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.

Test Environment: H100 GPU, BF16, 81-frame video (480P)

Performance Improvements:

• 🚀 LightVAE is 2-3x faster than official VAE, 50% less memory
• ⚡ LightTAE is 10+ times faster than official VAE, 95%+ less memory
• 🎨 Near-official VAE quality, surpasses open-source TAE

...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)

• 🔗 Wan2.1 Distilled Models and Wan2.2 Distilled Models
• ✅ Supports BF16 format
• ✅ Supports FP8 format (requires models with _comfyui.safetensors suffix)

Option 2: Original Models (20-step)

• 🔗 Wan2.1 Official Models and Wan2.2 Official Models
• ✅ Supports BF16 format
• ✅ Supports FP8 format (requires models with _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):

• 🔗 LightX2V Autoencoders

# 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 2.1
• Wan2.2_VAE.pth / .safetensors - Official VAE 2.2
• lightvaew2_1.pth / .safetensors - Optimized VAE 2.1 ⭐ Recommended
• taew2_1.pth / .safetensors - Open-source TAE 2.1
• taew2_2.pth / .safetensors - Open-source TAE 2.2
• lighttaew2_1.pth / .safetensors - Optimized TAE 2.1 ⚡ Fastest
• lighttaew2_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 object

Features:

• ✅ Automatically identifies VAE type from filename
• ✅ Supports all LightX2V VAE models

Input Parameters:

• vae - VAE object from Loader
• latent - Latent representation

Output:

• IMAGE - Decoded video frames

Supports:

• ✅ All VAE series (WanVAE, LightVAE)
• ✅ All TAE series (TAE, LightTAE)

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

• ⚠️ Wan2.1 VAE can only be used with Wan2.1/Wan2.2-A1B backbone models
• ⚠️ Wan2.2 VAE can only be used with Wan2.2 TI2V backbone models
• ❌ Do not mix different versions of VAE and backbone models

• Project Homepage: https://github.com/ModelTC/LightX2V
• VAE Models: https://huggingface.co/lightx2v/Autoencoders
• Video Generation Models: https://huggingface.co/lightx2v/
• ComfyUI-WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper
• TAE Series Models: https://github.com/madebyollin/taesd
• Wan-AI: https://huggingface.co/Wan-AI

If this project helps you, please give a ⭐ to LightX2V and this repository!

• GitHub Issues: Issues page of this repository
• LightX2V Issues: https://github.com/ModelTC/LightX2V/issues
• HuggingFace: https://huggingface.co/lightx2v