## Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild > [[Paper](https://arxiv.org/abs/2401.13627)]   [[Project Page](http://supir.xpixel.group/)]   [[Replicate Demo](https://replicate.com/cjwbw/supir)]
> Fanghua, Yu, [Jinjin Gu](https://www.jasongt.com/), Zheyuan Li, Jinfan Hu, Xiangtao Kong, [Xintao Wang](https://xinntao.github.io/), [Jingwen He](https://scholar.google.com.hk/citations?user=GUxrycUAAAAJ), [Yu Qiao](https://scholar.google.com.hk/citations?user=gFtI-8QAAAAJ), [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ)
> 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

--- ## 🔧 Dependencies and Installation 1. Clone repo ```bash git clone https://github.com/Fanghua-Yu/SUPIR.git cd SUPIR ``` 2. Install dependent packages ```bash conda create -n SUPIR python=3.8 -y conda activate SUPIR pip install --upgrade pip pip install -r requirements.txt ``` 3. Download Checkpoints #### Dependent Models * [SDXL CLIP Encoder-1](https://huggingface.co/openai/clip-vit-large-patch14) * [SDXL CLIP Encoder-2](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) * [SDXL base 1.0_0.9vae](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors) * [LLaVA CLIP](https://huggingface.co/openai/clip-vit-large-patch14-336) * [LLaVA v1.5 13B](https://huggingface.co/liuhaotian/llava-v1.5-13b) #### Models we provided: * `SUPIR-v0Q`: [Baidu Netdisk](https://pan.baidu.com/s/1lnefCZhBTeDWijqbj1jIyw?pwd=pjq6), [Google Drive](https://drive.google.com/drive/folders/1yELzm5SvAi9e7kPcO_jPp2XkTs4vK6aR?usp=sharing) Default training settings with paper. High generalization and high image quality in most cases. * `SUPIR-v0F`: [Baidu Netdisk](https://pan.baidu.com/s/1AECN8NjiVuE3hvO8o-Ua6A?pwd=k2uz), [Google Drive](https://drive.google.com/drive/folders/1yELzm5SvAi9e7kPcO_jPp2XkTs4vK6aR?usp=sharing) Training with light degradation settings. Stage1 encoder of `SUPIR-v0F` remains more details when facing light degradations. 4. 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 ``` --- ## ⚡ Quick Inference ### Val Dataset RealPhoto60: [Baidu Netdisk](https://pan.baidu.com/s/1CJKsPGtyfs8QEVCQ97voBA?pwd=aocg), [Google Drive](https://drive.google.com/drive/folders/1yELzm5SvAi9e7kPcO_jPp2XkTs4vK6aR?usp=sharing) ### Usage of SUPIR ```Shell 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'] ``` ### Python Script ```Shell # 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 ``` ### Gradio Demo ```Shell CUDA_VISIBLE_DEVICES=0,1 python gradio_demo.py --ip 0.0.0.0 --port 6688 --use_image_slider --log_history ```

### Online Demo (Coming Soon) --- ## BibTeX @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} } ## 📧 Contact If you have any question, please email `fanghuayu96@gmail.com`.