Dual Diffusion Implicit Bridges (DDIBs) offers a novel approach to image-to-image translation by decoupling source and target domain training, enhancing data privacy and model adaptability. This method is suitable for researchers and practitioners seeking flexible image translation solutions without requiring joint domain datasets.

How It Works

DDIBs leverage two independently trained diffusion models, one for the source and one for the target domain. The translation process involves encoding source images into a latent space using the source model and then decoding these latents into target images with the target model. This two-step process is mathematically framed as concatenating Schrödinger Bridges, a form of entropy-regularized optimal transport, enabling theoretical insights into the method's efficacy.

Quick Start & Requirements

• Install via pip install -e . followed by specific dependency versions (e.g., numpy==1.24.0, matplotlib==3.6.2).
• Requires Python 3.9.
• Pretrained synthetic models can be downloaded via python download.py --exp synthetic.
• ImageNet models require downloading weights and the ImageNet validation dataset.
• Official documentation and examples are available via provided GitHub, arXiv, and Colab links.

Highlighted Details

• Implemented based on OpenAI's guided-diffusion and improved-diffusion repositories.
• Supports translation for synthetic datasets (e.g., Moons, Checkerboards) and class-conditional ImageNet translation.
• Theoretical interpretation as concatenated Schrödinger Bridges provides optimal transport properties.
• Offers flexibility for new domain pair translations without retraining on joint datasets.

Maintenance & Community

The project is associated with ICLR 2023 and authors from Stanford University. A to-do list indicates plans for releasing models on AFHQ and Yosemite datasets and adding color translation experiments.

Licensing & Compatibility

The repository's license is not explicitly stated in the README. However, its reliance on OpenAI's diffusion models suggests potential compatibility with their licensing terms. Commercial use should be verified.

Limitations & Caveats

The cycle consistency is approximate, dependent on ODE solver discretization errors. The README mentions a to-do list for additional datasets and features, indicating ongoing development.