Advancing multi-subject text-to-image synthesis via Attention Refinement and Inheritance

Recent text-to-image diffusion models have shown significant advancements in high-quality image synthesis. Represented by Stable Diffusion, these models have been applied in various fields, such as art design, digital development, personalization, etc. However, they still fail to generate images that properly comply with the given text prompts, especially when encountering multiple subjects. Current approaches for multi-subject synthesis focus on latent code optimization at specific timesteps,yet yielding compromised text-image alignment. In this paper, we explore the generation of diffusion models through the lens of both noise initialization and denoising process, and propose an effective two-stage Attention Refinement and Inheritance (ARI) approach. Firstly, we update the initial noise by refining the cross-attention maps between different subject tokens, employing strategies of separation, energization, and centralization. Then, we introduce attention-level interpolation as an inheritancemechanism to explicitly regulate the denoising process. This enables the image generation to maintain high correspondence with the refined initial attention, thereby achieving a more controllable synthesis. Extensive experiments show that our approach achieves more faithful alignment between generated images and text prompts compared with state-of-the-art multi-subject image synthesis methods.