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--- |
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license: apache-2.0 |
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tags: |
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- diffusion-single-file |
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- comfyui |
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- distillation |
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- LoRA |
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- video |
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- video genration |
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base_model: |
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- Wan-AI/Wan2.2-I2V-A14B |
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pipeline_tags: |
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- image-to-video |
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- text-to-video |
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library_name: diffusers |
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--- |
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# 🎬 Wan2.2 Distilled Models |
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### ⚡ High-Performance Video Generation with 4-Step Inference |
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*Distillation-accelerated version of Wan2.2 - Dramatically faster speed with excellent quality* |
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--- |
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[](https://huggingface.co/lightx2v/Wan2.2-Distill-Models) |
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[](https://github.com/ModelTC/LightX2V) |
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[](LICENSE) |
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--- |
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## 🌟 What's Special? |
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<table> |
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<tr> |
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<td width="50%"> |
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### ⚡ Ultra-Fast Generation |
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- **4-step inference** (vs traditional 50+ steps) |
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- Approximately **2x faster** using LightX2V than ComfyUI |
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- Near real-time video generation capability |
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</td> |
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<td width="50%"> |
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### 🎯 Flexible Options |
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- **Dual noise control**: High/Low noise variants |
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- Multiple precision formats (BF16/FP8/INT8) |
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- Full 14B parameter models |
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</td> |
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</tr> |
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<tr> |
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<td width="50%"> |
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### 💾 Memory Efficient |
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- FP8/INT8: **~50% size reduction** |
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- CPU offload support |
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- Optimized for consumer GPUs |
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</td> |
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<td width="50%"> |
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### 🔧 Easy Integration |
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- Compatible with LightX2V framework |
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- ComfyUI support |
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- Simple configuration files |
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</td> |
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</tr> |
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</table> |
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--- |
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## 📦 Model Catalog |
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### 🎥 Model Types |
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<table> |
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<tr> |
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<td align="center" width="50%"> |
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#### 🖼️ **Image-to-Video (I2V) - 14B Parameters** |
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Transform static images into dynamic videos with advanced quality control |
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- 🎨 **High Noise**: More creative, diverse outputs |
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- 🎯 **Low Noise**: More faithful to input, stable outputs |
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</td> |
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<td align="center" width="50%"> |
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#### 📝 **Text-to-Video (T2V) - 14B Parameters** |
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Generate videos from text descriptions |
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- 🎨 **High Noise**: More creative, diverse outputs |
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- 🎯 **Low Noise**: More stable and controllable outputs |
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- 🚀 Full 14B parameter model |
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</td> |
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</tr> |
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</table> |
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### 🎯 Precision Versions |
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| Precision | Model Identifier | Model Size | Framework | Quality vs Speed | |
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|:---------:|:-----------------|:----------:|:---------:|:-----------------| |
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| 🏆 **BF16** | `lightx2v_4step` | ~28.6 GB | LightX2V | ⭐⭐⭐⭐⭐ Highest Quality | |
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| ⚡ **FP8** | `scaled_fp8_e4m3_lightx2v_4step` | ~15 GB | LightX2V | ⭐⭐⭐⭐ Excellent Balance | |
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| 🎯 **INT8** | `int8_lightx2v_4step` | ~15 GB | LightX2V | ⭐⭐⭐⭐ Fast & Efficient | |
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| 🔷 **FP8 ComfyUI** | `scaled_fp8_e4m3_lightx2v_4step_comfyui` | ~15 GB | ComfyUI | ⭐⭐⭐ ComfyUI Ready | |
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### 📝 Naming Convention |
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```bash |
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# Format: wan2.2_{task}_A14b_{noise_level}_{precision}_lightx2v_4step.safetensors |
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# I2V Examples: |
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wan2.2_i2v_A14b_high_noise_lightx2v_4step.safetensors # I2V High Noise - BF16 |
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wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors # I2V High Noise - FP8 |
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wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step.safetensors # I2V Low Noise - INT8 |
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wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step_comfyui.safetensors # I2V Low Noise - FP8 ComfyUI |
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``` |
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> 💡 **Browse All Models**: [View Full Model Collection →](https://huggingface.co/lightx2v/Wan2.2-Distill-Models/tree/main) |
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--- |
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## 🚀 Usage |
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### Method 1: LightX2V (Recommended ⭐) |
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**LightX2V is a high-performance inference framework optimized for these models, approximately 2x faster than ComfyUI with better quantization accuracy. Highly recommended!** |
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#### Quick Start |
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1. Download model (using I2V FP8 as example) |
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```bash |
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huggingface-cli download lightx2v/Wan2.2-Distill-Models \ |
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--local-dir ./models/wan2.2_i2v \ |
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--include "wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors" |
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``` |
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```bash |
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huggingface-cli download lightx2v/Wan2.2-Distill-Models \ |
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--local-dir ./models/wan2.2_i2v \ |
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--include "wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors" |
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``` |
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> 💡 **Tip**: For T2V models, follow the same steps but replace `i2v` with `t2v` in the filenames |
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2. Clone LightX2V repository |
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```bash |
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git clone https://github.com/ModelTC/LightX2V.git |
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cd LightX2V |
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``` |
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3. Install dependencies |
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```bash |
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pip install -r requirements.txt |
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``` |
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Or refer to [Quick Start Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/quickstart.html) to use docker |
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4. Select and modify configuration file |
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Choose appropriate configuration based on your GPU memory: |
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**80GB+ GPUs (A100/H100)** |
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- I2V: [wan_moe_i2v_distill.json](https://github.com/ModelTC/LightX2V/blob/main/configs/wan22/wan_moe_i2v_distill.json) |
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**24GB+ GPUs (RTX 4090)** |
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- I2V: [wan_moe_i2v_distill_4090.json](https://github.com/ModelTC/LightX2V/blob/main/configs/wan22/wan_moe_i2v_distill_4090.json) |
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5. Run inference (using [I2V]((https://github.com/ModelTC/LightX2V/blob/main/scripts/wan22/run_wan22_moe_i2v_distill.sh)) as example) |
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```bash |
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cd scripts |
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bash wan22/run_wan22_moe_i2v_distill.sh |
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``` |
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> 📝 **Note**: Update model paths in the script to point to your Wan2.2 model. Also refer to [LightX2V Model Structure Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/model_structure.html) |
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#### LightX2V Documentation |
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- **Quick Start Guide**: [LightX2V Quick Start](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/quickstart.html) |
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- **Complete Usage Guide**: [LightX2V Model Structure Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/model_structure.html) |
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- **Configuration File Instructions**: [Configuration Files](https://github.com/ModelTC/LightX2V/tree/main/configs/distill) |
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- **Quantized Model Usage**: [Quantization Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/quantization.html) |
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- **Parameter Offloading**: [Offload Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/offload.html) |
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--- |
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### Method 2: ComfyUI |
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Please refer to [workflow](https://huggingface.co/lightx2v/Wan2.2-Distill-Models/blob/main/wan2.2_moe_i2v_scale_fp8_comfyui.json) |
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## ⚠️ Important Notes |
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**Other Components**: These models only contain DIT weights. Additional components needed at runtime: |
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- T5 text encoder |
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- CLIP vision encoder |
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- VAE encoder/decoder |
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- Tokenizer |
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Please refer to [LightX2V Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/model_structure.html) for instructions on organizing the complete model directory. |
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## 🤝 Community |
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- **GitHub Issues**: https://github.com/ModelTC/LightX2V/issues |
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- **HuggingFace**: https://huggingface.co/lightx2v/Wan2.2-Distill-Models |
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If you find this project helpful, please give us a ⭐ on [GitHub](https://github.com/ModelTC/LightX2V) |
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</div> |
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