TokenCompose: Text-to-Image Diffusion with Token-level Supervision

Zirui Wang, Zhizhou Sha, Zheng Ding, Yilin Wang, Zhuowen Tu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 8553-8564

We present TokenCompose a Latent Diffusion Model for text-to-image generation that achieves enhanced consistency between user-specified text prompts and model-generated images. Despite its tremendous success the standard denoising process in the Latent Diffusion Model takes text prompts as conditions only absent explicit constraint for the consistency between the text prompts and the image contents leading to unsatisfactory results for composing multiple object categories. Our proposed TokenCompose aims to improve multi-category instance composition by introducing the token-wise consistency terms between the image content and object segmentation maps in the finetuning stage. TokenCompose can be applied directly to the existing training pipeline of text-conditioned diffusion models without extra human labeling information. By finetuning Stable Diffusion with our approach the model exhibits significant improvements in multi-category instance composition and enhanced photorealism for its generated images.