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from ..utils import common_annotator_call, create_node_input_types
import comfy.model_management as model_management
class LERES_Depth_Map_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return create_node_input_types(
rm_nearest=("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}),
rm_background=("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}),
boost=(["enable", "disable"], {"default": "disable"})
)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
def execute(self, image, rm_nearest, rm_background, resolution=512, **kwargs):
from controlnet_aux.leres import LeresDetector
model = LeresDetector.from_pretrained().to(model_management.get_torch_device())
out = common_annotator_call(model, image, resolution=resolution, thr_a=rm_nearest, thr_b=rm_background, boost=kwargs["boost"] == "enable")
del model
return (out, )
NODE_CLASS_MAPPINGS = {
"LeReS-DepthMapPreprocessor": LERES_Depth_Map_Preprocessor
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LeReS-DepthMapPreprocessor": "LeReS Depth Map (enable boost for leres++)"
} |