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# ComfyUI's ControlNet Auxiliary Preprocessors
Plug-and-play [ComfyUI](https://github.com/comfyanonymous/ComfyUI) node sets for making [ControlNet](https://github.com/lllyasviel/ControlNet/) hint images
The code is copy-pasted from the respective folders in https://github.com/lllyasviel/ControlNet/tree/main/annotator and connected to [the 🤗 Hub](https://huggingface.co/lllyasviel/Annotators).
All credit & copyright goes to https://github.com/lllyasviel.
# Marigold
Check out Marigold Depth Estimator which can generate very detailed and sharp depth map from high-resolution still 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](./UPDATES.md) to follow updates
# Installation:
## Using ComfyUI Manager (recommended):
Install [ComfyUI Manager](https://github.com/ltdrdata/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 <br> control_canny <br> t2iadapter_canny |
| HED Soft-Edge Lines | hed | control_v11p_sd15_softedge <br> 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 <br> control_mlsd |
| PiDiNet Soft-Edge Lines | pidinet | control_v11p_sd15_softedge <br> control_scribble |
| Scribble Lines | scribble | control_v11p_sd15_scribble <br> control_scribble |
| Scribble XDoG Lines | scribble_xdog | control_v11p_sd15_scribble <br> control_scribble |
| Fake Scribble Lines | scribble_hed | control_v11p_sd15_scribble <br> control_scribble |
| TEED Soft-Edge Lines | teed | [controlnet-sd-xl-1.0-softedge-dexined](https://huggingface.co/SargeZT/controlnet-sd-xl-1.0-softedge-dexined/blob/main/controlnet-sd-xl-1.0-softedge-dexined.safetensors) <br> control_v11p_sd15_softedge (Theoretically)
| Scribble PiDiNet Lines | scribble_pidinet | control_v11p_sd15_scribble <br> control_scribble |
| AnyLine Lineart | | mistoLine_fp16.safetensors <br> mistoLine_rank256 <br> control_v11p_sd15s2_lineart_anime <br> 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 <br> control_depth <br> t2iadapter_depth |
| LeReS Depth Map | depth_leres | control_v11f1p_sd15_depth <br> control_depth <br> t2iadapter_depth |
| Zoe Depth Map | depth_zoe | control_v11f1p_sd15_depth <br> control_depth <br> t2iadapter_depth |
| MiDaS Normal Map | normal_map | control_normal |
| BAE Normal Map | normal_bae | control_v11p_sd15_normalbae |
| MeshGraphormer Hand Refiner ([HandRefinder](https://github.com/wenquanlu/HandRefiner)) | depth_hand_refiner | [control_sd15_inpaint_depth_hand_fp16](https://huggingface.co/hr16/ControlNet-HandRefiner-pruned/blob/main/control_sd15_inpaint_depth_hand_fp16.safetensors) |
| Depth Anything | depth_anything | [Depth-Anything](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_controlnet/diffusion_pytorch_model.safetensors) |
| Zoe Depth Anything <br> (Basically Zoe but the encoder is replaced with DepthAnything) | depth_anything | [Depth-Anything](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_controlnet/diffusion_pytorch_model.safetensors) |
| Normal DSINE | | control_normal/control_v11p_sd15_normalbae |
| Metric3D Depth | | control_v11f1p_sd15_depth <br> control_depth <br> t2iadapter_depth |
| Metric3D Normal | | control_v11p_sd15_normalbae |
| Depth Anything V2 | | [Depth-Anything](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_controlnet/diffusion_pytorch_model.safetensors) |
## Faces and Poses Estimators
| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter |
|-----------------------------|---------------------------|-------------------------------------------|
| DWPose Estimator | dw_openpose_full | control_v11p_sd15_openpose <br> control_openpose <br> t2iadapter_openpose |
| OpenPose Estimator | openpose (detect_body) <br> openpose_hand (detect_body + detect_hand) <br> openpose_faceonly (detect_face) <br> openpose_full (detect_hand + detect_body + detect_face) | control_v11p_sd15_openpose <br> control_openpose <br> t2iadapter_openpose |
| MediaPipe Face Mesh | mediapipe_face | controlnet_sd21_laion_face_v2 |
| Animal Estimator | animal_openpose | [control_sd15_animal_openpose_fp16](https://huggingface.co/huchenlei/animal_openpose/blob/main/control_sd15_animal_openpose_fp16.pth) |
## Optical Flow Estimators
| Preprocessor Node | sd-webui-controlnet/other | ControlNet/T2I-Adapter |
|-----------------------------|---------------------------|-------------------------------------------|
| Unimatch Optical Flow | | [DragNUWA](https://github.com/ProjectNUWA/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](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md#json-output-format) 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):
```js
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
```js
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
```py
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 <br> 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](https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/ioclab_sd15_recolor.safetensors) <br> [sai_xl_recolor_256lora](https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/sai_xl_recolor_256lora.safetensors) <br> [bdsqlsz_controlllite_xl_recolor_luminance](https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/bdsqlsz_controlllite_xl_recolor_luminance.safetensors) |
| Image Intensity | recolor_intensity | Idk. Maybe same as above? |
# Examples
> A picture is worth a thousand words
Credit to https://huggingface.co/thibaud/controlnet-sd21 for most examples below. 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
### TEED Soft-Edge Lines
### Anyline Lineart
## Normal and Depth Map
### Depth (idk the preprocessor they use)
## Zoe - Depth Map
## BAE - Normal Map
## MeshGraphormer
## Depth Anything & Zoe Depth Anything
## DSINE
## Metric3D
## Depth Anything V2
## Faces and Poses
### OpenPose
### Animal Pose (AP-10K)
### DensePose
## Semantic Segmantation
### OneFormer ADE20K Segmentor
### Anime Face Segmentor
## T2IAdapter-only
### Color Pallete for T2I-Adapter
## Optical Flow
### Unimatch
## Recolor
# 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](https://github.com/Fannovel16/comfyui_controlnet_aux/issues) 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 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.
2. Add it into `requirements.txt`
3. Run `install.bat` or pip command mentioned in Installation
# Assets files of preprocessors
* anime_face_segment: [bdsqlsz/qinglong_controlnet-lllite/Annotators/UNet.pth](https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/blob/main/Annotators/UNet.pth), [anime-seg/isnetis.ckpt](https://huggingface.co/skytnt/anime-seg/blob/main/isnetis.ckpt)
* densepose: [LayerNorm/DensePose-TorchScript-with-hint-image/densepose_r50_fpn_dl.torchscript](https://huggingface.co/LayerNorm/DensePose-TorchScript-with-hint-image/blob/main/densepose_r50_fpn_dl.torchscript)
* dwpose:
* * bbox_detector: Either [yzd-v/DWPose/yolox_l.onnx](https://huggingface.co/yzd-v/DWPose/blob/main/yolox_l.onnx), [hr16/yolox-onnx/yolox_l.torchscript.pt](https://huggingface.co/hr16/yolox-onnx/blob/main/yolox_l.torchscript.pt), [hr16/yolo-nas-fp16/yolo_nas_l_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_l_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_m_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_m_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_s_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_s_fp16.onnx)
* * pose_estimator: Either [hr16/DWPose-TorchScript-BatchSize5/dw-ll_ucoco_384_bs5.torchscript.pt](https://huggingface.co/hr16/DWPose-TorchScript-BatchSize5/blob/main/dw-ll_ucoco_384_bs5.torchscript.pt), [yzd-v/DWPose/dw-ll_ucoco_384.onnx](https://huggingface.co/yzd-v/DWPose/blob/main/dw-ll_ucoco_384.onnx)
* animal_pose (ap10k):
* * bbox_detector: Either [yzd-v/DWPose/yolox_l.onnx](https://huggingface.co/yzd-v/DWPose/blob/main/yolox_l.onnx), [hr16/yolox-onnx/yolox_l.torchscript.pt](https://huggingface.co/hr16/yolox-onnx/blob/main/yolox_l.torchscript.pt), [hr16/yolo-nas-fp16/yolo_nas_l_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_l_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_m_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_m_fp16.onnx), [hr16/yolo-nas-fp16/yolo_nas_s_fp16.onnx](https://huggingface.co/hr16/yolo-nas-fp16/blob/main/yolo_nas_s_fp16.onnx)
* * pose_estimator: Either [hr16/DWPose-TorchScript-BatchSize5/rtmpose-m_ap10k_256_bs5.torchscript.pt](https://huggingface.co/hr16/DWPose-TorchScript-BatchSize5/blob/main/rtmpose-m_ap10k_256_bs5.torchscript.pt), [hr16/UnJIT-DWPose/rtmpose-m_ap10k_256.onnx](https://huggingface.co/hr16/UnJIT-DWPose/blob/main/rtmpose-m_ap10k_256.onnx)
* hed: [lllyasviel/Annotators/ControlNetHED.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/ControlNetHED.pth)
* leres: [lllyasviel/Annotators/res101.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/res101.pth), [lllyasviel/Annotators/latest_net_G.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/latest_net_G.pth)
* lineart: [lllyasviel/Annotators/sk_model.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/sk_model.pth), [lllyasviel/Annotators/sk_model2.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/sk_model2.pth)
* lineart_anime: [lllyasviel/Annotators/netG.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/netG.pth)
* manga_line: [lllyasviel/Annotators/erika.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/erika.pth)
* mesh_graphormer: [hr16/ControlNet-HandRefiner-pruned/graphormer_hand_state_dict.bin](https://huggingface.co/hr16/ControlNet-HandRefiner-pruned/blob/main/graphormer_hand_state_dict.bin), [hr16/ControlNet-HandRefiner-pruned/hrnetv2_w64_imagenet_pretrained.pth](https://huggingface.co/hr16/ControlNet-HandRefiner-pruned/blob/main/hrnetv2_w64_imagenet_pretrained.pth)
* midas: [lllyasviel/Annotators/dpt_hybrid-midas-501f0c75.pt](https://huggingface.co/lllyasviel/Annotators/blob/main/dpt_hybrid-midas-501f0c75.pt)
* mlsd: [lllyasviel/Annotators/mlsd_large_512_fp32.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/mlsd_large_512_fp32.pth)
* normalbae: [lllyasviel/Annotators/scannet.pt](https://huggingface.co/lllyasviel/Annotators/blob/main/scannet.pt)
* oneformer: [lllyasviel/Annotators/250_16_swin_l_oneformer_ade20k_160k.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/250_16_swin_l_oneformer_ade20k_160k.pth)
* open_pose: [lllyasviel/Annotators/body_pose_model.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/body_pose_model.pth), [lllyasviel/Annotators/hand_pose_model.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/hand_pose_model.pth), [lllyasviel/Annotators/facenet.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/facenet.pth)
* pidi: [lllyasviel/Annotators/table5_pidinet.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/table5_pidinet.pth)
* sam: [dhkim2810/MobileSAM/mobile_sam.pt](https://huggingface.co/dhkim2810/MobileSAM/blob/main/mobile_sam.pt)
* uniformer: [lllyasviel/Annotators/upernet_global_small.pth](https://huggingface.co/lllyasviel/Annotators/blob/main/upernet_global_small.pth)
* zoe: [lllyasviel/Annotators/ZoeD_M12_N.pt](https://huggingface.co/lllyasviel/Annotators/blob/main/ZoeD_M12_N.pt)
* teed: [bdsqlsz/qinglong_controlnet-lllite/7_model.pth](https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/blob/main/Annotators/7_model.pth)
* depth_anything: Either [LiheYoung/Depth-Anything/checkpoints/depth_anything_vitl14.pth](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth), [LiheYoung/Depth-Anything/checkpoints/depth_anything_vitb14.pth](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitb14.pth) or [LiheYoung/Depth-Anything/checkpoints/depth_anything_vits14.pth](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vits14.pth)
* diffusion_edge: Either [hr16/Diffusion-Edge/diffusion_edge_indoor.pt](https://huggingface.co/hr16/Diffusion-Edge/blob/main/diffusion_edge_indoor.pt), [hr16/Diffusion-Edge/diffusion_edge_urban.pt](https://huggingface.co/hr16/Diffusion-Edge/blob/main/diffusion_edge_urban.pt) or [hr16/Diffusion-Edge/diffusion_edge_natrual.pt](https://huggingface.co/hr16/Diffusion-Edge/blob/main/diffusion_edge_natrual.pt)
* unimatch: Either [hr16/Unimatch/gmflow-scale2-regrefine6-mixdata.pth](https://huggingface.co/hr16/Unimatch/blob/main/gmflow-scale2-regrefine6-mixdata.pth), [hr16/Unimatch/gmflow-scale2-mixdata.pth](https://huggingface.co/hr16/Unimatch/blob/main/gmflow-scale2-mixdata.pth) or [hr16/Unimatch/gmflow-scale1-mixdata.pth](https://huggingface.co/hr16/Unimatch/blob/main/gmflow-scale1-mixdata.pth)
* zoe_depth_anything: Either [LiheYoung/Depth-Anything/checkpoints_metric_depth/depth_anything_metric_depth_indoor.pt](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_metric_depth/depth_anything_metric_depth_indoor.pt) or [LiheYoung/Depth-Anything/checkpoints_metric_depth/depth_anything_metric_depth_outdoor.pt](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_metric_depth/depth_anything_metric_depth_outdoor.pt)
# 1500 Stars 😄
<a href="https://star-history.com/#Fannovel16/comfyui_controlnet_aux&Date">
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Thanks for yalls supports. I never thought the graph for stars would be linear lol.
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