Reproduce “Dual Diffusion Implicit Bridges for Image-to-Image Translation” on OpenResearch
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About this paper
Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon
Dual Diffusion Implicit Bridges (DDIBs) leverages two independently trained diffusion models to achieve unpaired image-to-image translation. This approach offers enhanced data privacy and scalability by decoupling training from domain pairs and intrinsically guarantees cycle consistency through its connection to probability flow ODEs and Schrödinger Bridges.
Key insights
- The deterministic nature of Probability Flow ODEs from DDIMs inherently guarantees exact cycle consistency for image-to-image translation.
- Unpaired image translation can be achieved by concatenating two independently learned diffusion processes, each mapping a domain to a shared Gaussian latent space.
- The proposed method has a strong theoretical grounding, as its probability flow ODEs are equivalent to optimal processes of Schrödinger Bridge Problems (entropy-regularized optimal transport).
Summary by alphaXiv.
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