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ComfyUI LayerDivider

ComfyUI LayerDivider is custom nodes that generating layered psd files inside ComfyUI, original implement is mattyamonaca/layerdivider

https://github.com/jtydhr88/ComfyUI-LayerDivider/assets/860985/3ceb0638-1ed7-4e01-b231-03c4408c95e3

Environment

I tested the following environment, it might work on other environment, but I don’t test:

Common

Windows 10/Ubuntu

GTX 3090

Cuda 12.1

Env 1 – see Method 1

ComfyUI embedded python (python 3.11) and ComfyUI Manager

Env 2 – see Method 2

conda

Python 3.11

Env 3 – see Method 3

conda

Python 3.11

Env 4 – see Method 4

Ubuntu

conda/Python 3.11

cuda 12.1

(Common) Installation – CUDA & cuDNN

This repo requires specific versions of CUDA and cuDNN to be installed locally:

For CUDA, I only install and test CUDA 12.1, you can find it from https://developer.nvidia.com/cuda-12-1-0-download-archive

For cuDNN, it MUST be v8.9.2 – CUDA 12.x (according to https://github.com/mdboom/pytoshop/issues/9), you can find it from https://developer.nvidia.com/rdp/cudnn-archive

After install and unzip, make sure you configure the PATH of your system variable

(Common) Installation – Visual Studio Build Tools

It might also require Visual Studio Build Tools.

However, I am not sure because my local already installed previously.

If it needs, you can find from Visual Studio Build Tools.

(Method 1) Installation – ComfyUI Embedded Plugin & ComfyUI Manager

You could clone this repo inside comfyUI/custom_notes directly git clone https://github.com/jtydhr88/ComfyUI-LayerDivider.git

or use ComfyUI Manager

However, no matter which way you choose, it will fail at first time

Stop ComfyUI

Then go to custom_nodes\ComfyUI-LayerDivider, and run install_windows_portable_win_py311_cu121.bat

Done!

(If you prefer to use conda and python 3.10, you could follow the next)

(Method 2) Installation – ComfyUI

You could use conda to manage and create the ComfyUI runtime environment:

use cmd/terminal to enter the comfyui root folder (which includes run_cpu.bat and run_nvidia_gpu.bat)

conda create --name comfy-py-310 python=3.10

conda activate comfy-py-310

pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu121

pip install -r ComfyUI\requirements.txt

Then you can run python -s ComfyUI\main.py --windows-standalone-build to check ComfyUI running properly.

(Method 2) Installation – ComfyUI LayerDivider

Then we can clone and configure this repo for ComfyUI:

cd ComfyUI\custom_nodes

pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/

pip install Cython

pip install pytoshop -I --no-cache-dir

pip install psd-tools --no-deps

git clone https://github.com/jtydhr88/ComfyUI-LayerDivider.git

cd ComfyUI-LayerDivider

pip install -r requirements.txt

Congratulation! You complete all installation!

(Method 3) Installation – ComfyUI LayerDivider

Assume you already have a conda python3.11 env

activate your env

go into this folder and run install_conda_win_py311_cu121.bat

Congratulation! You complete all installation!

(Method 4) Ubuntu Installation – ComfyUI LayerDivider

Assume you already have a python3.11 env + cuda 12.1

clone this repo inside custom_nodes folder

cd ComfyUI-LayerDivider/

pip install -r requirements.txt

Then make sure run them one by one:

pip install cython

pip install pytoshop -I –no-cache-dir

pip install psd_tools

pip install onnxruntime-gpu==1.17.0 –extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/

Congratulation! You complete all installation!

Node Introduction

Currently, this extension includes two modes with four custom nodes, plus two layer modes(normal and composite) for each mode:

Mode

There are two main layered segmentation modes:

Color Base – Layers based on similar colors, with parameters:

loops

init_cluster

ciede_threshold

blur_size

Segment Mask – First, the image is divided into segments using SAM – segment anything to generate corresponding masks, then layers are created based on these masks.

Load SAM Mask Generator, with parameters (These come from segment anything, please refer to here for more details):

pred_iou_thresh

stability_score_thresh

min_mask_region_area

LayerDivider – Segment Mask, with parameters:

area_th: determines the number of partitions. The smaller the value, the more partitions there will be; the larger the value, the fewer partitions there will be.

Layer Mode

Using in Divide Layer node to decide the layer mode:

normal – Generates three layers for each region:

base – The base layer is the starting point for image processing

bright – The bright layer focuses on the brightest parts of the image, enhancing the brightness and gloss of these areas

shadow – The shadow layer deals with the darker parts of the image, emphasizing the details of shadows and dark areas.

composite – Generates five layers for each region:

base – The base layer is the starting point of the image

screen – The screen layer simulates the effect of light overlay. It multiplies the color values of the image with the color values of the layer above it and then inverts the result, producing a brighter effect than the original image

multiply – The multiply layer simulates the effect of multiple images being overlaid. It directly multiplies the color values of the image with the color values of the layer above it, resulting in a darker effect than the original image.

subtract – The subtract layer subtracts the color values of the layer above from the base image, resulting in an image with lower color values.

addition – The addition layer adds the color values of the layer above to the base image, resulting in an image with higher color values.

Example workflows

Here are two workflows for reference:

layerdivider-color-base.json

layerdivider-seg-mask.json

Example outputs

output_color_base_composite.psd

output_color_base_normal.psd

output_seg_mask_composite.psd

output_seg_mask_normal.psd

Known issues

Sometimes, composite mode will fail on some images, such as ComfyUI example image, still under invesgating the cause

Credit & Thanks

mattyamonaca/layerdivider – Original implement

ComfyUI – A powerful and modular stable diffusion GUI.

My extensions for ComfyUI

ComfyUI-Unique3D – ComfyUI Unique3D is custom nodes that running Unique3D into ComfyUI

ComfyUI-LayerDivider – ComfyUI InstantMesh is custom nodes that generating layered psd files inside ComfyUI

ComfyUI-InstantMesh – ComfyUI InstantMesh is custom nodes that running InstantMesh into ComfyUI

ComfyUI-ImageMagick – This extension implements custom nodes that integreated ImageMagick into ComfyUI

ComfyUI-Workflow-Encrypt – Encrypt your comfyui workflow with key

My extensions for stable diffusion webui

3D Model/pose loader A custom extension for AUTOMATIC1111/stable-diffusion-webui that allows you to load your local 3D model/animation inside webui, or edit pose as well, then send screenshot to txt2img or img2img as your ControlNet’s reference image.

Canvas Editor A custom extension for AUTOMATIC1111/stable-diffusion-webui that integrated a full capability canvas editor which you can use layer, text, image, elements and so on, then send to ControlNet, basing on Polotno.

StableStudio Adapter A custom extension for AUTOMATIC1111/stable-diffusion-webui to extend rest APIs to do some local operations, using in StableStudio.

Txt/Img to 3D Model A custom extension for sd-webui that allow you to generate 3D model from txt or image, basing on OpenAI Shap-E.

3D Editor A custom extension for sd-webui that with 3D modeling features (add/edit basic elements, load your custom model, modify scene and so on), then send screenshot to txt2img or img2img as your ControlNet’s reference image, basing on ThreeJS editor.