# Alias-Free Latent Diffusion Models

Yifan Zhou1, Zeqi Xiao1, Shuai Yang2, Xingang Pan1✉
1S-Lab, Nanyang Technological University
2Wangxuan Institute of Computer Technology, Peking University
CVPR 2025
arXiv
Code

## Motivation

We found the VAE and denoising network in LDM are not equivariant to fractional shifts. We propose an alias-free framework to improve the fractional shift equivariance of LDM. We demonstrate the effectiveness of our method in various applications, including video editing, frame interpolation, super-resolution and normal estimation.

## Results

### Shifting I2SB Super-resolution Input

### Shifting YOSO Normal Estimation Input

## Video Editing

### Warping-equivariant Editing

Our alias-free model can produce consistent editing results for videos of smooth motion.
As discussed in the limitation section, our method sufferers similar issues to flow-base editing. For example, in the following video, the flow in background is hard to obtain implicitly, causing the flickering effects in the outputs.

### Splatting and Interpolation

The flickering is due to the overlapping of summation splatting, which can be addressed by depth estimation and softmax splatting.

## Ablation Study

### Shifting VAE Latent

### Shifting LDM Noisy Latent

## BibTeX

        @inproceedings{zhou2025afldm,
          title={Alias-Free Latent Diffusion Models: Improving Fractional Shift Equivariance of Diffusion Latent Space},
          author={Zhou, Yifan and Xiao, Zeqi and Yang, Shuai and Pan, Xingang },
          booktitle = {CVPR},
          year = {2025},
        }