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from ..utils import common_annotator_call, create_node_input_types
import comfy.model_management as model_management
class DensePose_Preprocessor:
@classmethod
def INPUT_TYPES(s):
return create_node_input_types(
model=(["densepose_r50_fpn_dl.torchscript", "densepose_r101_fpn_dl.torchscript"], {"default": "densepose_r50_fpn_dl.torchscript"}),
cmap=(["Viridis (MagicAnimate)", "Parula (CivitAI)"], {"default": "Viridis (MagicAnimate)"})
)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "tbox/ControlNet Preprocessors"
def execute(self, image, model, cmap, resolution=512):
from densepose import DenseposeDetector
model = DenseposeDetector \
.from_pretrained(filename=model) \
.to(model_management.get_torch_device())
return (common_annotator_call(model, image, cmap="viridis" if "Viridis" in cmap else "parula", resolution=resolution), )
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