Update app.py

app.py

@@ -195,7 +195,48 @@ def delete_old_files():
         time.sleep(600)
 
 
-threading.Thread(target=delete_old_files, daemon=True).start()
+##threading.Thread(target=delete_old_files, daemon=True).start()
+@spaces.GPU
+def generate(
+    prompt,
+    image_input,
+    seed_value,
+    scale_status,
+    rife_status,
+    progress=gr.Progress(track_tqdm=True)
+):
+    latents, seed = infer(
+        prompt,
+        image_input,
+        num_inference_steps=5,
+        guidance_scale=7.0,
+        seed=seed_value,
+        progress=progress,
+    )
+    if scale_status:
+        latents = upscale_batch_and_concatenate(upscale_model, latents, device)
+    if rife_status:
+        latents = rife_inference_with_latents(frame_interpolation_model, latents)
+
+    batch_size = latents.shape[0]
+    batch_video_frames = []
+    for batch_idx in range(batch_size):
+        pt_image = latents[batch_idx]
+        pt_image = torch.stack([pt_image[i] for i in range(pt_image.shape[0])])
+
+        image_np = VaeImageProcessor.pt_to_numpy(pt_image)
+        image_pil = VaeImageProcessor.numpy_to_pil(image_np)
+        batch_video_frames.append(image_pil)
+
+    video_path = save_video(batch_video_frames[0], fps=math.ceil((len(batch_video_frames[0]) - 1) / 6))
+    video_update = gr.update(visible=True, value=video_path)
+    gif_path = convert_to_gif(video_path)
+    gif_update = gr.update(visible=True, value=gif_path)
+    seed_update = gr.update(visible=True, value=seed)
+
+    return video_path, video_update, gif_update, seed_update
+
+
 examples_images = [
     ["asserts/example_images/1.png", "A woman adorned with a delicate flower crown, is standing amidst a field of gently swaying wildflowers. Her eyes sparkle with a serene gaze, and a faint smile graces her lips, suggesting a moment of peaceful contentment. The shot is framed from the waist up, highlighting the gentle breeze lightly tousling her hair. The background reveals an expansive meadow under a bright blue sky, capturing the tranquility of a sunny afternoon."],
     ["asserts/example_images/2.png", "The video captures a boy walking along a city street, filmed in black and white on a classic 35mm camera. His expression is thoughtful, his brow slightly furrowed as if he's lost in contemplation. The film grain adds a textured, timeless quality to the image, evoking a sense of nostalgia. Around him, the cityscape is filled with vintage buildings, cobblestone sidewalks, and softly blurred figures passing by, their outlines faint and indistinct. Streetlights cast a gentle glow, while shadows play across the boy's path, adding depth to the scene. The lighting highlights the boy's subtle smile, hinting at a fleeting moment of curiosity. The overall cinematic atmosphere, complete with classic film still aesthetics and dramatic contrasts, gives the scene an evocative and introspective feel."],
@@ -321,45 +362,7 @@ with gr.Blocks() as demo:
         </tr>
     </table>
         """)
-    @spaces.GPU
-    def generate(
-        prompt,
-        image_input,
-        seed_value,
-        scale_status,
-        rife_status,
-        progress=gr.Progress(track_tqdm=True)
-    ):
-        latents, seed = infer(
-            prompt,
-            image_input,
-            num_inference_steps=5,
-            guidance_scale=7.0,
-            seed=seed_value,
-            progress=progress,
-        )
-        if scale_status:
-            latents = upscale_batch_and_concatenate(upscale_model, latents, device)
-        if rife_status:
-            latents = rife_inference_with_latents(frame_interpolation_model, latents)
-
-        batch_size = latents.shape[0]
-        batch_video_frames = []
-        for batch_idx in range(batch_size):
-            pt_image = latents[batch_idx]
-            pt_image = torch.stack([pt_image[i] for i in range(pt_image.shape[0])])
-
-            image_np = VaeImageProcessor.pt_to_numpy(pt_image)
-            image_pil = VaeImageProcessor.numpy_to_pil(image_np)
-            batch_video_frames.append(image_pil)
-
-        video_path = save_video(batch_video_frames[0], fps=math.ceil((len(batch_video_frames[0]) - 1) / 6))
-        video_update = gr.update(visible=True, value=video_path)
-        gif_path = convert_to_gif(video_path)
-        gif_update = gr.update(visible=True, value=gif_path)
-        seed_update = gr.update(visible=True, value=seed)
-
-        return video_path, video_update, gif_update, seed_update
+    
 
     generate_button.click(
         generate,
@@ -367,6 +370,5 @@ with gr.Blocks() as demo:
         outputs=[video_output, download_video_button, download_gif_button, seed_text],
     )
 
-if __name__ == "__main__":
-    demo.queue(max_size=15)
-    demo.launch(debug=True)
+demo.queue(max_size=15)
+demo.launch(debug=True)