diff --git a/invokeai/app/invocations/seed_variance_enhancer.py b/invokeai/app/invocations/seed_variance_enhancer.py
index d08f51ec4a..3ee27b2a3e 100644
--- a/invokeai/app/invocations/seed_variance_enhancer.py
+++ b/invokeai/app/invocations/seed_variance_enhancer.py
@@ -20,6 +20,13 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ZImageConditioningInfo,
 )
 
+# Seed offset for noise mask generation (v2.2 behavior for consistency with prompt variations)
+NOISE_MASK_SEED_OFFSET = 1
+
+# Factors for suggested strength calculation based on embedding standard deviation
+MIN_STRENGTH_FACTOR = 0.1
+MAX_STRENGTH_FACTOR = 10.0
+
 
 class MaskStartPosition(str, Enum):
     """Which end of the prompt will be protected from noise."""
@@ -142,12 +149,12 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
         torch.manual_seed(self.seed)
         noise = torch.rand_like(prompt_embeds) * 2 * self.strength - self.strength
 
-        # Reset seed for value selection (v2.2 behavior for consistency with prompt variations)
-        torch.manual_seed(self.seed + 1)
-        noise_mask = torch.bernoulli(torch.ones_like(prompt_embeds) * randomize_percent).bool()
+        # Reset seed for value selection to ensure consistency with prompt variations
+        torch.manual_seed(self.seed + NOISE_MASK_SEED_OFFSET)
+        noise_mask = torch.bernoulli(torch.full_like(prompt_embeds, randomize_percent)).bool()
 
         # Check for null sequences (padding)
-        first_null, last_nonnull, null_sequences = self._find_null_sequences(prompt_embeds)
+        first_null, last_nonnull, is_null_list = self._find_null_sequences(prompt_embeds)
 
         # Apply masking if needed
         if mask_percent > 0 or last_nonnull < prompt_embeds.size(1) - 1:
@@ -174,8 +181,9 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
                 if self.log_statistics:
                     context.logger.info("Seed Variance Enhancer is masking null sequences from noise")
 
-                null_mask_tensor = ~torch.tensor(
-                    null_sequences, device=prompt_embeds.device, dtype=torch.bool
+                # Convert is_null_list to tensor: True where sequences should be protected (null sequences)
+                null_mask_tensor = torch.tensor(
+                    is_null_list, device=prompt_embeds.device, dtype=torch.bool
                 )
                 null_mask_tensor = null_mask_tensor.view(1, -1, 1).expand(
                     prompt_embeds.size(0), -1, prompt_embeds.size(2)
@@ -195,18 +203,19 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
         """Find sequences in tensor that contain all zeros (padding).
 
         Returns:
-            Tuple of (first_null_index, last_nonnull_index, null_sequences_list)
+            Tuple of (first_null_index, last_nonnull_index, is_null_list)
+            where is_null_list contains 1 for null sequences and 0 for non-null sequences
         """
         first_null = -1
         last_nonnull = -1
-        null_sequences = [0] * tensor.size(1)
+        is_null_list = [0] * tensor.size(1)
 
         if tensor.dim() == 3:
             for i in range(tensor.size(1)):
                 sequence = tensor[:, i, ...]
                 is_all_zero = torch.all(sequence == 0)
 
-                null_sequences[i] = 0 if is_all_zero else 1
+                is_null_list[i] = 1 if is_all_zero else 0
 
                 if not is_all_zero:
                     last_nonnull = i
@@ -214,7 +223,7 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
                 if is_all_zero and first_null == -1:
                     first_null = i
 
-        return first_null, last_nonnull, null_sequences
+        return first_null, last_nonnull, is_null_list
 
     def _log_statistics(self, context: InvocationContext, conditioning_data: ConditioningFieldData) -> None:
         """Log statistics about the conditioning tensor."""
@@ -233,7 +242,7 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
             return
 
         # Find null sequences
-        first_null, last_nonnull, null_sequences = self._find_null_sequences(tensor)
+        first_null, last_nonnull, is_null_list = self._find_null_sequences(tensor)
 
         # Calculate statistics on non-null portion
         if last_nonnull < tensor.size(1) - 1 and last_nonnull >= 0:
@@ -252,10 +261,12 @@ class SeedVarianceEnhancerInvocation(BaseInvocation):
         context.logger.info(f"Dimensions: {list(tensor.shape)}")
         context.logger.info(f"Min: {min_val:.6f}, Max: {max_val:.6f}")
         context.logger.info(f"Mean: {mean:.6f}, Std Dev: {std:.6f}")
-        context.logger.info(f"Suggested strength range: {std/10:.6f} - {std*10:.6f}")
+        context.logger.info(
+            f"Suggested strength range: {std * MIN_STRENGTH_FACTOR:.6f} - {std * MAX_STRENGTH_FACTOR:.6f}"
+        )
 
         if first_null != -1:
-            num_null = sum(1 for x in null_sequences if x == 0)
+            num_null = sum(1 for x in is_null_list if x == 1)
             context.logger.info(
                 f"Null sequences: First at {first_null}, Last non-null at {last_nonnull}, Total null: {num_null}"
             )