Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
[Paper] [Project Page] [Replicate Demo]
Fanghua, Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, Chao Dong
Shenzhen Institute of Advanced Technology; Shanghai AI Laboratory; University of Sydney; The Hong Kong Polytechnic University; ARC Lab, Tencent PCG; The Chinese University of Hong Kong
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Clone repo
git clone https://github.com/Fanghua-Yu/SUPIR.git cd SUPIR -
Install dependent packages
conda create -n SUPIR python=3.8 -y conda activate SUPIR pip install --upgrade pip pip install -r requirements.txt
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Download Checkpoints
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SUPIR-v0Q: Baidu Netdisk, Google DriveDefault training settings with paper. High generalization and high image quality in most cases.
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SUPIR-v0F: Baidu Netdisk, Google DriveTraining with light degradation settings. Stage1 encoder of
SUPIR-v0Fremains more details when facing light degradations.
- Edit Custom Path for Checkpoints
* [CKPT_PTH.py] --> LLAVA_CLIP_PATH, LLAVA_MODEL_PATH, SDXL_CLIP1_PATH, SDXL_CLIP2_CACHE_DIR * [options/SUPIR_v0.yaml] --> SDXL_CKPT, SUPIR_CKPT_Q, SUPIR_CKPT_F
RealPhoto60: Baidu Netdisk, Google Drive
Usage: -- python test.py [options] -- python gradio_demo.py [interactive options] --img_dir Input folder. --save_dir Output folder. --upscale Upsampling ratio of given inputs. Default: 1 --SUPIR_sign Model selection. Default: 'Q'; Options: ['F', 'Q'] --seed Random seed. Default: 1234 --min_size Minimum resolution of output images. Default: 1024 --edm_steps Numb of steps for EDM Sampling Scheduler. Default: 50 --s_stage1 Control Strength of Stage1. Default: -1 (negative means invalid) --s_churn Original hy-param of EDM. Default: 5 --s_noise Original hy-param of EDM. Default: 1.003 --s_cfg Classifier-free guidance scale for prompts. Default: 7.5 --s_stage2 Control Strength of Stage2. Default: 1.0 --num_samples Number of samples for each input. Default: 1 --a_prompt Additive positive prompt for all inputs. Default: 'Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.' --n_prompt Fixed negative prompt for all inputs. Default: 'painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth' --color_fix_type Color Fixing Type. Default: 'Wavelet'; Options: ['None', 'AdaIn', 'Wavelet'] --linear_CFG Linearly (with sigma) increase CFG from 'spt_linear_CFG' to s_cfg. Default: False --linear_s_stage2 Linearly (with sigma) increase s_stage2 from 'spt_linear_s_stage2' to s_stage2. Default: False --spt_linear_CFG Start point of linearly increasing CFG. Default: 1.0 --spt_linear_s_stage2 Start point of linearly increasing s_stage2. Default: 0.0 --ae_dtype Inference data type of AutoEncoder. Default: 'bf16'; Options: ['fp32', 'bf16'] --diff_dtype Inference data type of Diffusion. Default: 'fp16'; Options: ['fp32', 'fp16', 'bf16']
# Seek for best quality for most cases CUDA_VISIBLE_DEVICES=0,1 python test.py --img_dir '/opt/data/private/LV_Dataset/DiffGLV-Test-All/RealPhoto60/LQ' --save_dir ./results-Q --SUPIR_sign Q --upscale 2 # for light degradation and high fidelity CUDA_VISIBLE_DEVICES=0,1 python test.py --img_dir '/opt/data/private/LV_Dataset/DiffGLV-Test-All/RealPhoto60/LQ' --save_dir ./results-F --SUPIR_sign F --upscale 2 --s_cfg 4.0 --linear_CFG
CUDA_VISIBLE_DEVICES=0,1 python gradio_demo.py --ip 0.0.0.0 --port 6688 --use_image_slider --log_history
@misc{yu2024scaling,
title={Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild},
author={Fanghua Yu and Jinjin Gu and Zheyuan Li and Jinfan Hu and Xiangtao Kong and Xintao Wang and Jingwen He and Yu Qiao and Chao Dong},
year={2024},
eprint={2401.13627},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
If you have any question, please email fanghuayu96@gmail.com.