Distillation-accelerated version of Wan2.2 - Dramatically faster speed with excellent quality

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⚡ Ultra-Fast Generation 4-step inference (vs traditional 50+ steps) Approximately 2x faster using LightX2V than ComfyUI Near real-time video generation capability	🎯 Flexible Options Dual noise control: High/Low noise variants Multiple precision formats (BF16/FP8/INT8) Full 14B parameter models
💾 Memory Efficient FP8/INT8: ~50% size reduction CPU offload support Optimized for consumer GPUs	🔧 Easy Integration Compatible with LightX2V framework ComfyUI support Simple configuration files

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🖼️ Image-to-Video (I2V) - 14B Parameters Transform static images into dynamic videos with advanced quality control 🎨 High Noise: More creative, diverse outputs 🎯 Low Noise: More faithful to input, stable outputs

📝 Text-to-Video (T2V) - 14B Parameters Generate videos from text descriptions 🎨 High Noise: More creative, diverse outputs 🎯 Low Noise: More stable and controllable outputs 🚀 Full 14B parameter model

Precision	Model Identifier	Model Size	Framework	Quality vs Speed
🏆 BF16	lightx2v_4step	~28.6 GB	LightX2V	⭐⭐⭐⭐⭐ Highest Quality
⚡ FP8	scaled_fp8_e4m3_lightx2v_4step	~15 GB	LightX2V	⭐⭐⭐⭐ Excellent Balance
🎯 INT8	int8_lightx2v_4step	~15 GB	LightX2V	⭐⭐⭐⭐ Fast & Efficient
🔷 FP8 ComfyUI	scaled_fp8_e4m3_lightx2v_4step_comfyui	~15 GB	ComfyUI	⭐⭐⭐ ComfyUI Ready

# Format: wan2.2_{task}_A14b_{noise_level}_{precision}_lightx2v_4step.safetensors

# I2V Examples:
wan2.2_i2v_A14b_high_noise_lightx2v_4step.safetensors                       # I2V High Noise - BF16
wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors      # I2V High Noise - FP8
wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step.safetensors                  # I2V Low Noise - INT8
wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step_comfyui.safetensors  # I2V Low Noise - FP8 ComfyUI

💡 Browse All Models: View Full Model Collection →

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LightX2V is a high-performance inference framework optimized for these models, approximately 2x faster than ComfyUI with better quantization accuracy. Highly recommended!

1. Download model (using I2V FP8 as example)

huggingface-cli download lightx2v/Wan2.2-Distill-Models \
    --local-dir ./models/wan2.2_i2v \
    --include "wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors"

huggingface-cli download lightx2v/Wan2.2-Distill-Models \
    --local-dir ./models/wan2.2_i2v \
    --include "wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors"

💡 Tip: For T2V models, follow the same steps but replace i2v with t2v in the filenames

2. Clone LightX2V repository

git clone https://github.com/ModelTC/LightX2V.git
cd LightX2V

3. Install dependencies

pip install -r requirements.txt

Or refer to Quick Start Documentation to use docker

4. Select and modify configuration file

Choose appropriate configuration based on your GPU memory:

80GB+ GPUs (A100/H100)

• I2V: wan_moe_i2v_distill.json

24GB+ GPUs (RTX 4090)

• I2V: wan_moe_i2v_distill_4090.json

5. Run inference (using I2V as example)

cd scripts
bash wan22/run_wan22_moe_i2v_distill.sh

📝 Note: Update model paths in the script to point to your Wan2.2 model. Also refer to LightX2V Model Structure Documentation

• Quick Start Guide: LightX2V Quick Start
• Complete Usage Guide: LightX2V Model Structure Documentation
• Configuration File Instructions: Configuration Files
• Quantized Model Usage: Quantization Documentation
• Parameter Offloading: Offload Documentation

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Please refer to workflow

Other Components: These models only contain DIT weights. Additional components needed at runtime:

• T5 text encoder
• CLIP vision encoder
• VAE encoder/decoder
• Tokenizer

Please refer to LightX2V Documentation for instructions on organizing the complete model directory.

• GitHub Issues: https://github.com/ModelTC/LightX2V/issues
• HuggingFace: https://huggingface.co/lightx2v/Wan2.2-Distill-Models

If you find this project helpful, please give us a ⭐ on GitHub