Removed Redundant Code

Author: SquirrelRat

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "WanVideoLooper"
 description = "This is a set of custom nodes for ComfyUI designed for generating sequential long video clips based on the Wan 2.2 model architecture, handling continuity between segments and offering advanced control over the sampling process"
-version = "1.0.6"
+version = "1.0.7"
 license = {file = "LICENSE"}
 dependencies = [
     "color-match",

wan_looper_node.py

@@ -111,7 +111,7 @@ def _prepare_latent_window(positive_cond, negative_cond, vae, width, height, fra
     return positive_cond, negative_cond, {"samples": empty_latent}
 
 # ====================================================================================================
-# NEW Color Match Helper
+# Color Match Helper
 # ====================================================================================================
 def _apply_color_match(target_batch_tensor, reference_tensor, strength=1.0):
     """Applies MKL color matching from reference to target batch."""
@@ -131,6 +131,8 @@ def _apply_color_match(target_batch_tensor, reference_tensor, strength=1.0):
     cm = ColorMatcher()
 
     for i in range(batch_size):
+        if (i + 1) % 25 == 0 or i == 0: # Log every 25 frames
+         _log(f"Color matching frame {i+1}/{batch_size}...")
         try:
             target_img = target_np[i]
             transfer_result = cm.transfer(src=target_img, ref=ref_np, method='mkl')
@@ -389,20 +391,16 @@ def loop_video(
         # --- 4. Finalize & Return ---
         if not all_frames_collected:
             _log("Error: No frames were collected."); return (torch.zeros((1, 64, 64, 3)),)*2
-        _log("Concatenating final video batch.")
-        num_channels = all_frames_collected[0].shape[-1]
-        consistent_batches = []
-        for batch in all_frames_collected:
-             if batch.shape[-1] == num_channels: consistent_batches.append(batch)
-             else:
-                 _log(f"Warning: Channel mismatch found (Expected {num_channels}, Got {batch.shape[-1]}). Adjusting batch.")
-                 if batch.shape[-1] == 1 and num_channels == 3: batch = batch.repeat(1, 1, 1, 3)
-                 elif batch.shape[-1] == 4 and num_channels == 3: batch = batch[:, :, :, :3]
-                 elif batch.shape[-1] == 3 and num_channels == 4: alpha = torch.ones_like(batch[:, :, :, :1]); batch = torch.cat([batch, alpha], dim=-1)
-                 else: batch = torch.zeros((batch.shape[0], batch.shape[1], batch.shape[2], num_channels), dtype=batch.dtype)
-                 consistent_batches.append(batch)
-
-        final_batch = torch.cat(consistent_batches, dim=0)
+
+        _log("Concatenating final video batch...")
+        try:
+            final_batch = torch.cat(all_frames_collected, dim=0)
+            
+        except RuntimeError as e:
+            _log(f"CRITICAL ERROR: Failed to concatenate final batch. This may be a channel/shape mismatch between segments. {e}")
+            _log("This should not happen if the VAE is consistent. Please report this error.")
+            _log("Returning a dummy tensor to prevent a crash.")
+            return (torch.zeros((1, 64, 64, 3)),)*2
 
         # --- 4b. Final Frame Color Match (New Logic) ---
         if color_match and color_match_lastframe and final_batch.shape[0] > 1: