convert nodes_morphology.py to V3 schema (#10159)

Author: bigcat88

comfy_extras/nodes_morphology.py

@@ -1,24 +1,34 @@
 import torch
 import comfy.model_management
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat
 import kornia.color
 
 
-class Morphology:
+class Morphology(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"image": ("IMAGE",),
-                                "operation": (["erode",  "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],),
-                                "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}),
-                                }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Morphology",
+            display_name="ImageMorphology",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Combo.Input(
+                    "operation",
+                    options=["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],
+                ),
+                io.Int.Input("kernel_size", default=3, min=3, max=999, step=1),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "process"
-
-    CATEGORY = "image/postprocessing"
-
-    def process(self, image, operation, kernel_size):
+    @classmethod
+    def execute(cls, image, operation, kernel_size) -> io.NodeOutput:
         device = comfy.model_management.get_torch_device()
         kernel = torch.ones(kernel_size, kernel_size, device=device)
         image_k = image.to(device).movedim(-1, 1)
@@ -39,49 +49,63 @@ def process(self, image, operation, kernel_size):
         else:
             raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'")
         img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1)
-        return (img_out,)
+        return io.NodeOutput(img_out)
 
 
-class ImageRGBToYUV:
+class ImageRGBToYUV(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "image": ("IMAGE",),
-                              }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageRGBToYUV",
+            category="image/batch",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Image.Output(display_name="Y"),
+                io.Image.Output(display_name="U"),
+                io.Image.Output(display_name="V"),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
-    RETURN_NAMES = ("Y", "U", "V")
-    FUNCTION = "execute"
-
-    CATEGORY = "image/batch"
-
-    def execute(self, image):
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
         out = kornia.color.rgb_to_ycbcr(image.movedim(-1, 1)).movedim(1, -1)
-        return (out[..., 0:1].expand_as(image), out[..., 1:2].expand_as(image), out[..., 2:3].expand_as(image))
+        return io.NodeOutput(out[..., 0:1].expand_as(image), out[..., 1:2].expand_as(image), out[..., 2:3].expand_as(image))
 
-class ImageYUVToRGB:
+class ImageYUVToRGB(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"Y": ("IMAGE",),
-                             "U": ("IMAGE",),
-                             "V": ("IMAGE",),
-                              }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageYUVToRGB",
+            category="image/batch",
+            inputs=[
+                io.Image.Input("Y"),
+                io.Image.Input("U"),
+                io.Image.Input("V"),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "execute"
-
-    CATEGORY = "image/batch"
-
-    def execute(self, Y, U, V):
+    @classmethod
+    def execute(cls, Y, U, V) -> io.NodeOutput:
         image = torch.cat([torch.mean(Y, dim=-1, keepdim=True), torch.mean(U, dim=-1, keepdim=True), torch.mean(V, dim=-1, keepdim=True)], dim=-1)
         out = kornia.color.ycbcr_to_rgb(image.movedim(-1, 1)).movedim(1, -1)
-        return (out,)
+        return io.NodeOutput(out)
+
+
+class MorphologyExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Morphology,
+            ImageRGBToYUV,
+            ImageYUVToRGB,
+        ]
+
 
-NODE_CLASS_MAPPINGS = {
-    "Morphology": Morphology,
-    "ImageRGBToYUV": ImageRGBToYUV,
-    "ImageYUVToRGB": ImageYUVToRGB,
-}
+async def comfy_entrypoint() -> MorphologyExtension:
+    return MorphologyExtension()
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Morphology": "ImageMorphology",
-}