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--- |
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license: gpl-3.0 |
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pipeline_tag: image-to-image |
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library_name: diffusers |
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base_model: |
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- stabilityai/stable-diffusion-2 |
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--- |
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# SDMatte - SafeTensors Models for Interactive Matting |
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This repository provides **SafeTensors** versions of the SDMatte models for **interactive image matting**, optimized for seamless use with **ComfyUI**. |
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--- |
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## 🔍 About SDMatte |
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**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. |
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### ✨ Key Features |
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- **Diffusion-Powered**: Uses strong priors from diffusion models to extract high-fidelity details |
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- **Interactive Matting**: Visual prompt-driven control for intuitive editing |
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- **Edge & Texture Focus**: Excels in handling challenging edge regions and fine textures |
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- **Coordinate & Opacity Awareness**: Improves matting accuracy with spatial and opacity context |
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## 📦 Available Models |
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- `SDMatte.safetensors` – Standard version for interactive matting |
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- `SDMatte_plus.safetensors` – Enhanced version with improved performance |
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## 🧩 Built for ComfyUI: `ComfyUI-RMBG` |
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These models are designed for use with our **ComfyUI custom node**: |
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➡️ [ComfyUI-RMBG on GitHub](https://github.com/1038lab/ComfyUI-RMBG) |
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This custom node integrates SDMatte into ComfyUI workflows, enabling high-quality interactive matting inside a visual pipeline. |
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### 🔄 Latest Update |
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**Version:** `v2.9.0` |
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**Date:** `2025-08-18` |
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📄 [Read the update changelog](https://github.com/1038lab/ComfyUI-RMBG/blob/main/update.md#v290-20250818) |
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--- |
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## 🙌 Credits and Attribution |
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### 📚 Original Work |
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- **Authors**: vivoCameraResearch Team |
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- **Model Repository**: [Hugging Face – LongfeiHuang/SDMatte](https://huggingface.co/LongfeiHuang/SDMatte) |
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- **Official Code**: [GitHub – vivoCameraResearch/SDMatte](https://github.com/vivoCameraResearch/SDMatte) |
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- **Paper**: *SDMatte: Grafting Diffusion Models for Interactive Matting* |
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### 📝 Abstract (from the original paper) |
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> 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. |
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