Update README.md

README.md

@@ -9,3 +9,206 @@ base_model:
   - Wan-AI/Wan2.2-I2V-A14B
 library_name: diffusers
 ---
+# 🎬 Wan2.2 Distilled Models
+
+### ⚡ High-Performance Video Generation with 4-Step Inference
+
+*Distillation-accelerated version of Wan2.2 - Dramatically faster speed with excellent 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-Models)
+[![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?
+
+<table>
+<tr>
+<td width="50%">
+
+### ⚡ Ultra-Fast Generation
+- **4-step inference** (vs traditional 50+ steps)
+- Approximately **2x faster** using LightX2V than ComfyUI
+- Near real-time video generation capability
+
+</td>
+<td width="50%">
+
+### 🎯 Flexible Options
+- **Dual noise control**: High/Low noise variants
+- Multiple precision formats (BF16/FP8/INT8)
+- Full 14B parameter models
+
+</td>
+</tr>
+<tr>
+<td width="50%">
+
+### 💾 Memory Efficient
+- FP8/INT8: **~50% size reduction**
+- CPU offload support
+- Optimized for consumer GPUs
+
+</td>
+<td width="50%">
+
+### 🔧 Easy Integration
+- Compatible with LightX2V framework
+- ComfyUI support
+- Simple configuration files
+
+</td>
+</tr>
+</table>
+
+---
+
+## 📦 Model Catalog
+
+### 🎥 Model Types
+
+<table>
+<tr>
+<td align="center" width="50%">
+
+#### 🖼️ **Image-to-Video (I2V) - 14B Parameters**
+Transform static images into dynamic videos with advanced quality control
+
+- 🎨 **High Noise**: More creative, diverse outputs
+- 🎯 **Low Noise**: More faithful to input, stable outputs
+
+</td>
+<td align="center" width="50%">
+
+#### 📝 **Text-to-Video (T2V) - 14B Parameters**
+Generate videos from text descriptions
+
+- 🎨 **High Noise**: More creative, diverse outputs
+- 🎯 **Low Noise**: More stable and controllable outputs
+- 🚀 Full 14B parameter model
+
+</td>
+</tr>
+</table>
+
+### 🎯 Precision Versions
+
+| Precision | Model Identifier | Model Size | Framework | Quality vs Speed |
+|:---------:|:-----------------|:----------:|:---------:|:-----------------|
+| 🏆 **BF16** | `lightx2v_4step` | ~28.6 GB | LightX2V | ⭐⭐⭐⭐⭐ Highest Quality |
+| ⚡ **FP8** | `scaled_fp8_e4m3_lightx2v_4step` | ~15 GB | LightX2V | ⭐⭐⭐⭐ Excellent Balance |
+| 🎯 **INT8** | `int8_lightx2v_4step` | ~15 GB | LightX2V | ⭐⭐⭐⭐ Fast & Efficient |
+| 🔷 **FP8 ComfyUI** | `scaled_fp8_e4m3_lightx2v_4step_comfyui` | ~15 GB | ComfyUI | ⭐⭐⭐ ComfyUI Ready |
+
+### 📝 Naming Convention
+
+```bash
+# Format: wan2.2_{task}_A14b_{noise_level}_{precision}_lightx2v_4step.safetensors
+
+# I2V Examples:
+wan2.2_i2v_A14b_high_noise_lightx2v_4step.safetensors                       # I2V High Noise - BF16
+wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors      # I2V High Noise - FP8
+wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step.safetensors                  # I2V Low Noise - INT8
+wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step_comfyui.safetensors  # I2V Low Noise - FP8 ComfyUI
+
+```
+> 💡 **Browse All Models**: [View Full Model Collection →](https://huggingface.co/lightx2v/Wan2.2-Distill-Models/tree/main)
+
+---
+
+## 🚀 Usage
+
+### Method 1: LightX2V (Recommended ⭐)
+
+**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 (using I2V FP8 as example)
+```bash
+huggingface-cli download lightx2v/Wan2.2-Distill-Models \
+    --local-dir ./models/wan2.2_i2v \
+    --include "wan2.2_i2v_A14b_high_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors"
+```
+
+```bash
+huggingface-cli download lightx2v/Wan2.2-Distill-Models \
+    --local-dir ./models/wan2.2_i2v \
+    --include "wan2.2_i2v_A14b_low_noise_scaled_fp8_e4m3_lightx2v_4step.safetensors"
+```
+
+> 💡 **Tip**: For T2V models, follow the same steps but replace `i2v` with `t2v` in the filenames
+
+2. Clone LightX2V repository
+
+```bash
+git clone https://github.com/ModelTC/LightX2V.git
+cd LightX2V
+```
+
+3. Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+Or refer to [Quick Start Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/quickstart.html) to use docker
+
+4. Select and modify configuration file
+
+Choose appropriate configuration based on your GPU memory:
+
+**80GB+ GPUs (A100/H100)**
+- I2V: [wan_moe_i2v_distill.json](https://github.com/ModelTC/LightX2V/blob/main/configs/wan22/wan_moe_i2v_distill.json)
+
+**24GB+ GPUs (RTX 4090)**
+- I2V: [wan_moe_i2v_distill_4090.json](https://github.com/ModelTC/LightX2V/blob/main/configs/wan22/wan_moe_i2v_distill_4090.json)
+
+
+5. 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
+```
+
+> 📝 **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)
+
+
+#### LightX2V Documentation
+- **Quick Start Guide**: [LightX2V Quick Start](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/quickstart.html)
+- **Complete Usage Guide**: [LightX2V Model Structure Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/getting_started/model_structure.html)
+- **Configuration File Instructions**: [Configuration Files](https://github.com/ModelTC/LightX2V/tree/main/configs/distill)
+- **Quantized Model Usage**: [Quantization Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/quantization.html)
+- **Parameter Offloading**: [Offload Documentation](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/offload.html)
+
+---
+
+### Method 2: ComfyUI
+
+Please refer to [workflow](https://huggingface.co/lightx2v/Wan2.2-Distill-Models/blob/main/wan2.2_moe_i2v_scale_fp8_comfyui.json)
+
+
+
+## ⚠️ Important Notes
+
+**Other Components**: These models only contain DIT weights. Additional components needed 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 instructions on organizing the complete model directory.
+
+
+## 🤝 Community
+
+- **GitHub Issues**: https://github.com/ModelTC/LightX2V/issues
+- **HuggingFace**: https://huggingface.co/lightx2v/Wan2.2-Distill-Models
+
+If you find this project helpful, please give us a ⭐ on [GitHub](https://github.com/ModelTC/LightX2V)
+
+</div>