Controllable generation of Thangka artifact elements based on cross-domain feature alignment and high-order interaction mechanisms

Thangka artifact elements contain strict structures, intricate ornamentation, and rich cultural symbolism, posing challenges for controllable image generation. Existing text-to-image models often produce structural distortions, blurred details, and cultural semantic deviations. To address these issues, we propose a controllable diffusion-based method integrating cross-domain feature alignment and high-order interaction mechanisms. Built on a latent diffusion framework, the method introduces Frequency-Domain Cross-Normalization to align control and denoising features and Hierarchical High-Order Interaction Control to enhance long-range structural modeling and fine-grained ornamentation representation. We also construct TRA-15, a multimodal dataset of Thangka artifact elements. Experiments on TRA-15 demonstrate improved generation quality, structural accuracy, and ornamental detail preservation, while subjective evaluations indicate better controllability and visual-cultural consistency. Results on COCO further show moderate generalization ability.