HunyuanVideo

Overview

HunyuanVideo is Tencent's groundbreaking contribution to open-source AI video generation, featuring an unprecedented 13 billion parameters making it the most powerful open-source video generation model available. Launched on December 5, 2024, HunyuanVideo has set new standards for open-source video AI with impressive quality metrics: 68.5% text alignment and 96.4% visual quality scores.

The model leverages an advanced 3D Variational Autoencoder (VAE) architecture to ensure smooth, natural motion and exceptional visual consistency across generated frames. HunyuanVideo can generate clips up to 16 seconds long while maintaining consistent quality at 1280x720 pixels (720p HD), significantly outperforming previous open-source alternatives.

What sets HunyuanVideo apart is its comprehensive camera control system, allowing users to specify movements like zoom in, zoom out, pan up, pan down, tilt up, tilt down, orbit left, orbit right, static shots, and handheld camera movements directly in their prompts. This level of control, combined with full open-source access to code and model weights on GitHub, makes HunyuanVideo an invaluable resource for researchers, developers, and enterprises building custom video generation solutions without the constraints of proprietary APIs.

Key Features

• 13 billion parameters - largest open-source video generation model
• High-quality 720p HD video output at 1280x720 resolution
• Variable video length support up to 16 seconds
• Advanced 3D VAE architecture for smooth motion and visual consistency
• Comprehensive camera controls: zoom, pan, tilt, orbit, static, handheld
• 68.5% text alignment and 96.4% visual quality benchmark scores
• Complete open-source: code and model weights on GitHub
• Outperforms previous state-of-the-art open-source models
• Apache 2.0 license for commercial use
• Active community development and continuous improvements

Use Cases

• Research into large-scale video generation models
• Custom video generation pipeline development
• On-premises video AI deployment for enterprises
• Fine-tuning for specific video styles or domains
• Educational tool for understanding diffusion-based video generation
• Commercial video production without API dependencies
• Prototype and proof-of-concept video creation
• Cinematic shot generation with precise camera control

Technical Specifications

HunyuanVideo uses a 3D VAE with diffusion transformer architecture featuring 13 billion parameters. The model outputs 720p HD video at 1280x720 resolution with variable framerate and supports video lengths up to 16 seconds. Hardware requirements include a minimum of 60GB GPU memory for 720p generation, with 80GB recommended for optimal quality. Suitable GPUs include NVIDIA A100 (80GB), H100, and H200. The model achieves 68.5% text alignment and 96.4% visual quality performance metrics.

Camera Control Capabilities

HunyuanVideo features comprehensive camera control options that can be specified directly in prompts: zoom in and zoom out for focal length adjustments, pan up and pan down for vertical camera movement, tilt up and tilt down for angular adjustments, orbit left and orbit right for circular camera paths, static shots for fixed perspective, and handheld camera movement for dynamic, realistic motion. These controls enable precise cinematic composition and professional-quality video generation.

Open Source and Licensing

HunyuanVideo is completely free and open source under the Apache 2.0 license, allowing for both personal and commercial use. The model requires self-hosted deployment with associated GPU and cloud infrastructure costs, but there are no API fees. Complete access to code and model weights is available on GitHub and Hugging Face.

Code Example: Local Inference with Hugging Face

Deploy HunyuanVideo locally using the Hugging Face Diffusers library. This example demonstrates text-to-video generation with camera controls and memory optimization techniques for GPU-constrained environments.

import torch
from diffusers import HunyuanVideoPipeline
from diffusers.utils import export_to_video
import gc

# Configuration
MODEL_ID = "tencent/HunyuanVideo"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.float16  # Use fp16 for memory efficiency

try:
    # Initialize pipeline with memory optimizations
    print("Loading HunyuanVideo model...")
    pipe = HunyuanVideoPipeline.from_pretrained(
        MODEL_ID,
        torch_dtype=DTYPE,
        variant="fp16",  # Use fp16 variant for reduced memory
    )
    
    # Enable memory-efficient attention and CPU offloading
    pipe.enable_model_cpu_offload()  # Offload to CPU when not in use
    pipe.enable_vae_slicing()  # Process VAE in slices
    pipe.enable_vae_tiling()  # Tile VAE for large resolutions
    
    # Text-to-video generation with camera controls
    prompt = "A majestic eagle soaring over snow-capped mountains at sunset, zoom out, cinematic lighting, 720p HD"
    negative_prompt = "blurry, low quality, distorted, watermark"
    
    print(f"Generating video: {prompt}")
    
    # Generation parameters
    video_frames = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_frames=129,  # ~5 seconds at 24fps
        height=720,
        width=1280,
        num_inference_steps=50,  # More steps = higher quality
        guidance_scale=7.5,  # Controls prompt adherence
        generator=torch.Generator(device=DEVICE).manual_seed(42)
    ).frames[0]
    
    # Export to video file
    output_path = "hunyuan_video_output.mp4"
    export_to_video(video_frames, output_path, fps=24)
    print(f"Video saved to: {output_path}")
    
    # Clean up GPU memory
    del pipe
    gc.collect()
    torch.cuda.empty_cache()
    
    print("Generation complete!")
    
except RuntimeError as e:
    if "out of memory" in str(e):
        print("GPU out of memory. Try reducing resolution or num_frames.")
        print("Recommended: Use GPU with 60GB+ VRAM (A100, H100)")
    else:
        raise e
except Exception as e:
    print(f"Error during generation: {e}")
    raise

# Advanced example: Camera control variations
camera_prompts = [
    "A bustling city street at night, pan right, neon lights",
    "A serene lake reflection, tilt down, morning mist",
    "A racing car on track, orbit left, motion blur",
    "A coffee being poured, zoom in, macro shot",
    "A mountain landscape, static shot, golden hour"
]

# Batch generation with different camera movements
for idx, camera_prompt in enumerate(camera_prompts):
    print(f"\nGenerating video {idx+1}/{len(camera_prompts)}...")
    # Implementation would follow similar pattern as above

Code Example: Cloud API Inference

While HunyuanVideo is primarily designed for local deployment, several cloud providers offer hosted inference endpoints. This example demonstrates integration with Replicate's API for serverless video generation without managing GPU infrastructure.

import replicate
import os
import time
import requests
from pathlib import Path

# Set your Replicate API token
os.environ["REPLICATE_API_TOKEN"] = "r8_your_api_token_here"

def generate_video_cloud(prompt, camera_control="static shot", duration=5):
    """
    Generate video using HunyuanVideo via cloud API
    
    Args:
        prompt: Text description of the video
        camera_control: Camera movement (zoom in/out, pan, tilt, orbit, static)
        duration: Video duration in seconds (up to 16)
    
    Returns:
        Path to downloaded video file
    """
    try:
        # Construct full prompt with camera control
        full_prompt = f"{prompt}, {camera_control}, 720p HD, high quality"
        
        print(f"Submitting generation request...")
        print(f"Prompt: {full_prompt}")
        
        # Submit generation request
        output = replicate.run(
            "tencent/hunyuan-video:latest",
            input={
                "prompt": full_prompt,
                "negative_prompt": "blurry, low quality, distorted, text, watermark",
                "num_frames": duration * 24,  # 24 fps
                "height": 720,
                "width": 1280,
                "guidance_scale": 7.5,
                "num_inference_steps": 50
            }
        )
        
        # Output is a URL to the generated video
        video_url = output
        print(f"Video generated: {video_url}")
        
        # Download the video
        response = requests.get(video_url, stream=True)
        response.raise_for_status()
        
        output_path = Path(f"hunyuan_cloud_{int(time.time())}.mp4")
        with open(output_path, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)
        
        print(f"Video downloaded to: {output_path}")
        return output_path
        
    except replicate.exceptions.ReplicateError as e:
        print(f"Replicate API error: {e}")
        raise
    except requests.exceptions.RequestException as e:
        print(f"Download error: {e}")
        raise
    except Exception as e:
        print(f"Unexpected error: {e}")
        raise

# Business use case: Marketing video generation
if __name__ == "__main__":
    # Example 1: Product showcase with zoom
    video1 = generate_video_cloud(
        prompt="Luxury watch on velvet cushion, studio lighting, reflections",
        camera_control="zoom in",
        duration=8
    )
    
    # Example 2: Real estate walkthrough
    video2 = generate_video_cloud(
        prompt="Modern kitchen with marble countertops, natural sunlight",
        camera_control="pan right",
        duration=10
    )
    
    # Example 3: Product demo with orbit
    video3 = generate_video_cloud(
        prompt="Smartphone displaying app interface, clean background",
        camera_control="orbit right",
        duration=6
    )
    
    print("\nAll marketing videos generated successfully!")
    print(f"Videos saved: {video1}, {video2}, {video3}")

Professional Integration Services by 21medien

Implementing HunyuanVideo in production environments requires expertise in GPU infrastructure, model optimization, and video processing pipelines. 21medien offers comprehensive integration services to help businesses leverage this powerful open-source technology effectively.

Our services include: Infrastructure Planning and GPU cluster deployment for on-premises or cloud-based HunyuanVideo hosting, Custom API Development for integrating video generation into existing workflows and applications, Workflow Automation including batch processing, queue management, and rendering optimization, Prompt Engineering consultation to maximize video quality and achieve specific camera movements and visual styles, Model Fine-tuning for domain-specific applications such as product visualization, real estate, or brand-specific content, Performance Optimization including memory management, inference speed improvements, and cost reduction strategies, and Technical Training for your development team on deployment, maintenance, and troubleshooting.

Whether you need a turnkey video generation platform, custom integration with your existing systems, or expert consultation on optimizing HunyuanVideo for your specific use case, our team of AI engineers and video technology specialists is ready to help. Schedule a free consultation call through our contact page to discuss your video AI requirements and explore how HunyuanVideo can transform your content creation workflow.