Model Architecture
What type of model is Wan AI?
Wan AI uses a diffusion transformer (DiT) architecture for video generation. Unlike earlier video generation models that adapted image diffusion U-Nets for temporal processing, Wan AI uses a transformer-based architecture that processes spatial and temporal dimensions simultaneously.
The key architectural components:
- 3D VAE (Variational Autoencoder): Encodes input frames into a compressed latent space and decodes generated latents back to pixel space
- Diffusion Transformer: The core generation model that iteratively denoises random noise in latent space to produce coherent video
- Text Encoder: Processes text prompts into conditioning embeddings (based on T5-XXL)
- Temporal attention layers: Ensure consistency across video frames
What’s the difference between the 14B and 1.3B models?
| Specification | Wan 2.1 (14B) | Wan 2.1 (1.3B) |
|---|---|---|
| Parameters | 14 billion | 1.3 billion |
| Min VRAM | 24GB | 8GB |
| Max resolution | 1080p | 720p |
| Max duration | 10 seconds | 6 seconds |
| Visual quality | Professional | Good |
| Motion quality | Excellent | Adequate |
| Generation speed (720p, 4s) | ~3 min (RTX 4090) | ~1 min (RTX 3060) |
| Model size on disk | ~28GB | ~5GB |
The 14B model is the flagship, producing the highest quality output. The 1.3B model is a distilled version optimized for consumer hardware, trading quality for accessibility.
Does Wan AI support image-to-video?
Yes. Both models support:
- Text-to-video: Generate video from text descriptions
- Image-to-video: Animate a still image with text-described motion
- First-frame conditioning: Provide a reference image that becomes the first frame
Image-to-video mode is particularly useful for animating illustrations, photographs, and AI-generated images while maintaining the visual quality and style of the input.
What frame rates does Wan AI support?
Wan AI natively generates at 16fps and 24fps. The output can be post-processed to other frame rates:
- 30fps: Interpolate using RIFE or other frame interpolation tools
- 60fps: Double interpolation (may introduce artifacts)
- Slow motion: Generate at 24fps and slow to 12fps or 8fps for stylized slow-motion effects
Hardware Requirements
What’s the absolute minimum hardware?
For the 1.3B model:
- GPU: NVIDIA GPU with 8GB VRAM (RTX 3060, RTX 4060, etc.)
- CPU: Any modern quad-core processor
- RAM: 16GB
- Storage: 15GB free space
For the 14B model:
- GPU: NVIDIA GPU with 24GB VRAM (RTX 4090, A5000, A100)
- CPU: Modern 6-core processor
- RAM: 32GB minimum (64GB recommended)
- Storage: 50GB free space
Can I run Wan AI on AMD GPUs?
Limited support. The primary codebase is optimized for NVIDIA CUDA. Community efforts have enabled partial AMD ROCm support, but performance is significantly lower (2-4× slower) and some features may not work reliably. For production use, NVIDIA GPUs are strongly recommended.
Can I run Wan AI on Apple Silicon?
Experimental support exists for M-series Macs through MLX and MPS backends. The 1.3B model can run on M2 Pro/Max/Ultra with 32GB+ unified memory. Performance is approximately 3-5× slower than an equivalent NVIDIA GPU. The 14B model requires M2 Ultra with 192GB memory or M3 Max with 128GB memory — expensive configurations.
For serious use, an NVIDIA-based system is more cost-effective.
Can I run multiple models on one GPU?
Not simultaneously. Each model requires its full VRAM allocation during generation. However, you can:
- Load/unload models between generations (adds 20-60 seconds per switch)
- Use a multi-GPU setup with different models on different GPUs
- Queue generations from different models sequentially
How much storage do I need?
| Component | Size |
|---|---|
| 14B model weights | ~28GB |
| 1.3B model weights | ~5GB |
| Text encoder (T5-XXL) | ~10GB |
| VAE | ~1GB |
| ComfyUI + dependencies | ~5GB |
| Generated video output | ~50-200MB per clip |
| Working space | 20GB minimum |
Plan for at least 100GB of free space for a comfortable working environment, more if you’re storing many generated clips.
Fine-Tuning
Can I fine-tune Wan AI?
Yes. Wan AI supports:
- LoRA fine-tuning: Lower-resource adaptation that modifies a subset of model weights. Recommended for most users.
- Full fine-tuning: Modifies all model weights. Requires significantly more compute but produces better results for radical style changes.
What do I need for LoRA fine-tuning?
- Hardware: GPU with 24GB+ VRAM (RTX 4090 for 14B LoRA; RTX 3090/4070 Ti for 1.3B LoRA)
- Training data: 50-200 video clips (5-10 seconds each) representing the target style
- Time: 4-12 hours for a basic LoRA (14B model on RTX 4090)
- Software: Kohya_ss (adapted for video), or custom training scripts from the Wan AI repository
What kind of training data works best?
- Consistent style: All clips should share the visual properties you want the model to learn
- Variety in content: Different subjects and compositions within the consistent style
- Good quality: Clean, high-resolution source material
- Proper framing: Clips that represent the kind of output you want to generate
- Moderate quantity: 50-200 clips is the sweet spot. Fewer risks overfitting; more adds diminishing returns.
How do I share or distribute my fine-tuned models?
LoRA files are small (50-500MB) and can be shared freely. Wan AI’s license permits derivative works, including fine-tuned models, for both personal and commercial use. Common distribution platforms:
- CivitAI (largest community)
- Hugging Face (developer-oriented)
- GitHub (for associated code)
Output Specifications
What resolutions are supported?
| Model | Minimum | Default | Maximum |
|---|---|---|---|
| 14B | 480×480 | 832×480 | 1920×1080 |
| 1.3B | 320×320 | 512×320 | 1280×720 |
Aspect ratios are flexible. Common options: 16:9, 9:16 (vertical), 4:3, 1:1, 21:9 (ultrawide).
What’s the maximum video duration?
The 14B model reliably generates up to 10 seconds at 720p or 6 seconds at 1080p in a single pass. The 1.3B model generates up to 6 seconds at 480p.
For longer videos, use:
- Clip chaining: Generate overlapping clips and blend transitions
- Temporal extension: Use specialized tools to extend clips while maintaining coherence
- Traditional editing: Cut between AI-generated clips as you would with filmed footage
What file formats can I output?
Wan AI generates raw frame sequences. Post-processing (automated in ComfyUI workflows) produces:
- MP4 (H.264 or H.265): Most common, good compression
- MOV (ProRes): Professional editing format, larger files
- WebM (VP9): Web-optimized format
- PNG sequence: Individual frames for maximum flexibility
Licensing
What license is Wan AI released under?
Wan AI 2.1 is released under the Apache 2.0 license, which permits:
- Commercial use
- Modification and distribution
- Private use
- Patent use
With requirements to:
- Include the license notice
- State changes if you modify the code
This is one of the most permissive open-source licenses available.
Can I use Wan AI-generated video commercially?
Yes. The Apache 2.0 license does not restrict commercial use of outputs. You can use Wan AI-generated video in:
- Commercial films and videos
- Advertising and marketing
- Products and services
- Client deliverables
- Social media and content platforms
Are there content restrictions?
The model itself has no built-in content restrictions. However:
- Your jurisdiction’s laws apply to any content you create
- Platform terms of service apply if you host content on third-party platforms
- Ethical and legal responsibility rests with the creator
Troubleshooting
Common Issues
“CUDA out of memory”: Your GPU doesn’t have enough VRAM. Solutions: Use the 1.3B model, reduce resolution, enable CPU offloading in ComfyUI, or use a GPU with more VRAM.
Slow generation speed: Check that you’re using the GPU (not CPU) for inference. Verify CUDA is properly installed. Close other GPU-consuming applications.
Visual artifacts in output: Try increasing the number of inference steps. Use a different scheduler. Reduce resolution if running near VRAM limits.
Temporal flickering: Increase temporal attention strength if your framework supports it. Use a higher number of inference steps. Consider post-processing with video stabilization tools.
Model fails to load: Verify model files are complete (check SHA256 hashes). Ensure sufficient RAM and VRAM. Update to the latest version of your generation framework.
Where to Get Help
- GitHub Issues: github.com/Wan-Video/Wan2.1/issues
- ComfyUI Discord: Active community for workflow help
- Reddit r/StableDiffusion: Community discussion (covers all open-source generation models)
- Hugging Face Forums: Technical discussions and model-specific threads
References
- Wan AI GitHub: github.com/Wan-Video/Wan2.1
- Wan AI Technical Report: arXiv (Tongyi Wanxiang team)
- Hugging Face Model Cards: Wan AI model documentation
- ComfyUI Documentation: github.com/comfyanonymous/ComfyUI
- Apache License 2.0: apache.org/licenses/LICENSE-2.0
- NVIDIA CUDA Toolkit: developer.nvidia.com/cuda-toolkit