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Ly820359009/
comfyui_controlnet_aux

Name: comfyui_controlnet_aux
Availability: InStock

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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.

Know your onnxruntime build:

- NVidia CUDA 11.x or bellow/AMD GPU: onnxruntime-gpu
- NVidia CUDA 12.x: onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
- 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.

Add it into requirements.txt
Run install.bat or pip command mentioned in Installation

Assets files of preprocessors

anime_face_segment: bdsqlsz/qinglong_controlnet-lllite/Annotators/UNet.pth, anime-seg/isnetis.ckpt
densepose: LayerNorm/DensePose-TorchScript-with-hint-image/densepose_r50_fpn_dl.torchscript
dwpose:
- bbox_detector: Either yzd-v/DWPose/yolox_l.onnx, hr16/yolox-onnx/yolox_l.torchscript.pt, hr16/yolo-nas-fp16/yolo_nas_l_fp16.onnx, hr16/yolo-nas-fp16/yolo_nas_m_fp16.onnx, hr16/yolo-nas-fp16/yolo_nas_s_fp16.onnx
- pose_estimator: Either hr16/DWPose-TorchScript-BatchSize5/dw-ll_ucoco_384_bs5.torchscript.pt, yzd-v/DWPose/dw-ll_ucoco_384.onnx
animal_pose (ap10k):
- bbox_detector: Either yzd-v/DWPose/yolox_l.onnx, hr16/yolox-onnx/yolox_l.torchscript.pt, hr16/yolo-nas-fp16/yolo_nas_l_fp16.onnx, hr16/yolo-nas-fp16/yolo_nas_m_fp16.onnx, hr16/yolo-nas-fp16/yolo_nas_s_fp16.onnx
- pose_estimator: Either hr16/DWPose-TorchScript-BatchSize5/rtmpose-m_ap10k_256_bs5.torchscript.pt, hr16/UnJIT-DWPose/rtmpose-m_ap10k_256.onnx
hed: lllyasviel/Annotators/ControlNetHED.pth
leres: lllyasviel/Annotators/res101.pth, lllyasviel/Annotators/latest_net_G.pth
lineart: lllyasviel/Annotators/sk_model.pth, lllyasviel/Annotators/sk_model2.pth
lineart_anime: lllyasviel/Annotators/netG.pth
manga_line: lllyasviel/Annotators/erika.pth
mesh_graphormer: hr16/ControlNet-HandRefiner-pruned/graphormer_hand_state_dict.bin, hr16/ControlNet-HandRefiner-pruned/hrnetv2_w64_imagenet_pretrained.pth
midas: lllyasviel/Annotators/dpt_hybrid-midas-501f0c75.pt
mlsd: lllyasviel/Annotators/mlsd_large_512_fp32.pth
normalbae: lllyasviel/Annotators/scannet.pt
oneformer: lllyasviel/Annotators/250_16_swin_l_oneformer_ade20k_160k.pth
open_pose: lllyasviel/Annotators/body_pose_model.pth, lllyasviel/Annotators/hand_pose_model.pth, lllyasviel/Annotators/facenet.pth
pidi: lllyasviel/Annotators/table5_pidinet.pth
sam: dhkim2810/MobileSAM/mobile_sam.pt
uniformer: lllyasviel/Annotators/upernet_global_small.pth
zoe: lllyasviel/Annotators/ZoeD_M12_N.pt
teed: bdsqlsz/qinglong_controlnet-lllite/7_model.pth
depth_anything: Either LiheYoung/Depth-Anything/checkpoints/depth_anything_vitl14.pth, LiheYoung/Depth-Anything/checkpoints/depth_anything_vitb14.pth or LiheYoung/Depth-Anything/checkpoints/depth_anything_vits14.pth
diffusion_edge: Either hr16/Diffusion-Edge/diffusion_edge_indoor.pt, hr16/Diffusion-Edge/diffusion_edge_urban.pt or hr16/Diffusion-Edge/diffusion_edge_natrual.pt
unimatch: Either hr16/Unimatch/gmflow-scale2-regrefine6-mixdata.pth, hr16/Unimatch/gmflow-scale2-mixdata.pth or hr16/Unimatch/gmflow-scale1-mixdata.pth
zoe_depth_anything: Either LiheYoung/Depth-Anything/checkpoints_metric_depth/depth_anything_metric_depth_indoor.pt or LiheYoung/Depth-Anything/checkpoints_metric_depth/depth_anything_metric_depth_outdoor.pt

2000 Stars 😄

Thanks for yalls supports. I never thought the graph for stars would be linear lol.

Ly820359009/comfyui_controlnet_aux