DiffusionKit

An inference tool for running diffusion models on Apple silicon.

CommonProductImageImage GenerationDeep Learning

DiffusionKit is an open-source project aimed at providing local inference capabilities for diffusion models on Apple silicon devices. It achieves efficient image processing by converting PyTorch models to Core ML format and using MLX for image generation. The project supports Stable Diffusion 3 and FLUX models, enabling both image generation and image-to-image transformations.

Convert PyTorch models to Core ML format for efficient inference on Apple silicon devices.
Use MLX for image generation
supporting various parameter adjustments to control the generation process.
Support for Stable Diffusion 3 and FLUX models
offering diverse image generation options.
Generate images using Python CLI or Swift code for flexible usage.
Support for using seeds to ensure result reproducibility.
Local checkpoint usage is supported
allowing users to load custom models.

\The target audience primarily consists of developers and researchers who require efficient image generation on Apple silicon devices. They may have specific needs for deploying and inferring deep learning models
aiming to enhance efficiency and response speed through local processing.\

Use DiffusionKit to generate artwork with specific styles or content.
Transform existing images into different styles or scenes using the image-to-image functionality.
Utilize DiffusionKit for rapid prototyping and testing of models in research projects.

1. Install the necessary Python environment and dependencies.
2. Use pip to install DiffusionKit as per the project documentation.
3. If necessary
download from Hugging Face Hub and accept the usage terms for Stable Diffusion 3.
4. Convert the PyTorch model to Core ML format.

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Bounce Rate

37.31%

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5.8

Visit Duration

00:06:52

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