Update app.py

app.py

@@ -35,14 +35,41 @@ pipe = WanImageToVideoPipeline.from_pretrained(
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
 pipe.to("cuda")
 
+# Load FusionX enhancement LoRAs
+from huggingface_hub import hf_hub_download
+import torch
+
 # Load FusionX enhancement LoRAs
 lora_adapters = []
 lora_weights = []
 
+# Helper to print model keys
+def print_model_keys(model, show_values=False):
+    print("\n🔑 Model State Dict Keys:")
+    for k, v in model.state_dict().items():
+        if show_values:
+            print(f"{k}: {v.shape}")
+        else:
+            print(k)
+
+# Print keys in UNet, Text Encoder, VAE
+print_model_keys(pipe.unet, show_values=True)
+print_model_keys(pipe.text_encoder, show_values=True)
+print_model_keys(pipe.vae, show_values=True)
+
 # Download LoRA file
 lora_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
 
-# Load and fuse it — use any adapter name (e.g., "default", "main", etc.)
+# Print keys from LoRA checkpoint
+print("\n📦 LoRA Checkpoint Keys:")
+try:
+    lora_checkpoint = torch.load(lora_path, map_location="cpu")
+    for k, v in lora_checkpoint.items():
+        print(f"{k}: {v.shape}")
+except Exception as e:
+    print(f"❌ Failed to load LoRA file for key inspection: {e}")
+
+# Load and fuse LoRA
 try:
     pipe.load_lora_weights(lora_path, adapter_name="main")
     pipe.set_adapters(["main"], adapter_weights=[1.0])