metadata

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

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