[doc] add feature diffusion v2, bloom, auto-parallel
diff --git a/README-zh-Hans.md b/README-zh-Hans.md
index 57cf90586004..ec9014deb361 100644
--- a/README-zh-Hans.md
+++ b/README-zh-Hans.md
@@ -38,12 +38,12 @@
  <li>
    <a href="#并行训练样例展示">并行训练样例展示</a> 
    <ul>
-     <li><a href="#ViT">ViT</a></li>
      <li><a href="#GPT-3">GPT-3</a></li>
      <li><a href="#GPT-2">GPT-2</a></li>
      <li><a href="#BERT">BERT</a></li>
      <li><a href="#PaLM">PaLM</a></li>
      <li><a href="#OPT">OPT</a></li>
+     <li><a href="#ViT">ViT</a></li>
      <li><a href="#推荐系统模型">推荐系统模型</a></li>
    </ul>
  </li>
@@ -59,6 +59,7 @@
    <ul>
      <li><a href="#GPT-3-Inference">GPT-3</a></li>
      <li><a href="#OPT-Serving">1750亿参数OPT在线推理服务</a></li>
+     <li><a href="#BLOOM-Inference">1750亿参数 BLOOM</a></li>
    </ul>
  </li>
 <li>
@@ -102,6 +103,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
   - 1维, [2维](https://arxiv.org/abs/2104.05343), [2.5维](https://arxiv.org/abs/2105.14500), [3维](https://arxiv.org/abs/2105.14450) 张量并行
   - [序列并行](https://arxiv.org/abs/2105.13120)
   - [零冗余优化器 (ZeRO)](https://arxiv.org/abs/1910.02054)
+  - [自动并行](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt/auto_parallel_with_gpt)
 - 异构内存管理
   - [PatrickStar](https://arxiv.org/abs/2108.05818)
 - 使用友好
@@ -113,12 +115,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
 <p align="right">(<a href="#top">返回顶端</a>)</p>
 
 ## 并行训练样例展示
-### ViT
-<p align="center">
-<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width="450" />
-</p>
 
-- 14倍批大小和5倍训练速度（张量并行=64）
 
 ### GPT-3
 <p align="center">
@@ -153,6 +150,12 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
 
 请访问我们的 [文档](https://www.colossalai.org/) 和 [例程](https://github.com/hpcaitech/ColossalAI-Examples) 以了解详情。
 
+### ViT
+<p align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width="450" />
+</p>
+
+- 14倍批大小和5倍训练速度（张量并行=64）
 
 ### 推荐系统模型
 - [Cached Embedding](https://github.com/hpcaitech/CachedEmbedding), 使用软件Cache实现Embeddings，用更少GPU显存训练更大的模型。
@@ -199,23 +202,38 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
 
 - [OPT推理服务](https://service.colossalai.org/opt): 无需注册，免费体验1750亿参数OPT在线推理服务
 
+<p id="BLOOM-Inference" align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BLOOM%20Inference.PNG" width=800/>
+</p>
+
+- [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): 降低1750亿参数BLOOM模型部署推理成本超10倍
 
 <p align="right">(<a href="#top">返回顶端</a>)</p>
 
 ## Colossal-AI 成功案例
 
 ### AIGC
-加速AIGC(AI内容生成)模型，如[Stable Diffusion](https://github.com/CompVis/stable-diffusion)
+加速AIGC(AI内容生成)模型，如[Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) 和 [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion)
+
 <p id="diffusion_train" align="center">
-<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_train.png" width=800/>
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width=800/>
 </p>
 
-- [Colossal-AI优化Stable Diffusion](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 6.5倍训练加速和预训练成本降低, 微调硬件成本下降约7倍(从RTX3090/4090到RTX3050/2070)
+- [训练](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 减少5.6倍显存消耗，硬件成本最高降低46倍(从A100到RTX3060)
 
 <p id="diffusion_demo" align="center">
-<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_demo.png" width=800/>
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png" width=800/>
 </p>
 
+- [DreamBooth微调](https://github.com/hpcaitech/ColossalAI/tree/hotfix/doc/examples/images/dreambooth): 仅需3-5张目标主题图像个性化微调
+
+<p id="inference" align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
+</p>
+
+- [推理](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): GPU推理显存消耗降低2.5倍
+
+
 <p align="right">(<a href="#top">返回顶端</a>)</p>
 
 ### 生物医药
diff --git a/README.md b/README.md
index 36d5c2e820b6..c58ad5e5c4ee 100644
--- a/README.md
+++ b/README.md
@@ -38,12 +38,12 @@
  <li>
    <a href="#Parallel-Training-Demo">Parallel Training Demo</a> 
    <ul>
-     <li><a href="#ViT">ViT</a></li>
      <li><a href="#GPT-3">GPT-3</a></li>
      <li><a href="#GPT-2">GPT-2</a></li>
      <li><a href="#BERT">BERT</a></li>
      <li><a href="#PaLM">PaLM</a></li>
      <li><a href="#OPT">OPT</a></li>
+     <li><a href="#ViT">ViT</a></li>
      <li><a href="#Recommendation-System-Models">Recommendation System Models</a></li>
    </ul>
  </li>
@@ -59,6 +59,7 @@
    <ul>
      <li><a href="#GPT-3-Inference">GPT-3</a></li>
      <li><a href="#OPT-Serving">OPT-175B Online Serving for Text Generation</a></li>
+     <li><a href="#BLOOM-Inference">175B BLOOM</a></li>
    </ul>
  </li>
    <li>
@@ -104,6 +105,7 @@ distributed training and inference in a few lines.
   - 1D, [2D](https://arxiv.org/abs/2104.05343), [2.5D](https://arxiv.org/abs/2105.14500), [3D](https://arxiv.org/abs/2105.14450) Tensor Parallelism
   - [Sequence Parallelism](https://arxiv.org/abs/2105.13120)
   - [Zero Redundancy Optimizer (ZeRO)](https://arxiv.org/abs/1910.02054)
+  - [Auto-Parallelism](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt/auto_parallel_with_gpt)
 
 - Heterogeneous Memory Management 
   - [PatrickStar](https://arxiv.org/abs/2108.05818)
@@ -119,12 +121,6 @@ distributed training and inference in a few lines.
 <p align="right">(<a href="#top">back to top</a>)</p>
 
 ## Parallel Training Demo
-### ViT
-<p align="center">
-<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width="450" />
-</p>
-
-- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64
 
 ### GPT-3
 <p align="center">
@@ -158,6 +154,13 @@ distributed training and inference in a few lines.
 
 Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI-Examples) for more details.
 
+### ViT
+<p align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/ViT.png" width="450" />
+</p>
+
+- 14x larger batch size, and 5x faster training for Tensor Parallelism = 64
+
 ### Recommendation System Models
 - [Cached Embedding](https://github.com/hpcaitech/CachedEmbedding), utilize software cache to train larger embedding tables with a smaller GPU memory budget.
 
@@ -202,22 +205,37 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt
 
 - [OPT Serving](https://service.colossalai.org/opt): Try 175-billion-parameter OPT online services for free, without any registration whatsoever.
 
+<p id="BLOOM-Inference" align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/BLOOM%20Inference.PNG" width=800/>
+</p>
+
+- [BLOOM](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce hardware deployment costs of 175-billion-parameter BLOOM by more than 10 times.
+
 <p align="right">(<a href="#top">back to top</a>)</p>
 
 ## Colossal-AI in the Real World
 
 ### AIGC
-Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion](https://github.com/CompVis/stable-diffusion)
+Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion).
 <p id="diffusion_train" align="center">
-<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_train.png" width=800/>
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width=800/>
 </p>
 
-- [Stable Diffusion with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): 6.5x faster training and pretraining cost saving, the hardware cost of fine-tuning can be almost 7X cheaper (from RTX3090/4090 to RTX3050/2070)
+- [Training](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).
 
 <p id="diffusion_demo" align="center">
-<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_demo.png" width=800/>
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/DreamBooth.png" width=800/>
 </p>
 
+- [DreamBooth Fine-tuning](https://github.com/hpcaitech/ColossalAI/tree/hotfix/doc/examples/images/dreambooth): Personalize your model using just 3-5 images of the desired subject.
+
+<p id="inference" align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
+</p>
+
+- [Inference](https://github.com/hpcaitech/EnergonAI/tree/main/examples/bloom): Reduce inference GPU memory consumption by 2.5x.
+
+
 <p align="right">(<a href="#top">back to top</a>)</p>
 
 ### Biomedicine
