Update app.py

app.py

@@ -530,6 +530,33 @@ def prepare_lora_cache(
     progress(1.0, desc="Done!")
     return f"Built and cached LoRA state: {cache_path.name}"
 
+# =============================================================================
+# LoRA State Application (called BEFORE pipeline generation)
+# =============================================================================
+
+def apply_prepared_lora_state_to_pipeline():
+    """
+    Apply the prepared LoRA state from pipeline._cached_state to the preloaded
+    transformer. This should be called BEFORE pipeline generation, not during.
+    """
+    if pipeline._cached_state is None:
+        print("[LoRA] No prepared LoRA state available; skipping.")
+        return False
+    
+    try:
+        existing_transformer = _transformer  # The preloaded transformer from globals
+        with torch.no_grad():
+            missing, unexpected = existing_transformer.load_state_dict(pipeline._cached_state, strict=False)
+            if missing:
+                print(f"[LoRA] load_state_dict mismatch: missing={len(missing)} keys")
+            if unexpected:
+                print(f"[LoRA] load_state_dict mismatch: unexpected={len(unexpected)} keys")
+        
+        print("[LoRA] Prepared LoRA state applied to the pipeline.")
+        return True
+    except Exception as e:
+        print(f"[LoRA] Failed to apply LoRA state: {type(e).__name__}: {e}")
+        return False
 
 # =============================================================================
 # Helper Functions
@@ -722,6 +749,8 @@ def generate_video(
 
         log_memory("before pipeline call")
 
+        apply_prepared_lora_state_to_pipeline()
+
         video, audio = pipeline(
             prompt=prompt,
             negative_prompt=negative_prompt,