  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.
