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stevenlearns/
Wan2.2-distilled-models

Name: Wan2.2-distilled-models
Availability: InStock

diffusers

safetensors

i2v

diffusion-single-file

Wan-AI/Wan2.2-I2V-A14B

finetune

apache-2.0

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🎬 Wan2.2 Distilled Models

⚡ High-Performance Video Generation with 4-Step Inference

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

GitHub

License

🔥 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

📦 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

bash

# 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 →

🚀 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

Download model (using I2V FP8 as example)

bash

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"

bash

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

Clone LightX2V repository

bash

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

Install dependencies

pip install -r requirements.txt

Or refer to Quick Start Documentation to use docker

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

Run inference (using I2V as example)

bash

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

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

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

stevenlearns/Wan2.2-distilled-models

🎬 Wan2.2 Distilled Models

⚡ High-Performance Video Generation with 4-Step Inference

🔥 News

🌟 What's Special?

⚡ Ultra-Fast Generation

🎯 Flexible Options

💾 Memory Efficient

🔧 Easy Integration

📦 Model Catalog

🎥 Model Types

🖼️ Image-to-Video (I2V) - 14B Parameters

📝 Text-to-Video (T2V) - 14B Parameters

🎯 Precision Versions

📝 Naming Convention

🚀 Usage

Method 1: LightX2V (Recommended ⭐)

Quick Start

LightX2V Documentation

Method 2: ComfyUI

⚠️ Important Notes

🤝 Community

stevenlearns/
Wan2.2-distilled-models