ComfyUI-LightVAE is a collection of LightX2V VAE custom nodes designed for ComfyUI, supporting high-performance video VAE models including LightVAE and LightTAE.
The LightX2V team has deeply optimized VAE, creating two major series: LightVAE and LightTAE, which significantly reduce memory usage and improve inference speed while maintaining high quality.
🎯 LightVAE SeriesFeature: Best Balance ⚖️
|
⚡ LightTAE SeriesFeature: Ultra-fast + High Quality 🏆
|
Test Environment: H100 GPU, BF16, 81-frame video (480P)
| Model | Encode Time | Decode Time | Encode Memory | Decode Memory | Quality |
|---|---|---|---|---|---|
| lightvaew2_1 | 1.5s | 2.1s | 4.8GB | 5.6GB | ⭐⭐⭐⭐⭐ |
| lighttaew2_1 | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐⭐ |
| Wan2.1_VAE | 4.2s | 5.5s | 8.5GB | 10.1GB | ⭐⭐⭐⭐ |
| taew2_1 | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐ |
Performance Improvements:
...comfy\python_embeded\python.exe -s -m pip install -r requirements.txt
# Clone LightX2V repository
git clone https://github.com/ModelTC/LightX2V
cd LightX2V
python setup_vae.py install
# windows example
...comfy\python_embeded\python.exe setup_vae.py install
LightVAE nodes depend on WanVideoWrapper for main model support:
cd ComfyUI/custom_nodes
git clone https://github.com/kijai/ComfyUI-WanVideoWrapper
cd ComfyUI/custom_nodes
git clone https://github.com/YOUR_USERNAME/ComfyUI-LightVAE
Option 1: Distilled Models (Recommended, 4-step)
_comfyui.safetensors suffix)Option 2: Original Models (20-step)
_comfyui.safetensors suffix)# Download to ComfyUI/models/diffusion_models/
huggingface-cli download lightx2v/Wan2.1/2-Distill-Models \
--local-dir ./ComfyUI/models/diffusion_models/
All VAE Models (Required):
# Download all VAE models
huggingface-cli download lightx2v/Autoencoders \
--local-dir ./ComfyUI/models/vae/
# Or download only what you need (Recommended)
huggingface-cli download lightx2v/Autoencoders lightvaew2_1.pth \
--local-dir ./ComfyUI/models/vae/
Supported VAE Models:
Wan2.1_VAE.pth / .safetensors - Official VAE 2.1Wan2.2_VAE.pth / .safetensors - Official VAE 2.2lightvaew2_1.pth / .safetensors - Optimized VAE 2.1 ⭐ Recommendedtaew2_1.pth / .safetensors - Open-source TAE 2.1taew2_2.pth / .safetensors - Open-source TAE 2.2lighttaew2_1.pth / .safetensors - Optimized TAE 2.1 ⚡ Fastestlighttaew2_2.pth / .safetensors - Optimized TAE 2.2
Input Parameters:
vae_filename - VAE model filename (automatically lists from ./models/vae/)dtype - Data type (bfloat16 / float16 / float32)device - Compute device (cuda / cpu)Output:
vae - VAE model objectFeatures:

Input Parameters:
vae - VAE object from Loaderlatent - Latent representationOutput:
IMAGE - Decoded video framesSupports:
High-performance configuration using 4-step distilled model + LightVAE optimized decoder.
Workflow File: example/workflows/wan2.1_I2V_4step_fp8_lightvae.json
Wan2.2 Text-Image-to-Video + LightVAE decoding.
Workflow File: example/workflows/wan2.2_TI2V_lightvae.json
If this project helps you, please give a ⭐ to LightX2V and this repository!
Enjoy using LightX2V VAE! 🚀
7 commits
Python
100.0%
ComfyUI-LightVAE is a collection of LightX2V VAE custom nodes designed for ComfyUI, supporting high-performance video VAE models including LightVAE and LightTAE.
The LightX2V team has deeply optimized VAE, creating two major series: LightVAE and LightTAE, which significantly reduce memory usage and improve inference speed while maintaining high quality.
🎯 LightVAE SeriesFeature: Best Balance ⚖️
|
⚡ LightTAE SeriesFeature: Ultra-fast + High Quality 🏆
|
Test Environment: H100 GPU, BF16, 81-frame video (480P)
| Model | Encode Time | Decode Time | Encode Memory | Decode Memory | Quality |
|---|---|---|---|---|---|
| lightvaew2_1 | 1.5s | 2.1s | 4.8GB | 5.6GB | ⭐⭐⭐⭐⭐ |
| lighttaew2_1 | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐⭐ |
| Wan2.1_VAE | 4.2s | 5.5s | 8.5GB | 10.1GB | ⭐⭐⭐⭐ |
| taew2_1 | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐ |
Performance Improvements:
...comfy\python_embeded\python.exe -s -m pip install -r requirements.txt
# Clone LightX2V repository
git clone https://github.com/ModelTC/LightX2V
cd LightX2V
python setup_vae.py install
# windows example
...comfy\python_embeded\python.exe setup_vae.py install
LightVAE nodes depend on WanVideoWrapper for main model support:
cd ComfyUI/custom_nodes
git clone https://github.com/kijai/ComfyUI-WanVideoWrapper
cd ComfyUI/custom_nodes
git clone https://github.com/YOUR_USERNAME/ComfyUI-LightVAE
Option 1: Distilled Models (Recommended, 4-step)
_comfyui.safetensors suffix)Option 2: Original Models (20-step)
_comfyui.safetensors suffix)# Download to ComfyUI/models/diffusion_models/
huggingface-cli download lightx2v/Wan2.1/2-Distill-Models \
--local-dir ./ComfyUI/models/diffusion_models/
All VAE Models (Required):
# Download all VAE models
huggingface-cli download lightx2v/Autoencoders \
--local-dir ./ComfyUI/models/vae/
# Or download only what you need (Recommended)
huggingface-cli download lightx2v/Autoencoders lightvaew2_1.pth \
--local-dir ./ComfyUI/models/vae/
Supported VAE Models:
Wan2.1_VAE.pth / .safetensors - Official VAE 2.1Wan2.2_VAE.pth / .safetensors - Official VAE 2.2lightvaew2_1.pth / .safetensors - Optimized VAE 2.1 ⭐ Recommendedtaew2_1.pth / .safetensors - Open-source TAE 2.1taew2_2.pth / .safetensors - Open-source TAE 2.2lighttaew2_1.pth / .safetensors - Optimized TAE 2.1 ⚡ Fastestlighttaew2_2.pth / .safetensors - Optimized TAE 2.2
Input Parameters:
vae_filename - VAE model filename (automatically lists from ./models/vae/)dtype - Data type (bfloat16 / float16 / float32)device - Compute device (cuda / cpu)Output:
vae - VAE model objectFeatures:

Input Parameters:
vae - VAE object from Loaderlatent - Latent representationOutput:
IMAGE - Decoded video framesSupports:
High-performance configuration using 4-step distilled model + LightVAE optimized decoder.
Workflow File: example/workflows/wan2.1_I2V_4step_fp8_lightvae.json
Wan2.2 Text-Image-to-Video + LightVAE decoding.
Workflow File: example/workflows/wan2.2_TI2V_lightvae.json
If this project helps you, please give a ⭐ to LightX2V and this repository!
Enjoy using LightX2V VAE! 🚀
7 commits
Python
100.0%