---
license: mit
language:
- en
---
ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models
Qin Zhou, Zhiyang Zhang, Jinglong Wang, Xiaobin Li, Jing Zhang*, Qian Yu, Lu Sheng, Dong Xu
Beihang University, University of Hong Kong
## Abstract
Diffusion models excel at image generation. Recent studies have shown that these models not only generate high-quality images but also encode text-image alignment information through attention maps or loss functions. This information is valuable for various downstream tasks, including segmentation, text-guided image editing, and compositional image generation. However, current methods heavily rely on the assumption of perfect text-image alignment in diffusion models, which is not the case. In this paper, we propose using zero-shot referring image segmentation as a proxy task to evaluate the pixel-level image and class-level text alignment of popular diffusion models. We conduct an in-depth analysis of pixel-text misalignment in diffusion models from the perspective of training data bias. We find that misalignment occurs in images with small-sized, occluded, or rare object classes. Therefore, we propose ELBO-T2IAlign—a simple yet effective method to calibrate pixel-text alignment in diffusion models based on the evidence lower bound (ELBO) of likelihood. ELBO-T2IAlign is training-free and generic: it requires no additional annotations, model retraining, or architectural modifications, and it can be directly applied to different diffusion backbones. Extensive experiments on zero-shot referring image segmentation, text-guided image editing, and compositional image generation verify that the proposed calibration improves pixel-text alignment across complementary downstream tasks.
## Details
This repository contains all datasets used in our paper, including COCO, VOC, Context...
Only validation sets are included, which cost about 7G memory.
```bash
# unzip command
cat dataset.tar.gz.* > dataset.tar.gz
tar -xzf dataset.tar.gz
```
## Citation
```bibtex
@article{zhou2025elbo,
title={ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models},
author={Zhou, Qin and Zhang, Zhiyang and Wang, Jinglong and Li, Xiaobin and Zhang, Jing and Yu, Qian and Sheng, Lu and Xu, Dong},
journal={arXiv preprint arXiv:2506.09740},
year={2025}
}
``` 
