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Sponsored by
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lightx2v / Autoencoders
Model's Last Updated: November 21 2025
Introduction of Autoencoders
Model Details of Autoencoders
🎨 LightVAE
⚡ Efficient Video Autoencoder (VAE) Model Collection
From Official Models to Lightx2v Distilled Optimized Versions - Balancing Quality, Speed and Memory
For VAE, the LightX2V team has conducted a series of deep optimizations, deriving two major series: LightVAE and LightTAE , which significantly reduce memory consumption and improve inference speed while maintaining high quality.
💡 Core Advantages
📦 Available Models
🎯 Wan2.1 Series VAE
| Model Name | Type | Architecture | Description |
|---|---|---|---|
Wan2.1_VAE
|
Official VAE | Causal Conv3D |
Wan2.1 official video VAE model
Highest quality, large memory, slow speed |
taew2_1
|
Open Source Small AE | Conv2D |
Open source model based on
taeHV
Small memory, fast speed, average quality |
lighttaew2_1
|
LightTAE Series | Conv2D |
Our distilled optimized version based on
taew2_1
Small memory, fast speed, quality close to official ✨ |
lightvaew2_1
|
LightVAE Series | Causal Conv3D |
Our pruned 75% on WanVAE2.1 architecture then trained+distilled
Best balance: high quality + low memory + fast speed 🏆 |
🎯 Wan2.2 Series VAE
| Model Name | Type | Architecture | Description |
|---|---|---|---|
Wan2.2_VAE
|
Official VAE | Causal Conv3D |
Wan2.2 official video VAE model
Highest quality, large memory, slow speed |
taew2_2
|
Open Source Small AE | Conv2D |
Open source model based on
taeHV
Small memory, fast speed, average quality |
lighttaew2_2
|
LightTAE Series | Conv2D |
Our distilled optimized version based on
taew2_2
Small memory, fast speed, quality close to official ✨ |
📊 Wan2.1 Series Performance Comparison
- Precision : BF16
- Test Hardware : NVIDIA H100
Video Reconstruction (5s 81-frame video)
| Speed | Wan2.1_VAE | taew2_1 | lighttaew2_1 | lightvaew2_1 |
|---|---|---|---|---|
| Encode Speed | 4.1721 s | 0.3956 s | 0.3956 s | 1.5014s |
| Decode Speed | 5.4649 s | 0.2463 s | 0.2463 s | 2.0697s |
| GPU Memory | Wan2.1_VAE | taew2_1 | lighttaew2_1 | lightvaew2_1 |
|---|---|---|---|---|
| Encode Memory | 8.4954 GB | 0.00858 GB | 0.00858 GB | 4.7631 GB |
| Decode Memory | 10.1287 GB | 0.41199 GB | 0.41199 GB | 5.5673 GB |
Video Generation
Task: s2v(speech to video)
Model: seko-talk
|
Wan2.1_VAE
|
taew2_1
|
lighttaew2_1
|
lightvaew2_1
|
📊 Wan2.2 Series Performance Comparison
- Precision : BF16
- Test Hardware : NVIDIA H100
Video Reconstruction
| Speed | Wan2.2_VAE | taew2_2 | lighttaew2_2 |
|---|---|---|---|
| Encode Speed | 1.1369s | 0.3499 s | 0.3499 s |
| Decode Speed | 3.1268 s | 0.0891 s | 0.0891 s |
| GPU Memory | Wan2.2_VAE | taew2_2 | lighttaew2_2 |
|---|---|---|---|
| Encode Memory | 6.1991 GB | 0.0064 GB | 0.0064 GB |
| Decode Memory | 12.3487 GB | 0.4120 GB | 0.4120 GB |
Video Generation
Task: t2v(text to video)
Model:
Wan2.2-TI2V-5B
|
Wan2.2_VAE
|
taew2_2
|
lighttaew2_2
|
🎯 Model Selection Recommendations
Selection by Use Case
🔥 Our Optimization Results Comparison
| Comparison | Open Source TAE | LightTAE (Ours) | Official VAE | LightVAE (Ours) |
|---|---|---|---|---|
| Architecture | Conv2D | Conv2D | Causal Conv3D | Causal Conv3D |
| Memory Usage | Minimal (~0.4 GB) | Minimal (~0.4 GB) | Large (~8-12 GB) | Medium (~4-5 GB) |
| Inference Speed | Extremely Fast ⚡⚡⚡⚡⚡ | Extremely Fast ⚡⚡⚡⚡⚡ | Slow ⚡⚡ | Fast ⚡⚡⚡⚡ |
| Generation Quality | Average ⭐⭐⭐ | Close to Official ⭐⭐⭐⭐⭐ | Highest ⭐⭐⭐⭐⭐ | Close to Official ⭐⭐⭐⭐⭐ |
🚀 Usage
Download VAE Models
# Download Wan2.1 official VAE
huggingface-cli download lightx2v/Autoencoders \
--local-dir ./models/vae/
Use in LightX2V
Specify the VAE path in the configuration file:
Using Official VAE Series:
{
"vae_pth": "./models/vae/Wan2.1_VAE.pth"
}
Using LightVAE Series:
{
"use_lightvae": true,
"vae_pth": "./models/vae/lightvaew2_1.pth"
}
Using LightTAE Series:
{
"use_tiny_vae": true,
"need_scaled": true,
"tiny_vae_path": "./models/vae/lighttaew2_1.pth"
}
Using TAE Series:
{
"use_tiny_vae": true,
"tiny_vae_path": "./models/vae/taew2_1.pth"
}
Then run the inference script:
cd LightX2V/scripts
bash wan/run_wan_i2v.sh # or other inference scripts
⚠️ Important Notes
1. Compatibility
- Wan2.1 series VAE only works with Wan2.1 backbone models
- Wan2.2 series VAE only works with Wan2.2 backbone models
- Do not mix different versions of VAE and backbone models
📚 Related Resources
Documentation Links
- LightX2V Quick Start : Quick Start Documentation
- Model Structure Description : Model Structure Documentation
- taeHV Project : GitHub - madebyollin/taeHV
Related Models
- Wan2.1 Backbone Models : Wan-AI Model Collection
- Wan2.2 Backbone Models : Wan-AI/Wan2.2-TI2V-5B
- LightX2V Optimized Models : lightx2v Model Collection
🤝 Community & Support
- GitHub Issues : https://github.com/ModelTC/LightX2V/issues
- HuggingFace : https://huggingface.co/lightx2v
- LightX2V Homepage : https://github.com/ModelTC/LightX2V
If you find this project helpful, please give us a ⭐ on GitHub
Runs of lightx2v Autoencoders on huggingface.co
7.0K
Total runs
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1.3K
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More Information About Autoencoders huggingface.co Model
Url of Autoencoders
Autoencoders huggingface.co Url
Provider of Autoencoders huggingface.co
Other API from lightx2v
Updated:October 16 2026
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