* Two great Web UI's to choose from: Streamlit or Gradio
* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders
* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN
* Run additional upscaling models on CPU to save VRAM
* 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
* Advanced img2img editor with Mask and crop capabilities
* Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)
* More diffusion samplers 🔥🔥: A great collection of samplers to use, including:
-`k_euler` (Default)
-`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 🏋️: Adjust the strength of different terms in your prompt
* Selectable GPU usage with `--gpu <id>`
* Memory Monitoring 🔥: Shows VRAM usage and generation time after outputting
* Word Seeds 🔥: Use words instead of seed numbers
* CFG: Classifier free guidance scale, a feature for fine-tuning your output
* Automatic Launcher: Activate conda and run Stable Diffusion with a single command
* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB
* Prompt validation: If your prompt is too long, you will get a warning in the text output field
* Copy-paste generation parameters: A text output provides generation parameters in an easy to copy-paste form for easy sharing.
* 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`.
* Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.
* 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.
# Stable Diffusion Web UI
A fully-integrated and easy way to work with Stable Diffusion right from a browser window.
## Streamlit
**Features:**
- Clean UI with an easy to use design, with support for widescreen displays.
- Dynamic live preview of your generations
- Easily customizable presets right from the WebUI (Coming Soon!)
- An integrated gallery to show the generations for a prompt or session (Coming soon!)
- Better optimization VRAM usage optimization, less errors for bigger generations.
- Text2Video - Generate video clips from text prompts right from the WEb UI (WIP)
- Concepts Library - Run custom embeddings others have made via textual inversion.
- Actively being developed with new features being added and planned - Stay Tuned!
- Streamlit is now the new primary UI for the project moving forward.
- *Currently in active development and still missing some of the features present in the Gradio Interface.*
Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.
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.
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 `stable-diffusion-webui/src/realesrgan/experiments/pretrained_models` directory.
### GoBig, LSDR, and GoLatent *(Currently Gradio Only)*
More powerful upscalers that uses a seperate Latent Diffusion model to more cleanly upscale images.
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/src/latent-diffusion/experiments/pretrained_models/
Please see the [Image Enhancers Documentation](docs/5.image_enhancers.md) to learn more.
-----
### *Original Information From The Stable Diffusion Repo*
*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/)<br/>
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 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).
- Our codebase 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).
