Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,449 Bytes
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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)
@spaces.GPU(duration=120)
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.",
examples=[
["image.png", "generated.mp4"]
]
)
# Launch the interface
iface.launch() |