--- title: Wan2.2-Distill-Loras canonical_url: "https://www.modelscope.cn/models/lightx2v/Wan2.2-Distill-Loras" md_url: "https://www.modelscope.cn/models/lightx2v/Wan2.2-Distill-Loras.md" repository: lightx2v/Wan2.2-Distill-Loras last_updated: 2025-12-17 license: apache-2.0 pipeline_tag: image-to-video tasks: - image-to-video base_model: - Wan-AI/Wan2.2-I2V-A14B base_model_relation: adapter parameters: 686.9M tensor_type: - BF16 library_name: - lora - safetensors - pytorch frameworks: - pytorch downloads: 10540 stars: 11 tags: - diffusion-single-file - comfyui - distillation - LoRA - video - "video genration" - lora --- # Wan2.2-Distill-Loras > Wan2.2-Distill-Loras - lightx2v 在 ModelScope 开源的模型。🎬 Wan2.2 Distilled LoRA Models lightx2v/Wan2.2-Distill-Loras 是 ModelScope 魔搭社区上的 686.9M 参数image-to-video模型，采用 apache-2.0 许可，基于 Wan-AI/Wan2.2-I2V-A14B 构建。 - **Repository**: lightx2v/Wan2.2-Distill-Loras - **License**: apache-2.0 - **Tasks**: image-to-video - **Parameters**: 686.9M - **Base model**: Wan-AI/Wan2.2-I2V-A14B - **Tags**: diffusion-single-file, comfyui, distillation, LoRA, video, video genration, lora - **Downloads**: 10540 - **Stars**: 11 - **Last updated**: 2025-12-17 Source: https://www.modelscope.cn/models/lightx2v/Wan2.2-Distill-Loras --- # 🎬 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* ![img_lightx2v](https://cdn-uploads.huggingface.co/production/uploads/680de13385293771bc57400b/tTnp8-ARpj3wGxfo5P55c.png) --- [![🤗 HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-yellow)](https://huggingface.co/lightx2v/Wan2.2-Distill-Loras) [![GitHub](https://img.shields.io/badge/GitHub-LightX2V-blue?logo=github)](https://github.com/ModelTC/LightX2V) [![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](LICENSE) --- ## 🌟 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](https://huggingface.co/lightx2v/Wan2.2-Distill-Loras/tree/main) 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 model huggingface-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 model huggingface-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** ```bash huggingface-cli download Wan-AI/Wan2.2-I2V-A14B \ --local-dir ./models/Wan2.2-I2V-A14B ``` > 💡 **Note**: [lightx2v/Wan2.2-Official-Models](https://huggingface.co/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 filename huggingface-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](https://github.com/ModelTC/LightX2V/blob/main/tools/convert/readme_zh.md) --- ### 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 filename huggingface-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](https://github.com/ModelTC/LightX2V/blob/main/configs/wan22/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](https://huggingface.co/lightx2v/Wan2.2-Distill-Loras/tree/main) for the latest version #### 2.3 Run Inference Using [I2V](https://github.com/ModelTC/LightX2V/blob/main/scripts/wan22/run_wan22_moe_i2v_distill.sh) as example: ```bash cd scripts bash wan22/run_wan22_moe_i2v_distill.sh ``` ### Method 3: ComfyUI Please refer to [workflow](https://huggingface.co/lightx2v/Wan2.2-Distill-Loras/blob/main/wan2.2_i2v_scale_fp8_comfyui_with_lora.json) ## ⚠️ Important Notes 1. **Base Model Requirement**: These LoRAs must be used with Wan2.2-I2V-A14B base model, cannot be used standalone 2. **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](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/model_structure.html) for how to organize complete model directory 3. **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](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/quickstart.html) - **Model Conversion Tool**: [Conversion Tool Documentation](https://github.com/ModelTC/LightX2V/blob/main/tools/convert/readme_zh.md) - **Online LoRA Loading**: [Configuration File Example](https://github.com/ModelTC/LightX2V/blob/main/configs/wan22/wan_moe_i2v_distil_with_lora.json) - **Quantization Guide**: [Quantization Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/quantization.html) - **Model Structure**: [Model Structure Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/model_structure.html) ### Related Models - **Distilled Full Models**: [Wan2.2-Distill-Models](https://huggingface.co/lightx2v/Wan2.2-Distill-Models) - **Wan2.2 Official Models**: [Wan2.2-Official-Models](https://huggingface.co/lightx2v/Wan2.2-Official-Models) - Contains high noise and low noise base models - **Base Model (Wan-AI)**: [Wan2.2-I2V-A14B](https://huggingface.co/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](https://github.com/ModelTC/LightX2V)