Update README.md

README.md

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 # SDMatte - SafeTensors Models for Interactive Matting
 
-This repository contains SafeTensors versions of the SDMatte models for interactive image matting, optimized for ComfyUI usage.
+This repository provides **SafeTensors** versions of the SDMatte models for **interactive image matting**, optimized for seamless use with **ComfyUI**.
 
-## About SDMatte
+---
+
+## 🔍 About SDMatte
+
+**SDMatte: Grafting Diffusion Models for Interactive Matting** is a state-of-the-art model that leverages the power of **diffusion priors** to achieve high-precision matting — especially around fine details and complex edges.
 
-**SDMatte: Grafting Diffusion Models for Interactive Matting**
+### ✨ Key Features
 
-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.
+- **Diffusion-Powered**: Uses strong priors from diffusion models to extract high-fidelity details  
+- **Interactive Matting**: Visual prompt-driven control for intuitive editing  
+- **Edge & Texture Focus**: Excels in handling challenging edge regions and fine textures  
+- **Coordinate & Opacity Awareness**: Improves matting accuracy with spatial and opacity context  
+
+---
+
+## 📦 Available Models
+
+- `SDMatte.safetensors` – Standard version for interactive matting  
+- `SDMatte_plus.safetensors` – Enhanced version with improved performance  
+
+---
 
-### 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
+## 🧩 Built for ComfyUI: `ComfyUI-RMBG`
 
-## Available Models
+These models are designed for use with our **ComfyUI custom node**:  
+➡️ [ComfyUI-RMBG on GitHub](https://github.com/1038lab/ComfyUI-RMBG)
 
-- **SDMatte.safetensors** - Standard interactive matting model
-- **SDMatte_plus.safetensors** - Enhanced version with improved performance
+This custom node integrates SDMatte into ComfyUI workflows, enabling high-quality interactive matting inside a visual pipeline.
 
-## Credits and Attribution
+### 🔄 Latest Update  
+**Version:** `v2.9.0`  
+**Date:** `2025-08-18`  
+📄 [Read the update changelog](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v290-20250818)
 
-### 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
+---
+
+## 🙌 Credits and Attribution
+
+### 📚 Original Work
+
+- **Authors**: vivoCameraResearch Team  
+- **Model Repository**: [Hugging Face – LongfeiHuang/SDMatte](https://huggingface.co/LongfeiHuang/SDMatte)  
+- **Official Code**: [GitHub – vivoCameraResearch/SDMatte](https://github.com/vivoCameraResearch/SDMatte)  
+- **Paper**: *SDMatte: Grafting Diffusion Models for Interactive Matting*
+
+### 📝 Abstract (from the original paper)
+
+> 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.
+
+---
 
-### 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.*