Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion
  Models
Chen Wu Fernando De la Torre
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.