Update app.py

app.py

@@ -3,6 +3,8 @@ import spaces
 import torch
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 from diffusers.utils import export_to_video
+import cv2
+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)
@@ -11,7 +13,14 @@ pipe.enable_model_cpu_offload()
 @spaces.GPU(duration=250)
 def generate(prompt, num_inference_steps=25):
     video_frames = pipe(prompt, num_inference_steps).frames
-    video_path = export_to_video(video_frames)
+    resized_frames = []
+    for frame in video_frames:
+        height, width, _ = frame.shape
+        new_height = (height // 8) * 8
+        new_width = (width // 8) * 8
+        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
 
 prompt = gr.Textbox("Enter prompt to generate a video")