Text-to-image diffusion models have emerged as powerful tools for high-quality image generation and editing. Many existing approaches rely on text prompts as editing guidance. However, these methods are constrained by the need for manual prompt crafting, which can be time-consuming, introduce irrelevant details, and significantly limit editing performance. In this work, we propose optimizing semantic embeddings guided by attribute classifiers to steer text-to-image models toward desired edits, without relying on text prompts or requiring any training or fine-tuning of the diffusion model. We utilize classifiers to learn precise semantic embeddings at the dataset level. The learned embeddings are theoretically justified as the optimal representation of attribute semantics, enabling disentangled and accurate edits. Experiments further demonstrate that our method achieves high levels of disentanglement and strong generalization across different domains of data.
View on arXiv@article{chang2025_2505.14254,
title={ Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic Optimization },
author={ Yuanyuan Chang and Yinghua Yao and Tao Qin and Mengmeng Wang and Ivor Tsang and Guang Dai },
journal={arXiv preprint arXiv:2505.14254},
year={ 2025 }
}