Detector Guidance for Multi-Object Text-to-Image Generation

TL;DR

This paper introduces Detector Guidance, a method that uses a latent object detection model to improve multi-object text-to-image generation by reducing object mixing and enhancing object separation.

Contribution

The paper proposes a novel Detector Guidance approach that integrates latent object detection into diffusion models to better handle multiple objects in generated images.

Findings

DG improves object separation in generated images.

Human evaluations show 8-22% reduction in object mixing.

DG outperforms baseline methods on COCO, CC, and MRO benchmarks.

Abstract

Diffusion models have demonstrated impressive performance in text-to-image generation. They utilize a text encoder and cross-attention blocks to infuse textual information into images at a pixel level. However, their capability to generate images with text containing multiple objects is still restricted. Previous works identify the problem of information mixing in the CLIP text encoder and introduce the T5 text encoder or incorporate strong prior knowledge to assist with the alignment. We find that mixing problems also occur on the image side and in the cross-attention blocks. The noisy images can cause different objects to appear similar, and the cross-attention blocks inject information at a pixel level, leading to leakage of global object understanding and resulting in object mixing. In this paper, we introduce Detector Guidance (DG), which integrates a latent object detection model to separate different objects during the generation process. DG first performs latent object detection on cross-attention maps (CAMs) to obtain object information. Based on this information, DG then masks conflicting prompts and enhances related prompts by manipulating the following CAMs. We evaluate the effectiveness of DG using Stable Diffusion on COCO, CC, and a novel multi-related object benchmark, MRO. Human evaluations demonstrate that DG provides an 8-22\% advantage in preventing the amalgamation of conflicting concepts and ensuring that each object possesses its unique region without any human involvement and additional iterations. Our implementation is available at \url{https://github.com/luping-liu/Detector-Guidance}.