coreml-stable-diffusion-v1-5

Maintainer: apple

Total Score

53

Last updated 5/28/2024

๐ŸŽฒ

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The coreml-stable-diffusion-v1-5 model is a version of the Stable Diffusion v1-5 model that has been converted to Core ML format for use on Apple Silicon hardware. It was developed by Hugging Face using Apple's repository, which has an ASCL license. The Stable Diffusion v1-5 model is a latent text-to-image diffusion model capable of generating photo-realistic images from text prompts. This model was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned to improve classifier-free guidance sampling. There are four variants of the Core ML weights available, including different attention implementations and compilation options for Swift and Python inference.

Model inputs and outputs

Inputs

  • Text prompt: The text prompt describing the desired image to be generated.

Outputs

  • Generated image: The photo-realistic image generated based on the input text prompt.

Capabilities

The coreml-stable-diffusion-v1-5 model is capable of generating a wide variety of photo-realistic images from text prompts, ranging from landscapes and scenes to intricate illustrations and creative concepts. Like other Stable Diffusion models, it excels at rendering detailed, imaginative imagery, but may struggle with tasks involving more complex compositionality or generating legible text.

What can I use it for?

The coreml-stable-diffusion-v1-5 model is intended for research purposes, such as exploring the capabilities and limitations of generative models, generating artworks and creative content, and developing educational or creative tools. However, the model should not be used to intentionally create or disseminate images that could be harmful, disturbing, or offensive, or to impersonate individuals without their consent.

Things to try

One interesting aspect of the coreml-stable-diffusion-v1-5 model is the availability of different attention implementations and compilation options, which can affect the performance and memory usage of the model on Apple Silicon hardware. Developers may want to experiment with these variants to find the best balance of speed and efficiency for their specific use cases.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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