  Diffusion models have demonstrated great success in the field of
text-to-image generation. However, alleviating the misalignment between the
text prompts and images is still challenging. The root reason behind the
misalignment has not been extensively investigated. We observe that the
misalignment is caused by inadequate token attention activation. We further
attribute this phenomenon to the diffusion model's insufficient condition
utilization, which is caused by its training paradigm. To address the issue, we
propose CoMat, an end-to-end diffusion model fine-tuning strategy with an
image-to-text concept matching mechanism. We leverage an image captioning model
to measure image-to-text alignment and guide the diffusion model to revisit
ignored tokens. A novel attribute concentration module is also proposed to
address the attribute binding problem. Without any image or human preference
data, we use only 20K text prompts to fine-tune SDXL to obtain CoMat-SDXL.
Extensive experiments show that CoMat-SDXL significantly outperforms the
baseline model SDXL in two text-to-image alignment benchmarks and achieves
start-of-the-art performance.
