OT-ALD: Aligning Latent Distributions with Optimal Transport for Accelerated Image-to-Image Translation
This addresses efficiency and quality issues in image-to-image translation for computer vision applications, representing an incremental improvement over existing diffusion-based methods.
The paper tackles the problem of slow and inaccurate image-to-image translation in diffusion-based methods by proposing OT-ALD, which uses optimal transport to align latent distributions, resulting in 20.29% faster sampling and a 2.6-point FID score improvement on average.
The Dual Diffusion Implicit Bridge (DDIB) is an emerging image-to-image (I2I) translation method that preserves cycle consistency while achieving strong flexibility. It links two independently trained diffusion models (DMs) in the source and target domains by first adding noise to a source image to obtain a latent code, then denoising it in the target domain to generate the translated image. However, this method faces two key challenges: (1) low translation efficiency, and (2) translation trajectory deviations caused by mismatched latent distributions. To address these issues, we propose a novel I2I translation framework, OT-ALD, grounded in optimal transport (OT) theory, which retains the strengths of DDIB-based approach. Specifically, we compute an OT map from the latent distribution of the source domain to that of the target domain, and use the mapped distribution as the starting point for the reverse diffusion process in the target domain. Our error analysis confirms that OT-ALD eliminates latent distribution mismatches. Moreover, OT-ALD effectively balances faster image translation with improved image quality. Experiments on four translation tasks across three high-resolution datasets show that OT-ALD improves sampling efficiency by 20.29% and reduces the FID score by 2.6 on average compared to the top-performing baseline models.