Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2402.13490v1
• Date: Wed, 21 Feb 2024 03:01:17 GMT
• Title: Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models
• Authors: Chen Wu and Fernando De la Torre
• Abstract summary: We show 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. 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.
• Score: 68.47333676663312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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