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from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
import comfy.model_management as model_management
import numpy as np
class MIDAS_Normal_Map_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return define_preprocessor_inputs(
a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0),
bg_threshold=INPUT.FLOAT(default=0.1),
resolution=INPUT.RESOLUTION()
)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
def execute(self, image, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs):
from custom_controlnet_aux.midas import MidasDetector
model = MidasDetector.from_pretrained().to(model_management.get_torch_device())
#Dirty hack :))
cb = lambda image, **kargs: model(image, **kargs)[1]
out = common_annotator_call(cb, image, resolution=resolution, a=a, bg_th=bg_threshold, depth_and_normal=True)
del model
return (out, )
class MIDAS_Depth_Map_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return define_preprocessor_inputs(
a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0),
bg_threshold=INPUT.FLOAT(default=0.1),
resolution=INPUT.RESOLUTION()
)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
def execute(self, image, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs):
from custom_controlnet_aux.midas import MidasDetector
# Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py
model = MidasDetector.from_pretrained().to(model_management.get_torch_device())
out = common_annotator_call(model, image, resolution=resolution, a=a, bg_th=bg_threshold)
del model
return (out, )
NODE_CLASS_MAPPINGS = {
"MiDaS-NormalMapPreprocessor": MIDAS_Normal_Map_Preprocessor,
"MiDaS-DepthMapPreprocessor": MIDAS_Depth_Map_Preprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
"MiDaS-NormalMapPreprocessor": "MiDaS Normal Map",
"MiDaS-DepthMapPreprocessor": "MiDaS Depth Map"
} |