints.append((label, now))
def report(self, is_clear_checkpoints = True):
# Determine the max label width for alignment
max_label_length = max(len(label) for label, _ in self.checkpoints)
prev_time = self.checkpoints[0][1]
for label, curr_time in self.checkpoints[1:]:
elapsed = curr_time - prev_time
print(f"{label.ljust(max_label_length)}: {elapsed:.3f} seconds")
prev_time = curr_time
if is_clear_checkpoints:
self.checkpoints.clear()
self.checkpoint() # Store checkpoints
def report_all(self):
"""Print all recorded checkpoints and total execution time with aligned formatting."""
print("\n> Execution Time Report:")
# Determine the max label width for alignment
max_label_length = max(len(label) for label, _ in self.checkpoints) if len(self.checkpoints) > 0 else 0
prev_time = self.start_time
for label, curr_time in self.checkpoints[1:]:
elapsed = curr_time - prev_time
print(f"{label.ljust(max_label_length)}: {elapsed:.3f} seconds")
prev_time = curr_time
total_time = self.checkpoints[-1][1] - self.start_time
print(f"{'Total Execution Time'.ljust(max_label_length)}: {total_time:.3f} seconds\n")
self.checkpoints.clear()
def restart(self):
self.start_time = time.perf_counter() # Record the start time
self.checkpoints = [("Start", self.start_time)] # Store checkpoints
def get_selection_from_gallery(selected_state: gr.SelectData):
"""
Extracts the selected image path and caption from the gallery selection state.
Args:
selected_state (gr.SelectData): The selection state from a Gradio gallery,
containing information about the selected image.
Returns:
tuple: A tuple containing:
- str: The file path of the selected image.
- str: The caption of the selected image.
If `selected_state` is None or invalid, it returns `None`.
"""
if not selected_state:
return selected_state
return (selected_state.value["image"]["path"], selected_state.value["caption"])
def limit_gallery(gallery):
"""
Ensures the gallery does not exceed input_images_limit.
Args:
gallery (list): Current gallery images.
Returns:
list: Trimmed gallery with a maximum of input_images_limit images.
"""
return gallery[:input_images_limit] if input_images_limit > 0 and gallery else gallery
def append_gallery(gallery: list, image: str):
"""
Append a single image to the gallery while respecting input_images_limit.
Parameters:
- gallery (list): Existing list of images. If None, initializes an empty list.
- image (str): The image to be added. If None or empty, no action is taken.
Returns:
- list: Updated gallery.
"""
if gallery is None:
gallery = []
if not image:
return gallery, None
if input_images_limit == -1 or len(gallery) < input_images_limit:
gallery.append(image)
return gallery, None
def extend_gallery(gallery: list, images):
"""
Extend the gallery with new images while respecting the input_images_limit.
Parameters:
- gallery (list): Existing list of images. If None, initializes an empty list.
- images (list): New images to be added. If None, defaults to an empty list.
Returns:
- list: Updated gallery with the new images added.
"""
if gallery is None:
gallery = []
if not images:
return gallery
# Add new images to the gallery
gallery.extend(images)
# Trim gallery to the specified limit, if applicable
if input_images_limit > 0:
gallery = gallery[:input_images_limit]
return gallery
def remove_image_from_gallery(gallery: list, selected_image: str):
"""
Removes a selected image from the gallery if it exists.
Args:
gallery (list): The current list of images in the gallery.
selected_image (str): The image to be removed, represented as a string
that needs to be parsed into a tuple.
Returns:
list: The updated gallery after removing the selected image.
"""
if not gallery or not selected_image:
return gallery
selected_image = ast.literal_eval(selected_image) # Use ast.literal_eval to parse text into a tuple in remove_image_from_gallery.
# Remove the selected image from the gallery
if selected_image in gallery:
gallery.remove(selected_image)
return gallery
def main():
if torch.cuda.is_available():
torch.cuda.set_per_process_memory_fraction(0.975, device='cuda:0')
# set torch options to avoid get black image for RTX16xx card
# https://github.com/CompVis/stable-diffusion/issues/69#issuecomment-1260722801
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# Ensure the target directory exists
os.makedirs('output', exist_ok=True)
title = "Image Upscaling & Restoration using GFPGAN / RestoreFormerPlusPlus / CodeFormer / GPEN Algorithm"
description = r"""
<a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration and Upscalling of the image with a Generative Facial Prior</b></a>. <br>
<a href='https://github.com/wzhouxiff/RestoreFormerPlusPlus' target='_blank'><b>RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs</b></a>. <br>
<a href='https://github.com/sczhou/CodeFormer' target='_blank'><b>CodeFormer: Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022)</b></a>. <br>
<a href='https://github.com/yangxy/GPEN' target='_blank'><b>GPEN: GAN Prior Embedded Network for Blind Face Restoration in the Wild</b></a>. <br>
<br>
Practically, the aforementioned algorithm is used to restore your **old photos** or improve **AI-generated faces**.<br>
To use it, simply just upload the concerned image.<br>
"""
# Custom CSS to set the height of the gr.Dropdown menu
css = """
ul.options {
max-height: 500px !important; /* Set the maximum height of the dropdown menu */
overflow-y: auto !important; /* Enable vertical scrolling if the content exceeds the height */
}
div.progress-level div.progress-level-inner {
text-align: left !important;
width: 55.5% !important;
}
"""
upscale = Upscale()
rows = []
tmptype = None
upscale_model_tables = []
for key, _ in typed_upscale_models.items():
upscale_type, upscale_model = key.split(", ", 1)
if tmptype and tmptype != upscale_type:#RRDB ESRGAN
speed = "Fast" if tmptype == "SRVGG" else ("Slow" if any(value == tmptype for value in ("DAT", "HAT", "DRCT", "ATD", "SRFormer")) else "Normal")
upscale_model_header = f"| Upscale Model | Info, Type: {tmptype}, Model execution speed: {speed} | Download URL |\n|------------|------|--------------|"
upscale_model_tables.append(upscale_model_header + "\n" + "\n".join(rows))
rows.clear()
tmptype = upscale_type
value = upscale_models[upscale_model]
row = f"| [{upscale_model}]({value[1]}) | " + value[2].replace("\n", "<br>") + " | [download]({value[0]}) |"
rows.append(row)
speed = "Fast" if tmptype == "SRVGG" else ("Slow" if any(value == tmptype for value in ("DAT", "HAT", "DRCT", "ATD", "SRFormer")) else "Normal")
upscale_model_header = f"| Upscale Model Name | Info, Type: {tmptype}, Model execution speed: {speed} | Download URL |\n|------------|------|--------------|"
upscale_model_tables.append(upscale_model_header + "\n" + "\n".join(rows))
with gr.Blocks(title = title, css = css) as demo:
gr.Markdown(value=f"<h1 style=\"text-align:center;\">{title}</h1><br>{description}")
with gr.Row():
with gr.Column(variant="panel"):
submit = gr.Button(value="Submit", variant="primary", size="lg")
# Create an Image component for uploading images
input_image = gr.Image(label="Upload an Image or clicking paste from clipboard button", type="filepath", format="png", height=150)
with gr.Row():
upload_button = gr.UploadButton("Upload multiple images", file_types=["image"], file_count="multiple", size="sm")
remove_button = gr.Button("Remove Selected Image", size="sm")
input_gallery = gr.Gallery(columns=5, rows=5, show_share_button=False, interactive=True, height="500px", label="Gallery that displaying a grid of images" + (f"(The online environment image limit is {input_images_limit})" if input_images_limit > 0 else ""))
face_model = gr.Dropdown([None]+list(face_models.keys()), type="value", value='GFPGANv1.4.pth', label='Face Restoration version', info="Face Restoration and RealESR can be freely combined in different ways, or one can be set to \"None\" to use only the other model. Face Restoration is primarily used for face restoration in real-life images, while RealESR serves as a background restoration model.")
upscale_model = gr.Dropdown([None]+list(typed_upscale_models.keys()), type="value", value='SRVGG, realesr-general-x4v3.pth', label='UpScale version')
upscale_scale = gr.Number(label="Rescaling factor", value=4)
face_detection = gr.Dropdown(["retinaface_resnet50", "YOLOv5l", "YOLOv5n"], type="value", value="retinaface_resnet50", label="Face Detection type")
face_detection_threshold = gr.Number(label="Face eye dist threshold", value=10, info="A threshold to filter out faces with too small an eye distance (e.g., side faces).")
face_detection_only_center = gr.Checkbox(value=False, label="Face detection only center", info="If set to True, only the face closest to the center of the image will be kept.")
with_model_name = gr.Checkbox(label="Output image files name with model name", value=True)
# Define the event listener to add the uploaded image to the gallery
input_image.change(append_gallery, inputs=[input_gallery, input_image], outputs=[input_gallery, input_image])
# When the upload button is clicked, add the new images to the gallery
upload_button.upload(extend_gallery, inputs=[input_gallery, upload_button], outputs=input_gallery)
# Event to update the selected image when an image is clicked in the gallery
selected_image = gr.Textbox(label="Selected Image", visible=False)
input_gallery.select(get_selection_from_gallery, inputs=None, outputs=selected_image)
# Trigger update when gallery changes
input_gallery.change(limit_gallery, input_gallery, input_gallery)
# Event to remove a selected image from the gallery
remove_button.click(remove_image_from_gallery, inputs=[input_gallery, selected_image], outputs=input_gallery)
with gr.Row():
clear = gr.ClearButton(
components=[
input_gallery,
face_model,
upscale_model,
upscale_scale,
face_detection,
face_detection_threshold,
face_detection_only_center,
with_model_name,
], variant="secondary", size="lg",)
with gr.Column(variant="panel"):
gallerys = gr.Gallery(type="filepath", label="Output (The whole image)", format="png")
outputs = gr.File(label="Download the output image")
with gr.Row(variant="panel"):
# Generate output array
output_arr = []
for file_name in example_list:
output_arr.append([file_name,])
gr.Examples(output_arr, inputs=[input_image,], examples_per_page=20)
with gr.Row(variant="panel"):
# Convert to Markdown table
header = "| Face Model Name | Info | Download URL |\n|------------|------|--------------|"
rows = [
f"| [{key}]({value[1]}) | " + value[2].replace("\n", "<br>") + f" | [download]({value[0]}) |"
for key, value in face_models.items()
]
markdown_table = header + "\n" + "\n".join(rows)
gr.Markdown(value=markdown_table)
for table in upscale_model_tables:
with gr.Row(variant="panel"):
gr.Markdown(value=table)
submit.click(
upscale.inference,
inputs=[
input_gallery,
face_model,
upscale_model,
upscale_scale,
face_detection,
face_detection_threshold,
face_detection_only_center,
with_model_name,
],
outputs=[gallerys, outputs],
)
demo.queue(default_concurrency_limit=1)
demo.launch(inbrowser=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input_images_limit", type=int, default=5)
args = parser.parse_args()
input_images_limit = args.input_images_limit
main() |