* If the issue involves _a bug_ in **textual-inversion** create the issue on **_[sd-webui/stable-diffusion-webui](https://github.com/sd-webui/stable-diffusion-webui)_**
* If you want to know how to **activate** or **use** textual-inversion see **_[hlky/sd-enable-textual-inversion](https://github.com/hlky/sd-enable-textual-inversion)_**. Activation not working? create the issue on **_[sd-webui/stable-diffusion-webui](https://github.com/sd-webui/stable-diffusion-webui)_**
Need more support with Gradio? We have a discord channel called `gradio-stable-diffusion` for Q&A with the gradio authors, to join use this link https://discord.gg/Qs8AsnX7Jd, then go to `role-assignment` and click gradio to join the `gradio` channels.
🔥 NEW! webui.cmd updates with any changes in environment.yaml file so the environment will always be up to date as long as you get the new environment.yaml file 🔥
:fire: no need to remove environment, delete src folder and create again, MUCH simpler! 🔥
* GFPGAN Face Correction 🔥: [Download the model](/docs/1.installation.md#optional-additional-models) Automatically correct distorted faces with a built-in GFPGAN option, fixes them in less than half a second
* RealESRGAN Upscaling 🔥: [Download the models](/docs/1.installation.md#optional-additional-models) Boosts the resolution of images with a built-in RealESRGAN option
* 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
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.
### LDSR
* 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/
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.
For example, if you use `a busy city street in a modern city|illustration|cinematic lighting` prompt, there are four combinations possible (first part of prompt is always kept):
There are three options for resizing input images in img2img mode:
- Just resize - simply resizes source image to target resolution, resulting in incorrect aspect ratio
- Crop and resize - resize source image preserving aspect ratio so that entirety of target resolution is occupied by it, and crop parts that stick out
- Resize and fill - resize source image preserving aspect ratio so that it entirely fits target resolution, and fill empty space by rows/columns from source image
*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
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 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).
## 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},
