aliensmn
/

comfyui_controlnet_aux

@@ -1,252 +1,51 @@
-# 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
-"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
-![](./examples/CNAuxBanner.jpg)
-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.
-# 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
-![](./examples/example_save_kps.png)
-#### 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
-![](./examples/ExecuteAll1.jpg)
-![](./examples/ExecuteAll2.jpg)
-# 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](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.
-![](./examples/example_torchscript.png)
-### 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
-![](./examples/example_onnx.png)
-# 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)
-# 2000 Stars 😄
-<a href="https://star-history.com/#Fannovel16/comfyui_controlnet_aux&Date">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Fannovel16/comfyui_controlnet_aux&type=Date&theme=dark" />
-    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=Fannovel16/comfyui_controlnet_aux&type=Date" />
-    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=Fannovel16/comfyui_controlnet_aux&type=Date" />
-  </picture>
-</a>
-Thanks for yalls supports. I never thought the graph for stars would be linear lol.

+---
+license: mit
+tags:
+- comfyui
+- comfyui-custom-nodes
+- mirror
+- github-mirror
+library_name: comfyui
+---
+# comfyui_controlnet_aux
+🔄 **Mirror of GitHub Repository**
+This is a personal mirror of the original GitHub repository for easy access and backup.
+## 📦 Original Source
+**GitHub:** [https://github.com/Fannovel16/comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux)
+## 🎯 Purpose
+This mirror is maintained for:
+- ✅ Easy access and backup
+- ✅ Integration with Hugging Face workflows
+- ✅ Personal development and testing
+- ✅ Offline availability
+## 📖 Usage
+Please refer to the original GitHub repository for:
+- Latest updates and releases
+- Installation instructions
+- Documentation
+- Issues and support
+- Contributing guidelines
+## 🙏 Attribution
+All code and content in this repository belongs to the original authors and contributors.
+This is purely a mirror for personal convenience.
+**Please support the original project:** https://github.com/Fannovel16/comfyui_controlnet_aux
+---
+**Mirrored by:** aliensmn
+**Mirror Type:** ComfyUI Custom Node
+**Original Repository:** https://github.com/Fannovel16/comfyui_controlnet_aux
+*If you are the original author and would like this mirror removed, please contact me.*