diff --git a/nodes/model_optimization_nodes.py b/nodes/model_optimization_nodes.py
index b777ba2..9072bcd 100644
--- a/nodes/model_optimization_nodes.py
+++ b/nodes/model_optimization_nodes.py
@@ -1179,7 +1179,7 @@ class CFGZeroStarAndInit:
         return {"required": { 
                     "model": ("MODEL",),
                     "use_zero_init": ("BOOLEAN", {"default": True}),
-                    "zero_star_steps": ("INT", {"default": 0, "min": 0, "tooltip": "for zero init, starts from 0 so first step is always zeroed out if use_zero_init enabled"}),
+                    "zero_init_steps": ("INT", {"default": 0, "min": 0, "tooltip": "for zero init, starts from 0 so first step is always zeroed out if use_zero_init enabled"}),
                 }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
@@ -1187,12 +1187,13 @@ class CFGZeroStarAndInit:
     CATEGORY = "KJNodes/experimental"
     EXPERIMENTAL = True
 
-    def patch(self, model, use_zero_init, zero_star_steps):
+    def patch(self, model, use_zero_init, zero_init_steps):
         def cfg_zerostar(args):
             #zero init
+            cond = args["cond"]
             timestep = args["timestep"]
             sigmas = args["model_options"]["transformer_options"]["sample_sigmas"]
-            matched_step_index = (sigmas == timestep[0] ).nonzero()
+            matched_step_index = (sigmas == timestep[0]).nonzero()
             if len(matched_step_index) > 0:
                 current_step_index = matched_step_index.item()
             else:
@@ -1203,17 +1204,15 @@ class CFGZeroStarAndInit:
                 else:
                     current_step_index = 0
 
-            if (current_step_index <= zero_star_steps) and use_zero_init:
+            if (current_step_index <= zero_init_steps) and use_zero_init:
                 return cond * 0
                         
-            cond = args["cond"]
             uncond = args["uncond"]
             cond_scale = args["cond_scale"]
                 
             batch_size = cond.shape[0]
 
-            noise_pred_text = cond
-            positive_flat = noise_pred_text.view(batch_size, -1)
+            positive_flat = cond.view(batch_size, -1)
             negative_flat = uncond.view(batch_size, -1)
 
             dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
@@ -1221,7 +1220,7 @@ class CFGZeroStarAndInit:
             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)
+            noise_pred = uncond * alpha + cond_scale * (cond - uncond * alpha)
             return noise_pred
 
         m = model.clone()