ComfyUI's ControlNet Auxiliary Preprocessors

Plug-and-play ComfyUI node sets for making ControlNet hint images

"anime style, a protest in the street, cyberpunk city, a woman with pink hair and golden eyes (looking at the viewer) is holding a sign with the text "ComfyUI ControlNet Aux" in bold, neon pink" on Flux.1 Dev

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.

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 Soft-Edge 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 Soft-Edge 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 | | TEED Soft-Edge Lines | teed | controlnet-sd-xl-1.0-softedge-dexined

control_v11p_sd15_softedge (Theoretically) | Scribble PiDiNet Lines | scribble_pidinet | control_v11p_sd15_scribble

control_scribble | | AnyLine Lineart | | mistoLine_fp16.safetensors

mistoLine_rank256

control_v11p_sd15s2_lineart_anime

control_v11p_sd15_lineart |

Normal and Depth Estimators

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 | | MeshGraphormer Hand Refiner (HandRefinder) | depth_hand_refiner | control_sd15_inpaint_depth_hand_fp16 | | Depth Anything | depth_anything | Depth-Anything | | Zoe Depth Anything

(Basically Zoe but the encoder is replaced with DepthAnything) | depth_anything | Depth-Anything | | Normal DSINE | | control_normal/control_v11p_sd15_normalbae | | Metric3D Depth | | control_v11f1p_sd15_depth

control_depth

t2iadapter_depth | | Metric3D Normal | | control_v11p_sd15_normalbae | | Depth Anything V2 | | Depth-Anything |

Faces and Poses Estimators

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
DWPose Estimator	dw_openpose_full	control_v11p_sd15_openpose

control_openpose

t2iadapter_openpose | | OpenPose Estimator | 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 Estimator | animal_openpose | control_sd15_animal_openpose_fp16 |

Optical Flow Estimators

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
Unimatch Optical Flow		DragNUWA

How to get OpenPose-format JSON?

User-side

This workflow will save images to ComfyUI's output folder (the same location as output images). If you haven't found Save Pose Keypoints node, update this extension

Dev-side

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

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
OneFormer ADE20K Segmentor	oneformer_ade20k	control_v11p_sd15_seg
OneFormer COCO Segmentor	oneformer_coco	control_v11p_sd15_seg
UniFormer Segmentor	segmentation	control_sd15_seg

control_v11p_sd15_seg|

T2IAdapter-only

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
Color Pallete	color	t2iadapter_color
Content Shuffle	shuffle	t2iadapter_style

Recolor

Preprocessor Node	sd-webui-controlnet/other	ControlNet/T2I-Adapter
Image Luminance	recolor_luminance	ioclab_sd15_recolor

sai_xl_recolor_256lora

bdsqlsz_controlllite_xl_recolor_luminance | | Image Intensity | recolor_intensity | Idk. Maybe same as above? |

Examples

A picture is worth a thousand words

Testing workflow

https://github.com/Fannovel16/comfyui_controlnet_aux/blob/main/examples/ExecuteAll.png Input image: https://github.com/Fannovel16/comfyui_controlnet_aux/blob/main/examples/comfyui-controlnet-aux-logo.png

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:
2. • NVidia CUDA 11.x or bellow/AMD GPU: onnxruntime-gpu
3. • NVidia CUDA 12.x: `onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-