Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models
<|reference_start|>Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models: Text-to-image diffusion models have achieved remarkable performance in image synthesis, while the text interface does not always provide fine-grained control over certain image factors. For instance, changing a single token in the text can have unintended effects on the image. This paper shows a simple modification of classifier-free guidance can help disentangle image factors in text-to-image models. The key idea of our method, Contrastive Guidance, is to characterize an intended factor with two prompts that differ in minimal tokens: the positive prompt describes the image to be synthesized, and the baseline prompt serves as a "baseline" that disentangles other factors. Contrastive Guidance is a general method we illustrate whose benefits in three scenarios: (1) to guide domain-specific diffusion models trained on an object class, (2) to gain continuous, rig-like controls for text-to-image generation, and (3) to improve the performance of zero-shot image editors.<|reference_end|>
@article{wu2024contrastive,
    title={Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion
  Models},
    author={Chen Wu and Fernando De la Torre},
    journal={arXiv preprint arXiv:2402.13490},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13490},
    primaryClass={cs.CV}
}