Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising
  Trajectory Sharpening
Authors: name: Ye Tian, name: Ling Yang, name: Xinchen Zhang, name: Yunhai Tong, name: Mengdi Wang, name: Bin Cui

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