SDMatte 精细抠图

Every matting thread on r/comfyui eventually gets the same advice: for hair, fur, glass, and smoke, a plain background remover isn't enough - you need a matting model. RuiSDMatte is RUI-Nodes' implementation of SDMatte (vivo Camera Research, ICCV 2025), and it's the pack's flagship: you feed it a rough mask that roughly covers your subject, and it refines that mask down to a high-quality fractional alpha, right down to the flyaway strands and transparent glass edges that make segmentation models give up.

The mental model is important: this node doesn't find the subject, it polishes the edge. Your rough mask can come from anywhere - SAM, BiRefNet, rembg, a hand-drawn scribble. SDMatte's whole job is taking that sloppy silhouette and turning it into a precise cutout. The KB's background-removal essay covers why that distinction (segmentation vs. matting) is the entire game when it comes to hard edges.

How it works

Wire up the model object from RuiSDMatteLoader, your image, and a rough mask, and the node builds the visual prompt from that mask, runs the diffusion-based matting network, and returns alpha (a MASK, fractional 0–1) plus cutout (the image composited onto the alpha). The settings that matter:

• prompt_type - how the mask is turned into a prompt. bbox_mask (default) is the one to use; it's the official test path and the most robust. mask uses the mask directly (good when you already have an accurate coarse segmentation). point_mask samples 10 points inside the mask, and auto_mask uses no localization at all (single-subject images only).
• is_transparent - turn this on for glass, gauze, smoke, anything translucent. The author measured it changing output on ~25% of pixels for a glass subject. Forget it and you'll get a solid-looking glass that's wrong.
• inference_size - default 1024, matching the official config. Dropping to 512 cost the biggest quality hit of any setting in the author's own tests, so leave it at 1024 unless VRAM forces your hand.
• caption - an English description of the subject. Here's the sharp edge: it only works with the non-plus SDMatte.pth. The plus weights were trained with COCO-Matte instead of RefMatte and never learned text grounding - a caption on plus weights is at best inert, at worst slightly worse. Leave it blank with SDMatte_plus; only bother with it if you swapped in the original SDMatte.pth.
• point_radius / seed - only active for point_mask mode (and point_mask itself only works on SDMatte.pth).

Installing it

Install the pack (ComfyUI Manager → search "RUI-Nodes", or git clone + pip install -r requirements.txt), make sure diffusers, transformers, safetensors, scipy, and opencv-python are installed, drop a checkpoint into ComfyUI/models/SDMatte/, and restart. You do not need SD 2.1 weights - the config ships with the node.

Where people get burned

Three things, all grounded in real reports. First, the model-detection wall: this is the most common SDMatte failure in the wild - the node won't see your downloaded weights. Weights must be in models/SDMatte/, and the diffusers/transformers deps must be installed or the node never loads. Second, black output on point_mask with the plus weights - the node prints a warning when the alpha maxes near 0.1, which is your cue to switch to bbox_mask or the original SDMatte.pth. Third, the transformers 5.x trap: newer versions silently drop the text-encoder keys in other implementations; this node patches that automatically. Keep inference_size at 1024, keep is_transparent on for translucent subjects, and let the rough mask be rough - precision is the model's job, not yours.