This repository provides SafeTensors versions of the SDMatte models for interactive image matting, optimized for seamless use with ComfyUI.
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 β especially around fine details and complex edges.
SDMatte.safetensors β Standard version for interactive mattingSDMatte_plus.safetensors β Enhanced version with improved performanceComfyUI-RMBGThese models are designed for use with our ComfyUI custom node:
β‘οΈ ComfyUI-RMBG on GitHub
This custom node integrates SDMatte into ComfyUI workflows, enabling high-quality interactive matting inside a visual pipeline.
Version: v2.9.0
Date: 2025-08-18
π Read the update changelog
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 β making them an attractive solution for interactive matting.
7 commits
This repository provides SafeTensors versions of the SDMatte models for interactive image matting, optimized for seamless use with ComfyUI.
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 β especially around fine details and complex edges.
SDMatte.safetensors β Standard version for interactive mattingSDMatte_plus.safetensors β Enhanced version with improved performanceComfyUI-RMBGThese models are designed for use with our ComfyUI custom node:
β‘οΈ ComfyUI-RMBG on GitHub
This custom node integrates SDMatte into ComfyUI workflows, enabling high-quality interactive matting inside a visual pipeline.
Version: v2.9.0
Date: 2025-08-18
π Read the update changelog
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 β making them an attractive solution for interactive matting.
7 commits