(CVPR2024) Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

Original Image

Output Image (SR x4)

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⚠ RAM Requirement: 60G

⚠ VRAM : 30G x2

Online Demo

🚀 Check out the online demo

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🔧 Dependencies and Installation

1. Clone repo

   git clone https://github.com/Fanghua-Yu/SUPIR.git
   cd SUPIR

2. Install dependent packages

   conda create -n SUPIR python=3.8 -y
   conda activate SUPIR
   pip install --upgrade pip
   conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia
   pip install -r requirements.txt

3. Download Checkpoints

For users who can connect to huggingface, please setting LLAVA_CLIP_PATH, SDXL_CLIP1_PATH, SDXL_CLIP2_CKPT_PTH in CKPT_PTH.py as None. These CLIPs will be downloaded automatically.

Dependent Models

• SDXL CLIP Encoder-1
• SDXL CLIP Encoder-2
• SDXL base 1.0_0.9vae
• LLaVA CLIP
• LLaVA v1.5 13B
• (optional) Juggernaut-XL_v9_RunDiffusionPhoto_v2
  • Replacement of SDXL base 1.0_0.9vae for Photo Realistic
• (optional) Juggernaut_RunDiffusionPhoto2_Lightning_4Steps
  • Distilling model used in SUPIR_v0_Juggernautv9_lightning.yaml

# Download above models via following
python download_dependent_models_v2.py
cd my_models
gdown --id 1aTLhTLUKK8nkNAotB7kZgfKc8SGA_TL2 -O SUPIR-v0F.ckpt
gdown --id 1ohCIBV_RAej1zuiidHph5qXNuD4GRxO3 -O SUPIR-v0Q.ckpt
cd ..

# if gdown doesnot work then download v0F and v0Q via following links

Models we provided:

• SUPIR-v0Q: Baidu Netdisk, Google Drive

  Default training settings with paper. High generalization and high image quality in most cases.

• SUPIR-v0F: Baidu Netdisk, Google Drive

  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
   

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⚡ Quick Inference

Val Dataset

RealPhoto60: Baidu Netdisk, Google Drive

Usage of SUPIR

Usage: 
-- python test.py [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

# 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

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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}
}

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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.

For inquiries or to obtain permission for commercial use, please contact Dr. Jinjin Gu (hellojasongt@gmail.com).