摘要：传统的巴塔克（Batak）乌罗斯（Ulos）织锦产业面临着日益严峻的挑战，受限于传统的手工驱动设计方式，难以产出多样化且创新的纹样。本研究提出了一个多模态生成框架，将微调后的潜空间扩散模型（通过 LoRA 微调的 Stable Diffusion XL v1.0）与多模态大语言模型（LLaMA 1.5-7B）相结合，以实现可控且忠实于传统文化的乌罗斯纹样生成。文本、图像、表征以及语义映射（通过 ControlNet）四种互补的条件机制共同指导生成过程，各自控制从语义意图到空间布局的不同方面。在三种场景（形状变换、色彩变化和高复杂度输入）下的五级消融实验表明，条件控制的有效性与组合机制的数量并不成正比：“文本 + 图像 + 语义映射”获得了最佳的 FID（270）和 CLIP 分数（0.65 - 0.70），但 SSIM 最弱（0.65）；而“文本 + 图像 + 表征”在保持稳定 SSIM（0.84）和具备竞争力的 FID（280）之间取得了最佳综合平衡。结合所有四种机制反而产生了最弱的 FID（330），表明存在相互冲突的优化信号。九位织工与三十位公众参与者的定性评估证实了统计学上显著的正面认可度（Wilcoxon 检验，分别为 p=0.007 和 p<0.001）。研究还开发了支持文生图和图生图的 Web 端原型，为文化遗产保护提供了实用的数字化设计工具。

Abstract:The traditional Batak Ulos weaving industry faces growing challenges in producing diverse, innovative motifs due to limitations in conventional, manually driven design methods. This study proposes a multimodal generative framework integrating a fine-tuned Latent Diffusion Model (Stable Diffusion XL v1.0 via LoRA) with a Multimodal Large Language Model (LLaMA 1.5-7B) to enable controllable, culturally faithful Ulos motif generation. Four complementary conditioning mechanisms: text, image, representation, and semantic map (via ControlNet) jointly guide the generation process, each governing a distinct aspect from semantic intent to spatial layout. A five level ablation study across three scenarios (shape transformation, colour variation, and high-complexity input) shows that conditioning effectiveness is not proportional to the number of mechanisms combined: Text + Image + Semantic Map achieved the best FID (270) and CLIP Score (0.65 - 0.70) but the weakest SSIM (0.65), while Text + Image + Representation offered the best overall balance, with stable SSIM (0.84) and competitive FID (280). Combining all four mechanisms yielded the weakest FID (330), indicating conflicting optimization signals. Qualitative evaluation by nine weavers and thirty public participants confirmed statistically significant positive acceptance (Wilcoxon, p=0.007 and p<0.001, respectively). A web-based prototype supporting text-to-image and image-to-image generation was also developed, offering a practical digital design tool for cultural heritage preservation.