feat(wan): Add LoRA with improved quantization pipeline

mlx_video/models/wan/loading.py

@@ -6,14 +6,15 @@ import mlx.core as mx
 import mlx.nn as nn
 
 
-def load_wan_model(model_path: Path, config, quantization: dict | None = None):
-    """Load and initialize WanModel, with optional quantization support.
+def load_wan_model(model_path: Path, config, quantization: dict | None = None, loras: list | None = None):
+    """Load and initialize WanModel, with optional quantization and LoRA support.
 
     Args:
         model_path: Path to model safetensors file
         config: WanModelConfig
         quantization: Optional dict with 'bits' and 'group_size' keys.
                       If provided, creates QuantizedLinear stubs before loading.
+        loras: Optional list of (lora_path, strength) tuples to apply.
     """
     from mlx_video.models.wan.model import WanModel
 
@@ -30,6 +31,27 @@ def load_wan_model(model_path: Path, config, quantization: dict | None = None):
         )
 
     weights = mx.load(str(model_path))
+
+    # Apply LoRAs: dequantize+merge for quantized models, weight merge for bf16
+    if loras:
+        if quantization:
+            # Dequantize LoRA-targeted layers, merge delta, replace with bf16 Linear.
+            # Non-LoRA layers stay 4-bit. Zero per-step overhead.
+            from mlx_video.convert_wan import _load_lora_configs
+            from mlx_video.lora import apply_loras_to_model
+
+            model.load_weights(list(weights.items()), strict=False)
+            mx.eval(model.parameters())
+            module_to_loras = _load_lora_configs(loras)
+            apply_loras_to_model(model, module_to_loras)
+            mx.eval(model.parameters())
+            return model
+        else:
+            # Weight merging: fold LoRA into bf16 weights before loading
+            from mlx_video.convert_wan import load_and_apply_loras
+
+            weights = load_and_apply_loras(dict(weights), loras)
+
     model.load_weights(list(weights.items()), strict=False)
     mx.eval(model.parameters())
     return model