diff --git a/comfy/ldm/helios/model.py b/comfy/ldm/helios/model.py
index c1ea5f595..2faeea897 100644
--- a/comfy/ldm/helios/model.py
+++ b/comfy/ldm/helios/model.py
@@ -711,19 +711,9 @@ class HeliosModel(torch.nn.Module):
             )
             f_long = self._rope_downsample_3d(f_long, (long_t, hs, ws), (4, 4, 4))
             hidden_states = torch.cat([x_long, hidden_states], dim=1)
-            freqs = torch.cat([f_long, freqs], dim=1)
+        freqs = torch.cat([f_long, freqs], dim=1)
 
         history_context_length = hidden_states.shape[1] - original_context_length
-        mismatch = hidden_states.shape[1] != freqs.shape[1]
-        summary_key = (
-            int(post_t),
-            int(post_h),
-            int(post_w),
-            int(original_context_length),
-            int(hidden_states.shape[1]),
-            int(freqs.shape[1]),
-            int(history_context_length),
-        )
 
         if timestep.ndim == 0:
             timestep = timestep.unsqueeze(0)
@@ -770,28 +760,28 @@ class HeliosModel(torch.nn.Module):
     def unpatchify(self, x, grid_sizes):
         """
         Unpatchify the output from proj_out back to video format.
-        
+
         Args:
             x: [batch, num_patches, out_dim * prod(patch_size)]
             grid_sizes: (num_frames, height, width) in patch space
-        
+
         Returns:
             [batch, out_dim, num_frames, height, width] in pixel space
         """
         b = x.shape[0]
         post_t, post_h, post_w = grid_sizes
         p_t, p_h, p_w = self.patch_size
-        
+
         # Reshape: [B, T*H*W, out_dim*p_t*p_h*p_w] -> [B, T, H, W, p_t, p_h, p_w, out_dim]
         # Use -1 to let PyTorch infer the channel dimension (out_dim)
         hidden_states = x.reshape(b, post_t, post_h, post_w, p_t, p_h, p_w, -1)
-        
+
         # Permute: [B, T, H, W, p_t, p_h, p_w, C] -> [B, C, T, p_t, H, p_h, W, p_w]
         hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
-        
+
         # Flatten patches: [B, C, T, p_t, H, p_h, W, p_w] -> [B, C, T*p_t, H*p_h, W*p_w]
         output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
-        
+
         return output
     def _rope_downsample_3d(self, freqs, grid_sizes, kernel_size):
         b, _, one, d, i2, j2 = freqs.shape
diff --git a/comfy_extras/nodes_helios.py b/comfy_extras/nodes_helios.py
index 89a63c3fd..2fcf0a64f 100644
--- a/comfy_extras/nodes_helios.py
+++ b/comfy_extras/nodes_helios.py
@@ -412,7 +412,6 @@ def _helios_dmd_sample(
 
     for i in range(len(sigmas) - 1):
         sigma = sigmas[i]
-        sigma_next = sigmas[i + 1]
         timestep = all_timesteps[i] if i < len(all_timesteps) else i
         denoised = model(x, sigma * s_in, **extra_args)