Heat Dissipation Flow Matching for Multi-Scale Image Generation
A new AI method, Heat Dissipation Flow Matching (HDFM), integrates blur-based corruption into ODE-based frameworks for image generation. Unlike standard diffusion models that rely on noise, HDFM uses heat dissipation to preserve color budgets and multi-scale details. It addresses the ill-posed inverse heat-dissipation problem and adopts x-prediction to handle high-dimensional regression. The approach aligns an interpolated heat-dissipation path within Flow Matching, offering multi-scale priors. The paper is published on arXiv under ID 2605.19371.
Key facts
- HDFM introduces a continuous blurred (heat-dissipation) process into Flow Matching.
- It aligns an interpolated heat-dissipation path to address ill-posedness.
- It adopts x-prediction to mitigate high-dimensional regression difficulties.
- Blur-based corruption preserves better color budgets and multi-scale detail.
- The method is distinct from noise-based diffusion models.
- The paper is available on arXiv with ID 2605.19371.
- HDFM is an ODE-based framework, unlike previous blur-based SDE models.
- It provides multi-scale priors for image generation.
Entities
Institutions
- arXiv