单步完成无配对图像翻译，无需对抗训练。
Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation
- 用隐式桥模型连接分布轨迹，实现单步双向翻译。
- 在基准数据集上达到当前最优性能，一步生成。
- 适合追求高效生成的视觉应用开发者。
自CycleGAN提出以来，无配对图像到图像翻译取得了显著进展。然而，基于扩散模型或薛定谔桥的方法因迭代采样特性，尚未在真实场景中广泛应用。为此，我们提出一种新框架——隐式桥一致性蒸馏（IBCD），可在不使用对抗损失的情况下实现单步双向无配对翻译。IBCD通过扩散隐式桥模型连接不同分布间的PF-ODE轨迹，扩展了一致性蒸馏。此外，引入两项改进：1）用于一致性蒸馏的分布匹配；2）基于蒸馏难度的自适应加权方法。实验表明，IBCD在基准数据集上以单次生成步骤达到最先进性能。项目页面见https://hyn2028.github.io/project_page/IBCD/index.html。
原文摘要 · Abstract (English)
Unpaired image-to-image translation has seen significant progress since the introduction of CycleGAN. However, methods based on diffusion models or Schrödinger bridges have yet to be widely adopted in real-world applications due to their iterative sampling nature. To address this challenge, we propose a novel framework, Implicit Bridge Consistency Distillation (IBCD), which enables single-step bidirectional unpaired translation without using adversarial loss. IBCD extends consistency distillation by using a diffusion implicit bridge model that connects PF-ODE trajectories between distributions. Additionally, we introduce two key improvements: 1) distribution matching for consistency distillation and 2) adaptive weighting method based on distillation difficulty. Experimental results demonstrate that IBCD achieves state-of-the-art performance on benchmark datasets in a single generation step. Project page available at https://hyn2028.github.io/project_page/IBCD/index.html
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