We introduce StableMaterials, a novel approach for generating photorealistic physical-based rendering (PBR) materials that integrate semi-supervised learning with Latent Diffusion Models (LDMs). Our method employs adversarial training to distill knowledge from existing large-scale image generation models, minimizing the reliance on annotated data and enhancing the diversity in generation. This distillation approach aligns the distribution of the generated materials with that of image textures from an SDXL model, enabling the generation of novel materials that are not present in the initial training dataset. Furthermore, we employ a diffusion-based refiner model to improve the visual quality of the samples and achieve high-resolution generation. Finally, we distill a latent consistency model for fast generation in just four steps and propose a new tileability technique that removes visual artifacts typically associated with fewer diffusion steps. We detail the architecture and training process of StableMaterials, the integration of semi-supervised training within existing LDM frameworks and show the advantages of our approach. Comparative evaluations with state-of-the-art methods show the effectiveness of StableMaterials, highlighting its potential applications in computer graphics and beyond. StableMaterials is publicly available at https://gvecchio.com/stablematerials.

翻译：本文提出StableMaterials，一种生成基于物理渲染（PBR）的真实感材质的新方法，该方法将半监督学习与潜在扩散模型（LDMs）相结合。我们的方法采用对抗训练从现有大规模图像生成模型中提取知识，从而减少对标注数据的依赖并增强生成多样性。这种知识蒸馏方法使生成材质的分布与SDXL模型的图像纹理分布对齐，能够生成初始训练数据集中不存在的新颖材质。此外，我们采用基于扩散的细化模型来提升样本的视觉质量，实现高分辨率生成。最后，我们蒸馏出潜在一致性模型以实现仅需四步的快速生成，并提出一种新的可平铺技术以消除通常因较少扩散步数而产生的视觉伪影。我们详细阐述了StableMaterials的架构与训练流程、半监督训练在现有LDM框架中的集成方式，并展示了本方法的优势。与前沿方法的对比评估证明了StableMaterials的有效性，凸显了其在计算机图形学等领域的潜在应用价值。StableMaterials已在https://gvecchio.com/stablematerials公开提供。