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How to Generate AI Videos with HunyuanVideo on RunPod: A Complete Guide

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Are you interested in state-of-the-art AI video generation? Learn how to set up and use HunyuanVideo, Tencent’s powerful text-to-video model, on RunPod. This guide walks you through the entire process, from deployment to generating your first AI video.

The EU AI Video Generation Challenge (and Our Solution)

With OpenAI’s Sora making headlines, many creators and developers are excited about AI video generation. However, due to regulatory challenges, OpenAI services (including Sora) aren’t available in the EU. This creates a significant gap for European users looking to leverage AI video technology.

Enter HunyuanVideo: an open-source alternative that you can run anywhere, including the EU. By combining HunyuanVideo with RunPod’s infrastructure, you get:

What is HunyuanVideo?

HunyuanVideo is an open-source video generation model that rivals, and in some cases surpasses, leading closed-source models like Runway Gen-3 and Luma. It can create high-quality videos from text descriptions, making it a powerful tool for creators and developers.

Prerequisites

Before starting, ensure you have:

Quick Start Guide

  1. Deploy the Template
    • Visit our RunPod template
    • Select an A100 GPU with at least 60GB VRAM
    • Add a volume (minimum 200GB for model storage)
    • Click Deploy
  2. First-Time Setup The template automatically handles:
    • Environment configuration
    • Model downloads (approximately 150GB)
    • Required dependencies
  3. Generate Your First Video cd /workspace/HunyuanVideo python3 sample_video.py \ --video-size 720 1280 \ --video-length 129 \ --infer-steps 30 \ --prompt "a cat is running, realistic." \ --flow-reverse \ --seed 0 \ --save-path ./results

Advanced Usage

Multi-GPU Generation

For faster generation using multiple GPUs:

torchrun --nproc_per_node=8 sample_video.py \
    --video-size 1280 720 \
    --video-length 129 \
    --infer-steps 50 \
    --prompt "A cat walks on the grass, realistic style." \
    --flow-reverse \
    --seed 42 \
    --ulysses-degree 8 \
    --ring-degree 1 \
    --save-path ./results

Web Interface

Launch the Gradio interface for a user-friendly experience:

SERVER_NAME=0.0.0.0 SERVER_PORT=7860 python3 gradio_server.py --flow-reverse

Performance Tips

  1. Resolution Settings
    • 720p (1280×720) is recommended for most uses
    • Higher resolutions require more VRAM
  2. Generation Parameters
    • Adjust infer-steps (20-50) for quality vs. speed
    • Use seed for reproducible results
    • flow-reverse typically produces better quality
  3. Resource Management
    • Monitor GPU memory with nvidia-smi
    • Use appropriate batch sizes for your GPU

Troubleshooting Common Issues

  1. Out of Memory Errors
    • Reduce video resolution
    • Decrease batch size
    • Use fewer inference steps
  2. Model Loading Issues
    • Check available disk space
    • Verify model downloads in /workspace/HunyuanVideo/ckpts
  3. Generation Quality
    • Experiment with different prompts
    • Adjust inference steps
    • Try different seeds

Technical Specifications

Best Practices

  1. Prompt Engineering
    • Be specific in descriptions
    • Include style references
    • Mention desired camera movements
  2. Resource Optimization
    • Start with shorter videos
    • Use appropriate resolution
    • Clean up old generations
  3. Workflow Integration
    • Save successful prompts
    • Document parameter combinations
    • Maintain organized output folders

Conclusion

HunyuanVideo on RunPod provides a powerful platform for AI video generation. Whether you’re a content creator, developer, or researcher, this setup offers the flexibility and performance needed for high-quality video generation.

Additional Resources

This guide is regularly updated to reflect the latest improvements in HunyuanVideo and RunPod infrastructure. Last updated: December 2024

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