Stage-wise Distortion-Perception Traversal in Zero-shot Inverse Problems with Diffusion Models

2026-05-27 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

A new framework, MAP-RPS, and its latent space extension, LMAP-RPS, enable flexible distortion-perception (D-P) tradeoff traversal in zero-shot inverse problems using a single diffusion model. Published as 2605.28711, this method addresses the inherent tension between distortion performance and perceptual quality in Bayesian inverse problems. MAP-RPS operates in two stages: an initial MAP estimation stage approximates the MMSE solution for low-distortion initialization, followed by a re-noised posterior sampling stage that progressively enhances perceptual quality. Theoretical analyses validate its design. LMAP-RPS extends this approach to latent space, leveraging large-scale pre-trained latent diffusion backbones for broader applicability. Extensive experiments confirm that MAP-RPS and LMAP-RPS effectively traverse the D-P tradeoff across various tasks and serve as efficient solvers for real-world inverse problems.

Key takeaway

For Machine Learning Engineers developing zero-shot inverse problem solvers, MAP-RPS and LMAP-RPS offer a principled approach to manage the distortion-perception tradeoff. You can achieve flexible control over output quality, from low-distortion to high-perception, without retraining. Consider integrating LMAP-RPS with your existing latent diffusion backbones to enhance applicability and efficiency across diverse real-world tasks. This method provides a robust solution for balancing fidelity and visual quality in your generative models.

Key insights

A stage-wise diffusion model framework enables flexible distortion-perception tradeoff traversal in zero-shot inverse problems.

Principles

• Bayesian inverse problems exhibit a fundamental distortion-perception tradeoff.
• Zero-shot diffusion models can achieve D-P traversal with a single model.
• Combining MAP estimation with posterior sampling improves quality.

Method

MAP-RPS starts with MAP estimation for low-distortion initialization, then uses re-noised posterior sampling to progressively improve perceptual quality. LMAP-RPS extends this to latent space.

In practice

• Apply MAP-RPS for efficient zero-shot inverse problem solving.
• Utilize LMAP-RPS with pre-trained latent diffusion backbones for broader tasks.

Topics

• Diffusion Models
• Inverse Problems
• Distortion-Perception Tradeoff
• Zero-shot Learning
• MAP-RPS
• Latent Diffusion Models

Code references

• mlvlab/DAVI
• devzhk/enkg-pytorch

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.