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import gradio as gr
import moviepy.editor as mp
from diffusers import DiffusionPipeline
import spaces
# Load diffusion pipelines
image_pipeline = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo")
video_pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt")
@spaces.GPU(duration=120)
def generate_images(prompt, num_images):
"""Generates images using the image pipeline."""
images = []
for _ in range(num_images):
generated_image = image_pipeline(prompt=prompt).images[0]
images.append(generated_image)
return images
@spaces.GPU(duration=120)
def generate_videos(images):
"""Generates videos from a list of images using the video pipeline."""
videos = []
for image in images:
# Wrap the image in a list as expected by the pipeline
generated_video = video_pipeline(images=[image]).images[0]
videos.append(generated_video)
return videos
def combine_videos(video_clips):
"""Combines video clips into a single video using moviepy."""
final_clip = mp.concatenate_videoclips(video_clips)
return final_clip
def generate(prompt):
"""Generates and combines images and videos."""
images = generate_images(prompt, 2)
video_clips = generate_videos(images)
combined_video = combine_videos(video_clips)
return combined_video
# Gradio interface with improved formatting and video output
interface = gr.Interface(
fn=generate,
inputs="text",
outputs="video",
title="AI Video Generation",
description="Enter a prompt to generate a video using diffusion models.",
css="""
.output-video {
width: 100%; /* Adjust width as needed */
height: 400px; /* Adjust height as desired */
}
""",
)
# Launch the interface
interface.launch()
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