用条件一致性模型实现多领域图像翻译与增强
Conditional Consistency Guided Image Translation and Enhancement
- 引入条件输入引导去噪过程，保持输出结构一致
- 在10个数据集上验证，生成图像质量高且跨域适应性强
- 适合图像增强、跨模态转换等应用，代码已开源
一致性模型作为扩散模型的有前景替代方案，通过单步生成实现高质量图像生成。然而，其在多领域图像翻译任务（如跨模态翻译和低光图像增强）中的应用仍不充分。本文提出条件一致性模型（CCMs），通过引入特定任务的条件输入，实现多领域图像翻译。该方法在去噪过程中融入条件信息，确保生成结果保留对应输入域的结构与上下文特征。我们在10个不同数据集上评估了CCMs，结果表明其在多个领域中均能生成高质量翻译图像。代码已公开于https://github.com/amilbhagat/Conditional-Consistency-Models。
原文摘要 · Abstract (English)
Consistency models have emerged as a promising alternative to diffusion models, offering high-quality generative capabilities through single-step sample generation. However, their application to multi-domain image translation tasks, such as cross-modal translation and low-light image enhancement remains largely unexplored. In this paper, we introduce Conditional Consistency Models (CCMs) for multi-domain image translation by incorporating additional conditional inputs. We implement these modifications by introducing task-specific conditional inputs that guide the denoising process, ensuring that the generated outputs retain structural and contextual information from the corresponding input domain. We evaluate CCMs on 10 different datasets demonstrating their effectiveness in producing high-quality translated images across multiple domains. Code is available at https://github.com/amilbhagat/Conditional-Consistency-Models.
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