用可微搜索找到更快的扩散采样方法，10步生成高质量图像
Differentiable Solver Search for Fast Diffusion Sampling
- 提出可微搜索算法，优化时间步与求解器系数组合
- 10步采样下，ImageNet256上FID达2.33~2.40，超越传统方法
- 适用于多种模型架构与尺寸，通用性强
扩散模型虽生成质量高，但需大量函数评估。近期基于ODE的求解器在有限采样步数下缓解了逆扩散计算负担。然而，这些求解器多借鉴亚当斯类多步法，仅依赖t相关的拉格朗日插值，我们发现该方式对扩散模型并非最优，并揭示了一个由时间步与求解器系数构成的紧凑搜索空间。基于此分析，提出新型可微求解器搜索算法以寻找更优解。采用所搜求解器后，修正流模型如SiT-XL/2和FlowDCN-XL/2在ImageNet256上仅用10步即达到2.40和2.35的FID分数；同时，DDPM模型DiT-XL/2也实现2.33的FID分数。值得注意的是，所搜求解器显著优于传统方法，且在不同模型架构、分辨率与规模间具强泛化性。
原文摘要 · Abstract (English)
Diffusion models have demonstrated remarkable generation quality but at the cost of numerous function evaluations. Recently, advanced ODE-based solvers have been developed to mitigate the substantial computational demands of reverse-diffusion solving under limited sampling steps. However, these solvers, heavily inspired by Adams-like multistep methods, rely solely on t-related Lagrange interpolation. We show that t-related Lagrange interpolation is suboptimal for diffusion model and reveal a compact search space comprised of time steps and solver coefficients. Building on our analysis, we propose a novel differentiable solver search algorithm to identify more optimal solver. Equipped with the searched solver, rectified-flow models, e.g., SiT-XL/2 and FlowDCN-XL/2, achieve FID scores of 2.40 and 2.35, respectively, on ImageNet256 with only 10 steps. Meanwhile, DDPM model, DiT-XL/2, reaches a FID score of 2.33 with only 10 steps. Notably, our searched solver outperforms traditional solvers by a significant margin. Moreover, our searched solver demonstrates generality across various model architectures, resolutions, and model sizes.
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