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import streamlit as st
import torch
from transformers import pipeline
from PIL import Image
from diffusers import LTXVideoProcessor, LTXVideoPipeline
import numpy as np
from moviepy.editor import ImageSequenceClip
import tempfile
import os
def generate_video_from_image(image, duration_seconds=10, progress_bar=None):
"""
Generate a video from an image using LTX-Video and image captioning.
Args:
image: PIL Image object
duration_seconds: Duration of output video in seconds
progress_bar: Streamlit progress bar object
"""
try:
if progress_bar:
progress_bar.progress(0.1, "Generating image caption...")
# Setup image captioning pipeline
captioner = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
# Generate caption
caption = captioner(image)[0]['generated_text']
st.write(f"Generated caption: *{caption}*")
if progress_bar:
progress_bar.progress(0.3, "Loading LTX-Video model...")
# Initialize LTX-Video pipeline
processor = LTXVideoProcessor()
pipeline = LTXVideoPipeline.from_pretrained("Lightricks/ltx-video")
if progress_bar:
progress_bar.progress(0.4, "Processing image...")
# Process image for video generation
processed_image = processor(image).pixel_values
processed_image = torch.from_numpy(processed_image).unsqueeze(0)
if progress_bar:
progress_bar.progress(0.5, "Generating video frames...")
# Generate video frames
num_frames = duration_seconds * 30 # 30 FPS
video_frames = pipeline(
processed_image,
num_inference_steps=50,
num_frames=num_frames,
guidance_scale=7.5,
prompt=caption,
).videos
if progress_bar:
progress_bar.progress(0.8, "Creating final video...")
# Convert frames to format suitable for moviepy
frames = [np.array(frame) for frame in video_frames[0]]
# Create temporary file for video
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_file:
output_path = tmp_file.name
# Create and save video
clip = ImageSequenceClip(frames, fps=30)
clip.write_videofile(output_path, codec='libx264', audio=False)
if progress_bar:
progress_bar.progress(1.0, "Video generation complete!")
return output_path, caption
except Exception as e:
st.error(f"Error generating video: {str(e)}")
return None, None
def main():
st.set_page_config(page_title="Video Generator", page_icon="🎥")
st.title("🎥 AI Video Generator")
st.write("""
Upload an image to generate a video with AI-powered motion and transitions.
The app will automatically generate a caption for your image and use it as inspiration for the video.
""")
# File uploader
uploaded_file = st.file_uploader("Choose an image", type=['png', 'jpg', 'jpeg'])
# Duration selector
duration = st.slider("Video duration (seconds)", min_value=1, max_value=30, value=10)
if uploaded_file is not None:
# Display uploaded image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Generate button
if st.button("Generate Video"):
# Create a progress bar
progress_text = "Operation in progress. Please wait..."
my_bar = st.progress(0, text=progress_text)
# Generate video
video_path, caption = generate_video_from_image(image, duration, my_bar)
if video_path and os.path.exists(video_path):
# Read the video file
with open(video_path, 'rb') as video_file:
video_bytes = video_file.read()
# Create download button
st.download_button(
label="Download Video",
data=video_bytes,
file_name="generated_video.mp4",
mime="video/mp4"
)
# Display video
st.video(video_bytes)
# Clean up temporary file
os.unlink(video_path)
else:
st.error("Failed to generate video. Please try again.")
if __name__ == "__main__":
main() |