Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models
Latent diffusion models have emerged as the dominant framework for high-fidelity and efficient image generation, owing to their ability to learn diffusion processes in compact latent spaces. However, while previous research has focused primarily on reconstruction accuracy and semantic alignment of the latent space, we observe that another critical factor, robustness to sampling perturbations, also plays a crucial role in determining generation quality. Through empirical and theoretical analyses, we show that the commonly used $β$-VAE-based tokenizers in latent diffusion models, tend to produce overly compact latent manifolds that are highly sensitive to stochastic perturbations during dif...
| Authors | Qifan Li, Xingyu Zhou, Jinhua Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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