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• W4387294554 abstract "We present Direct Reward Fine-Tuning (DRaFT), a simple and effective method for fine-tuning diffusion models to maximize differentiable reward functions, such as scores from human preference models. We first show that it is possible to backpropagate the reward function gradient through the full sampling procedure, and that doing so achieves strong performance on a variety of rewards, outperforming reinforcement learning-based approaches. We then propose more efficient variants of DRaFT: DRaFT-K, which truncates backpropagation to only the last K steps of sampling, and DRaFT-LV, which obtains lower-variance gradient estimates for the case when K=1. We show that our methods work well for a variety of reward functions and can be used to substantially improve the aesthetic quality of images generated by Stable Diffusion 1.4. Finally, we draw connections between our approach and prior work, providing a unifying perspective on the design space of gradient-based fine-tuning algorithms." @default.
• W4387294554 created "2023-10-03" @default.
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• W4387294554 date "2023-09-29" @default.
• W4387294554 modified "2023-10-04" @default.
• W4387294554 title "Directly Fine-Tuning Diffusion Models on Differentiable Rewards" @default.
• W4387294554 doi "https://doi.org/10.48550/arxiv.2309.17400" @default.
• W4387294554 hasPublicationYear "2023" @default.
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