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import torch,os,imageio
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
from PIL import Image
from huggingface_hub import login
login(token=os.getenv("TOKEN"))
# 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
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 |