Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Sygil-Dev/sygil-webui

Stable Diffusion web UI
https://github.com/Sygil-Dev/sygil-webui

Last synced: about 2 months ago
JSON representation

Stable Diffusion web UI

Awesome Lists containing this project

README 

        
# Web-based UI for Stable Diffusion



## Created by [Sygil.Dev](https://github.com/sygil-dev)



## Join us at Sygil.Dev's Discord Server [![Generic badge](https://flat.badgen.net/discord/members/ttM8Tm6wge?icon=discord)](https://discord.gg/ttM8Tm6wge)



## Installation instructions for:



- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/Installation/windows-installation)**

- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/Installation/linux-installation)**



### Want to ask a question or request a feature?



Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/sygil-dev/sygil-webui/discussions).



## Documentation



[Documentation is located here](https://sygil-dev.github.io/sygil-webui/)



## Want to contribute?



Check the [Contribution Guide](CONTRIBUTING.md)



[Sygil-Dev](https://github.com/Sygil-Dev) main devs:



* ![ZeroCool940711's avatar](https://avatars.githubusercontent.com/u/5977640?s=40&v=4)[ZeroCool940711](https://github.com/ZeroCool940711)

* ![Kasiya13's avatar](https://avatars.githubusercontent.com/u/26075839?s=40&v=4)[Kasiya13](https://github.com/Kasiya13)



### Project Features:



* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN



* Generator Preview: See your image as its being made



* Run additional upscaling models on CPU to save VRAM



* Textual inversion: [Reaserch Paper](https://textual-inversion.github.io/)



* K-Diffusion Samplers: A great collection of samplers to use, including:



  - `k_euler`

  - `k_lms`

  - `k_euler_a`

  - `k_dpm_2`

  - `k_dpm_2_a`

  - `k_heun`

  - `PLMS`

  - `DDIM`



* Loopback: Automatically feed the last generated sample back into img2img



* Prompt Weighting & Negative Prompts: Gain more control over your creations



* Selectable GPU usage from Settings tab



* Word Seeds: Use words instead of seed numbers



* Automated Launcher: Activate conda and run Stable Diffusion with a single command



* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB (with *optimized* mode enabled in Settings)



* Prompt validation: If your prompt is too long, you will get a warning in the text output field



* Sequential seeds for batches: If you use a seed of 1000 to generate two batches of two images each, four generated images will have seeds: `1000, 1001, 1002, 1003`.



* Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.



* [Gradio] Advanced img2img editor with Mask and crop capabilities



* [Gradio] Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)



# SD WebUI



An easy way to work with Stable Diffusion right from your browser.



## Streamlit



![](images/streamlit/streamlit-t2i.png)



**Features:**



- Clean UI with an easy to use design, with support for widescreen displays

- *Dynamic live preview* of your generations

- Easily customizable defaults, right from the WebUI's Settings tab

- An integrated gallery to show the generations for a prompt

- *Optimized VRAM* usage for bigger generations or usage on lower end GPUs

- *Text to Video:* Generate video clips from text prompts right from the WebUI (WIP)

- Image to Text: Use [CLIP Interrogator](https://github.com/pharmapsychotic/clip-interrogator) to interrogate an image and get a prompt that you can use to generate a similar image using Stable Diffusion.

- *Concepts Library:* Run custom embeddings others have made via textual inversion.

- Textual Inversion training: Train your own embeddings on any photo you want and use it on your prompt.

- **Currently in development: [Stable Horde](https://stablehorde.net/) integration; ImgLab, batch inputs, & mask editor from Gradio



**Prompt Weights & Negative Prompts:**



To give a token (tag recognized by the AI) a specific or increased weight (emphasis), add `:0.##` to the prompt, where `0.##` is a decimal that will specify the weight of all tokens before the colon.

Ex: `cat:0.30, dog:0.70` or `guy riding a bicycle :0.7, incoming car :0.30`



Negative prompts can be added by using  `###` , after which any tokens will be seen as negative.

Ex: `cat playing with string ### yarn` will negate `yarn` from the generated image.



Negatives are a very powerful tool to get rid of contextually similar or related topics, but **be careful when adding them since the AI might see connections you can't**, and end up outputting gibberish



**Tip:* Try using the same seed with different prompt configurations or weight values see how the AI understands them, it can lead to prompts that are more well-tuned and less prone to error.



Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.



## Gradio [Legacy]



![](images/gradio/gradio-t2i.png)



**Features:**



- Older UI that is functional and feature complete.

- Has access to all upscaling models, including LSDR.

- Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.

- Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.



**Note: the Gradio interface is no longer being actively developed by Sygil.Dev and is only receiving bug fixes.**



Please see the [Gradio Documentation](https://sygil-dev.github.io/sygil-webui/docs/Gradio/gradio-interface/) to learn more.



## Image Upscalers



---



### GFPGAN



![](images/GFPGAN.png)



Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strong the effect is.



If you want to use GFPGAN to improve generated faces, you need to install it separately.

Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it

into the `/sygil-webui/models/gfpgan` directory.



### RealESRGAN



![](images/RealESRGAN.png)



Lets you double the resolution of generated images. There is a checkbox in every tab to use RealESRGAN, and you can choose between the regular upscaler and the anime version.

There is also a separate tab for using RealESRGAN on any picture.



Download [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) and [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth).

Put them into the `sygil-webui/models/realesrgan` directory.



### LSDR



Download **LDSR** [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1). Rename `last.ckpt` to `model.ckpt` and place both under `sygil-webui/models/ldsr/`



### GoBig, and GoLatent *(Currently on the Gradio version Only)*



More powerful upscalers that uses a separate Latent Diffusion model to more cleanly upscale images.



Please see the [Post-Processing Documentation](https://sygil-dev.github.io/sygil-webui/docs/post-processing) to learn more.



-----



### *Original Information From The Stable Diffusion Repo:*



# Stable Diffusion



*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*



[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)

[Robin Rombach](https://github.com/rromb)\*,

[Andreas Blattmann](https://github.com/ablattmann)\*,

[Dominik Lorenz](https://github.com/qp-qp)\,

[Patrick Esser](https://github.com/pesser),

[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)



**CVPR '22 Oral**



which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).



[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion

model.

Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.

Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),

this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.

With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.

See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).



## Stable Diffusion v1



Stable Diffusion v1 refers to a specific configuration of the model

architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet

and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and

then finetuned on 512x512 images.



*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present

in its training data.

Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).



## Comments



- Our code base for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)

  and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).

  Thanks for open-sourcing!



- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).



## BibTeX



```

@misc{rombach2021highresolution,

      title={High-Resolution Image Synthesis with Latent Diffusion Models},

      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},

      year={2021},

      eprint={2112.10752},

      archivePrefix={arXiv},

      primaryClass={cs.CV}

}

```