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 controlnet-aux==0.0.5
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 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_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"]
processor_id = 'scribble_hed'
processor = Processor(processor_id)
processed_image = processor(image, 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
# 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("./weight_path")
# instantiate
canny = CannyDetector()
content = ContentShuffleDetector()
face_detector = MediapipeFaceDetector()
# 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_canny = canny(img)
processed_image_content = content(img)
processed_image_mediapipe_face = face_detector(img)