lightx2v/Wan2.2-Distill-Loras

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Cataloged from lightx2v/Wan2.2-Distill-Loras

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🎬 Wan2.2 Distilled LoRA Models

⚡ High-Performance Video Generation with 4-Step Inference Using LoRA

LoRA weights extracted from Wan2.2 distilled models - Flexible deployment with excellent generation quality

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🌟 What's Special?

⚡ Flexible Deployment

Base Model + LoRA: Can be combined with base models
Offline Merging: Pre-merge LoRA into models
Online Loading: Dynamically load LoRA during inference
Multiple Frameworks: Supports LightX2V and ComfyUI

🎯 Dual Noise Control

High Noise: More creative, diverse outputs
Low Noise: More faithful to input, stable outputs
Rank 64 LoRA, compact size

💾 Storage Efficient

Small LoRA Size: Significantly smaller than full models
Flexible Combination: Can be combined with quantization
Easy Sharing: Convenient for model weight distribution

🚀 4-Step Inference

Ultra-Fast Generation: Generate high-quality videos in just 4 steps
Distillation Acceleration: Inherits advantages of distilled models
Quality Assurance: Maintains excellent generation quality

📦 LoRA Model Catalog

🎥 Available LoRA Models

Task Type	Noise Level	Model File	Rank	Purpose
I2V	High Noise	`wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_xxx.safetensors`	64	More creative image-to-video
I2V	Low Noise	`wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_xxx.safetensors`	64	More stable image-to-video

💡 Note:

xxx in filenames represents version number or timestamp, please check HuggingFace repository for the latest version

These LoRAs must be used with Wan2.2 base models

🚀 Usage

Prerequisites

Base Model: You need to prepare Wan2.2 I2V base model (original model without distillation)

Download base model (choose one):

Method 1: From LightX2V Official Repository (Recommended)

bash

# Download high noise base modelhuggingface-cli download lightx2v/Wan2.2-Official-Models \    wan2.2_i2v_A14b_high_noise_lightx2v.safetensors \    --local-dir ./models/Wan2.2-Official-Models
# Download low noise base modelhuggingface-cli download lightx2v/Wan2.2-Official-Models \    wan2.2_i2v_A14b_low_noise_lightx2v.safetensors \    --local-dir ./models/Wan2.2-Official-Models

Method 2: From Wan-AI Official Repository

💡 Note: lightx2v/Wan2.2-Official-Models provides separate high noise and low noise base models, download as needed

Method 1: LightX2V - Offline LoRA Merging (Recommended ⭐)

Offline LoRA merging provides best performance and supports quantization simultaneously.

1.1 Download LoRA Models

bash

# Download both LoRAs (high noise and low noise)# Note: xxx represents version number, please check HuggingFace for actual filenamehuggingface-cli download lightx2v/Wan2.2-Distill-Loras \    wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --local-dir ./loras/

1.2 Merge LoRA (Basic Merging)

Merge LoRA:

bash

cd LightX2V/tools/convert
# For directory-based base model: --source /path/to/Wan2.2-I2V-A14B/high_noise_model/python converter.py \    --source ./models/Wan2.2-Official-Models/wan2.2_i2v_A14b_high_noise_lightx2v.safetensors \    --output /path/to/output/ \    --output_ext .safetensors \    --output_name wan2.2_i2v_A14b_high_noise_lightx2v_4step \    --model_type wan_dit \    --lora_path /path/to/loras/wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --lora_strength 1.0 \    --single_file
# For directory-based base model: --source /path/to/Wan2.2-I2V-A14B/low_noise_model/python converter.py \    --source ./models/Wan2.2-Official-Models/wan2.2_i2v_A14b_low_noise_lightx2v.safetensors \    --output /path/to/output/ \    --output_ext .safetensors \    --output_name wan2.2_i2v_A14b_low_noise_lightx2v_4step \    --model_type wan_dit \    --lora_path /path/to/loras/wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --lora_strength 1.0 \    --single_file

1.3 Merge LoRA + Quantization (Recommended)

Merge LoRA + FP8 Quantization:

bash

cd LightX2V/tools/convert
# For directory-based base model: --source /path/to/Wan2.2-I2V-A14B/high_noise_model/python converter.py \    --source ./models/Wan2.2-Official-Models/wan2.2_i2v_A14b_high_noise_lightx2v.safetensors \    --output /path/to/output/ \    --output_ext .safetensors \    --output_name wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step \    --model_type wan_dit \    --lora_path /path/to/loras/wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --lora_strength 1.0 \    --quantized \    --linear_dtype torch.float8_e4m3fn \    --non_linear_dtype torch.bfloat16 \    --single_file
# For directory-based base model: --source /path/to/Wan2.2-I2V-A14B/low_noise_model/python converter.py \    --source ./models/Wan2.2-Official-Models/wan2.2_i2v_A14b_low_noise_lightx2v.safetensors \    --output /path/to/output/ \    --output_ext .safetensors \    --output_name wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step \    --model_type wan_dit \    --lora_path /path/to/loras/wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --lora_strength 1.0 \    --quantized \    --linear_dtype torch.float8_e4m3fn \    --non_linear_dtype torch.bfloat16 \    --single_file

Merge LoRA + ComfyUI FP8 Format:

bash

cd LightX2V/tools/convert
# For directory-based base model: --source /path/to/Wan2.2-I2V-A14B/high_noise_model/python converter.py \    --source ./models/Wan2.2-Official-Models/wan2.2_i2v_A14b_high_noise_lightx2v.safetensors \    --output /path/to/output/ \    --output_ext .safetensors \    --output_name wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step_comfyui \    --model_type wan_dit \    --lora_path /path/to/loras/wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --lora_strength 1.0 \    --quantized \    --linear_dtype torch.float8_e4m3fn \    --non_linear_dtype torch.bfloat16 \    --single_file \    --comfyui_mode
# For directory-based base model: --source /path/to/Wan2.2-I2V-A14B/low_noise_model/python converter.py \    --source ./models/Wan2.2-Official-Models/wan2.2_i2v_A14b_low_noise_lightx2v.safetensors \    --output /path/to/output/ \    --output_ext .safetensors \    --output_name wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step_comfyui \    --model_type wan_dit \    --lora_path /path/to/loras/wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --lora_strength 1.0 \    --quantized \    --linear_dtype torch.float8_e4m3fn \    --non_linear_dtype torch.bfloat16 \    --single_file \    --comfyui_mode

📝 Reference Documentation: For more merging options, see LightX2V Model Conversion Documentation

Method 2: LightX2V - Online LoRA Loading

Online LoRA loading requires no pre-merging, loads dynamically during inference, more flexible.

2.1 Download LoRA Models

bash

# Download both LoRAs (high noise and low noise)# Note: xxx represents version number, please check HuggingFace for actual filenamehuggingface-cli download lightx2v/Wan2.2-Distill-Loras \    wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_xxx.safetensors \    --local-dir ./loras/

2.2 Use Configuration File

Reference configuration file: wan_moe_i2v_distil_with_lora.json

LoRA configuration example in config file:

json

{    "lora_configs": [        {            "name": "high_noise_model",            "path": "/path/to/loras/wan2.2_i2v_A14b_high_noise_lora_rank64_lightx2v_4step_xxx.safetensors",            "strength": 1.0        },        {            "name": "low_noise_model",            "path": "/path/to/loras/wan2.2_i2v_A14b_low_noise_lora_rank64_lightx2v_4step_xxx.safetensors",            "strength": 1.0        }    ]}

💡 Tip: Replace xxx with actual version number (e.g., 1022). Check HuggingFace repository for the latest version

2.3 Run Inference

Using I2V as example:

Method 3: ComfyUI

Please refer to workflow

⚠️ Important Notes

Base Model Requirement: These LoRAs must be used with Wan2.2-I2V-A14B base model, cannot be used standalone
Other Components: In addition to DIT model and LoRA, the following are also required at runtime:
- T5 text encoder
- CLIP vision encoder
- VAE encoder/decoder
- Tokenizer
Please refer to LightX2V Documentation for how to organize complete model directory
Inference Configuration: When using 4-step inference, configure correct denoising_step_list, recommended: [1000, 750, 500, 250]

📚 Related Resources

Documentation Links

LightX2V Quick Start: Quick Start Documentation
Model Conversion Tool: Conversion Tool Documentation
Online LoRA Loading: Configuration File Example
Quantization Guide: Quantization Documentation
Model Structure: Model Structure Documentation

Related Models

Distilled Full Models: Wan2.2-Distill-Models
Wan2.2 Official Models: Wan2.2-Official-Models - Contains high noise and low noise base models
Base Model (Wan-AI): Wan2.2-I2V-A14B

🤝 Community & Support

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

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