[](https://colab.research.google.com/github/altryne/sd-webui-colab/blob/main/Stable_Diffusion_WebUi_Altryne.ipynb)
# Installation instructions for [Windows](https://github.com/sd-webui/stable-diffusion-webui/wiki/Installation), [Linux](https://github.com/sd-webui/stable-diffusion-webui/wiki/Linux-Automated-Setup-Guide), or [Google Colab](https://colab.research.google.com/github/altryne/sd-webui-colab/blob/main/Stable_Diffusion_WebUi_Altryne.ipynb)
* 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)_**
🔥 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](https://github.com/sd-webui/stable-diffusion-webui/wiki/Installation#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](https://github.com/sd-webui/stable-diffusion-webui/wiki/Installation#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
**GFPGAN**, **RealESRGAN** and **LDSR** optional models are supported. Detailed download instructios is available in the wiki [wiki](https://github.com/sd-webui/stable-diffusion-webui/wiki/Installation#optional-additional-models).
Please ensure that you run `webui.cmd` OR `webui_streamlit.cmd`**first** before downloading and initializing `/stable-diffusion-webui/src` folder.
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):
### Flagging (Broken after UI changed to gradio.Blocks() see [Flag button missing from new UI](https://github.com/sd-webui/stable-diffusion-webui/issues/50))
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
dir to write txt2img results to (overrides --outdir)
--outdir_img2img [OUTDIR_IMG2IMG]
dir to write img2img results to (overrides --outdir)
--save-metadata Whether to embed the generation parameters in the sample images
--skip-grid do not save a grid, only individual samples. Helpful when evaluating lots of samples
--skip-save do not save indiviual samples. For speed measurements.
--n_rows N_ROWS rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default:
-1)
--config CONFIG path to config which constructs model
--ckpt CKPT path to checkpoint of model
--precision {full,autocast}
evaluate at this precision
--gfpgan-dir GFPGAN_DIR
GFPGAN directory
--realesrgan-dir REALESRGAN_DIR
RealESRGAN directory
--realesrgan-model REALESRGAN_MODEL
Upscaling model for RealESRGAN
--no-verify-input do not verify input to check if it's too long
--no-half do not switch the model to 16-bit floats
--no-progressbar-hiding
do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware
accleration in browser)
--defaults DEFAULTS path to configuration file providing UI defaults, uses same format as cli parameter
--gpu GPU choose which GPU to use if you have multiple
--extra-models-cpu run extra models (GFGPAN/ESRGAN) on cpu
--esrgan-cpu run ESRGAN on cpu
--gfpgan-cpu run GFPGAN on cpu
--cli CLI don't launch web server, take Python function kwargs from this file.
```
-----
# 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/)<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},
