diff --git a/docs/zh_CN/vae.md b/docs/zh_CN/vae.md
index 75e3ccd..b27339c 100644
--- a/docs/zh_CN/vae.md
+++ b/docs/zh_CN/vae.md
@@ -1,65 +1,58 @@
-# VAE Report
+# VAE 技术报告
 
-As [Pixart-Sigma](https://arxiv.org/abs/2403.04692) finds that adapting to a new VAE is simple, we develop an additional temporal VAE.
-Specifically, our VAE consists of a pipeline of a [spatial VAE](https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers) followed by a temporal VAE.
-For the temporal VAE, we follow the implementation of [MAGVIT-v2](https://arxiv.org/abs/2310.05737), with the following modifications:
+由于 [Pixart-Sigma](https://arxiv.org/abs/2403.04692) 论文中指出适应新的VAE很简单，因此我们开发了一个额外的时间VAE。
+具体而言, 我们的VAE由一个[空间 VAE](https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers)和一个时间VA相接的形式组成.
+对于时间VAE，我们遵循 [MAGVIT-v2](https://arxiv.org/abs/2310.05737)的实现, 并做了以下修改:
 
-* We remove the architecture specific to the codebook.
-* We do not use the discriminator, and use the VAE reconstruction loss, kl loss, and perceptual loss for training.
-* In the last linear layer of the encoder, we scale down to a diagonal Gaussian Distribution of 4 channels, following our previously trained STDiT that takes in 4 channels input.
-* Our decoder is symmetric to the encoder architecture.
+* 我们删除了码本特有的架构。
+* 我们不使用鉴别​​器（discriminator），而是使用VAE重建损失、kl损失和感知损失进行训练。
+* 在编码器的最后一个线性层中，我们缩小到 4 通道的对角高斯分布，遵循我们之前训练的接受 4 通道输入的 STDiT。
+* 我们的解码器与编码器架构对称。
 
-## Training
+## 训练
+我们分不同阶段训练模型。
 
-We train the model in different stages.
-
-We first train the temporal VAE only by freezing the spatial VAE for 380k steps on a single machine (8 GPUs).
-We use an additional identity loss to make features from the 3D VAE similar to the features from the 2D VAE.
-We train the VAE using 20% images and 80% videos with 17 frames.
+我们首先通过在单台机器（8 个 GPU）上冻结空间 VAE 380k 步来训练时间 VAE。我们使用额外的身份损失使 3D VAE 的特征与 2D VAE 的特征相似。我们使用 20% 的图像和 80% 的视频（17 帧）来训练 VAE。
 
 ```bash
 torchrun --nnodes=1 --nproc_per_node=8 scripts/train_vae.py configs/vae/train/stage1.py --data-path YOUR_CSV_PATH
 ```
 
-Next, we remove the indentity loss and train the 3D VAE pipeline to reconstructe the 2D-compressed videos for 260k steps.
+接下来，我们移除身份损失并训练 3D VAE 管道以重建 260k 步的 2D 压缩视频。
 
 ```bash
 torchrun --nnodes=1 --nproc_per_node=8 scripts/train_vae.py configs/vae/train/stage2.py --data-path YOUR_CSV_PATH
 ```
 
-Finally, we remove the reconstruction loss for the 2D-compressed videos and train the VAE pipeline to construct the 3D videos for 540k steps.
-We train our VAE with a random number within 34 frames to make it more robust to different video lengths.
-This stage is trained on 24 GPUs.
+最后，我们移除了 2D 压缩视频的重建损失，并训练 VAE 管道以构建 540k 步的 3D 视频。我们在 34 帧内使用随机数训练 VAE，使其对不同长度的视频更具鲁棒性。此阶段在 24 个 GPU 上进行训练。
 
 ```bash
 torchrun --nnodes=3 --nproc_per_node=8 scripts/train_vae.py configs/vae/train/stage3.py --data-path YOUR_CSV_PATH
 ```
 
-Note that you need to adjust the `epochs` in the config file accordingly with respect to your own csv data size.
+请注意，您需要根据自己的 csv 数据大小相应地调整配置文件中的 `epochs` 。
 
-## Inference
+## 推理
 
-To visually check the performance of the VAE, you may run the following inference.
-It saves the original video to your specified video directory with `_ori` postfix (i.e. `"YOUR_VIDEO_DIR"_ori`), the reconstructed video from the full pipeline with the `_rec` postfix (i.e. `"YOUR_VIDEO_DIR"_rec`), and the reconstructed video from the 2D compression and decompression with the `_spatial` postfix (i.e. `"YOUR_VIDEO_DIR"_spatial`).
+为了直观地检查 VAE 的性能，您可以运行以下推理。它使用 `_ori` 后缀（即 `"YOUR_VIDEO_DIR"_ori`）将原始视频保存到您指定的视频目录中，使用`_rec`后缀（即`"YOUR_VIDEO_DIR"_rec`）将来自完整管道的重建视频保存到指定的视频目录中，并使用 `_spatial`后缀（即`"YOUR_VIDEO_DIR"_spatial`）将来自 2D 压缩和解压缩的重建视频保存到指定的视频目录中。
 
 ```bash
 torchrun --standalone --nnodes=1 --nproc_per_node=1 scripts/inference_vae.py configs/vae/inference/video.py --ckpt-path YOUR_VAE_CKPT_PATH --data-path YOUR_CSV_PATH --save-dir YOUR_VIDEO_DIR
 ```
-## Evaluation
+## 评估
+然后，我们可以计算 VAE 在 SSIM、PSNR、LPIPS 和 FLOLPIPS 指标上的表现得分。
 
-We can then calculate the scores of the VAE performances on metrics of SSIM, PSNR, LPIPS, and FLOLPIPS.
-
-* SSIM: structural similarity index measure, the higher the better
-* PSNR: peak-signal-to-noise ratio, the higher the better
-* LPIPS:  learned perceptual image quality degradation, the lower the better
-* [FloLPIPS](https://arxiv.org/pdf/2207.08119): LPIPS with video interpolation, the lower the better.
+* SSIM: 结构相似性指数度量，越高越好
+* PSNR: 峰值信噪比，越高越好
+* LPIPS: 学习感知图像质量下降，越低越好
+* [FloLPIPS](https://arxiv.org/pdf/2207.08119): 带有视频插值的LPIPS，越低越好。
 
 ```bash
 python eval/vae/eval_common_metric.py --batch_size 2 --real_video_dir YOUR_VIDEO_DIR_ori --generated_video_dir YOUR_VIDEO_DIR_rec --device cuda --sample_fps 24 --crop_size 256 --resolution 256 --num_frames 17 --sample_rate 1 --metric ssim psnr lpips flolpips
 ```
 
-## Acknowledgement
-We are grateful for the following work:
+## 致谢
+我们非常感谢以下工作：
 * [MAGVIT-v2](https://arxiv.org/abs/2310.05737): Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
 * [Taming Transformers](https://github.com/CompVis/taming-transformers): Taming Transformers for High-Resolution Image Synthesis
 * [3D blur pooling](https://github.com/adobe/antialiased-cnns/pull/39/commits/3d6f02b6943c58b68c19c07bc26fad57492ff3bc)