Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910883559505920 |
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| author | Lee, Suhyeon Kim, Kwanyoung Ye, Jong Chul |
| author_facet | Lee, Suhyeon Kim, Kwanyoung Ye, Jong Chul |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_15056 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation Lee, Suhyeon Kim, Kwanyoung Ye, Jong Chul Computer Vision and Pattern Recognition 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 |
| title | Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15056 |