Update app.py

app.py

@@ -1,23 +1,19 @@
+import spaces
 import gradio as gr
-#import gradio.helpers
 import torch
 import os
 from glob import glob
 from pathlib import Path
 from typing import Optional
-
 from diffusers import StableVideoDiffusionPipeline
 from diffusers.utils import load_image, export_to_video
 from PIL import Image
-
 import uuid
 import random
 from huggingface_hub import hf_hub_download
 
-#gradio.helpers.CACHED_FOLDER = '/data/cache'
-
 pipe = StableVideoDiffusionPipeline.from_pretrained(
-    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
+    "stabilityai/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16, variant="fp16"
 )
 pipe.to("cuda")
 pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
@@ -25,6 +21,7 @@ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
 
 max_64_bit_int = 2**63 - 1
 
+@spaces.GPU
 def sample(
     image: Image,
     seed: Optional[int] = 42,
@@ -48,7 +45,7 @@ def sample(
     base_count = len(glob(os.path.join(output_folder, "*.mp4")))
     video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
 
-    frames = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=25).frames[0]
+    frames = pipe(image, decode_chunk_size=decoding_t, generator=generator, motion_bucket_id=motion_bucket_id, noise_aug_strength=0.1, num_frames=14).frames[0]
     export_to_video(frames, video_path, fps=fps_id)
     torch.manual_seed(seed)