AlexMaOLS/EluCD
Elucidating The Design Space of Classifier-Guided Diffusion Generation
This project helps researchers and developers working with image generation models to achieve higher quality results. It takes existing diffusion models like DDPM or EDM, along with readily available pre-trained image classifiers (e.g., ResNet), and combines them to produce more accurate and visually coherent images conditioned on specific categories. The primary users are machine learning researchers and practitioners focused on generative AI for image synthesis.
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Use this if you are generating conditional images using diffusion models and want to improve the fidelity and relevance of the generated output by leveraging off-the-shelf image classifiers.
Not ideal if you are looking for a general-purpose image generation tool without needing to integrate specific classifier guidance, or if you are not working with established diffusion model architectures.
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Language
Python
License
MIT
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Last pushed
Jan 20, 2024
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