Create node.py

node.py

@@ -0,0 +1,31 @@
+import torch
+
+class CustomScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        inputs = {
+            "required": {                
+                "steps": ("INT", {"default": 4, "min": 1, "max": 25}),
+            },
+            "optional": {},             
+        }
+
+        defaults = [4.12, 1.62, 0.7, 0.04]
+        for i in range(0, 26):
+            default = defaults[i] if i < len(defaults) else 0.0
+            inputs["optional"][f"sigma_{i}"] = ("FLOAT", {"default": default, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.0001})
+        
+        return inputs
+        
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, steps, **kwargs):
+        sigmas = []
+        for i in range(0, steps + 1):
+            sigma = kwargs[f"sigma_{i}"]
+            sigmas.append(sigma)
+
+        sigmas = torch.FloatTensor(sigmas).cpu()
+        return (sigmas,)