Position-LoRA: Enhanced Relation Customization through Structural Prior in Initial Latent Noise

Recent advancements in concept customization via diffusion models have significantly enhanced controllability and quality. However, precise relation customization, which controls the position of interactions among multiple instances, remains challenging due to unpredictable initial latent noise. Existing methods primarily rely on conditional prompts and attention control, overlooking the structured potential of initial noise. This paper introduces Position-LoRA, a novel framework leveraging structural prior in initial noise to improve relation customization and layout control. Position-LoRA employs a differential fine-tuning scheme and a latent noise encoder. The guided fine-tuning enhances generation tendencies from structured initial noise, embedding explicit relationship-specific spatial information. The latent noise encoder dynamically manipulates latent noises, enabling precise spatial control and flexibility in relational image generation. Furthermore, a fine-grained guidance and control strategy is employed during generation to enhance the image-text alignment and layout alignment. Experiments demonstrate that Position-LoRA improves stability, controllability, and fidelity in relational image generation with layout control, surpassing existing concept customization and layout-to-image methods in qualitative and quantitative evaluations. Code is available at https://github.com/liyiming09/Position-LoRA.