options/Video_model/Model.py

import spaces
import torch,os,imageio
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
from PIL import Image
import numpy as np

# Check if CUDA (GPU) is available, otherwise use CPU
device = "cuda" if torch.cuda.is_available() else "cpu"

def save_video(frames, save_path, fps, quality=9):
    writer = imageio.get_writer(save_path, fps=fps, quality=quality)
    for frame in frames:
        frame = np.array(frame)
        writer.append_data(frame)
    writer.close()

# Function to generate the video
@spaces.GPU(duration=100)
def Video(image):


    pipeline = StableVideoDiffusionPipeline.from_pretrained(
        "stabilityai/stable-video-diffusion-img2vid-xt-1-1", torch_dtype=torch.float16
    ).to(device)

    # Enable model offloading if using the CPU
    if device == "cpu":
        pipeline.enable_model_cpu_offload()
    else:
        pipeline.enable_sequential_cpu_offload()

    
    image = Image.fromarray(image)
    image = image.resize((1024, 576))

    # Set random seed for reproducibility
    generator = torch.manual_seed(42)
    
    # Ensure the image is moved to the appropriate device (GPU or CPU)
    # image = image.to(device)
    
    # Generate the video frames
    frames = pipeline(image, decode_chunk_size=8, generator=generator).frames[0]
    path="generated.mp4"
    # Export the frames to a video file
    save_video(frames, path, fps=7)
    # vid=cv2.VideoCapture(path)
    return path