load wan in global

app.py

@@ -97,23 +97,15 @@ if not hasattr(vggt4track_model, 'infer'):
 tracker_model = Predictor.from_pretrained("Yuxihenry/SpatialTrackerV2-Offline")
 tracker_model.eval()
 
-# Lazy loading for Wan to save memory until needed
-wan_pipeline = None
+wan_pipeline = WanImageToVideoTTMPipeline.from_pretrained(
+    WAN_MODEL_ID,
+    torch_dtype=torch.bfloat16
+)
+wan_pipeline.vae.enable_tiling()
+wan_pipeline.vae.enable_slicing()
+wan_pipeline.to("cuda")
 
 
-def get_wan_pipeline():
-    global wan_pipeline
-    if wan_pipeline is None:
-        print("🚀 Loading Wan TTM Pipeline (14B)...")
-        wan_pipeline = WanImageToVideoTTMPipeline.from_pretrained(
-            WAN_MODEL_ID,
-            torch_dtype=torch.bfloat16
-        )
-        wan_pipeline.vae.enable_tiling()
-        wan_pipeline.vae.enable_slicing()
-        wan_pipeline.to("cuda")
-    return wan_pipeline
-
 
 print("✅ Tracking models loaded successfully!")
 
@@ -290,7 +282,6 @@ def run_wan_ttm_generation(prompt, tweak_index, tstrong_index, first_frame_path,
         return None, "❌ TTM Inputs missing. Please run 3D tracking first."
 
     progress(0, desc="Loading Wan TTM Pipeline...")
-    pipe = get_wan_pipeline()
 
     progress(0.2, desc="Preparing inputs...")
     image = load_image(first_frame_path)
@@ -304,8 +295,8 @@ def run_wan_ttm_generation(prompt, tweak_index, tstrong_index, first_frame_path,
 
     # Match resolution logic from run_wan.py
     max_area = 480 * 832
-    mod_value = pipe.vae_scale_factor_spatial * \
-        pipe.transformer.config.patch_size[1]
+    mod_value = wan_pipeline.vae_scale_factor_spatial * \
+        wan_pipeline.transformer.config.patch_size[1]
     height, width = compute_hw_from_area(
         image.height, image.width, max_area, mod_value)
     image = image.resize((width, height))
@@ -314,7 +305,7 @@ def run_wan_ttm_generation(prompt, tweak_index, tstrong_index, first_frame_path,
     generator = torch.Generator(device="cuda").manual_seed(0)
 
     with torch.inference_mode():
-        result = pipe(
+        result = wan_pipeline(
             image=image,
             prompt=prompt,
             negative_prompt=negative_prompt,