controlnet-aux-openpose-fix 0.0.10

Auxillary models for controlnet

ControlNet auxiliary models

This is a PyPi installable package of lllyasviel's ControlNet Annotators

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 .

Install

pip install -U controlnet-aux

DWPose

To use DWPose, you can install easy-dwpose. It's a lightweight package that uses ONNX models without requiring the installation of MMDetection, MMCV, or MMPose.

pip install easy-dwpose

from easy_dwpose import DWposeDetector

device = "cuda:0" if torch.cuda.is_available() else "cpu"
dwpose = DWposeDetector(device=device)

skeleton = dwpose(input_image, output_type="pil", include_hands=True, include_face=True)

Usage

You can use the processor class, which can load each of the auxiliary models with the following code

import requests
from PIL import Image
from io import BytesIO

from controlnet_aux.processor import Processor

# load image
url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"

response = requests.get(url)
img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))

# load processor from processor_id
# options are:
# ["canny", "depth_leres", "depth_leres++", "depth_midas", "depth_zoe", "lineart_anime",
#  "lineart_coarse", "lineart_realistic", "mediapipe_face", "mlsd", "normal_bae", "normal_midas",
#  "openpose", "openpose_face", "openpose_faceonly", "openpose_full", "openpose_hand",
#  "scribble_hed, "scribble_pidinet", "shuffle", "softedge_hed", "softedge_hedsafe",
#  "softedge_pidinet", "softedge_pidsafe", "dwpose"]
processor_id = 'scribble_hed'
processor = Processor(processor_id)

processed_image = processor(img, to_pil=True)

Each model can be loaded individually by importing and instantiating them as follows

from PIL import Image
import requests
from io import BytesIO
from controlnet_aux import HEDdetector, MidasDetector, MLSDdetector, OpenposeDetector, PidiNetDetector, NormalBaeDetector, LineartDetector, LineartAnimeDetector, CannyDetector, ContentShuffleDetector, ZoeDetector, MediapipeFaceDetector, SamDetector, LeresDetector, DWposeDetector

# load image
url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"

response = requests.get(url)
img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))

# load checkpoints
hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
mlsd = MLSDdetector.from_pretrained("lllyasviel/Annotators")
open_pose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
pidi = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
normal_bae = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
lineart = LineartDetector.from_pretrained("lllyasviel/Annotators")
lineart_anime = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
sam = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
mobile_sam = SamDetector.from_pretrained("dhkim2810/MobileSAM", model_type="vit_t", filename="mobile_sam.pt")
leres = LeresDetector.from_pretrained("lllyasviel/Annotators")
teed = TEEDdetector.from_pretrained("fal-ai/teed", filename="5_model.pth")
anyline = AnylineDetector.from_pretrained(
    "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
)

# specify configs, ckpts and device, or it will be downloaded automatically and use cpu by default
# det_config: ./src/controlnet_aux/dwpose/yolox_config/yolox_l_8xb8-300e_coco.py
# det_ckpt: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
# pose_config: ./src/controlnet_aux/dwpose/dwpose_config/dwpose-l_384x288.py
# pose_ckpt: https://huggingface.co/wanghaofan/dw-ll_ucoco_384/resolve/main/dw-ll_ucoco_384.pth
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dwpose = DWposeDetector(det_config=det_config, det_ckpt=det_ckpt, pose_config=pose_config, pose_ckpt=pose_ckpt, device=device)

# instantiate
canny = CannyDetector()
content = ContentShuffleDetector()
face_detector = MediapipeFaceDetector()
lineart_standard = LineartStandardDetector()


# process
processed_image_hed = hed(img)
processed_image_midas = midas(img)
processed_image_mlsd = mlsd(img)
processed_image_open_pose = open_pose(img, hand_and_face=True)
processed_image_pidi = pidi(img, safe=True)
processed_image_normal_bae = normal_bae(img)
processed_image_lineart = lineart(img, coarse=True)
processed_image_lineart_anime = lineart_anime(img)
processed_image_zoe = zoe(img)
processed_image_sam = sam(img)
processed_image_leres = leres(img)
processed_image_teed = teed(img, detect_resolution=1024)
processed_image_anyline = anyline(img, detect_resolution=1280)

processed_image_canny = canny(img)
processed_image_content = content(img)
processed_image_mediapipe_face = face_detector(img)
processed_image_dwpose = dwpose(img)
processed_image_lineart_standard = lineart_standard(img, detect_resolution=1024)

Image resolution

In order to maintain the image aspect ratio, detect_resolution, image_resolution and images sizes need to be using multiple of 64.