[V3] convert nodes_fresca.py to V3 schema

comfy_extras/nodes_fresca.py

@@ -1,6 +1,8 @@
 # Code based on https://github.com/WikiChao/FreSca (MIT License)
 import torch
 import torch.fft as fft
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 
 def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
@@ -51,25 +53,31 @@ def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
     return x_filtered
 
 
-class FreSca:
+class FreSca(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "scale_low": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.01,
-                                        "tooltip": "Scaling factor for low-frequency components"}),
-                "scale_high": ("FLOAT", {"default": 1.25, "min": 0, "max": 10, "step": 0.01,
-                                        "tooltip": "Scaling factor for high-frequency components"}),
-                "freq_cutoff": ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1,
-                                        "tooltip": "Number of frequency indices around center to consider as low-frequency"}),
-            }
-        }
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-    CATEGORY = "_for_testing"
-    DESCRIPTION = "Applies frequency-dependent scaling to the guidance"
-    def patch(self, model, scale_low, scale_high, freq_cutoff):
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FreSca",
+            display_name="FreSca",
+            category="_for_testing",
+            description="Applies frequency-dependent scaling to the guidance",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
+                               tooltip="Scaling factor for low-frequency components"),
+                io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
+                               tooltip="Scaling factor for high-frequency components"),
+                io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
+                             tooltip="Number of frequency indices around center to consider as low-frequency"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, scale_low, scale_high, freq_cutoff):
         def custom_cfg_function(args):
             conds_out = args["conds_out"]
             if len(conds_out) <= 1 or None in args["conds"][:2]:
@@ -91,13 +99,16 @@ def custom_cfg_function(args):
         m = model.clone()
         m.set_model_sampler_pre_cfg_function(custom_cfg_function)
 
-        return (m,)
+        return io.NodeOutput(m)
+
 
+class FreScaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            FreSca,
+        ]
 
-NODE_CLASS_MAPPINGS = {
-    "FreSca": FreSca,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "FreSca": "FreSca",
-}
+async def comfy_entrypoint() -> FreScaExtension:
+    return FreScaExtension()