Update README.md

README.md

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 ---
 title: StableMaterials
 emoji: 🧱
+thumbnail: https://gvecchio.com/stablematerials/static/images/teaser.jpg
 colorFrom: blue
 colorTo: blue
 sdk: gradio
@@ -10,4 +11,45 @@ pinned: false
 license: openrail
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# StableMaterials
+
+**StableMaterials** is a diffusion-based model designed for generating photorealistic physical-based rendering (PBR) materials. This model integrates semi-supervised learning with Latent Diffusion Models (LDMs) to produce high-resolution, tileable material maps from text or image prompts. StableMaterials can infer both diffuse (Basecolor) and specular (Roughness, Metallic) properties, as well as the material mesostructure (Height, Normal). 🌟
+
+For more details, visit the [project page](https://gvecchio.com/stablematerials/) or read the full paper on [arXiv](https://arxiv.org/abs/2406.09293).
+
+<center>
+    <img src="https://gvecchio.com/stablematerials/static/images/teaser.jpg" style="border-radius:10px;">
+</center>
+
+## Model Architecture
+
+<center>
+    <img src="https://gvecchio.com/stablematerials/static/images/architecture.png" style="border-radius:10px;">
+</center>
+
+### 🧩 Base Model 
+The base model generates low-resolution (512x512) material maps using a compression VAE (Variational Autoencoder) followed by a latent diffusion process. The architecture is based on the MatFuse adaptation of the LDM paradigm, optimized for material map generation with a focus on diversity and high visual fidelity. 🖼️
+
+### 🔑 Key Features
+- **Semi-Supervised Learning**: The model is trained using both annotated and unannotated data, leveraging adversarial training to distill knowledge from large-scale pretrained image generation models. 📚
+- **Knowledge Distillation**: Incorporates unannotated texture samples generated using the SDXL model into the training process, bridging the gap between different data distributions. 🌐
+- **Latent Consistency**: Employs a latent consistency model to facilitate fast generation, reducing the inference steps required to produce high-quality outputs. ⚡
+- **Feature Rolling**: Introduces a novel tileability technique by rolling feature maps for each convolutional and attention layer in the U-Net architecture. 🎢
+
+## Intended Use
+
+StableMaterials is designed for generating high-quality, realistic PBR materials for applications in computer graphics, such as video game development, architectural visualization, and digital content creation. The model supports both text and image-based prompting, allowing for versatile and intuitive material generation. 🕹️🏛️📸
+
+
+## 📖 Citation 
+
+If you use this model in your research, please cite the following paper:
+
+```
+@article{vecchio2024stablematerials,
+  title={StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning},
+  author={Vecchio, Giuseppe},
+  journal={arXiv preprint arXiv:2406.09293},
+  year={2024}
+}
+```