Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import numpy as np
 from PIL import Image
+import cv2
 from moviepy.editor import VideoFileClip
 from share_btn import community_icon_html, loading_icon_html, share_js
 import torch
@@ -13,11 +14,39 @@ pipe_xl.scheduler = DPMSolverMultistepScheduler.from_config(pipe_xl.scheduler.co
 pipe_xl.enable_model_cpu_offload()
 pipe_xl.to("cuda")
 
+def convert_mp4_to_frames(video_path):
+    # Read the video file
+    video = cv2.VideoCapture(video_path)
+
+    frames = []
+    
+    # Iterate through each frame
+    while True:
+        # Read a frame
+        ret, frame = video.read()
+        
+        # If the frame was not successfully read, then we have reached the end of the video
+        if not ret:
+            break
+        
+        # Convert the frame to grayscale if needed
+        # frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        # Append the frame to the list of frames
+        frames.append(frame)
+
+    # Release the video object
+    video.release()
+
+    # Convert the list of frames to a numpy array
+    frames = np.array(frames)
+
+    return frames
 
 def infer(prompt, video_in):
 
-   
-    video_frames = pipe_xl(prompt, video=video_in, strength=0.6).frames
+    video = convert_mp4_to_frames(video_in)
+    video_frames = pipe_xl(prompt, video=video, strength=0.6).frames
     video_path = export_to_video(video_frames, output_video_path="xl_result.mp4")
     
     return "xl_result.mp4", gr.Group.update(visible=True)