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---
license: openrail++
tags:
- stable-diffusion
- text-to-image
- core-ml
---
# Stable Diffusion v2-1-base 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). This version contains 4-bit linearly quantized Core ML weights for iOS 17 or macOS 14. To use weights without quantization, please visit [this model instead](https://huggingface.co/apple/coreml-stable-diffusion-2-1-base).
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.
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-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
```
There are also two zip archives suitable for use in the [Hugging Face demo app](https://github.com/huggingface/swift-coreml-diffusers) and other third party tools:
- `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.
Please, refer to https://huggingface.co/blog/diffusers-coreml for details.
- Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-1-base#examples)
- 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).
## 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}
}
*This model was quantized by Vishnou Vinayagame and adapted from the original by Pedro Cuenca, itself adapted from Robin Rombach, Patrick Esser and David Ha
*This model card was adapted by Pedro Cuenca from the original written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
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