ICML 2025

Angle Domain Guidance: Latent Diffusion Requires Rotation Rather Than
Extrapolation
Cheng Jin 1 Zhenyu Xiao * 1 Chutao Liu * 2 Yuantao Gu 1
Abstract
Classifier-free guidance (CFG) has emerged as a
pivotal advancement in text-to-image latent diffu-
sion models, establishing itself as a cornerstone
technique for achieving high-quality image syn-
thesis. However, under high guidance weights,
where text-image alignment is significantly en-
hanced, CFG also leads to pronounced color dis-
tortions in the generated images. We identify
that these distortions stem from the amplifica-
tion of sample norms in the latent space. We
present a theoretical framework that elucidates
the mechanisms of norm amplification and anoma-
lous diffusion phenomena induced by classifier-
free guidance. Leveraging our theoretical insights
and the latent space structure, we propose an An-
gle Domain Guidance (ADG) algorithm. ADG
constrains magnitude variations while optimizing
angular alignment, thereby mitigating color dis-
tortions while preserving the enhanced text-image
alignment achieved at higher guidance weights.
Experimental results demonstrate that ADG sig-
nificantly outperforms existing methods, generat-
ing images that not only maintain superior text
alignment but also exhibit improved color fidelity
and better alignment with human perceptual pref-
erences.
1. Introduction
The Stable Diffusion series (Rombach et al., 2022; Esser
et al., 2024), along with other latent-diffusion-based genera-
tive models (Ramesh et al., 2022; Labs, 2024), has revolu-
tionized text-to-image generation and related downstream
tasks (Zhang et al., 2023; Zhao et al., 2024; Zhang et al.,
*Equal contribution
1Department of Electronic Engineering,
Tsinghua University, Beijing, China 2Zhili College, Tsinghua
University, Beijing, China.
Correspondence to: Yuantao Gu
<gyt@tsinghua.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by t...