README.md

metadata

title: Wan2.2 Video Generation
emoji: 🎥
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 5.49.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - video-generation
  - text-to-video
  - image-to-video
  - diffusers
  - wan
  - ai-video
  - zero-gpu
python_version: '3.10'

Wan2.2 Video Generation 🎥

Generate high-quality videos from text prompts or images using the powerful Wan2.2-TI2V-5B model!

This Space provides an easy-to-use interface for creating videos with state-of-the-art AI technology.

Features ✨

• Text-to-Video: Generate videos from descriptive text prompts
• Image-to-Video: Animate your images by adding an input image
• High Quality: 720P resolution at 24fps
• Customizable: Adjust resolution, number of frames, guidance scale, and more
• Reproducible: Use seeds to recreate your favorite generations

Model Information 🤖

Wan2.2-TI2V-5B is a unified text-to-video and image-to-video generation model with:

• 5 billion parameters optimized for consumer-grade GPUs
• 720P resolution support (1280x704 default)
• 24 fps smooth video output
• Optimized duration: Default 3 seconds (optimized for Zero GPU limits)

The model uses a Mixture-of-Experts (MoE) architecture and delivers outstanding video generation quality, surpassing many commercial models.

How to Use 🚀

Text-to-Video Generation

1. Enter your prompt describing the video you want to create
2. Adjust settings in "Advanced Settings" if desired
3. Click "Generate Video"
4. Wait for generation (typically 2-3 minutes on Zero GPU with default settings)

Image-to-Video Generation

1. Upload an input image
2. Enter a prompt describing how the image should animate
3. Click "Generate Video"
4. The output will maintain the aspect ratio of your input image
5. Generation takes 2-3 minutes with optimized settings

Advanced Settings ⚙️

• Width/Height: Video resolution (default: 1280x704)
• Number of Frames: Longer videos need more frames (default: 73 frames ≈ 3 seconds, max: 145)
• Inference Steps: More steps = better quality but slower (default: 35, optimized for speed)
• Guidance Scale: How closely to follow the prompt (default: 5.0)
• Seed: Set a specific seed for reproducible results

Note: Settings are optimized to complete within Zero GPU's 3-minute time limit for Pro users.

Tips for Best Results 💡

1. Detailed Prompts: Be specific about what you want to see

   • Good: "Two anthropomorphic cats in comfy boxing gear fight on stage with dramatic lighting"
   • Basic: "cats fighting"

2. Image-to-Video: Use clear, high-quality input images that match your prompt

3. Quality vs Speed (optimized for Zero GPU limits):

   • Fast: 25-30 steps (~2 minutes)
   • Balanced: 35 steps (default, ~2-3 minutes)
   • Higher Quality: 40-50 steps (~3+ minutes, may timeout)

4. Experiment: Try different guidance scales:

   • Lower (3-4): More creative, less literal
   • Default (5): Good balance
   • Higher (7-10): Strictly follows prompt

Example Prompts 📝

• "Two anthropomorphic cats in comfy boxing gear fight on stage"
• "A serene underwater scene with colorful coral reefs and tropical fish swimming gracefully"
• "A bustling futuristic city at night with neon lights and flying cars"
• "A peaceful mountain landscape with snow-capped peaks and a flowing river"
• "An astronaut riding a horse through a nebula in deep space"
• "A dragon flying over a medieval castle at sunset"

Technical Details 🔧

• Model: Wan-AI/Wan2.2-TI2V-5B-Diffusers
• Framework: Hugging Face Diffusers
• Backend: PyTorch with bfloat16 precision
• GPU: Hugging Face Zero GPU (H200 with 70GB VRAM, automatically allocated)
• GPU Duration: 180 seconds (3 minutes) for Pro users
• Generation Time: ~2-3 minutes with optimized settings (73 frames, 35 steps)

Limitations ⚠️

• Generation requires compute time (2-3 minutes with default settings)
• Zero GPU allocation is time-limited (3 minutes for Pro, 60 seconds for Free)
• Videos longer than 6 seconds (145 frames) may timeout
• Higher quality settings (50+ steps) may timeout on Zero GPU
• Complex scenes with many objects may be challenging

Credits 🙏

• Model: Wan-AI
• Original Repository: Wan2.2
• Framework: Hugging Face Diffusers

License 📄

This Space uses the Wan2.2 model which is released under Apache 2.0 license.

Related Links 🔗

• Wan-AI on Hugging Face
• Original Model Card
• Diffusers Documentation

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Note: This is a community-created Space for easy access to Wan2.2 video generation. Generation times may vary based on current GPU availability.