Abstract
Iterative generative models, like diffusion and flow-matching, create high-fidelity samples by progressively refining a noise vector into data. However, this process is notoriously slow, often requiring hundreds of function evaluations. We introduce the warm-start model, a simple, deterministic model that dramatically accelerates conditional generation by providing a better starting point. Instead of starting generation from an uninformed N(0, I) prior, our warm-start model predicts an informed prior N(mu, sigma), whose moments are conditioned on the input context. This "warm start" substantially reduces the distance the generative process must traverse, particularly when the conditioning information is strongly informative. On tasks like image inpainting, our method achieves results competitive with a 1000-step DDPM baseline using only 11 total function evaluations (1 for the warm start, 10 for generation). A simple conditional normalization trick makes our method compatible with any standard generative model and sampler without modification, allowing it to be combined with other efficient sampling techniques for further acceleration. Our implementation is available at this https URL.
Abstract (translated)
迭代生成模型，如扩散和流匹配模型，通过逐步将噪声向量细化为数据来创建高保真样本。然而，这一过程通常非常慢，往往需要进行数百次函数评估。我们引入了预热启动模型（warm-start model），这是一种简单且确定性的模型，可以通过提供一个更好的起点显著加速条件生成。我们的预热启动模型预测了一个基于输入上下文的有信息量的先验分布N(mu, sigma)，而不是从无信息的N(0, I)先验开始生成。这种“预热”大幅减少了生成过程必须覆盖的距离，尤其是在上下文信息非常有用的情况下。 在图像修复等任务上，我们的方法仅使用总共11次函数评估（一次用于预热启动，十次用于生成）就能达到与一个需要1000步的DDPM基线相当的结果。一种简单的条件归一化技巧使得该方法可以与其他标准生成模型和采样器兼容，无需进行任何修改，并允许它结合其他高效的采样技术以进一步加速。 我们的实现代码可在[此处](https://this https URL)获得。
URL
https://arxiv.org/abs/2507.09212
