Ryuukeisyou / comfyui_controlnet_aux

ComfyUI's ControlNet Auxiliary Preprocessors

ComfyUI's ControlNet Auxiliary Preprocessors

This is a rework of comfyui_controlnet_preprocessors based on ControlNet auxiliary models by 🤗. I think the old repo isn't good enough to maintain.

YOU NEED TO REMOVE comfyui_controlnet_preprocessors BEFORE USING THIS REPO. THESE TWO CONFLICT WITH EACH OTHER.

All old workflows still can be used with custom nodes in this repo but the version option won't do anything. Almost all v1 preprocessors are replaced by v1.1 except those doesn't apppear in v1.1.

You don't need to care about the differences between v1 and v1.1 lol.

The code is copy-pasted from the respective folders in https://github.com/lllyasviel/ControlNet/tree/main/annotator and connected to the 🤗 Hub.

All credit & copyright goes to https://github.com/lllyasviel.

Marigold

NEW! Check out Marigold Depth Estimator which can generate very detailed and sharp depth map from high-resolution images. The mesh created by it is even 3D-printable. Due to diffusers, it can't be implemented in this extension but there is an Comfy implementation by Kijai https://github.com/kijai/ComfyUI-Marigold

Updates

Go to Update page to follow updates

Installation:

Using ComfyUI Manager (recommended):

Install ComfyUI Manager and do steps introduced there to install this repo.

Alternative:

If you're running on Linux, or non-admin account on windows you'll want to ensure /ComfyUI/custom_nodes and comfyui_controlnet_aux has write permissions.

There is now a install.bat you can run to install to portable if detected. Otherwise it will default to system and assume you followed ConfyUI's manual installation steps.

If you can't run install.bat (e.g. you are a Linux user). Open the CMD/Shell and do the following:

• Navigate to your /ComfyUI/custom_nodes/ folder
• Run git clone https://github.com/Fannovel16/comfyui_controlnet_aux/
• Navigate to your comfyui_controlnet_aux folder
  • Portable/venv:
    • Run path/to/ComfUI/python_embeded/python.exe -s -m pip install -r requirements.txt
  • With system python
    • Run pip install -r requirements.txt
• Start ComfyUI

Nodes

Please note that this repo only supports preprocessors making hint images (e.g. stickman, canny edge, etc). All preprocessors except Inpaint are intergrated into AIO Aux Preprocessor node. This node allow you to quickly get the preprocessor but a preprocessor's own threshold parameters won't be able to set. You need to use its node directly to set thresholds.

Nodes (sections are categories in Comfy menu)

Line Extractors

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
Binary Lines	binary	control_scribble
Canny Edge	canny	control_v11p_sd15_canny control_canny t2iadapter_canny
HED Lines	hed	control_v11p_sd15_softedge control_hed
Standard Lineart	standard_lineart	control_v11p_sd15_lineart
Realistic Lineart	lineart (or lineart_coarse if coarse is enabled)	control_v11p_sd15_lineart
Anime Lineart	lineart_anime	control_v11p_sd15s2_lineart_anime
Manga Lineart	lineart_anime_denoise	control_v11p_sd15s2_lineart_anime
M-LSD Lines	mlsd	control_v11p_sd15_mlsd control_mlsd
PiDiNet Lines	pidinet	control_v11p_sd15_softedge control_scribble
Scribble Lines	scribble	control_v11p_sd15_scribble control_scribble
Scribble XDoG Lines	scribble_xdog	control_v11p_sd15_scribble control_scribble
Fake Scribble Lines	scribble_hed	control_v11p_sd15_scribble control_scribble

Normal and Depth Map

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
MiDaS - Depth Map	(normal) depth	control_v11f1p_sd15_depth control_depth t2iadapter_depth
LeReS - Depth Map	depth_leres	control_v11f1p_sd15_depth control_depth t2iadapter_depth
Zoe - Depth Map	depth_zoe	control_v11f1p_sd15_depth control_depth t2iadapter_depth
MiDaS - Normal Map	normal_map	control_normal
BAE - Normal Map	normal_bae	control_v11p_sd15_normalbae

Faces and Poses

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
DWPose Pose Estimation	dw_openpose_full	control_v11p_sd15_openpose control_openpose t2iadapter_openpose
OpenPose Pose Estimation	openpose (detect_body) openpose_hand (detect_body + detect_hand) openpose_faceonly (detect_face) openpose_full (detect_hand + detect_body + detect_face)	control_v11p_sd15_openpose control_openpose t2iadapter_openpose
MediaPipe Face Mesh	mediapipe_face	controlnet_sd21_laion_face_v2
Animal Pose Estimation	animal_openpose	control_sd15_animal_openpose_fp16.pth

An array of OpenPose-format JSON corresponsding to each frame in an IMAGE batch can be gotten from DWPose and OpenPose using app.nodeOutputs on the UI or /history API endpoint. JSON output from AnimalPose uses a kinda similar format to OpenPose JSON:

[
    {
        "version": "ap10k",
        "animals": [
            [[x1, y1, 1], [x2, y2, 1],..., [x17, y17, 1]],
            [[x1, y1, 1], [x2, y2, 1],..., [x17, y17, 1]],
            ...
        ],
        "canvas_height": 512,
        "canvas_width": 768
    },
    ...
]

For extension developers (e.g. Openpose editor):

const poseNodes = app.graph._nodes.filter(node => ["OpenposePreprocessor", "DWPreprocessor", "AnimalPosePreprocessor"].includes(node.type))
for (const poseNode of poseNodes) {
    const openposeResults = JSON.parse(app.nodeOutputs[poseNode.id].openpose_json[0])
    console.log(openposeResults) //An array containing Openpose JSON for each frame
}

For API users: Javascript

import fetch from "node-fetch" //Remember to add "type": "module" to "package.json"
async function main() {
    const promptId = '792c1905-ecfe-41f4-8114-83e6a4a09a9f' //Too lazy to POST /queue
    let history = await fetch(`http://127.0.0.1:8188/history/${promptId}`).then(re => re.json())
    history = history[promptId]
    const nodeOutputs = Object.values(history.outputs).filter(output => output.openpose_json)
    for (const nodeOutput of nodeOutputs) {
        const openposeResults = JSON.parse(nodeOutput.openpose_json[0])
        console.log(openposeResults) //An array containing Openpose JSON for each frame
    }
}
main()

Python

import json, urllib.request

server_address = "127.0.0.1:8188"
prompt_id = '' #Too lazy to POST /queue

def get_history(prompt_id):
    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
        return json.loads(response.read())

history = get_history(prompt_id)[prompt_id]
for o in history['outputs']:
    for node_id in history['outputs']:
        node_output = history['outputs'][node_id]
        if 'openpose_json' in node_output:
            print(json.loads(node_output['openpose_json'][0])) #An list containing Openpose JSON for each frame

Semantic Segmentation

• OneFormer ADE20K Segmentor
• UniFormer Segmentor
• OneFormer COCO Segmentor

T2IAdapter-only

• Color Pallete
• Content Shuffle

Examples

A picture is worth a thousand words

Credit to https://huggingface.co/thibaud/controlnet-sd21. You can get the same kind of results from preprocessor nodes of this repo.

Line Extractors

Canny Edge

HED Lines

Realistic Lineart

Scribble/Fake Scribble

Normal and Depth Map

Depth (idk the preprocessor they use)

Zoe - Depth Map

BAE - Normal Map

Faces and Poses

OpenPose

Animal Pose (AP-10K)

DensePose

Semantic Segmantation

OneFormer ADE20K Segmentor

Anime Face Segmentor

T2IAdapter-only

Color Pallete for T2I-Adapter

Testing workflow

https://github.com/Fannovel16/comfyui_controlnet_aux/blob/master/tests/test_cn_aux_full.json

Q&A:

Why some nodes doesn't appear after I installed this repo?

This repo has a new mechanism which will skip any custom node can't be imported. If you meet this case, please create a issue on Issues tab with the log from the command line.

DWPose/AnimalPose only uses CPU so it's so slow. How can I make it use GPU?

There are two ways to speed-up DWPose: using TorchScript checkpoints (.torchscript.pt) checkpoints or ONNXRuntime (.onnx). TorchScript way is little bit slower than ONNXRuntime but doesn't require any additional library and still way way faster than CPU.

A torchscript bbox detector is compatiable with an onnx pose estimator and vice versa.

TorchScript

Set bbox_detector and pose_estimator according to this picture. You can try other bbox detector endings with .torchscript.pt to reduce bbox detection time if input images are ideal.

ONNXRuntime

If onnxruntime is installed successfully and the checkpoint used endings with .onnx, it will replace default cv2 backend to take advantage of GPU. Note that if you are using NVidia card, this method currently can only works on CUDA 11.8 (ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z) unless you compile onnxruntime yourself.

1. Know your onnxruntime build:

• • NVidia/AMD GPU: onnxruntime-gpu
• • DirectML: onnxruntime-directml
• • OpenVINO: onnxruntime-openvino

Note that if this is your first time using ComfyUI, please test if it can run on your device before doing next steps.

2. Add it into requirements.txt

3. Run install.bat or pip command mentioned in Installation