siliconflow/ComfyUI-See-through

siliconflow/ComfyUI-See-through

Description: 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.

Language: Jupyter Notebook

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Forks: 0

Open issues: 0

Created: 2026-04-16T07:44:48Z

Pushed: 2026-04-16T08:44:29Z

Default branch: master

Fork: yes

Parent repository: jtydhr88/ComfyUI-See-through

Archived: no

README:

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](README_CN.md)

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
• VRAM Optimization — Tag embedding caching, text encoder unloading, group offload, and configurable depth resolution for low-VRAM GPUs

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 | | cache_tag_embeds | true | Pre-compute and cache tag embeddings, then unload text encoders to save VRAM | | group_offload | false | Enable group offload to drastically reduce peak VRAM (allocated ~0.2GB, reserved ~7GB) at cost of 2–3x slower speed. Requires diffusers>=0.37.0 | | resolution_depth | -1 | Resolution for depth inference. -1 uses the same as layers. Lower values (e.g. 720) save VRAM and speed up depth estimation |

VRAM Optimization Guide

For most users (12GB+ VRAM): The default settings work well. cache_tag_embeds=true is already enabled and saves ~2GB VRAM with zero speed impact. No other changes needed.

For low-VRAM users (8–12 GB): Try the following settings in order, from least to most impact on speed:

1. `cache_tag_embeds=true` (default, already enabled) — Caches text embeddings and unloads text encoders, saving ~2GB VRAM with no speed penalty 2. `resolution_depth=720` — Run depth estimation at a lower resolution, then upscale back. Saves VRAM with minimal quality loss 3. Lower `resolution` — E.g. 1024 instead of 1280, reduces both VRAM and computation 4. `group_offload=true` — Last resort. Moves individual model blocks on/off GPU as needed, reducing peak allocated VRAM to ~0.2GB but 2–3x slower due to frequent CPU↔GPU transfers. Requires pip install diffusers>=0.37.0

Benchmark (RTX 5090, steps=30, cache_tag_embeds=true)

`group_offload` ON vs OFF (resolution=1280):

| Stage | group_offload=OFF | group_offload=ON | |-------|-------------------|------------------| | UNet+VAE loaded | 7.94 GB | 0.21 GB | | LayerDiff peak (allocated / reserved) | 7.95 GB / 13.69 GB | 0.21 GB / 7.31 GB | | Marigold peak | 2.49 GB | 0.07 GB | | Total time | 138 s | 385 s (2.8x slower) |

Resolution scaling (group_offload=OFF):

| Resolution | LayerDiff peak (allocated / reserved) | Marigold peak | Total time | Min VRAM |…