# Conditional Consistency Guided Image Translation and Enhancement

arXiv: https://arxiv.org/abs/2501.01223 · HTML: https://mlsanity.com/paper/2501.01223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- First posted on arXiv 2 Jan 2025.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Built on by (most cited first, 2 of 2)

- [GANs vs. Diffusion Models for virtual staining with the HER2match dataset](https://mlsanity.com/paper/2506.18484/) (2025)
- [HAPS: Rethinking Image Similarity for Virtual Staining](https://mlsanity.com/paper/2605.20362/) (2026)

## Abstract (arXiv)

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.