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@ -34,7 +34,12 @@ In order to get started, we recommend taking a look at two notebooks:
- The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook summarizes diffuser model training methods. This notebook takes a step-by-step approach to training your
diffuser model on an image dataset, with explanatory graphics.
## **New** Stable Diffusion is now fully compatible with `diffusers`!
## **New 🎨🎨🎨** Stable Diffusion is now fully compatible with `diffusers`!
Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from [CompVis](https://github.com/CompVis), [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/). It's trained on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database. 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 the [model card](https://huggingface.co/CompVis/stable-diffusion) for more information.
**The Stable Diffusion weights are currently only available to universities, academics, research institutions and independent researchers. Please request access applying to <a href="https://stability.ai/academia-access-form" target="_blank">this</a> form**
```py
# make sure you're logged in with `huggingface-cli login`