[doc] updated the stable diffussion on docker usage (#2244)

* [doc] updated the stable diffussion on docker usage

* polish doc

examples/images/diffusion/README.md

@@ -26,7 +26,7 @@ this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on te
 
 ## Installation
 
-### Optional #1: install from source
+### Option #1: install from source
 #### Step 1: Requirements
 
 A suitable [conda](https://conda.io/) environment named `ldm` can be created
@@ -63,13 +63,54 @@ pip install colossalai==0.1.12+torch1.12cu11.3 -f https://release.colossalai.org
 
 > The specified version is due to the interface incompatibility caused by the latest update of [Lightning](https://github.com/Lightning-AI/lightning), which will be fixed in the near future.
 
-### Optional #2: install from dockerfile
+### Option #2: Use Docker
+
+To use the stable diffusion Docker image, you can either build using the provided the [Dockerfile](./docker/Dockerfile) or pull a Docker image from our Docker hub.
 
 ```
+# 1. build from dockerfile
 cd docker
 docker build -t hpcaitech/diffusion:0.2.0  .
+
+# 2. pull from our docker hub
+docker pull hpcaitech/diffusion:0.2.0
 ```
 
+Once you have the image ready, you can launch the image with the following command:
+
+```bash
+########################
+# On Your Host Machine #
+########################
+# make sure you start your image in the repository root directory
+cd Colossal-AI
+
+# run the docker container
+docker run --rm \
+  -it --gpus all \
+  -v $PWD:/workspace \
+  -v <your-data-dir>:/data/scratch \
+  -v <hf-cache-dir>:/root/.cache/huggingface \
+  hpcaitech/diffusion:0.2.0 \
+  /bin/bash
+
+########################
+#  Insider Container   #
+########################
+# Once you have entered the docker container, go to the stable diffusion directory for training
+cd examples/images/diffusion/
+
+# start training with colossalai
+bash train_colossalai.sh
+```
+
+It is important for you to configure your volume mapping in order to get the best training experience.
+1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v <your-data-dir>:/data/scratch`, where you need to replace `<your-data-dir>` with the actual data path on your machine.
+2. **Recommended**, store the downloaded model weights to your host machine instead of the container directory via `-v <hf-cache-dir>:/root/.cache/huggingface`, where you need to repliace the `<hf-cache-dir>` with the actual path. In this way, you don't have to repeatedly download the pretrained weights for every `docker run`.
+3. **Optional**, if you encounter any problem stating that shared memory is insufficient inside container, please add `-v /dev/shm:/dev/shm` to your `docker run` command.
+
+
+
 ## Download the model checkpoint from pretrained
 
 ### stable-diffusion-v1-4