---
title: A Video Crossover Generator
emoji: 🌍
colorFrom: green
colorTo: green
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: gpl-3.0
short_description: made by movies
---
pip install transformers torch torchvision

import torch
from transformers import VideoGPT, VideoProcessor

# Load a pre-trained video generation model
model = VideoGPT.from_pretrained("huggingface/video-gpt")
processor = VideoProcessor.from_pretrained("huggingface/video-gpt")

def generate_crossover_video(video1_path, video2_path, output_path):
    # Load and process the input videos
    video1 = processor(video1_path)
    video2 = processor(video2_path)

    # Generate a crossover video
    with torch.no_grad():
        crossover_video = model.generate(video1, video2)

    # Save the generated video
    crossover_video.save(output_path)

# Example usage
generate_crossover_video("path/to/video1.mp4", "path/to/video2.mp4", "path/to/output_video.mp4")

import torch
from transformers import VideoGPT, VideoProcessor
from moviepy.editor import VideoFileClip, concatenate_videoclips

# Load the model and processor
model = VideoGPT.from_pretrained("huggingface/video-gpt")
processor = VideoProcessor.from_pretrained("huggingface/video-gpt")

def generate_crossover_video(video1_path, video2_path, output_path):
    # Load and process the input videos
    video1 = processor(video1_path)
    video2 = processor(video2_path)
    
    # Generate crossover video
    with torch.no_grad():
        crossover_video = model.generate(video1, video2)
    
    # Save the generated video
    crossover_video.save(output_path)

    def combine_cars_videos(video1_path, video2_path, output_path):
    clip1 = VideoFileClip(video1_path).subclip(0, 10)  # Take the first 10 seconds of video1
    clip2 = VideoFileClip(video2_path).subclip(0, 10)  # Take the first 10 seconds of video2

    final_clip = concatenate_videoclips([clip1, clip2])
    final_clip.write_videofile(output_path, codec="libx264")

    # Example usage
generate_crossover_video("path/to/cars_video1.mp4", "path/to/cars_video2.mp4", "path/to/output_crossover_video.mp4")
combine_cars_videos("path/to/cars_video1.mp4", "path/to/cars_video2.mp4", "path/to/final_output_video.mp4")

import torch
from transformers import VideoGPT, VideoProcessor  # Hypothetical models
from moviepy.editor import VideoFileClip, concatenate_videoclips

# Load the model and processor (hypothetical)
model = VideoGPT.from_pretrained("huggingface/video-gpt")
processor = VideoProcessor.from_pretrained("huggingface/video-gpt")

def generate_crossover_video(video1_path, video2_path, output_path):
    # Load and process the input videos
    video1 = processor(video1_path)
    video2 = processor(video2_path)

    # Generate a crossover video
    with torch.no_grad():
        crossover_video = model.generate(video1, video2)

    # Save the generated video
    crossover_video.save(output_path)

    def combine_bfdi_videos(video1_path, video2_path, output_path):
    clip1 = VideoFileClip(video1_path).subclip(0, 10)  # Take the first 10 seconds of video1
    clip2 = VideoFileClip(video2_path).subclip(0, 10)  # Take the first 10 seconds of video2

    final_clip = concatenate_videoclips([clip1, clip2])
    final_clip.write_videofile(output_path, codec="libx264")

    # Example usage
generate_crossover_video("path/to/bfd1_video.mp4", "path/to/bfb_video.mp4", "path/to/output_crossover_video.mp4")
combine_bfdi_videos("path/to/bfdia_video.mp4", "path/to/tpot_video.mp4", "path/to/final_output_video.mp4")

import torch
from transformers import VideoGPT, VideoProcessor  # Note: These are hypothetical models
from moviepy.editor import VideoFileClip, concatenate_videoclips

# Load the model and processor
model = VideoGPT.from_pretrained("huggingface/video-gpt")  # Replace with an actual pre-trained model
processor = VideoProcessor.from_pretrained("huggingface/video-gpt")  # Replace with an actual pre-trained processor

def generate_crossover_video(video1_path, video2_path, output_path):
    # Load and process the input videos
    video1 = processor(video1_path)
    video2 = processor(video2_path)
    
    # Generate a crossover video
    with torch.no_grad():
        crossover_video = model.generate(video1, video2)
    
    # Save the generated video
    crossover_video.save(output_path)

    def combine_mario_videos(video1_path, video2_path, output_path):
    clip1 = VideoFileClip(video1_path).subclip(0, 10)  # Take the first 10 seconds of video1
    clip2 = VideoFileClip(video2_path).subclip(0, 10)  # Take the first 10 seconds of video2
    
    final_clip = concatenate_videoclips([clip1, clip2])
    final_clip.write_videofile(output_path, codec="libx264")

    # Example usage
generate_crossover_video("path/to/mario_video1.mp4", "path/to/mario_video2.mp4", "path/to/output_crossover_video.mp4")
combine_mario_videos("path/to/mario_video1.mp4", "path/to/mario_video2.mp4", "path/to/final_output_video.mp4")

# Load the model and processor
model = VideoGPT.from_pretrained("huggingface/video-gpt")  # Replace with an actual pre-trained model
processor = VideoProcessor.from_pretrained("huggingface/video-gpt")  # Replace with an actual pre-trained processor

import torch
from transformers import VideoGPT, VideoProcessor  # Note: These are hypothetical models
from moviepy.editor import VideoFileClip, concatenate_videoclips

def generate_crossover_video(video1_path, video2_path, output_path):
    # Load and process the input videos
    video1 = processor(video1_path)
    video2 = processor(video2_path)

    # Generate a crossover video
    with torch.no_grad():
        crossover_video = model.generate(video1, video2)

    # Save the generated video
    crossover_video.save(output_path)

    def combine_minecraft_videos(video1_path, video2_path, output_path):
    clip1 = VideoFileClip(video1_path).subclip(0, 10)  # Take the first 10 seconds of video1
    clip2 = VideoFileClip(video2_path).subclip(0, 10)  # Take the first 10 seconds of video2

    final_clip = concatenate_videoclips([clip1, clip2])
    final_clip.write_videofile(output_path, codec="libx264")

    # Example usage
generate_crossover_video("path/to/minecraft_video1.mp4", "path/to/minecraft_video2.mp4", "path/to/output_crossover_video.mp4")
combine_minecraft_videos("path/to/minecraft_video1.mp4", "path/to/minecraft_video2.mp4", "path/to/final_output_video.mp4")

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference