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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.

Updates

  • AIO Aux Preprocessor intergrating all loadable aux preprocessors as dropdown options. Easy to copy, paste and get the preprocessor faster.
  • Added OpenPose-format JSON output from OpenPose Preprocessor and DWPose Preprocessor. Checks here.
  • Fixed wrong model path when downloading DWPose.
  • Make hint images less blurry.
  • Added resolution option, PixelPerfectResolution and HintImageEnchance nodes (TODO: Documentation).
  • Added RAFT Optical Flow Embedder for TemporalNet2 (TODO: Workflow example).
  • Fixed opencv's conflicts between this extension, ReActor and Roop. Thanks Gourieff for the solution!
  • RAFT is removed as the code behind it doesn't match what what the original code does
  • Changed lineart's display name from Normal Lineart to Realistic Lineart. This change won't affect old workflows
  • Added support for onnxruntime to speed-up DWPose (see the Q&A)
  • Fixed TypeError: expected size to be one of int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], but got size with types [<class 'numpy.int64'>, <class 'numpy.int64'>]: Issue, PR)
  • Fixed ImageGenResolutionFromImage mishape (Fannovel16#74)
  • Fixed LeRes and MiDaS's incomatipility with MPS device
  • Fixed checking DWPose onnxruntime session multiple times: Fannovel16#89)
  • Added Anime Face Segmentor (in ControlNet Preprocessors/Semantic Segmentation) for ControlNet AnimeFaceSegmentV2. Checks here
  • Change download functions and fix download error: PR
  • Caching DWPose Onnxruntime during the first use of DWPose node instead of ComfyUI startup
  • Added alternative YOLOX models for faster speed when using DWPose
  • Added alternative DWPose models
  • Implemented the preprocessor for AnimalPose ControlNet. Check Animal Pose AP-10K
  • Added YOLO-NAS models which are drop-in replacements of YOLOX
  • Fixed Openpose Face/Hands no longer detecting: Fannovel16#54
  • Added TorchScript implementation of DWPose and AnimalPose

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.

  1. Add it into requirements.txt

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

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.

Line Extractors

  • Binary Lines
  • Canny Edge
  • HED Lines
  • Realistic Lineart (formerly Normal Lineart)
  • Anime Lineart
  • Manga Lineart
  • M-LSD Lines
  • PiDiNet Lines
  • Scribble Lines
  • Scribble XDoG Lines

Normal and Depth Map

  • LeReS - Depth Map
  • MiDaS - Normal Map
  • MiDaS - Depth Map
  • BAE - Normal Map
  • Zoe - Depth Map

Faces and Poses

  • DWPose Pose Estimation
  • OpenPose Pose Estimation
  • MediaPipe Face Mesh
  • Animal Pose Estimation

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)

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