readme: ---
  license: creativeml-openrail-m
  tags:
  - coreml
  - stable-diffusion
  - text-to-image
  ---
  
  # Core ML Converted Model
  
  This model was converted to Core ML for use on Apple Silicon devices by following Apple's instructions [here](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml).<br>
  Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br>
  
  `split_einsum` version is compatible with all compute unit options including Neural Engine.<br>
  `original` version is only compatible with CPU & GPU option.
  
  # Stable Diffusion v2-1-base Model Card
  This model card focuses on the model associated with the Stable Diffusion v2-1-base model.
  
  This `stable-diffusion-2-1-base` model fine-tunes [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) with 220k extra steps taken, with `punsafe=0.98` on the same dataset. 
  
  - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `v2-1_512-ema-pruned.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt).
  - Use it with 🧨 [`diffusers`](#examples)
  
  ## Model Details
  - **Developed by:** Robin Rombach, Patrick Esser
  - **Model type:** Diffusion-based text-to-image generation model
  - **Language(s):** English
  - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
  - **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 ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
  - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
  - **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}
        }
        
  
  ## Examples
  
  Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
  
  ```bash
  pip install diffusers transformers accelerate scipy safetensors
  ```
  Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler):
  
  ```python
  from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
  import torch
  
  model_id = "stabilityai/stable-diffusion-2-1-base"
  
  scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
  pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
  pipe = pipe.to("cuda")
  
  prompt = "a photo of an astronaut riding a horse on mars"
  image = pipe(prompt).images[0]  
      
  image.save("astronaut_rides_horse.png")
  ```
  
  **Notes**:
  - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
  - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
  
  # 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 originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
  
  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 mo