diff --git a/DCW_ModelPatch.py b/DCW_ModelPatch.py
index 56259c1..045167c 100644
--- a/DCW_ModelPatch.py
+++ b/DCW_ModelPatch.py
@@ -1,4 +1,5 @@
 import torch
+import torch.nn.functional as F
 from pytorch_wavelets import DWTForward, DWTInverse
 
 class DCW_ModelPatch:
@@ -35,19 +36,17 @@ class DCW_ModelPatch:
                 dwt.to(device=device, dtype=dtype)
                 idwt.to(device=device, dtype=dtype)
 
-                # --- DYNAMIC BROADCASTING FIX ---
-                # We count how many dimensions the latent has (4 for image, 5 for video)
-                # and expand the scale 's' to match.
+                # 1. Determine Dimensions
                 dims = len(input_x.shape)
+                # Correctly broadcast S to [B, 1, 1, 1] or [B, 1, 1, 1, 1]
                 s = timestep.view(-1, *([1] * (dims - 1)))
                 
                 if s.shape[0] != output.shape[0]:
                     s = s[0].repeat(output.shape[0], *([1] * (dims - 1)))
 
-                # If 5D (Video), we squeeze the 'Frames' dim to do Wavelet math, then unsqueeze
+                # 2. 5D to 4D Conversion for Wavelets
                 is_5d = (dims == 5)
                 if is_5d:
-                    # [B, C, F, H, W] -> [B*F, C, H, W]
                     b, c_feat, f, h, w = input_x.shape
                     work_input = input_x.transpose(1, 2).reshape(-1, c_feat, h, w)
                     work_output = output.transpose(1, 2).reshape(-1, c_feat, h, w)
@@ -57,10 +56,24 @@ class DCW_ModelPatch:
                     work_output = output
                     work_s = s
 
-                # 1. Calculate x0
-                x0 = work_input - (work_s * work_output)
+                # 3. Handle Padding
+                orig_h, orig_w = work_input.shape[-2:]
+                pad_h = orig_h % 2
+                pad_w = orig_w % 2
                 
-                # 2. Wavelet Logic
+                if pad_h > 0 or pad_w > 0:
+                    work_input = F.pad(work_input, (0, pad_w, 0, pad_h))
+                    work_output = F.pad(work_output, (0, pad_w, 0, pad_h))
+                    # Ensure work_s matches the padded spatial dimensions
+                    work_s_pad = work_s.expand(-1, -1, work_input.shape[-2], work_input.shape[-1])
+                else:
+                    work_s_pad = work_s
+
+                # 4. Calculate x0
+                # If it fails here, the debug prints above will show why
+                x0 = work_input - (work_s_pad * work_output)
+                
+                # 5. Wavelet Correction
                 yl_t, yh_t = dwt(work_input)
                 yl_0, yh_0 = dwt(x0)
                 
@@ -70,21 +83,24 @@ class DCW_ModelPatch:
                 yl_corrected = yl_0 + (strength * (yl_t - yl_0) * t_factor)
                 x0_corrected = idwt((yl_corrected, yh_0))
                 
-                # 3. Final Prediction
+                # 6. Crop and Return
+                if pad_h > 0 or pad_w > 0:
+                    x0_corrected = x0_corrected[:, :, :orig_h, :orig_w]
+                    work_input = work_input[:, :, :orig_h, :orig_w]
+
                 res_output = (work_input - x0_corrected) / torch.clamp(work_s, min=1e-7)
 
-                # 4. Restore 5D shape if necessary
                 if is_5d:
-                    # [B*F, C, H, W] -> [B, C, F, H, W]
-                    res_output = res_output.view(b, f, c_feat, h, w).transpose(1, 2)
-
-                if t_val % 10 == 0:
-                    print(f"[DCW] Success | Mode: {'5D' if is_5d else '4D'} | T: {t_val:.2f}")
+                    res_output = res_output.view(b, f, c_feat, orig_h, orig_w).transpose(1, 2)
 
                 return res_output
 
             except Exception as e:
-                print(f"[DCW ERROR] Still failing: {e}")
+                print(f"\n[DCW FATAL ERROR]: {e}")
+                try:
+                    print(f"Context Shapes -> Input: {work_input.shape}, Output: {work_output.shape}, S: {work_s_pad.shape}")
+                except:
+                    pass
                 return output
 
         m = model.clone()
@@ -92,4 +108,4 @@ class DCW_ModelPatch:
         return (m,)
 
 NODE_CLASS_MAPPINGS = {"DCW_ModelPatch": DCW_ModelPatch}
-NODE_DISPLAY_NAME_MAPPINGS = {"DCW_ModelPatch": "Apply DCW (Wavelet Patch)"}
\ No newline at end of file
+NODE_DISPLAY_NAME_MAPPINGS = {"DCW_ModelPatch": "Apply DCW (Wavelet Patch)"}