Update app.py

app.py

@@ -51,9 +51,9 @@ global pipe
 pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.bfloat16)
 pipe.to("cuda")
 # Enable memory savings
-pipe.vae.enable_slicing()
-pipe.vae.enable_tiling()
-pipe.enable_model_cpu_offload()
+# pipe.vae.enable_slicing()
+# pipe.vae.enable_tiling()
+# pipe.enable_model_cpu_offload()
 
 with torch.no_grad():  # enable image inputs
     initial_input_channels = pipe.transformer.config.in_channels
@@ -111,7 +111,8 @@ def resize_image_to_bucket(image: Union[Image.Image, np.ndarray], bucket_reso: T
     image = image[crop_top:crop_top + bucket_height, crop_left:crop_left + bucket_width]
     return image
 
-@spaces.GPU(duration=120)
+# @spaces.GPU(duration=120)
+@torch.inference_mode()
 def generate_video(prompt: str, frame1: Image.Image, frame2: Image.Image, resolution: str, guidance_scale: float, num_frames: int, num_inference_steps: int) -> bytes:
     # Debugging print statements
     print(f"Frame 1 Type: {type(frame1)}")