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ComfyUI-See-through

A ComfyUI plugin that wraps See-through — an AI system that decomposes a single anime illustration into manipulatable 2.5D layer-decomposed models with depth ordering, ready for Live2D workflows.

中文说明

Paper: arxiv:2602.03749 (Conditionally accepted to ACM SIGGRAPH 2026)

Features

  • Single-Image Layer Decomposition — Input one anime character image, get up to 24 semantic transparent layers (hair, face, eyes, clothing, accessories, etc.)
  • Depth Estimation — Automatic depth map generation for each layer via fine-tuned Marigold, establishing correct drawing order
  • Smart Splitting — Eyes, ears, handwear split into left/right; hair split into front/back via depth clustering
  • PSD Export — Download layered PSD files directly from the browser (frontend ag-psd, no Python dependency)
  • Depth PSD — Separate depth PSD export for 3D/parallax workflows
  • Preview Output — Blended reconstruction preview as a standard ComfyUI IMAGE output
  • HuggingFace Auto-Download — Models download automatically from HuggingFace on first use

Nodes

Node Description
SeeThrough Load LayerDiff Model Load the LayerDiff SDXL pipeline (layer generation)
SeeThrough Load Depth Model Load the Marigold depth estimation pipeline
SeeThrough Decompose Full pipeline: LayerDiff + Marigold depth + post-processing
SeeThrough Save PSD Save layers as PNGs + metadata; download PSD via browser button

Installation

Clone this repository into your ComfyUI custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/jtydhr88/ComfyUI-See-through.git

Install dependencies:

cd ComfyUI-See-through
pip install -r requirements.txt

Restart ComfyUI. The SeeThrough nodes will appear under the SeeThrough category.

Dependencies

Only 4 additional Python packages beyond ComfyUI's base:

  • diffusers — Hugging Face diffusion pipeline
  • accelerate — Model loading acceleration
  • opencv-python — Image processing
  • scikit-learn — KMeans clustering for depth-based layer splitting

Models

Models are downloaded automatically from HuggingFace on first use:

Model HuggingFace Repo Purpose
LayerDiff 3D layerdifforg/seethroughv0.0.2_layerdiff3d SDXL-based transparent layer generation
Marigold Depth 24yearsold/seethroughv0.0.1_marigold Fine-tuned monocular depth for anime

Alternatively, download models manually and place them in ComfyUI/models/SeeThrough/.

Usage

Basic Workflow

  1. Add SeeThrough Load LayerDiff Model and SeeThrough Load Depth Model nodes
  2. Add a SeeThrough Decompose node — connect both models and a Load Image node
  3. Add SeeThrough Save PSD — connect the parts output
  4. Add Preview Image — connect the preview output
  5. Run the workflow
  6. Click Download PSD button on the Save PSD node to generate and download the PSD file

Example Workflows

Pre-made workflows are available in the workflows/ directory:

Workflow Resolution Steps L/R Split Description
seethrough-basic.json 1280 30 Yes Standard quality, recommended

Drag any .json file into ComfyUI to load the workflow.

Parameters

Parameter Default Description
seed 42 Random seed for reproducibility
resolution 1280 Processing resolution (image is center-padded to square)
num_inference_steps 30 Diffusion denoising steps (more = better quality, slower)
tblr_split true Split symmetric parts (eyes, ears, handwear) into left/right

Output Layers

The decomposition produces semantic layers including:

Body parts: front hair, back hair, neck, topwear, handwear, bottomwear, legwear, footwear, tail, wings, objects

Head parts: headwear, face, irides, eyebrow, eyewhite, eyelash, eyewear, ears, earwear, nose, mouth

Each layer is an RGBA image with transparency, positioned at its correct location in the canvas.

Credits

This plugin wraps the See-through research project by shitagaki-lab.

PSD generation uses ag-psd in the browser.

License

MIT