Segmentation-Free Guidance for Text-to-Image Diffusion Models

TL;DR

This paper introduces segmentation-free guidance for text-to-image diffusion models, dynamically adjusting prompts without retraining, leading to improved image quality and human preference over existing methods.

Contribution

It proposes a novel segmentation-free guidance method that enhances text-to-image diffusion models without additional training or compute, outperforming classifier-free guidance.

Findings

Human evaluators preferred segmentation-free guidance 60% over 19%.

Segmentation-free guidance outperforms classifier-free in objective metrics.

Method requires no retraining or extra compute.

Abstract

We introduce segmentation-free guidance, a novel method designed for text-to-image diffusion models like Stable Diffusion. Our method does not require retraining of the diffusion model. At no additional compute cost, it uses the diffusion model itself as an implied segmentation network, hence named segmentation-free guidance, to dynamically adjust the negative prompt for each patch of the generated image, based on the patch's relevance to concepts in the prompt. We evaluate segmentation-free guidance both objectively, using FID, CLIP, IS, and PickScore, and subjectively, through human evaluators. For the subjective evaluation, we also propose a methodology for subsampling the prompts in a dataset like MS COCO-30K to keep the number of human evaluations manageable while ensuring that the selected subset is both representative in terms of content and fair in terms of model performance. The results demonstrate the superiority of our segmentation-free guidance to the widely used classifier-free method. Human evaluators preferred segmentation-free guidance over classifier-free 60% to 19%, with 18% of occasions showing a strong preference. Additionally, PickScore win-rate, a recently proposed metric mimicking human preference, also indicates a preference for our method over classifier-free.