Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques

TL;DR

This paper identifies a universal content shift phenomenon in diffusion model features that hampers their discriminative power, and proposes a suppression method called GATE to improve diffusion feature quality across tasks.

Contribution

The paper reveals the content shift issue in diffusion features and introduces GATE, a practical guideline and method to suppress content shift, enhancing diffusion model discriminative capabilities.

Findings

Suppression of content shift improves diffusion feature quality.

GATE effectively evaluates and enhances diffusion features.

Proposed method outperforms baselines on multiple tasks.

Abstract

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://github.com/Darkbblue/diffusion-content-shift.