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lightx2v/
Wan2.1-Distill-Models

Name: Wan2.1-Distill-Models
Availability: InStock

diffusers

safetensors

diffusion-single-file

Wan-AI/Wan2.1-I2V-14B-480P

adapter

apache-2.0

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🎬 Wan2.1 Distilled Models

⚡ High-Performance Video Generation with 4-Step Inference

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

GitHub

License

🌟 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

📦 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

bash

# 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

Download model (720P I2V FP8 example)

bash

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"

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 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)

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

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