From 183e1cb87133f68a00c9d310be404bbbc48f4f7d Mon Sep 17 00:00:00 2001
From: gushiqiao <975033167@qq.com>
Date: Tue, 28 Oct 2025 16:35:03 +0800
Subject: [PATCH] feat: Add LightX2V VAE custom nodes for ComfyUI
---
README.md | 240 ++-
README_CN.md | 239 +++
__init__.py | 16 +
example/imgs/example.jpg | Bin 0 -> 728760 bytes
example/imgs/vae_decode.png | Bin 0 -> 16999 bytes
example/imgs/vae_loader.png | Bin 0 -> 38060 bytes
.../wan2.1_I2V_4step_fp8_lightvae.json | 1487 +++++++++++++++++
example/workflows/wan2.2_TI2V_lightvae.json | 1149 +++++++++++++
nodes.py | 143 ++
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create mode 100644 example/workflows/wan2.2_TI2V_lightvae.json
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diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1 +1,239 @@
-# ComfyUI-LightVAE
\ No newline at end of file
+# ComfyUI-LightVAE
+
+
+
+
+
+**High-Performance VAE Custom Nodes**
+
+[](https://huggingface.co/lightx2v/Autoencoders)
+[](https://github.com/ModelTC/LightX2V)
+[](LICENSE)
+
+English | [简体中文](README_CN.md)
+
+
+
+
+## 📖 Introduction
+
+ComfyUI-LightVAE is a collection of LightX2V VAE custom nodes designed for ComfyUI, supporting high-performance video VAE models including **LightVAE** and **LightTAE**.
+
+The LightX2V team has deeply optimized VAE, creating two major series: **LightVAE** and **LightTAE**, which significantly reduce memory usage and improve inference speed while maintaining high quality.
+
+
+## ✨ Key Features
+
+
+
+|
+
+### 🎯 LightVAE Series
+**Feature: Best Balance ⚖️**
+
+- ✅ Uses **Causal 3D Conv** (same as official)
+- ✅ **Near-official quality** ⭐⭐⭐⭐
+- ✅ **~50% less memory** (~4-5 GB)
+- ✅ **2-3x faster**
+- ✅ Balances quality, speed, and memory 🏆
+
+ |
+
+
+### ⚡ LightTAE Series
+**Feature: Ultra-fast + High Quality 🏆**
+
+- ✅ Minimal memory usage (~0.4 GB)
+- ✅ Lightning-fast inference
+- ✅ **Near-official quality** ⭐⭐⭐⭐
+- ✅ **Surpasses open-source TAE**
+
+ |
+
+
+
+
+## 🚀 Performance Comparison
+
+**Test Environment**: H100 GPU, BF16, 81-frame video (480P)
+
+| Model | Encode Time | Decode Time | Encode Memory | Decode Memory | Quality |
+|:------|:------------|:------------|:--------------|:--------------|:--------|
+| **lightvaew2_1** | 1.5s | 2.1s | 4.8GB | 5.6GB | ⭐⭐⭐⭐⭐ |
+| **lighttaew2_1** | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐⭐ |
+| [Wan2.1_VAE](https://github.com/Wan-Video/Wan2.1) | 4.2s | 5.5s | 8.5GB | 10.1GB | ⭐⭐⭐⭐ |
+| [taew2_1](https://github.com/madebyollin/taesd) | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐ |
+
+**Performance Improvements:**
+- 🚀 LightVAE is **2-3x faster** than official VAE, **50% less memory**
+- ⚡ LightTAE is **10+ times faster** than official VAE, **95%+ less memory**
+- 🎨 Near-official [VAE](https://github.com/Wan-Video/Wan2.1) quality, surpasses open-source [TAE](https://github.com/madebyollin/taesd)
+
+
+## 📦 Installation
+
+### 1. Install LightX2V Dependencies
+
+```bash
+# Clone LightX2V repository
+git clone https://github.com/ModelTC/LightX2V
+cd LightX2V
+
+python setup_vae.py install
+```
+
+### 2. Install ComfyUI-WanVideoWrapper
+
+LightVAE nodes depend on WanVideoWrapper for main model support:
+
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/kijai/ComfyUI-WanVideoWrapper
+```
+
+### 3. Install ComfyUI-LightVAE
+
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/YOUR_USERNAME/ComfyUI-LightVAE
+```
+
+### 4. Restart ComfyUI
+
+
+## 📥 Download Models
+
+### Main Models (Diffusion Models)
+
+**Option 1: Distilled Models (Recommended, 4-step)**
+- 🔗 [Wan2.1 Distilled Models](https://huggingface.co/lightx2v/Wan2.1-Distill-Models) and [Wan2.2 Distilled Models](https://huggingface.co/lightx2v/Wan2.2-Distill-Models)
+- ✅ Supports BF16 format
+- ✅ Supports FP8 format (requires models with `_comfyui.safetensors` suffix)
+
+**Option 2: Original Models (20-step)**
+- 🔗 [Wan2.1 Official Models](https://huggingface.co/lightx2v/Wan2.1-Official-Models) and [Wan2.2 Official Models](https://huggingface.co/lightx2v/Wan2.2-Official-Models)
+- ✅ Supports BF16 format
+- ✅ Supports FP8 format (requires models with `_comfyui.safetensors` suffix)
+
+```bash
+# Download to ComfyUI/models/diffusion_models/
+huggingface-cli download lightx2v/Wan2.1/2-Distill-Models \
+ --local-dir ./ComfyUI/models/diffusion_models/
+```
+
+### VAE Models
+
+**All VAE Models** (Required):
+- 🔗 [LightX2V Autoencoders](https://huggingface.co/lightx2v/Autoencoders)
+
+```bash
+# Download all VAE models
+huggingface-cli download lightx2v/Autoencoders \
+ --local-dir ./ComfyUI/models/vae/
+
+# Or download only what you need (Recommended)
+huggingface-cli download lightx2v/Autoencoders lightvaew2_1.pth \
+ --local-dir ./ComfyUI/models/vae/
+```
+
+**Supported VAE Models**:
+- `Wan2.1_VAE.pth` / `.safetensors` - Official VAE 2.1
+- `Wan2.2_VAE.pth` / `.safetensors` - Official VAE 2.2
+- `lightvaew2_1.pth` / `.safetensors` - Optimized VAE 2.1 ⭐ **Recommended**
+- `taew2_1.pth` / `.safetensors` - Open-source TAE 2.1
+- `taew2_2.pth` / `.safetensors` - Open-source TAE 2.2
+- `lighttaew2_1.pth` / `.safetensors` - Optimized TAE 2.1 ⚡ **Fastest**
+- `lighttaew2_2.pth` / `.safetensors` - Optimized TAE 2.2
+
+
+## 🎯 Node Documentation
+
+### 1. LightX2V VAE Decoder Loader
+
+Loads VAE decoder models.
+
+**Input Parameters:**
+- `vae_filename` - VAE model filename (automatically lists from `./models/vae/`)
+- `dtype` - Data type (bfloat16 / float16 / float32)
+- `device` - Compute device (cuda / cpu)
+
+**Output:**
+- `vae` - VAE model object
+
+**Features:**
+- ✅ Automatically identifies VAE type from filename
+- ✅ Supports all LightX2V VAE models
+
+
+
+### 2. LightX2V VAE Decode
+
+Decodes latent to video frames.
+
+**Input Parameters:**
+- `vae` - VAE object from Loader
+- `latent` - Latent representation
+
+**Output:**
+- `IMAGE` - Decoded video frames
+
+**Supports:**
+- ✅ All VAE series (WanVAE, LightVAE)
+- ✅ All TAE series (TAE, LightTAE)
+
+
+
+## 🖼️ Example Workflows
+
+### Wan2.1 I2V 4-step FP8 + LightVAE
+
+High-performance configuration using 4-step distilled model + LightVAE optimized decoder.
+
+
+
+**Workflow File**: [`example/workflows/wan2.1_I2V_4step_fp8_lightvae.json`](example/workflows/wan2.1_I2V_4step_fp8_lightvae.json)
+
+### Wan2.2 TI2V + LightVAE
+
+Wan2.2 Text-Image-to-Video + LightVAE decoding.
+
+**Workflow File**: [`example/workflows/wan2.2_TI2V_lightvae.json`](example/workflows/wan2.2_TI2V_lightvae.json)
+
+
+
+## ⚠️ Important Notes
+
+### Model Compatibility
+- ⚠️ **Wan2.1 VAE** can only be used with **Wan2.1/Wan2.2-A1B** backbone models
+- ⚠️ **Wan2.2 VAE** can only be used with **Wan2.2 TI2V** backbone models
+- ❌ Do not mix different versions of VAE and backbone models
+
+
+## 📚 Related Resources
+
+- **Project Homepage**: https://github.com/ModelTC/LightX2V
+- **VAE Models**: https://huggingface.co/lightx2v/Autoencoders
+- **Video Generation Models**: https://huggingface.co/lightx2v/
+- **ComfyUI-WanVideoWrapper**: https://github.com/kijai/ComfyUI-WanVideoWrapper
+- **TAE Series Models**: https://github.com/madebyollin/taesd
+- **Wan-AI**: https://huggingface.co/Wan-AI
+
+
+## 🙏 Acknowledgements
+
+If this project helps you, please give a ⭐ to [LightX2V](https://github.com/ModelTC/LightX2V) and this repository!
+
+
+
+## 📞 Support
+
+- **GitHub Issues**: Issues page of this repository
+- **LightX2V Issues**: https://github.com/ModelTC/LightX2V/issues
+- **HuggingFace**: https://huggingface.co/lightx2v
+
+
+
+**Enjoy using LightX2V VAE!** 🚀
+
+
+
diff --git a/README_CN.md b/README_CN.md
new file mode 100644
index 0000000..43240ed
--- /dev/null
+++ b/README_CN.md
@@ -0,0 +1,239 @@
+# ComfyUI-LightVAE
+
+
+
+
+
+**高性能 VAE 自定义节点**
+
+[](https://huggingface.co/lightx2v/Autoencoders)
+[](https://github.com/ModelTC/LightX2V)
+[](LICENSE)
+
+[English](README.md) | 简体中文
+
+
+
+
+## 📖 简介
+
+ComfyUI-LightVAE 是为 ComfyUI 设计的 LightX2V VAE 自定义节点集合，支持包括 **LightVAE** 和 **LightTAE** 在内的高性能视频 VAE 模型。
+
+LightX2V 团队对 VAE 进行了深度优化，衍生出两大系列：**LightVAE** 和 **LightTAE**，在保持高质量的同时显著降低内存占用并提升推理速度。
+
+
+## ✨ 核心特性
+
+
+
+|
+
+### 🎯 LightVAE 系列
+**特点：最佳平衡 ⚖️**
+
+- ✅ 使用 **Causal 3D Conv**（与官方相同）
+- ✅ **质量接近官方** ⭐⭐⭐⭐
+- ✅ 内存减少 **~50%**（~4-5 GB）
+- ✅ 速度提升 **2-3x**
+- ✅ 平衡质量、速度和内存 🏆
+
+ |
+
+
+### ⚡ LightTAE 系列
+**特点：极速 + 高质量 🏆**
+
+- ✅ 极低内存占用（~0.4 GB）
+- ✅ 极速推理
+- ✅ **质量接近官方** ⭐⭐⭐⭐
+- ✅ **超越开源 TAE**
+
+ |
+
+
+
+
+## 🚀 性能对比
+
+**测试环境**: H100 GPU, BF16, 81 帧视频 (480P)
+
+| 模型 | 编码时间 | 解码时间 | 编码内存 | 解码内存 | 质量 |
+|:-----|:---------|:---------|:---------|:---------|:-----|
+| **lightvaew2_1** | 1.5s | 2.1s | 4.8GB | 5.6GB | ⭐⭐⭐⭐⭐ |
+| **lighttaew2_1** | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐⭐ |
+| [Wan2.1_VAE](https://github.com/Wan-Video/Wan2.1) | 4.2s | 5.5s | 8.5GB | 10.1GB | ⭐⭐⭐⭐ |
+| [taew2_1](https://github.com/madebyollin/taesd) | 0.4s | 0.25s | 0.009GB | 0.4GB | ⭐⭐⭐ |
+
+**性能提升：**
+- 🚀 LightVAE 比官方 VAE **快 2-3 倍**，内存**节省 50%**
+- ⚡ LightTAE 比官方 VAE **快 10+ 倍**，内存**节省 95%+**
+- 🎨 质量接近官方 [VAE](https://github.com/Wan-Video/Wan2.1)，超过开源 [TAE](https://github.com/madebyollin/taesd)
+
+
+## 📦 安装
+
+### 1. 安装 LightX2V 依赖
+
+```bash
+# 克隆 LightX2V 仓库
+git clone https://github.com/ModelTC/LightX2V
+cd LightX2V
+
+python setup_vae.py install
+```
+
+### 2. 安装 ComfyUI-WanVideoWrapper
+
+LightVAE 节点依赖 WanVideoWrapper 提供的主模型支持：
+
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/kijai/ComfyUI-WanVideoWrapper
+```
+
+### 3. 安装 ComfyUI-LightVAE
+
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/YOUR_USERNAME/ComfyUI-LightVAE
+```
+
+### 4. 重启 ComfyUI
+
+
+## 📥 下载模型
+
+### 主模型（Diffusion Models）
+
+**选项 1: 蒸馏模型（推荐，4-step）**
+- 🔗 [Wan2.1蒸馏模型](https://huggingface.co/lightx2v/Wan2.1-Distill-Models) 和 [Wan2.2蒸馏模型](https://huggingface.co/lightx2v/Wan2.2-Distill-Models)
+- ✅ 支持 BF16 格式
+- ✅ 支持 FP8 格式（需使用 `_comfyui.safetensors` 后缀的模型）
+
+**选项 2: 原始模型（20-step）**
+- 🔗 [Wan2.1官方模型](https://huggingface.co/lightx2v/Wan2.1-Official-Models) 和 [Wan2.2官方模型](https://huggingface.co/lightx2v/Wan2.2-Official-Models)
+- ✅ 支持 BF16 格式
+- ✅ 支持 FP8 格式（需使用 `_comfyui.safetensors` 后缀的模型）
+
+```bash
+# 下载到 ComfyUI/models/diffusion_models/
+huggingface-cli download lightx2v/Wan2.1/2-Distill-Models \
+ --local-dir ./ComfyUI/models/diffusion_models/
+```
+
+### VAE 模型
+
+**所有 VAE 模型**（必需）:
+- 🔗 [LightX2V Autoencoders](https://huggingface.co/lightx2v/Autoencoders)
+
+```bash
+# 下载所有 VAE 模型
+huggingface-cli download lightx2v/Autoencoders \
+ --local-dir ./ComfyUI/models/vae/
+
+# 或只下载需要的（推荐）
+huggingface-cli download lightx2v/Autoencoders lightvaew2_1.pth \
+ --local-dir ./ComfyUI/models/vae/
+```
+
+**支持的 VAE 模型**:
+- `Wan2.1_VAE.pth` / `.safetensors` - 官方 VAE 2.1
+- `Wan2.2_VAE.pth` / `.safetensors` - 官方 VAE 2.2
+- `lightvaew2_1.pth` / `.safetensors` - 优化 VAE 2.1 ⭐ **推荐**
+- `taew2_1.pth` / `.safetensors` - 开源 TAE 2.1
+- `taew2_2.pth` / `.safetensors` - 开源 TAE 2.2
+- `lighttaew2_1.pth` / `.safetensors` - 优化 TAE 2.1 ⚡ **最快**
+- `lighttaew2_2.pth` / `.safetensors` - 优化 TAE 2.2
+
+
+## 🎯 节点说明
+
+### 1. LightX2V VAE Decoder Loader
+
+加载 VAE 解码器模型。
+
+**输入参数:**
+- `vae_filename` - VAE 模型文件名（从 `./models/vae/` 目录自动列出）
+- `dtype` - 数据类型（bfloat16 / float16 / float32）
+- `device` - 计算设备（cuda / cpu）
+
+**输出:**
+- `vae` - VAE 模型对象
+
+**特点:**
+- ✅ 自动从文件名识别 VAE 类型
+- ✅ 支持所有 LightX2V VAE 模型
+
+
+
+### 2. LightX2V VAE Decode
+
+解码 latent 到视频帧。
+
+**输入参数:**
+- `vae` - 来自 Loader 的 VAE 对象
+- `latent` - 潜在表示
+
+**输出:**
+- `IMAGE` - 解码后的视频帧
+
+**支持:**
+- ✅ 所有 VAE 系列（WanVAE, LightVAE）
+- ✅ 所有 TAE 系列（TAE, LightTAE）
+
+
+
+## 🖼️ 示例工作流
+
+### Wan2.1 I2V 4-step FP8 + LightVAE
+
+使用 4-step 蒸馏模型 + LightVAE 优化解码器的高性能配置。
+
+
+
+**工作流文件**: [`example/workflows/wan2.1_I2V_4step_fp8_lightvae.json`](example/workflows/wan2.1_I2V_4step_fp8_lightvae.json)
+
+### Wan2.2 TI2V + LightVAE
+
+Wan2.2 文本图像到视频 + LightVAE 解码。
+
+**工作流文件**: [`example/workflows/wan2.2_TI2V_lightvae.json`](example/workflows/wan2.2_TI2V_lightvae.json)
+
+
+
+## ⚠️ 注意事项
+
+### 模型兼容性
+- ⚠️ **Wan2.1 VAE** 只能用于 **Wan2.1/Wan2.2-A1B** 主干模型
+- ⚠️ **Wan2.2 VAE** 只能用于 **Wan2.2 TI2V** 主干模型
+- ❌ 不要混用不同版本的 VAE 和主干模型
+
+
+## 📚 相关资源
+
+- **项目主页**: https://github.com/ModelTC/LightX2V
+- **VAE 模型**: https://huggingface.co/lightx2v/Autoencoders
+- **视频生成模型**: https://huggingface.co/lightx2v/
+- **ComfyUI-WanVideoWrapper**: https://github.com/kijai/ComfyUI-WanVideoWrapper
+- **tae系列模型**： https://github.com/madebyollin/taesd
+- **Wan-AI**： https://huggingface.co/Wan-AI
+
+
+## 🙏 致谢
+
+如果这个项目对你有帮助，请给 ⭐ [LightX2V](https://github.com/ModelTC/LightX2V) 和本仓库！
+
+
+
+## 📞 支持
+
+- **GitHub Issues**: 本仓库的 Issues 页面
+- **LightX2V Issues**: https://github.com/ModelTC/LightX2V/issues
+- **HuggingFace**: https://huggingface.co/lightx2v
+
+
+
+**享受使用 LightX2V VAE！** 🚀
+
+
+
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..1b3b42b
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,16 @@
+from .nodes import (
+ VAEDecoderLoader,
+ LightVAEDecode
+)
+
+NODE_CLASS_MAPPINGS = {
+ "Lightx2v-VAEDecoderLoader": VAEDecoderLoader,
+ "Lightx2v-VAEDecode": LightVAEDecode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "Lightx2v-VAEDecoderLoader": "LightX2V VAE Decoder Loader",
+ "Lightx2v-VAEDecode": "LightX2V VAE Decode",
+}
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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