Guiding Diffusion Models with Semantically Degraded Conditions

TL;DR

This paper introduces Condition-Degradation Guidance (CDG), a novel method that replaces the null prompt in classifier-free guidance with strategically degraded conditions, improving semantic precision in text-to-image diffusion models.

Contribution

The paper proposes a new guidance paradigm that uses semantically degraded conditions, enhancing compositional accuracy without additional training or external models.

Findings

CDG improves compositional accuracy across multiple architectures.

It enhances text-image alignment with negligible computational overhead.

The method demonstrates the importance of adaptive, semantically-aware negative samples.

Abstract

Classifier-Free Guidance (CFG) is a cornerstone of modern text-to-image models, yet its reliance on a semantically vacuous null prompt ($\varnothing$) generates a guidance signal prone to geometric entanglement. This is a key factor limiting its precision, leading to well-documented failures in complex compositional tasks. We propose Condition-Degradation Guidance (CDG), a novel paradigm that replaces the null prompt with a strategically degraded condition, $\boldsymbol{c}_{\text{deg}}$. This reframes guidance from a coarse "good vs. null" contrast to a more refined "good vs. almost good" discrimination, thereby compelling the model to capture fine-grained semantic distinctions. We find that tokens in transformer text encoders split into two functional roles: content tokens encoding object semantics, and context-aggregating tokens capturing global context. By selectively degrading only the former, CDG constructs $\boldsymbol{c}_{\text{deg}}$ without external models or training. Validated across diverse architectures including Stable Diffusion 3, FLUX, and Qwen-Image, CDG markedly improves compositional accuracy and text-image alignment. As a lightweight, plug-and-play module, it achieves this with negligible computational overhead. Our work challenges the reliance on static, information-sparse negative samples and establishes a new principle for diffusion guidance: the construction of adaptive, semantically-aware negative samples is critical to achieving precise semantic control. Code is available at https://github.com/Ming-321/Classifier-Degradation-Guidance.