Recent advances in diffusion models have significantly improved the synthesis of materials, textures, and 3D shapes. By conditioning these models via text or images, users can guide the generation, reducing the time required to create digital assets. In this paper, we address the synthesis of structured, stationary patterns, where diffusion models are generally less reliable and, more importantly, less controllable. Our approach leverages the generative capabilities of diffusion models specifically adapted for the pattern domain. It enables users to exercise direct control over the synthesis by expanding a partially hand-drawn pattern into a larger design while preserving the structure and details of the input. To enhance pattern quality, we fine-tune an image-pretrained diffusion model on structured patterns using Low-Rank Adaptation (LoRA), apply a noise rolling technique to ensure tileability, and utilize a patch-based approach to facilitate the generation of large-scale assets. We demonstrate the effectiveness of our method through a comprehensive set of experiments, showing that it outperforms existing models in generating diverse, consistent patterns that respond directly to user input.

本研究解决了扩散模型在合成结构化静态图案时的控制性不足问题，通过专门调整扩散模型生成能力，使用户能够直接控制部分手绘图案扩展为更大设计，同时保留原有结构和细节。实验结果表明，该方法在生成多样化、一致性的图案方面优于现有模型，具有重要影响。

基于扩散模型的结构化图案扩展