init

node_wrappers/hed.py

@@ -0,0 +1,53 @@
+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)"
+}