feat(wan): Add LoRA with improved quantization pipeline

mlx_video/models/wan/vae22.py

@@ -7,12 +7,15 @@ Weight keys mirror the PyTorch checkpoint hierarchy so only tensor format
 conversion (channels-first → channels-last) is needed.
 """
 
+import logging
 import math
 
 import mlx.core as mx
 import mlx.nn as nn
 import numpy as np
 
+logger = logging.getLogger(__name__)
+
 CACHE_T = 2
 
 # Per-channel normalization for z_dim=48 latent space
@@ -774,11 +777,13 @@ def sanitize_wan22_vae_weights(weights: dict, include_encoder: bool = False) ->
     Maps PyTorch nn.Sequential indices to our named layers.
     """
     sanitized = {}
+    consumed = set()
 
     for key, value in weights.items():
         # Skip encoder and conv1 unless requested
         if not include_encoder:
             if key.startswith("encoder.") or key.startswith("conv1."):
+                consumed.add(key)
                 continue
 
         new_key = key
@@ -832,5 +837,10 @@ def sanitize_wan22_vae_weights(weights: dict, include_encoder: bool = False) ->
             value = mx.array(np.array(value).squeeze())
 
         sanitized[new_key] = value
+        consumed.add(key)
+
+    unconsumed = set(weights.keys()) - consumed
+    if unconsumed:
+        logger.warning("Unconsumed Wan2.2 VAE weight keys: %s", sorted(unconsumed))
 
     return sanitized