diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py
index 47b1dd049702..25367560a605 100644
--- a/comfy_extras/nodes_gits.py
+++ b/comfy_extras/nodes_gits.py
@@ -1,6 +1,8 @@
 # from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
 import numpy as np
 import torch
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 def loglinear_interp(t_steps, num_steps):
     """
@@ -333,25 +335,28 @@ def loglinear_interp(t_steps, num_steps):
     ],
 }
 
-class GITSScheduler:
+class GITSScheduler(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
-                     "steps": ("INT", {"default": 10, "min": 2, "max": 1000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                      }
-               }
-    RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling/schedulers"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GITSScheduler",
+            category="sampling/custom_sampling/schedulers",
+            inputs=[
+                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05),
+                io.Int.Input("steps", default=10, min=2, max=1000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Sigmas.Output(),
+            ],
+        )
 
-    FUNCTION = "get_sigmas"
-
-    def get_sigmas(self, coeff, steps, denoise):
+    @classmethod
+    def execute(cls, coeff, steps, denoise):
         total_steps = steps
         if denoise < 1.0:
             if denoise <= 0.0:
-                return (torch.FloatTensor([]),)
+                return io.NodeOutput(torch.FloatTensor([]))
             total_steps = round(steps * denoise)
 
         if steps <= 20:
@@ -362,8 +367,16 @@ def get_sigmas(self, coeff, steps, denoise):
 
         sigmas = sigmas[-(total_steps + 1):]
         sigmas[-1] = 0
-        return (torch.FloatTensor(sigmas), )
+        return io.NodeOutput(torch.FloatTensor(sigmas))
 
-NODE_CLASS_MAPPINGS = {
-    "GITSScheduler": GITSScheduler,
-}
+
+class GITSSchedulerExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            GITSScheduler,
+        ]
+
+
+async def comfy_entrypoint() -> GITSSchedulerExtension:
+    return GITSSchedulerExtension()