lightx2v/Wan2.2-Distill-Models

🎬 Wan2.2 Distilled Models

⚡ High-Performance Video Generation with 4-Step Inference

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

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🔥 News

• 2026.04.12: We are excited to release the Wan2.2-I2V-A14B-4step-720p-high and Wan2.2-I2V-A14B-4step-720p-low models. Compared to previous iterations, this version was trained on a high-quality 720p dataset and features an optimized low-noise training algorithm. These enhancements significantly boost the model's performance in fine-grained detail rendering and visual texture.

🌟 What's Special?

⚡ 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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📦 Model Catalog

🎥 Model Types

🖼️ 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 Versions

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

📝 Naming Convention

# 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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🚀 Usage

Method 1: LightX2V (Recommended ⭐)

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

Quick Start

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

For the FP8 I2V weights downloaded above, choose:

• FP8 configuration
• RTX 4090 FP8 configuration with CPU offload

Set high_noise_quantized_ckpt, low_noise_quantized_ckpt, and t5_quantized_ckpt in the selected JSON to your local checkpoint paths. Use an attention backend supported by your GPU and installed environment.

For BF16 distilled weights, use the BF16 configuration instead, and update high_noise_original_ckpt and low_noise_original_ckpt.

5. Run inference

Edit the I2V launch script: set lightx2v_path, model_path, CUDA_VISIBLE_DEVICES, and the input/output paths. Set --config_json to the configuration selected above.

Keep model_path pointed at the complete Wan2.2-I2V-A14B model directory containing the required encoders, tokenizer, and VAE.

Run from the LightX2V repository root:

bash scripts/wan22/inference/distill/run_wan22_moe_i2v_distill.sh

LightX2V 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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Method 2: ComfyUI

Please refer to workflow

⚠️ Important Notes

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.

🤝 Community

• 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