Open Source Release

UniVideo: The Unified AI Video Generation, Editing & Understanding Model

Experience the next generation of AI video creation with UniVideo from Kling Team (KwaiVGI). Unify text-to-video generation, image-to-video conversion, in-context video editing, and multimodal understanding in one powerful open-source framework.

View on GitHub Hugging Face Read Paper →
UniVideo Studio - AI Video Generation Demo
83.5
MMBench Score
82.58
VBench Score
0.728
CLIP-I Score
Apache 2.0
License
About UniVideo

What is UniVideo? The Revolutionary Unified Video Model

UniVideo is a groundbreaking open-source unified video foundation model developed by the Kling Team at KwaiVGI. Unlike traditional task-specific video AI models, UniVideo combines video understanding, generation, and editing capabilities into a single cohesive framework.

Built on an innovative dual-stream architecture that pairs a Multimodal Large Language Model (MLLM) with a Multimodal Diffusion Transformer (MMDiT), UniVideo achieves state-of-the-art performance across multiple video AI benchmarks while enabling seamless task composition and zero-shot generalization.

Dual-Stream Architecture

MLLM (Qwen2.5-VL) + MMDiT (HunyuanVideo) for unified processing

Multi-Task Unification

Text-to-video, image-to-video, editing, and understanding in one model

Zero-Shot Generalization

Transfer image capabilities to video tasks without additional training

UniVideo Architecture Overview

Input Layer

Text / Image / Video Instructions

↓
MLLM Branch (Qwen2.5-VL-7B)

Multimodal Understanding & Instruction Processing

↓
MMDiT Branch (HunyuanVideo-T2V-13B)

Video Generation & Editing Engine

↓
Output Layer

Generated / Edited Video Output

Core Features

UniVideo Key Capabilities for AI Video Generation and Editing

Discover the powerful features that make UniVideo the most versatile unified video AI model available. From text-to-video generation to advanced in-context editing, UniVideo delivers exceptional results.

Text-to-Video Generation

Generate high-quality videos directly from text prompts using UniVideo's advanced natural language understanding and video synthesis capabilities.

Image-to-Video Conversion

Transform static images into dynamic videos with UniVideo's image-to-video AI capabilities, maintaining visual consistency and natural motion.

In-Context Video Generation

Create videos with multiple consistent characters and objects using in-context learning, enabling multi-ID video generation without additional training.

Free-Form Video Editing

Edit videos without masks using UniVideo's instruction-based editing. Replace backgrounds, change materials, insert objects, and more with natural language commands.

Video Style Transfer

Apply artistic styles to videos while preserving content structure. Transform video aesthetics with UniVideo's powerful style transfer capabilities.

Visual Prompt Video Generation

Use images and diagrams as visual prompts to guide video generation. UniVideo understands visual references for precise creative control.

Applications

UniVideo Use Cases: AI Video Generation for Every Industry

From filmmaking to marketing, UniVideo transforms how professionals create and edit video content across diverse industries.

Filmmaking & Pre-visualization

Create pre-viz sequences, concept videos, and storyboard animations for film and TV production.

  • Rapid concept visualization
  • Scene pre-visualization
  • VFX placeholder generation

Game Design & Animation

Generate cinematics, character animations, and promotional content for games and interactive media.

  • Game trailer creation
  • Cutscene generation
  • Character animation prototypes

Advertising & Social Media

Produce engaging video ads, social media content, and marketing videos at scale.

  • Product showcase videos
  • Social media clips
  • A/B testing video variants

Education & Training

Create educational videos, training materials, and explainer content for learning applications.

  • Instructional videos
  • Training simulations
  • Educational animations
For Whom

Who Uses UniVideo? Target Audience for AI Video Generation

UniVideo serves diverse professionals from AI researchers to content creators, providing tools tailored to each user's needs.

AI Researchers & Engineers

Access source code, model weights, and technical documentation for research and development.

Video Creators & Studios

Generate and edit professional video content with intuitive tools and workflow templates.

Product Builders & Startups

Integrate UniVideo into applications and services with comprehensive API documentation.

Designers & Animators

Accelerate creative workflows with AI-powered video generation and style transfer tools.

Technical Deep Dive

UniVideo Architecture: Dual-Stream Multimodal Video Model

UniVideo employs an innovative dual-stream architecture that combines the strengths of Multimodal Large Language Models (MLLM) for understanding with Multimodal Diffusion Transformers (MMDiT) for generation.

The MLLM branch, based on Qwen2.5-VL-7B-Instruct, processes multimodal instructions and provides semantic understanding. The MMDiT branch, derived from HunyuanVideo-T2V-13B, handles the actual video generation and editing through diffusion-based synthesis.

This architecture enables UniVideo to handle diverse tasks including text-to-video generation, image-to-video conversion, in-context generation, and free-form video editing through a unified instruction paradigm.

Qwen2.5-VL HunyuanVideo MMDiT PyTorch 2.4.1 CUDA 12.1 Python 3.11
Input Processing
Text + Image + Video Instructions
↓
MLLM Branch
Semantic Understanding
MMDiT Branch
Video Synthesis
↓
Unified Output
Generated / Edited Video
Performance

UniVideo Benchmarks: AI Video Model Performance Comparison

UniVideo achieves state-of-the-art results across multiple video AI benchmarks, demonstrating superior performance in understanding, generation, and editing tasks.

Benchmark UniVideo Score Previous SOTA Improvement
MMBench (Understanding) 83.5 81.2 +2.8%
VBench (Generation) 82.58 80.1 +3.1%
CLIP-I (Identity Alignment) 0.728 0.695 +4.7%
Video Editing Quality 87.3 82.6 +5.7%
Quick Start

Getting Started with UniVideo: Installation and Setup Guide

Get UniVideo running in minutes with our streamlined installation process. Follow these steps to start generating AI videos.

01

Clone Repository

Download UniVideo source code from GitHub and set up your development environment with Python 3.11 and CUDA 12.1.

02

Download Weights

Fetch UniVideo model weights from Hugging Face. The model includes both MLLM and MMDiT components.

03

Run Inference

Execute the provided inference scripts to generate videos from text prompts or edit existing video content.

bash 
# Clone the UniVideo repository
git clone https://github.com/KwaiVGI/UniVideo.git
cd UniVideo

# Install dependencies
pip install -r requirements.txt

# Download model weights from Hugging Face
huggingface-cli download KwaiVGI/UniVideo --local-dir ./checkpoints

# Run text-to-video generation
python inference.py --prompt "A cat walking through a beautiful garden"
Templates

UniVideo Workflow Templates: AI Video Generation Pipelines

Ready-to-use workflow templates for common video generation scenarios. Copy and customize for your specific needs.

Advertising Video Production Marketing

Create compelling 5-15 second product showcase videos from text descriptions and product images.

Product Image → Text Prompt → UniVideo I2V → Final Video

Educational Content Creation Education

Transform lesson scripts into engaging educational videos with visual explanations and animations.

Script → Storyboard → UniVideo T2V → Educational Video

Game Cinematic Production Gaming

Generate consistent game cinematics from concept art with style-unified video sequences.

Concept Art → Style Reference → UniVideo → Game Cinematic
Core Capability

Text-to-Video Generation with UniVideo AI

UniVideo's text-to-video generation capability transforms natural language descriptions into high-quality video content. Using the powerful combination of MLLM for text understanding and MMDiT for video synthesis, UniVideo generates coherent, visually appealing videos that match your text prompts.

  • High-resolution video output up to 2K resolution
  • Natural language prompt understanding
  • Smooth motion and temporal consistency
  • Multiple aspect ratio support
Example Prompt

"A majestic eagle soaring through mountain peaks at golden hour, cinematic lighting, 4K quality"

Core Capability

Image-to-Video Conversion: Bring Static Images to Life

Transform static images into dynamic video content with UniVideo's image-to-video (I2V) capability. The model preserves visual consistency while adding natural motion and animation to your source images.

  • Maintains visual fidelity to source image
  • Adds natural, physics-aware motion
  • Supports multiple reference images
  • Control motion with text instructions
I2V Workflow

Input: Portrait photograph
Instruction: "Make the person smile and turn their head slightly"
Output: Animated video with natural facial movement

Advanced Editing

AI Video Editing with UniVideo: Free-Form Instruction-Based Editing

UniVideo enables powerful video editing capabilities through natural language instructions, eliminating the need for masks or complex selection tools.

Green Screen Replacement

Replace video backgrounds instantly with natural language commands. No manual masking required.

Material & Texture Changes

Transform object materials and textures throughout the video while maintaining temporal consistency.

Object Insertion & Removal

Add or remove objects from videos seamlessly with instruction-based editing powered by AI.

Advanced Generation

In-Context Video Generation: Multi-ID and Reference-Based Creation

UniVideo's in-context generation capability enables creating videos with multiple consistent characters, objects, or references without additional training. Simply provide reference images, and UniVideo maintains identity consistency throughout the generated video.

  • Support for 5-7 reference images per generation
  • Consistent identity preservation across frames
  • Multi-character video generation
  • Zero-shot generalization from image references
In-Context Example

References: [Image of person A], [Image of person B]
Prompt: "Person A and Person B having a conversation at a café"
Result: Video with consistent character identities

Creative Control

Visual Prompt-Based Video Generation with UniVideo

Use images, diagrams, and visual references to guide video generation with unprecedented precision. UniVideo understands visual prompts to create videos that match your creative vision.

  • Diagram-to-video generation
  • Style reference image support
  • Layout and composition control
  • Combined text + visual prompting
Visual Prompting

Input: Sketch diagram of scene layout
Style Ref: Oil painting reference image
Text: "Sunset over mountains"
Output: Video matching layout in oil painting style

Unified Capabilities

Task Composition & Generalization: UniVideo's Unified Approach

UniVideo's unified architecture enables combining multiple tasks in single operations, such as editing + style transfer or generation + in-context references.

Video Editing
+
Style Transfer
=
Styled Edited Video
Integration

UniVideo API Integration: Build AI Video Applications

Integrate UniVideo into your applications with our comprehensive API. Build custom video generation and editing workflows programmatically.

python 
from univideo import UniVideoModel

# Initialize UniVideo model
model = UniVideoModel(checkpoint_path="./checkpoints")

# Text-to-Video generation
video = model.generate(
    prompt="A beautiful sunset over the ocean with waves",
    num_frames=48,
    resolution=(1280, 720)
)

# Image-to-Video conversion
video = model.image_to_video(
    image="input_image.jpg",
    motion_prompt="Camera slowly pans right"
)

# Video editing
edited = model.edit_video(
    video="source_video.mp4",
    instruction="Replace the background with a forest scene"
)

# Save output
video.save("output_video.mp4")
Resources

UniVideo Documentation: Guides, Tutorials & API Reference

Comprehensive documentation to help you get the most out of UniVideo's capabilities.

Installation Guide

Step-by-step instructions for setting up UniVideo on your local machine or cloud environment.

Read Guide →

API Reference

Complete API documentation with function signatures, parameters, and usage examples.

View API →

Video Tutorials

Watch video walkthroughs covering UniVideo features and best practices.

Watch Now →
Academic

UniVideo Research: Papers and Technical Publications

Dive into the research behind UniVideo's groundbreaking unified video model architecture.

UniVideo: Unified Video Understanding, Generation and Editing

Comprehensive paper detailing the dual-stream architecture, training methodology, and benchmark results.

arXiv:2510.08377 Kling Team, KwaiVGI 2025
Read Paper Download PDF
Comparison

UniVideo vs Other AI Video Models: Feature Comparison

See how UniVideo compares to other leading AI video generation models in terms of capabilities and features.

Feature UniVideo Kling v1.6 Sora Pika
Text-to-Video ✓ ✓ ✓ ✓
Image-to-Video ✓ ✓ ✓ ✓
In-Context Generation ✓ — — —
Free-Form Editing ✓ — ✓ ✓
Task Composition ✓ — — —
Open Source ✓ — — —
Video Understanding ✓ — — —
Community

Join the UniVideo Community: Connect with AI Video Creators

Connect with researchers, developers, and creators working with UniVideo and AI video generation.

GitHub

Star the repo, contribute code, and report issues.

Visit GitHub

Hugging Face

Access model weights and try online demos.

Visit HF

X (Twitter)

Follow for updates and community highlights.

Follow Us

Discord

Join discussions with the AI video community.

Join Discord
FAQ

Frequently Asked Questions About UniVideo AI Video Model

Find answers to common questions about UniVideo installation, usage, capabilities, and requirements.

UniVideo is an open-source unified video foundation model developed by the Kling Team at KwaiVGI. It uses a dual-stream architecture combining a Multimodal Large Language Model (MLLM based on Qwen2.5-VL) for understanding with a Multimodal Diffusion Transformer (MMDiT based on HunyuanVideo) for generation. This enables UniVideo to handle video understanding, generation, and editing tasks in a single unified framework.

UniVideo requires a GPU with at least 24GB VRAM for basic usage. For optimal performance, 40GB or 80GB GPUs are recommended. The software requirements include Python 3.11, PyTorch 2.4.1, and CUDA 12.1. Cloud platforms like Google Colab Pro or Hugging Face Spaces can also be used to run UniVideo without local GPU hardware.

Yes, UniVideo is released under the Apache-2.0 license, which permits both personal and commercial use. You can freely use, modify, and distribute UniVideo in your projects. The source code is available on GitHub and model weights are hosted on Hugging Face for easy access.

UniVideo differentiates itself by being fully open-source and offering unified capabilities that most other models lack. While Sora, Runway, and Pika are proprietary and task-specific, UniVideo combines video understanding, generation, and editing in one model. UniVideo uniquely supports in-context generation, task composition, and zero-shot generalization from images to videos.

UniVideo supports comprehensive free-form video editing through natural language instructions. Capabilities include green screen background replacement, material and texture changes, object insertion and removal, style transfer, and in-context editing. Unlike traditional tools, UniVideo doesn't require manual masking or complex selection processes.

Yes, UniVideo can be run on cloud platforms. Hugging Face Spaces offers hosted demos for quick testing. For more control, you can use Google Colab Pro with A100 GPUs or cloud providers like AWS, GCP, or Azure with appropriate GPU instances. The community has also created various notebooks and deployment guides for cloud setup.

In-context video generation allows you to provide reference images of specific characters, objects, or styles, and UniVideo will maintain their consistency throughout the generated video. This enables multi-ID video generation where multiple distinct characters appear with preserved identities, without requiring additional model training or fine-tuning.

Installation involves three main steps: 1) Clone the GitHub repository, 2) Install Python dependencies using pip install -r requirements.txt, and 3) Download model weights from Hugging Face using huggingface-cli. Detailed installation instructions are available in the GitHub repository README and our Getting Started guide above.

Updates

UniVideo Development Roadmap: Future Features and Updates

Track the development progress of UniVideo and upcoming features planned by the Kling Team.

Q4 2025

Initial Open Source Release

UniVideo model weights, source code, and documentation released on GitHub and Hugging Face.

Q1 2026

Enhanced Editing Capabilities

Improved video editing with better temporal consistency and expanded instruction support.

Q2 2026

Higher Resolution Support

Extended support for 4K video generation and longer video durations.

Q3 2026

Audio Integration

Planned audio generation and synchronization capabilities for complete video production.

Success Stories

UniVideo Case Studies: Real-World AI Video Applications

Discover how creators and businesses are using UniVideo for AI video generation and editing.

Film Production

Pre-Visualization Pipeline

How a film studio reduced pre-viz production time by 60% using UniVideo for rapid scene conceptualization.

Marketing

Social Media Campaign

A marketing agency created 50+ personalized video ads in a week using UniVideo's batch generation capabilities.

Education

E-Learning Content

An EdTech startup built an entire course library with AI-generated explainer videos using UniVideo.

System Requirements

UniVideo Hardware Requirements: GPU and System Specifications

Choose the right hardware configuration for your UniVideo deployment based on your usage needs.

24GB
Minimum VRAM

Basic Usage

  • RTX 3090 / RTX 4090
  • 720p video generation
  • Basic editing tasks
  • Single batch processing
40GB
Recommended VRAM

Professional Usage

  • A100 40GB / A6000
  • 1080p video generation
  • All editing capabilities
  • In-context generation
80GB
Optimal VRAM

Enterprise Usage

  • A100 80GB / H100
  • 2K+ video generation
  • Batch processing
  • Production workloads
Open Source

Contribute to UniVideo: Join Open Source Development

UniVideo is open source and welcomes contributions from the community. Here's how you can get involved.

Code Contributions

Submit pull requests to improve UniVideo's functionality, fix bugs, or add new features.

Documentation

Help improve documentation, write tutorials, or translate content to other languages.

Issue Reports

Report bugs, suggest features, or share feedback to help improve UniVideo.

About

Kling Team & KwaiVGI: The Creators of UniVideo

UniVideo is developed by the Kling Team at KwaiVGI (Kuaishou Vision Generation & Intelligence), a leading research group focused on AI video generation and multimodal understanding.

The team has previously released influential models including the Kling video generation series, and continues to push the boundaries of what's possible with AI-powered video creation and editing.

KwaiVGI GitHub Project Page →

Start Creating with UniVideo Today

Join thousands of creators and researchers using UniVideo for AI video generation and editing.

Get Started on GitHub View on Hugging Face