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import gradio as gr
import torch
import os
import numpy as np
import ffmpeg
from moviepy.editor import AudioFileClip, ImageSequenceClip
from diffusers import StableDiffusionPipeline
from tempfile import TemporaryDirectory
# Load AI Image Generation Model (Stable Diffusion)
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(device)
def generate_frames(prompt, num_frames=30):
"""Generate a series of images based on the given prompt."""
images = []
for i in range(num_frames):
image = pipe(prompt).images[0]
images.append(image)
return images
def create_music_video(song, prompt):
"""Generate AI-based video from the uploaded song."""
with TemporaryDirectory() as temp_dir:
audio_path = os.path.join(temp_dir, "song.mp3")
video_path = os.path.join(temp_dir, "output.mp4")
# Save uploaded song
with open(audio_path, "wb") as f:
f.write(song)
# Extract duration
audio_clip = AudioFileClip(audio_path)
duration = audio_clip.duration
fps = 10
num_frames = int(duration * fps)
# Generate images
images = generate_frames(prompt, num_frames)
# Create video from images
image_sequence_clip = ImageSequenceClip([np.array(img) for img in images], fps=fps)
image_sequence_clip = image_sequence_clip.set_audio(audio_clip)
image_sequence_clip.write_videofile(video_path, codec="libx264", audio_codec="aac")
return video_path
# Gradio UI
interface = gr.Interface(
fn=create_music_video,
inputs=[
gr.Audio(type="file", label="Upload Song"),
gr.Textbox(label="Describe the Music Video (Prompt)")
],
outputs=gr.Video(label="Generated AI Music Video"),
title="AI Music Video Generator",
description="Upload a song and describe the music video you want. AI will generate a video using Stable Diffusion.",
)
# Launc
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