| --- |
| 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 🎥 |
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| Generate high-quality videos from text prompts or images using the powerful **Wan2.2-TI2V-5B** model! |
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| This Space provides an easy-to-use interface for creating videos with state-of-the-art AI technology. |
|
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| ## Features ✨ |
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| - **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 |
|
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| ## Model Information 🤖 |
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| **Wan2.2-TI2V-5B** is a unified text-to-video and image-to-video generation model with: |
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| - **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) |
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| The model uses a Mixture-of-Experts (MoE) architecture and delivers outstanding video generation quality, surpassing many commercial models. |
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| ## How to Use 🚀 |
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| ### Text-to-Video Generation |
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| 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) |
|
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| ### Image-to-Video Generation |
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| 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 |
|
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| ## Advanced Settings ⚙️ |
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| - **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 |
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| **Note**: Settings are optimized to complete within Zero GPU's 3-minute time limit for Pro users. |
|
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| ## Tips for Best Results 💡 |
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| 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" |
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| 2. **Image-to-Video**: Use clear, high-quality input images that match your prompt |
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| 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) |
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| 4. **Experiment**: Try different guidance scales: |
| - Lower (3-4): More creative, less literal |
| - Default (5): Good balance |
| - Higher (7-10): Strictly follows prompt |
|
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| ## Example Prompts 📝 |
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| - "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" |
|
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| ## Technical Details 🔧 |
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| - **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) |
|
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| ## Limitations ⚠️ |
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| - 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 |
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| ## Credits 🙏 |
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| - **Model**: [Wan-AI](https://huggingface.co/Wan-AI) |
| - **Original Repository**: [Wan2.2](https://github.com/Wan-Video/Wan2.2) |
| - **Framework**: [Hugging Face Diffusers](https://github.com/huggingface/diffusers) |
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| ## License 📄 |
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| This Space uses the Wan2.2 model which is released under Apache 2.0 license. |
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| ## Related Links 🔗 |
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| - [Wan-AI on Hugging Face](https://huggingface.co/Wan-AI) |
| - [Original Model Card](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers) |
| - [Diffusers Documentation](https://huggingface.co/docs/diffusers) |
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| --- |
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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. |
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