Abstract
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 this https URL.
Abstract (translated)
一致性模型作为一种有前景的扩散模型替代方案已出现，通过一步式样本生成提供高质量的生成能力。然而，在跨模态翻译和低光照图像增强等多域图像转换任务中的应用仍然鲜为人知。在本文中，我们引入了条件一致性模型（CCMs），用于多域图像转换，并通过纳入额外的条件输入来实现这一点。我们通过引入特定于任务的条件输入来实施这些修改，以指导去噪过程，确保生成的输出保留来自相应输入领域的结构和上下文信息。我们在10个不同的数据集上对CCMs进行了评估，证明了它们在多个领域内生成高质量转换图像的有效性。代码可在[此处](https://this https URL)获取。请注意，上述链接需要替换为实际可访问的URL地址。
URL
https://arxiv.org/abs/2501.01223
