ZiyiZhang27/tdpo
[ICML 2024] Code for the paper "Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases"
This project helps AI researchers and practitioners fine-tune diffusion models to generate higher-quality images. It takes a pre-trained diffusion model and a reward function (like aesthetic score or PickScore) as input, then outputs an optimized model that produces images better aligned with human preferences, without over-optimizing for specific rewards. Researchers working with generative AI for image creation would find this useful.
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Use this if you are developing or experimenting with diffusion models and need to fine-tune them more effectively to align with diverse human preferences, especially when aiming for generalization across different quality metrics.
Not ideal if you are looking for a plug-and-play solution for general image editing or generation without deep involvement in model training and research.
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Language
Python
License
MIT
Category
Last pushed
Jul 12, 2024
Commits (30d)
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