提出新蒸馏方法，让扩散桥模型推理速度提升4到100倍。
Inverse Bridge Matching Distillation
- 基于逆桥匹配构建可训练目标，支持条件与无条件模型蒸馏。
- 仅用噪声图像训练，一步生成器实现加速，最高提速100倍。
- 适用超分辨率、去JPEG伪影等任务，部分场景生成质量更优。
学习扩散桥模型（DBMs）很容易；但使其快速实用则是一门艺术。扩散桥模型是扩散模型在图像到图像翻译中的有前景扩展，但和许多现代扩散与流模型一样，面临推理缓慢的问题。为此，我们提出一种基于逆桥匹配公式的新型蒸馏技术，并推导出可实际求解的可计算目标。与以往的DBM蒸馏方法不同，该方法能同时蒸馏条件与无条件类型模型，实现一步生成器蒸馏，且仅需使用带噪声图像进行训练。我们在多种任务设置下评估该方法，包括超分辨率、JPEG恢复、草图转图像等，结果表明，所提蒸馏技术可使DBM推理速度提升4至100倍，某些情况下甚至优于原教师模型的生成质量。代码已开源：https://github.com/ngushchin/IBMD。
原文摘要 · Abstract (English)
Learning diffusion bridge models is easy; making them fast and practical is an art. Diffusion bridge models (DBMs) are a promising extension of diffusion models for applications in image-to-image translation. However, like many modern diffusion and flow models, DBMs suffer from the problem of slow inference. To address it, we propose a novel distillation technique based on the inverse bridge matching formulation and derive the tractable objective to solve it in practice. Unlike previously developed DBM distillation techniques, the proposed method can distill both conditional and unconditional types of DBMs, distill models in a one-step generator, and use only the corrupted images for training. We evaluate our approach for both conditional and unconditional types of bridge matching on a wide set of setups, including super-resolution, JPEG restoration, sketch-to-image, and other tasks, and show that our distillation technique allows us to accelerate the inference of DBMs from 4x to 100x and even provide better generation quality than used teacher model depending on particular setup. We provide the code at https://github.com/ngushchin/IBMD
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