Stable Diffusion v2-1-base Model Card

coreml-stable-diffusion-2-1-base
├── 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

• coreml-stable-diffusion-2-1-base-palettized_original_compiled.zip contains the compiled, 6-bit model with ORIGINAL attention implementation.
• coreml-stable-diffusion-2-1-base-palettized_split_einsum_v2_compiled.zip contains the compiled, 6-bit model with SPLIT_EINSUM_V2 attention implementation.

• Use it with 🧨 diffusers
• Use it with the stablediffusion repository: download the v2-1_512-ema-pruned.ckpt here.

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
• 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 that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).
• Resources for more information: GitHub Repository.
• 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}
  }