Update app.py

app.py

@@ -9,21 +9,13 @@ import numpy as np
 pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b", torch_dtype=torch.float16, variant="fp16")
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
+pipe.enable_vae_slicing()
 
 @spaces.GPU(duration=250)
-def generate(prompt, num_inference_steps=25):
-  video_frames = pipe(prompt, num_inference_steps).frames
-  resized_frames = []
-  allowed_resolutions = [16, 24, 32, 40, 48, 56, 64, 128, 256, 512]  # Define allowed resolutions (multiples of 8)
-  for frame in video_frames:
-    height, width, _ = frame.shape
-    # Find the closest allowed resolution smaller than the original
-    new_height = max(res for res in allowed_resolutions if res < height)
-    new_width = max(res for res in allowed_resolutions if res < width)
-    resized_frame = cv2.resize(frame, (new_width, new_height))
-    resized_frames.append(resized_frame)
-  video_path = export_to_video(np.array(resized_frames))
-  return video_path
+def generate(prompt, num_inference_steps=25, frames=200):
+    video_frames = pipe(prompt, num_inference_steps, num_frames).frames
+    video_path = export_to_video(video_frames)
+    return video_path
 
 prompt = gr.Textbox("Enter prompt to generate a video")
 num_inference_steps = gr.Slider(10, 50, value=25)