CompVis / stable-diffusion

# Stable Diffusion
*Stable Diffusion was made possible thanks to a collaboration with Stability AI and Runway and builds upon our previous work:*
**High-Resolution Image Synthesis with Latent Diffusion Models** 
Robin Rombach\*,
Andreas Blattmann\*,
Dominik Lorenz\,
Patrick Esser,
Björn Ommer 
_CVPR '22 Oral |
GitHub | arXiv | Project page_
Stable Diffusion is a latent text-to-image diffusion
model.
Thanks to a generous compute donation from Stability AI and support from LAION, we were able to train a Latent Diffusion Model on 512x512 images from a subset of the LAION-5B database. 
Similar to Google's Imagen, 
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 below and the model card.
## Requirements
A suitable conda environment named `ldm` can be created
and activated with:
 [code] 
You can also update an existing latent diffusion environment by running
 [code] 
## 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.*
The weights are available via the CompVis organization at Hugging Face under a license which contains specific use-based restrictions to prevent misuse and harm as informed by the model card, but otherwise remains permissive. While commercial use is permitted under the terms of the license, **we do not recommend using the provided weights for services or products without additional safety mechanisms and considerations**, since there are known limitations and biases of the weights, and research on safe and ethical deployment of general text-to-image models is an ongoing effort. **The weights are research artifacts and should be treated as such.**
The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based.
### Weights
We currently provide the following checkpoints:
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on laion2B-en.
 194k steps at resolution `512x512` on laion-high-resolution (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
 515k steps at resolution `512x512` on laion-aesthetics v2 5+ (a subset of laion2B-en with estimated aesthetics score `> 5