标题： SenseFlow：基于流的文本到图像蒸馏的分布匹配扩展 显示英文标题

标题： SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation

摘要： 分布匹配蒸馏（DMD）已被成功应用于文本到图像的扩散模型，例如Stable Diffusion（SD）1.5。 然而，原始的DMD在大规模基于流的文本到图像模型上（如SD 3.5和FLUX）遇到了收敛困难的问题。 在本文中，我们首先分析了在大规模模型上应用原始DMD时遇到的问题。 然后，为了克服可扩展性挑战，我们提出了隐式分布对齐（IDA），以正则化生成器与虚假分布之间的距离。 此外，我们提出了段内引导（ISG），以重新分配教师模型的时间步重要性分布。 仅使用IDA，DMD可以在SD 3.5上收敛；同时使用IDA和ISG，DMD可以在SD 3.5和FLUX.1上收敛。 除了其他改进（如扩展的判别器模型），我们的最终模型\textbf{SenseFlow}在蒸馏基于扩散的文本到图像模型（如SDXL）和基于流匹配的模型（如SD 3.5 Large和FLUX）方面表现优异。 源代码将在https://github.com/XingtongGe/SenseFlow上提供。 显示英文摘要

摘要： The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to regularize the distance between the generator and fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep importance distribution from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Along with other improvements such as scaled up discriminator models, our final model, dubbed \textbf{SenseFlow}, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX. The source code will be avaliable at https://github.com/XingtongGe/SenseFlow.