Summary This repository serves as a comprehensive, curated collection of research papers focused on diffusion models applied to image-to-image translation and style transfer tasks. It targets researchers, engineers, and practitioners seeking to stay abreast of the latest advancements in generative AI for image manipulation. The primary benefit is providing a centralized, organized resource that consolidates cutting-edge techniques, methodologies, and associated datasets, streamlining the exploration of this rapidly evolving field.

How It Works The collection organizes and links to academic papers detailing various diffusion-based approaches for image translation. These methods encompass diverse applications such as style transfer, domain adaptation, image editing, and cross-modality translation. Papers often introduce novel architectural modifications, advanced sampling strategies, or sophisticated conditioning mechanisms within diffusion frameworks to enhance translation quality and control. This structured compilation highlights the breadth of research and the innovative techniques employed by the community.

Quick Start & Requirements This repository is a curated list of research papers and does not offer a direct installation or execution command. To utilize the content, users must refer to the individual papers and their linked project pages or code repositories. Prerequisites will vary per project but typically involve Python environments, deep learning frameworks (e.g., PyTorch), and often require significant computational resources, including GPUs with specific CUDA versions. Links to official documentation, demos, or code are provided within the README for each relevant paper.

Highlighted Details

• Broad Task Coverage: Encompasses a wide array of image-to-image translation applications, including artistic style transfer, domain adaptation (e.g., SAR-to-optical), medical imaging, and general image editing.
• State-of-the-Art Research: Features papers from 2021 up to recent preprints (2025), reflecting the forefront of diffusion model research in this domain.
• Extensive Dataset Links: Provides direct links to numerous datasets crucial for training and evaluating image translation models, such as ImageNet, MS-COCO, Cityscapes, and medical imaging datasets.
• Code Accessibility: Many entries include direct links to GitHub repositories, facilitating reproducibility and enabling users to experiment with specific implementations.

Maintenance & Community The repository appears to be a static collection of research papers. No specific maintainers, community channels (e.g., Discord, Slack), or roadmaps are indicated within the provided README.

Licensing & Compatibility As a curated list of external research papers, this repository does not impose a specific license. Users must adhere to the licensing terms of the individual papers, associated code repositories, and datasets they choose to utilize. Compatibility for commercial use or closed-source linking will depend entirely on the licenses of the referenced projects.

Limitations & Caveats This resource is a compilation of research, not a unified software framework or tool. Users are responsible for evaluating, obtaining, and implementing the individual research projects. Many papers are preprints or recent publications, meaning their associated code may be experimental, lack comprehensive documentation, or be subject to rapid changes. The repository itself offers no direct support or unified API.