# StableSR for Stable Diffusion WebUI

Licensed under S-Lab License 1.0

[![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]

English｜[中文](README_CN.md)

- StableSR is a competitive super-resolution method originally proposed by Jianyi Wang et al.
- This repository is a migration of the StableSR project to the Automatic1111 WebUI.

Relevant Links

> Click to view high-quality official examples!

- [Project Page](https://iceclear.github.io/projects/stablesr/)
- [Official Repository](https://github.com/IceClear/StableSR)
- [Paper on arXiv](https://arxiv.org/abs/2305.07015)

> If you find this project useful, please give me & Jianyi Wang a star! ⭐

***

## Features

1. **High-fidelity detailed image upscaling**:
    - Being very detailed while keeping the face identity of your characters.
    - Suitable for most images (Realistic or Anime, Photography or AIGC, SD 1.5 or Midjourney images...) [Official Examples](https://iceclear.github.io/projects/stablesr/)
2. **Less VRAM consumption**
    - I remove the VRAM-expensive modules in the official implementation.
    - The remaining model is much smaller than ControlNet Tile model and requires less VRAM.
    - When combined with Tiled Diffusion & VAE, you can do 4k image super-resolution with limited VRAM (e.g., < 12 GB).
    > Please be aware that sdp may lead to OOM for some unknown reasons. You may use xformers instead.
3. **Wavelet Color Fix**
    - The official StableSR will significantly change the color of the generated image. The problem will be even more prominent when upscaling in tiles.
    - I implement a powerful post-processing technique that effectively matches the color of the upscaled image to the original. See [Wavelet Color Fix Example](https://imgsli.com/MTgwNDg2/).

***

## Usage

### 1. Installation

⚪ Method 1: Official Market

- Open Automatic1111 WebUI -> Click Tab "Extensions" -> Click Tab "Available" -> Find "StableSR" -> Click "Install"

⚪ Method 2: URL Install

- Open Automatic1111 WebUI -> Click Tab "Extensions" -> Click Tab "Install from URL" -> type in https://github.com/pkuliyi2015/sd-webui-stablesr.git -> Click "Install" 

![installation](https://github.com/pkuliyi2015/multidiffusion-img-demo/blob/master/installation.png?raw=true)

### 2. Download the main components

- You MUST use the Stable Diffusion V2.1 512 **EMA** checkpoint (~5.21GB) from StabilityAI
    - You can download it from [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)
    - Put into stable-diffusion-webui/models/Stable-Diffusion/

    > While it requires a SD2.1 checkpoint, you can still upscale ANY image (even from SD1.5 or NSFW). Your image won't be censored and the output quality won't be affected.

- Download the extracted StableSR module
    - Official resources: [HuggingFace](https://huggingface.co/Iceclear/StableSR/resolve/main/weibu_models.zip) (~1.2 G). Note that this is a zip file containing both the StableSR module and the VQVAE.
    - My resources: <[GoogleDrive](https://drive.google.com/file/d/1tWjkZQhfj07sHDR4r9Ta5Fk4iMp1t3Qw/view?usp=sharing)> <[百度网盘-提取码aguq](https://pan.baidu.com/s/1Nq_6ciGgKnTu0W14QcKKWg?pwd=aguq)>
    - Put the StableSR module (~400MB) into your stable-diffusion-webui/extensions/sd-webui-stablesr/models/

### 3. Optional components

- Install [Tiled Diffusion & VAE]((https://github.com/pkuliyi2015/multidiffusion-upscaler-for-automatic1111)) extension
    - The original StableSR easily gets OOM for large images > 512.
    - For better quality and less VRAM usage, we recommend Tiled Diffusion & VAE.
- Use the Official VQGAN VAE
    - Official resources: See the link in 2.
    - My resources: <[GoogleDrive](https://drive.google.com/file/d/1ARtDMia3_CbwNsGxxGcZ5UP75W4PeIEI/view?usp=share_link)> <[百度网盘-提取码83u9](https://pan.baidu.com/s/1YCYmGBethR9JZ8-eypoIiQ?pwd=83u9)>
    - Put the VQVAE (~700MB) into your stable-diffusion-webui/models/VAE

### 4. Extension Usage

- At the top of the WebUI, select the v2-1_512-ema-pruned checkpoint you downloaded.
- Switch to img2img tag. Find the "Scripts" dropdown at the bottom of the page.
    - Select the StableSR script.
    - Click the refresh button and select the StableSR checkpoint you have downloaded.
    - Choose a scale factor.
- Upload your image and start generation (can work without prompts).
- Euler a sampler is recommended. CFG Scale<=2, Steps >= 20.
- For output image size > 512, we recommend using Tiled Diffusion & VAE, otherwise, the image quality may not be ideal, and the VRAM usage will be huge. 
- Here are the official Tiled Diffusion settings:
    - Method = Mixture of Diffusers
    - Latent tile size = 64, Latent tile overlap = 32
    - Latent tile batch size as large as possible before Out of Memory.
    - Upscaler MUST be None (will not upscale here; instead, upscale in StableSR).
- The following figure shows the recommended settings for 24GB VRAM.
    - For a 6GB device, **just change Tiled Diffusion Latent tile batch size to 1, Tiled VAE Encoder Tile Size to 1024, Decoder Tile Size to 128.**
    - SDP attention optimization may lead to OOM. Please use xformers in that case.
    - You DON'T need to change other settings in Tiled Diffusion & Tiled VAE unless you have a very deep understanding. **These params are almost optimal for StableSR.**
![recommended settings](https://github.com/pkuliyi2015/multidiffusion-img-demo/blob/master/recommended_settings_24GB.jpg?raw=true)

    

### 5. Options Explained

- What is "Pure Noise"?
    - Pure Noise refers to starting from a fully random noise tensor instead of your image. **This is the default behavior in the StableSR paper.**
    - When enabling it, the script ignores your denoising strength and gives you much more detailed images, but also changes the color & sharpness significantly
    - When disabling it, the script starts by adding some noise to your image. The result will be not fully detailed, even if you set denoising strength = 1 (but maybe aesthetically good). See [Comparison](https://imgsli.com/MTgwMTMx).
    - If you disable Pure Noise, we recommend denoising strength=1
- What is "Color Fix"?
    - This is to mitigate the color shift problem from StableSR and the tiling process.
    - AdaIN simply adjusts the color statistics between the original and the outcome images. This is the official algorithm but ineffective in many cases.
    - Wavelet decomposes the original and the outcome images into low and high frequency, and then replace the outcome image's low-frequency part (colors) with the original image's. This is very powerful for uneven color shifting. The algorithm is from GIMP and Krita, which will take several seconds for each image.
    - When enabling color fix, the original image will also show up in your preview window, but will NOT be saved automatically.

### 6. Important Notice

> Why my results are different from the offical examples?

- It is not your or our fault.
    - This extension has the same UNet model weights as the StableSR if installed correctly. 
    - If you install the optional VQVAE, the whole model weights will be the same as the official model with fusion weights=0.
- However, your result will be **not as good as** the official results, because:
    - Sampler Difference: 
        - The official repo does 100 or 200 steps of legacy DDPM sampling with a custom timestep scheduler, and samples without negative prompts.
        - However, WebUI doesn't offer such a sampler, and it must sample with negative prompts. **This is the main difference.**
    - VQVAE Decoder Difference: 
        - The official VQVAE Decoder takes some Encoder features as input. 
        - However, in practice, I found these features are astonishingly huge for large images. (>10G for 4k images even in float16!) 
        - Hence, **I removed the CFW component in VAE Decoder**. As this lead to inferior fidelity in details, I will try to add it back later as an option.

***
## License

This project is licensed under:

- S-Lab License 1.0.
- [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa], due to the use of the NVIDIA SPADE module.

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg

### Disclaimer

- All code in this extension is for research purposes only. 
- The commercial use of the code and checkpoint is **strictly prohibited**.

### Important Notice for Outcome Images

- Please note that the CC BY-NC-SA 4.0 license in the NVIDIA SPADE module also prohibits the commercial use of outcome images. 
- Jianyi Wang may change the SPADE module to a commercial-friendly one but he is busy.
- If you wish to *speed up* his process for commercial purposes, please contact him through email: iceclearwjy@gmail.com

## Acknowledgments

I would like to thank Jianyi Wang et al. for the original StableSR method.