diff --git a/examples/images/diffusion/README.md b/examples/images/diffusion/README.md
index fa164de94..80e1e6ec7 100644
--- a/examples/images/diffusion/README.md
+++ b/examples/images/diffusion/README.md
@@ -1,29 +1,26 @@
 # ColoDiffusion: Stable Diffusion with Colossal-AI
 
-*[Colosssal-AI](https://github.com/hpcaitech/ColossalAI) provides a faster and lower cost solution for pretraining and
-fine-tuning for AIGC (AI-Generated Content) applications such as the model [stable-diffusion](https://github.com/CompVis/stable-diffusion) from [Stability AI](https://stability.ai/).*
-
-We take advantage of [Colosssal-AI](https://github.com/hpcaitech/ColossalAI) to exploit multiple optimization strategies
-, e.g. data parallelism, tensor parallelism, mixed precision & ZeRO, to scale the training to multiple GPUs.
-
-## Stable Diffusion
-
-[Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) is a latent text-to-image diffusion
-model.
-Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
-Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
-this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
-
+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) provides **6.5x faster training and pretraining cost saving, the hardware cost of fine-tuning can be almost 7X cheaper** (from RTX3090/4090 24GB to RTX3050/2070 8GB).
+- [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.
+
+More details can be found in our [blog of Stable Diffusion v1](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) and [blog of Stable Diffusion v2](https://www.hpc-ai.tech/blog/colossal-ai-0-2-0).
+
 ## Installation
 
 ### Option #1: install from source