CompVis / stable-diffusion

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 .

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 . 194,000 steps at resolution 512x512 on 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 ).
• 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
• 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 .
• 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 .

Model Access

Each checkpoint can be used both with Hugging Face's 🧨 Diffusers library or the original Stable Diffusion GitHub repository . Note that you have to "click-request" them on each respective model repository.

Demo

To quickly try out the model, you can try out the Stable Diffusion Space .

License

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.

Citation

    @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 .