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from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
import comfy.model_management as model_management
class HED_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return define_preprocessor_inputs(
safe=INPUT.COMBO(["enable", "disable"]),
resolution=INPUT.RESOLUTION()
)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/Line Extractors"
def execute(self, image, resolution=512, **kwargs):
from custom_controlnet_aux.hed import HEDdetector
model = HEDdetector.from_pretrained().to(model_management.get_torch_device())
out = common_annotator_call(model, image, resolution=resolution, safe = kwargs["safe"] == "enable")
del model
return (out, )
class Fake_Scribble_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return define_preprocessor_inputs(
safe=INPUT.COMBO(["enable", "disable"]),
resolution=INPUT.RESOLUTION()
)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/Line Extractors"
def execute(self, image, resolution=512, **kwargs):
from custom_controlnet_aux.hed import HEDdetector
model = HEDdetector.from_pretrained().to(model_management.get_torch_device())
out = common_annotator_call(model, image, resolution=resolution, scribble=True, safe=kwargs["safe"]=="enable")
del model
return (out, )
NODE_CLASS_MAPPINGS = {
"HEDPreprocessor": HED_Preprocessor,
"FakeScribblePreprocessor": Fake_Scribble_Preprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
"HEDPreprocessor": "HED Soft-Edge Lines",
"FakeScribblePreprocessor": "Fake Scribble Lines (aka scribble_hed)"
}