Update app.py

app.py

@@ -17,7 +17,7 @@ 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, duration=3):
+def convert_mp4_to_frames(video_path):
     # Read the video file
     video = cv2.VideoCapture(video_path)
 
@@ -26,7 +26,10 @@ def convert_mp4_to_frames(video_path, duration=3):
 
     # Calculate the number of frames to extract
     # Note: we cannot go beyond 3 seconds on the large A10G
-    num_frames = int(fps * min(duration, 3))
+    # num_frames = int(fps * min(duration, 3))
+
+    # let's use a fixed max for now, no need to have a duration field
+    num_frames = int(fps * 3)
 
     frames = []
     frame_count = 0
@@ -64,7 +67,7 @@ def infer(prompt, video_in, denoise_strength, secret_token):
     negative_prompt = "text, watermark, copyright, blurry, cropped, noisy, pixelated, nsfw"
 
 
-    video = convert_mp4_to_frames(video_in, duration)
+    video = convert_mp4_to_frames(video_in)
     video_resized = [Image.fromarray(frame).resize((1024, 576)) for frame in video]
     video_frames = pipe_xl(prompt, negative_prompt=negative_prompt, video=video_resized, strength=denoise_strength).frames
     video_path = export_to_video(video_frames, output_video_path="xl_result.mp4")