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215 lines
10 KiB
Markdown
215 lines
10 KiB
Markdown
# <center>Web-based UI for Stable Diffusion</center>
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## Created by [Sygil.Dev](https://github.com/sygil-dev)
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## [Join us at Sygil.Dev's Discord Server](https://discord.gg/gyXNe4NySY) [](https://discord.gg/gyXNe4NySY)
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## Installation instructions for:
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- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)**
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- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/2.linux-installation.html)**
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### Want to ask a question or request a feature?
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Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/sygil-dev/sygil-webui/discussions).
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## Documentation
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[Documentation is located here](https://sygil-dev.github.io/sygil-webui/)
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## Want to contribute?
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Check the [Contribution Guide](CONTRIBUTING.md)
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[sygil-dev](https://github.com/sygil-dev) main devs:
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*  [hlky](https://github.com/hlky)
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* [ZeroCool940711](https://github.com/ZeroCool940711)
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* [codedealer](https://github.com/codedealer)
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### Project Features:
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* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN
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* Generator Preview: See your image as its being made
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* Run additional upscaling models on CPU to save VRAM
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* Textual inversion: [Reaserch Paper](https://textual-inversion.github.io/)
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* K-Diffusion Samplers: A great collection of samplers to use, including:
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- `k_euler`
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- `k_lms`
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- `k_euler_a`
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- `k_dpm_2`
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- `k_dpm_2_a`
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- `k_heun`
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- `PLMS`
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- `DDIM`
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* Loopback: Automatically feed the last generated sample back into img2img
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* Prompt Weighting & Negative Prompts: Gain more control over your creations
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* Selectable GPU usage from Settings tab
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* Word Seeds: Use words instead of seed numbers
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* Automated Launcher: Activate conda and run Stable Diffusion with a single command
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* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB (with *optimized* mode enabled in Settings)
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* Prompt validation: If your prompt is too long, you will get a warning in the text output field
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* 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`.
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* Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.
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* [Gradio] Advanced img2img editor with Mask and crop capabilities
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* [Gradio] Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)
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# SD WebUI
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An easy way to work with Stable Diffusion right from your browser.
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## Streamlit
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**Features:**
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- Clean UI with an easy to use design, with support for widescreen displays
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- *Dynamic live preview* of your generations
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- Easily customizable defaults, right from the WebUI's Settings tab
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- An integrated gallery to show the generations for a prompt
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- *Optimized VRAM* usage for bigger generations or usage on lower end GPUs
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- *Text to Video:* Generate video clips from text prompts right from the WebUI (WIP)
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- 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.
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- *Concepts Library:* Run custom embeddings others have made via textual inversion.
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- Textual Inversion training: Train your own embeddings on any photo you want and use it on your prompt.
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- **Currently in development: [Stable Horde](https://stablehorde.net/) integration; ImgLab, batch inputs, & mask editor from Gradio
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**Prompt Weights & Negative Prompts:**
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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.
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Ex: `cat:0.30, dog:0.70` or `guy riding a bicycle :0.7, incoming car :0.30`
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Negative prompts can be added by using `###` , after which any tokens will be seen as negative.
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Ex: `cat playing with string ### yarn` will negate `yarn` from the generated image.
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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
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**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.
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Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.
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## Gradio [Legacy]
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**Features:**
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- Older UI that is functional and feature complete.
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- Has access to all upscaling models, including LSDR.
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- Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.
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- Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.
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**Note: the Gradio interface is no longer being actively developed by Sygil.Dev and is only receiving bug fixes.**
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Please see the [Gradio Documentation](docs/5.gradio-interface.md) to learn more.
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## Image Upscalers
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---
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### GFPGAN
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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.
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If you want to use GFPGAN to improve generated faces, you need to install it separately.
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Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it
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into the `/sygil-webui/models/gfpgan` directory.
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### RealESRGAN
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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.
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There is also a separate tab for using RealESRGAN on any picture.
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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).
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Put them into the `sygil-webui/models/realesrgan` directory.
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### LSDR
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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/`
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### GoBig, and GoLatent *(Currently on the Gradio version Only)*
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More powerful upscalers that uses a seperate Latent Diffusion model to more cleanly upscale images.
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Please see the [Image Enhancers Documentation](docs/6.image_enhancers.md) to learn more.
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-----
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### *Original Information From The Stable Diffusion Repo:*
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# Stable Diffusion
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*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:*
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[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
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[Robin Rombach](https://github.com/rromb)\*,
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[Andreas Blattmann](https://github.com/ablattmann)\*,
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[Dominik Lorenz](https://github.com/qp-qp)\,
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[Patrick Esser](https://github.com/pesser),
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[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
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**CVPR '22 Oral**
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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/).
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[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
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model.
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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.
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Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
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this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
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With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
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See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).
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## Stable Diffusion v1
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Stable Diffusion v1 refers to a specific configuration of the model
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architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
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and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
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then finetuned on 512x512 images.
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*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
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in its training data.
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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).
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## Comments
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- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
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and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
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Thanks for open-sourcing!
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- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
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## BibTeX
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```
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@misc{rombach2021highresolution,
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title={High-Resolution Image Synthesis with Latent Diffusion Models},
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author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
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year={2021},
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eprint={2112.10752},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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