Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising
  Trajectory Sharpening
We propose Diffusion-Sharpening, a fine-tuning approach that enhances
downstream alignment by optimizing sampling trajectories. Existing RL-based
fine-tuning methods focus on single training timesteps and neglect
trajectory-level alignment, while recent sampling trajectory optimization
methods incur significant inference NFE costs. Diffusion-Sharpening overcomes
this by using a path integral framework to select optimal trajectories during
training, leveraging reward feedback, and amortizing inference costs. Our
method demonstrates superior training efficiency with faster convergence, and
best inference efficiency without requiring additional NFEs. Extensive
experiments show that Diffusion-Sharpening outperforms RL-based fine-tuning
methods (e.g., Diffusion-DPO) and sampling trajectory optimization methods
(e.g., Inference Scaling) across diverse metrics including text alignment,
compositional capabilities, and human preferences, offering a scalable and
efficient solution for future diffusion model fine-tuning. Code:
https://github.com/Gen-Verse/Diffusion-Sharpening
67b40ce4d3c5f50aa9b71df5
Ye Tian
claimed_verified
2025-03-25T08:21:50.804Z
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tyfeld
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tyfeld
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Ling Yang
claimed_verified
2025-02-20T09:37:40.151Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64fde4e252e82dd432b74ce9/-CQZbBP7FsPPyawYrsi4z.jpeg
Ling Yang
user
Lingaaaaaaa
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Xinchen Zhang
claimed_verified
2025-02-18T09:31:29.697Z
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Xinchen Zhang
user
comin
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Yunhai Tong
67b40ce4d3c5f50aa9b71df9
Mengdi Wang
67b40ce4d3c5f50aa9b71dfa
Bin Cui
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising
  Trajectory Sharpening
We propose Diffusion-Sharpening, a fine-tuning approach that enhances
downstream alignment by optimizing sampling trajectories. Existing RL-based
fine-tuning methods focus on single training timesteps and neglect
trajectory-level alignment, while recent sampling trajectory optimization
methods incur significant inference NFE costs. Diffusion-Sharpening overcomes
this by using a path integral framework to select optimal trajectories during
training, leveraging reward feedback, and amortizing inference costs. Our
method demonstrates superior training efficiency with faster convergence, and
best inference efficiency without requiring additional NFEs. Extensive
experiments show that Diffusion-Sharpening outperforms RL-based fine-tuning
methods (e.g., Diffusion-DPO) and sampling trajectory optimization methods
(e.g., Inference Scaling) across diverse metrics including text alignment,
compositional capabilities, and human preferences, offering a scalable and
efficient solution for future diffusion model fine-tuning. Code:
https://github.com/Gen-Verse/Diffusion-Sharpening
Diffusion-Sharpening enhances diffusion model fine-tuning by optimizing sampling trajectories using a path integral framework, improving training and inference efficiency without additional computational costs.
Diffusion-Sharpening fine-tuning sampling trajectories path integral framework reward feedback amortizing inference costs Diffusion-DPO Inference Scaling text alignment compositional capabilities human preferences