lightx2v/Wan2.1-Distill-Models

🎬 Wan2.1 Distilled Models

⚡ High-Performance Video Generation with 4-Step Inference

Distillation-accelerated versions of Wan2.1 - Dramatically faster while maintaining exceptional quality

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🌟 What's Special?

⚡ Ultra-Fast Generation 4-step inference (vs traditional 50+ steps) Up to 2x faster than ComfyUI Real-time video generation capability	🎯 Flexible Options Multiple resolutions (480P/720P) Various precision formats (BF16/FP8/INT8) I2V and T2V support
💾 Memory Efficient FP8/INT8: ~50% size reduction CPU offload support Optimized for consumer GPUs	🔧 Easy Integration Compatible with LightX2V framework ComfyUI support available Simple configuration files

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📦 Model Catalog

🎥 Model Types

🖼️ Image-to-Video (I2V) Transform still images into dynamic videos 📺 480P Resolution 🎬 720P Resolution

📝 Text-to-Video (T2V) Generate videos from text descriptions 🚀 14B Parameters 🎨 High-quality synthesis

🎯 Precision Variants

Precision	Model Identifier	Model Size	Framework	Quality vs Speed
🏆 BF16	lightx2v_4step	~28-32 GB	LightX2V	⭐⭐⭐⭐⭐ Highest quality
⚡ FP8	scaled_fp8_e4m3_lightx2v_4step	~15-17 GB	LightX2V	⭐⭐⭐⭐ Excellent balance
🎯 INT8	int8_lightx2v_4step	~15-17 GB	LightX2V	⭐⭐⭐⭐ Fast & efficient
🔷 FP8 ComfyUI	scaled_fp8_e4m3_lightx2v_4step_comfyui	~15-17 GB	ComfyUI	⭐⭐⭐ ComfyUI ready

📝 Naming Convention

# Pattern: wan2.1_{task}_{resolution}_{precision}.safetensors

# Examples:
wan2.1_i2v_720p_lightx2v_4step.safetensors                          # 720P I2V - BF16
wan2.1_i2v_720p_scaled_fp8_e4m3_lightx2v_4step.safetensors         # 720P I2V - FP8
wan2.1_i2v_480p_int8_lightx2v_4step.safetensors                    # 480P I2V - INT8
wan2.1_t2v_14b_scaled_fp8_e4m3_lightx2v_4step_comfyui.safetensors  # T2V - FP8 ComfyUI

💡 Explore all models: Browse Full Model Collection →

🚀 Usage

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 (720P I2V FP8 example)

huggingface-cli download lightx2v/Wan2.1-Distill-Models \
    --local-dir ./models/wan2.1_i2v_720p \
    --include "wan2.1_i2v_720p_scaled_fp8_e4m3_lightx2v_4step.safetensors"

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 the appropriate configuration based on your GPU memory:

For 80GB+ GPU (A100/H100)

• I2V: wan_i2v_distill_4step_cfg.json
• T2V: wan_t2v_distill_4step_cfg.json

For 24GB+ GPU (RTX 4090)

• I2V: wan_i2v_distill_4step_cfg_4090.json
• T2V: wan_t2v_distill_4step_cfg_4090.json

5. Run inference

cd scripts
bash wan/run_wan_i2v_distill_4step_cfg.sh

Documentation

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

Performance Advantages

• ⚡ Fast: Approximately 2x faster than ComfyUI
• 🎯 Optimized: Deeply optimized for distilled models
• 💾 Memory Efficient: Supports CPU offload and other memory optimization techniques
• 🛠️ Flexible: Supports multiple quantization formats and configuration options

Community

• Issues: https://github.com/ModelTC/LightX2V/issues

⚠️ Important Notes

1. Additional Components: These models only contain DIT weights. You also need:

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

   Refer to LightX2V Documentation for how to organize the complete model directory.

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