Update app.py

app.py

@@ -7,26 +7,47 @@ from diffusers.utils import export_to_video
 import tempfile
 import time
 
-pipe = None
+# ============================================================
+# Model loads at CONTAINER STARTUP (free time, no GPU limit)
+# Download + CPU load happens ONCE when Space boots
+# ============================================================
+print("📦 Loading Wan2.1-1.3B on CPU (startup - no GPU timer)...")
+start = time.time()
+
+pipe = WanPipeline.from_pretrained(
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+)
+pipe.vae.enable_tiling()
+gc.collect()
+
+print(f"✅ Model ready in {time.time()-start:.0f}s | GPU time saved for generation only!")
+
 
 @spaces.GPU(duration=240)
 def generate_video(prompt, negative_prompt, num_frames, height, width, num_inference_steps, guidance_scale):
-    global pipe
-    if pipe is None:
-        print("📦 Loading Wan2.1-1.3B to GPU...")
-        pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", torch_dtype=torch.float16, low_cpu_mem_usage=True)
-        pipe.to("cuda")
-        pipe.vae.enable_tiling()
-        print("✅ Loaded!")
-    
+    pipe.to("cuda")
+
+    start = time.time()
     with torch.inference_mode():
-        result = pipe(prompt=prompt, negative_prompt=negative_prompt, num_frames=int(num_frames), height=int(height), width=int(width), num_inference_steps=int(num_inference_steps), guidance_scale=float(guidance_scale)).frames[0]
-    
+        result = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            num_frames=int(num_frames),
+            height=int(height),
+            width=int(width),
+            num_inference_steps=int(num_inference_steps),
+            guidance_scale=float(guidance_scale),
+        ).frames[0]
+
+    print(f"✅ Generated in {time.time()-start:.1f}s")
     output_path = tempfile.mktemp(suffix=".mp4")
     export_to_video(result, output_path, fps=16)
     gc.collect(); torch.cuda.empty_cache()
     return output_path
 
+
 with gr.Blocks(title="Shotarch Video Gen", theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🎬 Shotarch Video Generator\n### Wan2.1-1.3B on ZeroGPU")
     with gr.Row():