comfyui_controlnet_aux插件

前言

​ ComfyUI ControlNet Aux 是一个即插即用的 ComfyUI 节点集合，用于制作 ControlNet 提示图像 。该插件集成了各种计算机视觉模型作为预处理器，将输入图像转换为专门的控制图，然后可以通过 ControlNet 指导图像生成。

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安装方法

第一种

通过manager搜索 controlnet下载安装即可。

第二种

1. 进入 ComfyUI 的 custom_nodes 目录，并打开cmd
2. 使用以下命令克隆仓库：

git clone https://github.com/Fannovel16/comfyui_controlnet_aux.git

3. 进入文件夹

cd comfyui_controlnet_au

4. 安装相应库文件

==虚拟环境==

pip install -r requirements.txt

==内置python环境==

python -m pip install -r requirements.txt

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模型下载

使用该插件使用到的模型下载，按需下载

• 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