COT Flow：通过对比对学习最优传输图像采样与编辑
COT Flow: Learning Optimal-Transport Image Sampling and Editing by Contrastive Pairs
AI总结：
针对扩散模型迭代生成速度慢、受高斯噪声先验限制编辑灵活性的问题，提出基于最优传输的COT Flow方法，实现一步高质量非配对I2I转换，可编辑空间翻倍，还推出灵活的COT Editor编辑工具。
摘要：
扩散模型已在多模态数据的采样和编辑中展现出强劲性能，生成质量较高，但它们依赖迭代生成过程，计算成本高且速度慢。此外，大多数方法受限于从高斯噪声生成数据，这限制了其采样和编辑的灵活性。为克服这两个缺陷，我们提出对比最优传输流（Contrastive Optimal Transport，简称COT Flow），这是一种全新方法，与现有扩散模型相比，能够实现快速高质量生成，且零样本编辑灵活性更强。得益于最优传输（OT），我们的方法对先验分布没有限制，可实现非配对图像到图像（I2I）转换，并且与其他零样本编辑方法相比，可编辑空间翻倍（覆盖轨迹的起点和终点两端）。在质量方面，COT Flow仅需一步就能生成与现有最先进的非配对图像到图像（I2I）转换方法相当的结果。为了凸显引入OT后COT Flow的优势，我们推出了COT Editor，可执行用户引导的编辑，兼具出色的灵活性与质量。代码将在https://github.com/zuxinrui/cot_flow发布。
英文摘要：
Diffusion models have demonstrated strong performance in sampling and editing multi-modal data with high generation quality, yet they suffer from the iterative generation process which is computationally expensive and slow. In addition, most methods are constrained to generate data from Gaussian noise, which limits their sampling and editing flexibility. To overcome both disadvantages, we present Contrastive Optimal Transport Flow (COT Flow), a new method that achieves fast and high-quality generation with improved zero-shot editing flexibility compared to previous diffusion models. Benefiting from optimal transport (OT), our method has no limitation on the prior distribution, enabling unpaired image-to-image (I2I) translation and doubling the editable space (at both the start and end of the trajectory) compared to other zero-shot editing methods. In terms of quality, COT Flow can generate competitive results in merely one step compared to previous state-of-the-art unpaired image-to-image (I2I) translation methods. To highlight the advantages of COT Flow through the introduction of OT, we introduce the COT Editor to perform user-guided editing with excellent flexibility and quality. The code will be released at https://github.com/zuxinrui/cot_flow.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
