RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories

• URL: http://arxiv.org/abs/2503.07699v2
• Date: Tue, 25 Mar 2025 06:11:23 GMT
• Title: RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories
• Authors: Huiyang Shao, Xin Xia, Yuhong Yang, Yuxi Ren, Xing Wang, Xuefeng Xiao,
• Abstract summary: Existing acceleration methods compromise sample quality, controllability, or introduce training complexities.<n>We propose RayFlow, a novel diffusion framework that addresses these limitations.<n>Extensive experiments demonstrate RayFlow's superiority in generating high-quality images with improved speed, control, and training efficiency.
• Score: 17.934379261227388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have achieved remarkable success across various domains. However, their slow generation speed remains a critical challenge. Existing acceleration methods, while aiming to reduce steps, often compromise sample quality, controllability, or introduce training complexities. Therefore, we propose RayFlow, a novel diffusion framework that addresses these limitations. Unlike previous methods, RayFlow guides each sample along a unique path towards an instance-specific target distribution. This method minimizes sampling steps while preserving generation diversity and stability. Furthermore, we introduce Time Sampler, an importance sampling technique to enhance training efficiency by focusing on crucial timesteps. Extensive experiments demonstrate RayFlow's superiority in generating high-quality images with improved speed, control, and training efficiency compared to existing acceleration techniques.

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