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217 lines
10 KiB
Markdown
217 lines
10 KiB
Markdown
# <center>Web-based UI for Stable Diffusion</center>
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## Created by [sd-webui](https://github.com/sd-webui)
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## [Visit sd-webui's Discord Server](https://discord.gg/gyXNe4NySY) [](https://discord.gg/gyXNe4NySY)
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## Installation instructions for:
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- **[Windows](https://sd-webui.github.io/stable-diffusion-webui/docs/1.windows-installation.html)**
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- **[Linux](https://sd-webui.github.io/stable-diffusion-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/sd-webui/stable-diffusion-webui/discussions).
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## Documentation
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[Documentation is located here](https://sd-webui.github.io/stable-diffusion-webui/)
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## Want to contribute?
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Check the [Contribution Guide](CONTRIBUTING.md)
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[sd-webui](https://github.com/sd-webui) 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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* Two great Web UI's to choose from: Streamlit or Gradio
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* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders
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* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN
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* Run additional upscaling models on CPU to save VRAM
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* Textual inversion 🔥: [info](https://textual-inversion.github.io/) - requires enabling, see [here](https://github.com/hlky/sd-enable-textual-inversion), script works as usual without it enabled
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* Advanced img2img editor with Mask and crop capabilities
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* Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)
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* More diffusion samplers 🔥🔥: A great collection of samplers to use, including:
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- `k_euler` (Default)
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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 🏋️: Adjust the strength of different terms in your prompt
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* Selectable GPU usage with `--gpu <id>`
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* Memory Monitoring 🔥: Shows VRAM usage and generation time after outputting
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* Word Seeds 🔥: Use words instead of seed numbers
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* CFG: Classifier free guidance scale, a feature for fine-tuning your output
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* Automatic 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
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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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* Copy-paste generation parameters: A text output provides generation parameters in an easy to copy-paste form for easy sharing.
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* Correct 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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* Loopback for Image2Image: A checkbox for img2img allowing to automatically feed output image as input for the next batch. Equivalent to saving output image, and replacing input image with it.
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# Stable Diffusion Web UI
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A fully-integrated and easy way to work with Stable Diffusion right from a browser window.
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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 presets right from the WebUI (Coming Soon!)
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- An integrated gallery to show the generations for a prompt or session (Coming soon!)
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- Better optimization VRAM usage optimization, less errors for bigger generations.
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- Text2Video - Generate video clips from text prompts right from the WEb UI (WIP)
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- Concepts Library - Run custom embeddings others have made via textual inversion.
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- Actively being developed with new features being added and planned - Stay Tuned!
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- Streamlit is now the new primary UI for the project moving forward.
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- *Currently in active development and still missing some of the features present in the Gradio Interface.*
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Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.
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## Gradio
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**Features:**
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- Older UI design that is fully 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 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 `/stable-diffusion-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 `stable-diffusion-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 `stable-diffusion-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/5.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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