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on
Zero
Running
on
Zero
import torch | |
import gradio as gr | |
from diffusers import StableVideoDiffusionPipeline | |
from diffusers.utils import load_image, export_to_video | |
import spaces | |
# Check if GPU is available | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
# Load the pipeline | |
pipeline = StableVideoDiffusionPipeline.from_pretrained( | |
"stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16" | |
) | |
pipeline.to(device) | |
def generate_video(image_path, seed): | |
# Load and preprocess the image | |
image = load_image(image_path) | |
image = image.resize((1024, 576)) | |
# Set the generator seed | |
generator = torch.Generator(device=device).manual_seed(seed) | |
# Generate the video frames | |
frames = pipeline(image, decode_chunk_size=8, generator=generator).frames[0] | |
# Export the frames to a video file | |
output_video_path = "generated.mp4" | |
export_to_video(frames, output_video_path, fps=7) | |
return output_video_path | |
# Create the Gradio interface | |
iface = gr.Interface( | |
fn=generate_video, | |
inputs=[ | |
gr.Image(type="filepath", label="Upload Image"), | |
gr.Number(label="Seed", value=42) | |
], | |
outputs=gr.Video(label="Generated Video"), | |
title="Stable Video Diffusion", | |
description="Generate a video from an uploaded image using Stable Video Diffusion.", | |
) | |
# Launch the interface | |
iface.launch() | |