Create README.md

README.md

@@ -0,0 +1,39 @@
+---
+license: mit
+pipeline_tag: image-to-image
+library_name: diffusers
+base_model:
+- stabilityai/stable-diffusion-2
+---
+
+# SDMatte - SafeTensors Models for Interactive Matting
+
+This repository contains SafeTensors versions of the SDMatte models for interactive image matting, optimized for ComfyUI usage.
+
+## About SDMatte
+
+**SDMatte: Grafting Diffusion Models for Interactive Matting**
+
+SDMatte is a state-of-the-art diffusion-driven interactive matting model that leverages the powerful priors of diffusion models to achieve exceptional performance in extracting fine-grained details, especially in edge regions.
+
+### Key Features
+- **Diffusion-powered**: Utilizes diffusion model priors for superior detail extraction
+- **Interactive matting**: Visual prompt-driven interaction for precise control
+- **Fine-grained details**: Excels at capturing complex edge regions and texture details
+- **Coordinate & opacity awareness**: Enhanced spatial and opacity information processing
+
+## Available Models
+
+- **SDMatte.safetensors** - Standard interactive matting model
+- **SDMatte_plus.safetensors** - Enhanced version with improved performance
+
+## Credits and Attribution
+
+### Original Work
+**Authors**: vivoCameraResearch Team  
+**Original Repository**: https://huggingface.co/LongfeiHuang/SDMatte  
+**Official Code**: https://github.com/vivoCameraResearch/SDMatte  
+**Paper**: SDMatte: Grafting Diffusion Models for Interactive Matting
+
+### Abstract
+*Recent interactive matting methods have shown satisfactory performance in capturing the primary regions of objects, but they fall short in extracting fine-grained details in edge regions. Diffusion models trained on billions of image-text pairs, demonstrate exceptional capability in modeling highly complex data distributions and synthesizing realistic texture details, while exhibiting robust text-driven interaction capabilities, making them an attractive solution for interactive matting.*