diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index f42f301..ae42d81 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -142,12 +142,16 @@ class StableCascadeSampling(ModelSamplingDiscrete):
         else:
             sampling_settings = {}
 
-        self.num_timesteps = 1000
-        self.shift = sampling_settings.get("shift", 1.0)
-        cosine_s=8e-3
+        self.set_parameters(sampling_settings.get("shift", 1.0))
+
+    def set_parameters(self, shift=1.0, cosine_s=8e-3):
+        self.shift = shift
         self.cosine_s = torch.tensor(cosine_s)
-        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
         self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
+
+        #This part is just for compatibility with some schedulers in the codebase
+        self.num_timesteps = 1000
+        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
         for x in range(self.num_timesteps):
             t = x / self.num_timesteps
             sigmas[x] = self.sigma(t)
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 541ce8f..ac7c1c1 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -99,6 +99,32 @@ class ModelSamplingDiscrete:
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )
 
+class ModelSamplingStableCascade:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, shift):
+        m = model.clone()
+
+        sampling_base = comfy.model_sampling.StableCascadeSampling
+        sampling_type = comfy.model_sampling.EPS
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
 class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
@@ -171,5 +197,6 @@ class RescaleCFG:
 NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
     "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
+    "ModelSamplingStableCascade": ModelSamplingStableCascade,
     "RescaleCFG": RescaleCFG,
 }