readme: ---
  license: other
  tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - core-ml
  ---
  
  # Stable Diffusion v1-5 Model Card
  
  This model was generated by Hugging Face using [Apple’s repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md).
  
  Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
  For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).
  
  The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) 
  checkpoint and subsequently fine-tuned on 595k 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).
  
  
  These weights here have been converted to Core ML for use on Apple Silicon hardware.
  
  There are 4 variants of the Core ML weights:
  
  ```
  coreml-stable-diffusion-v1-5
  ├── original
  │   ├── compiled              # Swift inference, "original" attention
  │   └── packages              # Python inference, "original" attention
  └── split_einsum
      ├── compiled              # Swift inference, "split_einsum" attention
      └── packages              # Python inference, "split_einsum" attention
  ```
  
  Please, refer to https://huggingface.co/blog/diffusers-coreml for details.
  
  If you need weights for the 🧨 Diffusers library, please [visit this model instead](https://huggingface.co/runwayml/stable-diffusion-v1-5).
  
  ## Model Details
  - **Developed by:** Robin Rombach, Patrick Esser
  - **Model type:** Diffusion-based text-to-image generation model
  - **Language(s):** English
  - **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.
  - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
  - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
  - **Cite as:**
  
        @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}
        }
  
  # Uses
  
  ## Direct Use 
  The model is intended for research purposes only. Possible research areas and
  tasks include
  
  - Safe deployment of models which have the potential to generate harmful content.
  - Probing and understanding the limitations and biases of generative models.
  - Generation of artworks and use in design and other artistic processes.
  - Applications in educational or creative tools.
  - Research on generative models.
  
  Excluded uses are described below.
  
   ### Misuse, Malicious Use, and Out-of-Scope Use
  _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion_.
  
  
  The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
  
  #### Out-of-Scope Use
  The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
  
  #### Misuse and Malicious Use
  Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
  
  - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
  - Intentionally promoting or propagating discriminatory content or harmful stereotype