| | --- |
| | license: creativeml-openrail-m |
| | tags: |
| | - stable-diffusion |
| | - text-to-image |
| | inference: false |
| | --- |
| | # Stable Diffusion |
| |
|
| | Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. |
| | This model card gives an overview of all available model checkpoints. For more in-detail model cards, please have a look at the model repositories listed under [Model Access](#model-access). |
| |
|
| | ## Stable Diffusion Version 1 |
| |
|
| | For the first version 4 model checkpoints are released. |
| | *Higher* versions have been trained for longer and are thus usually better in terms of image generation quality then *lower* versions. More specifically: |
| |
|
| | - **stable-diffusion-v1-1**: The checkpoint is randomly initialized and has been trained on 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). |
| | 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). |
| | - **stable-diffusion-v1-2**: The checkpoint resumed training from `stable-diffusion-v1-1`. |
| | 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, |
| | filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). |
| | - **stable-diffusion-v1-3**: The checkpoint resumed training from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598) |
| | - **stable-diffusion-v1-4**: The checkpoint resumed training from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). |
| | - [**`stable-diffusion-v1-4`**](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). |
| |
|
| | ### Model Access |
| |
|
| | Each checkpoint can be used both with Hugging Face's [ 🧨 Diffusers library](https://github.com/huggingface/diffusers) or the original [Stable Diffusion GitHub repository](https://github.com/CompVis/stable-diffusion). Note that you have to *"click-request"* them on each respective model repository. |
| |
|
| | | **[🤗's 🧨 Diffusers library](https://github.com/huggingface/diffusers)** | **[Stable Diffusion GitHub repository](https://github.com/CompVis/stable-diffusion)** | |
| | | ----------- | ----------- | |
| | | [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1) | [`stable-diffusion-v-1-1-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-1-original) | |
| | | [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2) | [`stable-diffusion-v-1-2-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original) | |
| | | [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3) | [`stable-diffusion-v-1-3-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-3-original) | |
| | | [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) | [`stable-diffusion-v-1-4-original`](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) | |
| |
|
| | ### Demo |
| |
|
| | To quickly try out the model, you can try out the [Stable Diffusion Space](https://huggingface.co/spaces/stabilityai/stable-diffusion). |
| |
|
| | ### License |
| |
|
| | [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. |
| |
|
| | ## Citation |
| |
|
| | ```bibtex |
| | @InProceedings{Rombach_2022_CVPR, |
| | author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, |
| | title = {High-Resolution Image Synthesis With Latent Diffusion Models}, |
| | booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| | month = {June}, |
| | year = {2022}, |
| | pages = {10684-10695} |
| | } |
| | ``` |
| |
|
| | *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
| |
|
