diff --git a/__init__.py b/__init__.py
index 4bf3951..6d891b6 100644
--- a/__init__.py
+++ b/__init__.py
@@ -189,7 +189,7 @@ NODE_CONFIG = {
     "SkipLayerGuidanceWanVideo": {"class": SkipLayerGuidanceWanVideo, "name": "Skip Layer Guidance WanVideo"},
     "TimerNodeKJ": {"class": TimerNodeKJ, "name": "Timer Node KJ"},
     "HunyuanVideoEncodeKeyframesToCond": {"class": HunyuanVideoEncodeKeyframesToCond, "name": "HunyuanVideo Encode Keyframes To Cond"},
-    "CFGZeroStar": {"class": CFGZeroStar, "name": "CFG Zero Star"},
+    "CFGZeroStarAndInit": {"class": CFGZeroStarAndInit, "name": "CFG Zero Star/Init"},
 
     #instance diffusion
     "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},
diff --git a/nodes/model_optimization_nodes.py b/nodes/model_optimization_nodes.py
index cc193bc..44f8c84 100644
--- a/nodes/model_optimization_nodes.py
+++ b/nodes/model_optimization_nodes.py
@@ -1173,7 +1173,7 @@ class SkipLayerGuidanceWanVideo:
 
         return (m, )
 
-class CFGZeroStar:
+class CFGZeroStarAndInit:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { 
@@ -1195,17 +1195,6 @@ class CFGZeroStar:
             timestep = args["timestep"]
             sigmas = args["model_options"]["transformer_options"]["sample_sigmas"]
 
-            batch_size = cond.shape[0]
-
-            noise_pred_text = cond
-            positive_flat = noise_pred_text.view(batch_size, -1)
-            negative_flat = uncond.view(batch_size, -1)
-
-            dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
-            squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
-            alpha = dot_product / squared_norm
-            alpha = alpha.view(batch_size, *([1] * (len(cond.shape) - 1)))
-
             matched_step_index = (sigmas == timestep[0] ).nonzero()
             if len(matched_step_index) > 0:
                 current_step_index = matched_step_index.item()
@@ -1219,9 +1208,20 @@ class CFGZeroStar:
                     current_step_index = 0
 
             if (current_step_index <= zero_star_steps) and use_zero_init:
-                noise_pred = noise_pred_text * 0
-            else:
-                noise_pred = uncond * alpha + cond_scale * (noise_pred_text - uncond * alpha)
+                return cond * 0 
+                
+            batch_size = cond.shape[0]
+
+            noise_pred_text = cond
+            positive_flat = noise_pred_text.view(batch_size, -1)
+            negative_flat = uncond.view(batch_size, -1)
+
+            dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
+            squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
+            alpha = dot_product / squared_norm
+            alpha = alpha.view(batch_size, *([1] * (len(cond.shape) - 1)))
+
+            noise_pred = uncond * alpha + cond_scale * (noise_pred_text - uncond * alpha)
             return noise_pred
 
         m = model.clone()