Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models

2026-04-10

AI Summary

This work addresses the challenge in diffusion models where directly generating high-resolution images incurs substantial computational costs, while low-resolution previews often fail to preserve perceptual consistency with the final output. The authors propose a training-free, efficient preview method that, for the first time, incorporates the zero commutator condition into diffusion models. By constructing a downsampling matrix and guidance mechanism that satisfy this condition, the approach produces high-quality low-resolution previews without compromising perceptual fidelity. The method is compatible with existing acceleration techniques, reducing computational costs by up to 33% and achieving up to 3× inference speedup while maintaining image quality. Furthermore, it generalizes naturally to broader image transformation tasks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Image generative models have become indispensable tools to yield exquisite high-resolution (HR) images for everyone, ranging from general users to professional designers. However, a desired outcome often requires generating a large number of HR images with different prompts and seeds, resulting in high computational cost for both users and service providers. Generating low-resolution (LR) images first could alleviate computational burden, but it is not straightforward how to generate LR images that are perceptually consistent with their HR counterparts. Here, we consider the task of generating high-fidelity LR images, called Previews, that preserve perceptual similarity of their HR counterparts for an efficient workflow, allowing users to identify promising candidates before generating the final HR image. We propose the commutator-zero condition to ensure the LR-HR perceptual consistency for flow matching models, leading to the proposed training-free solution with downsampling matrix selection and commutator-zero guidance. Extensive experiments show that our method can generate LR images with up to 33\% computation reduction while maintaining HR perceptual consistency. When combined with existing acceleration techniques, our method achieves up to 3$\times$ speedup. Moreover, our formulation can be extended to image manipulations, such as warping and translation, demonstrating its generalizability.

Problem

• low-resolution preview
• perceptual consistency
• diffusion models
• high-resolution image
• computational efficiency

Innovation

• training-free
• perceptual consistency
• flow matching
• low-resolution preview
• commutator-zero condition