Enhancing Image Layout Control with Loss-Guided Diffusion Models

TL;DR

This paper explores how combining attention map modification and loss-guided guidance in diffusion models enhances image layout control without fine-tuning, leading to improved spatial constraint application.

Contribution

It provides an interpretation of two attention-based spatial control methods and demonstrates their combined effectiveness in diffusion models.

Findings

Combined methods outperform individual approaches in spatial control.

Interpretation reveals complementary features of the two methods.

Training-free techniques achieve better image layout precision.

Abstract

Diffusion models are a powerful class of generative models capable of producing high-quality images from pure noise using a simple text prompt. While most methods which introduce additional spatial constraints into the generated images (e.g., bounding boxes) require fine-tuning, a smaller and more recent subset of these methods take advantage of the models' attention mechanism, and are training-free. These methods generally fall into one of two categories. The first entails modifying the cross-attention maps of specific tokens directly to enhance the signal in certain regions of the image. The second works by defining a loss function over the cross-attention maps, and using the gradient of this loss to guide the latent. While previous work explores these as alternative strategies, we provide an interpretation for these methods which highlights their complimentary features, and demonstrate that it is possible to obtain superior performance when both methods are used in concert.