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@@ -11,13 +11,13 @@ The implementation makes minimum changes over the official codebase of Textual I
 ### Preparation
 To fine-tune a stable diffusion model, you need to obtain the pre-trained stable diffusion models following their [instructions](https://github.com/CompVis/stable-diffusion#stable-diffusion-v1). Weights can be downloaded on [HuggingFace](https://huggingface.co/CompVis). You can decide which version of checkpoint to use, but I use ```sd-v1-4-full-ema.ckpt```.
 
-We also need to create a set of images for regularization, as the fine-tuning algorithm of Dreambooth requires that. Details of the algorithm can be found in the paper. The text prompt can be ```photo of a <class>```, where ```<class>``` is a word that describes the class of your object, such as ```dog```. The command is
+We also need to create a set of images for regularization, as the fine-tuning algorithm of Dreambooth requires that. Details of the algorithm can be found in the paper. Note that in the original paper, the regularization images seem to be generated on-the-fly. However, here I generated a set of regularization images before the training. The text prompt for generating regularization images can be ```photo of a <class>```, where ```<class>``` is a word that describes the class of your object, such as ```dog```. The command is
 
 ```
 python scripts/stable_txt2img.py --ddim_eta 0.0 --n_samples 8 --n_iter 1 --scale 10.0 --ddim_steps 50  --ckpt /path/to/original/stable-diffusion/sd-v1-4-full-ema.ckpt --prompt "a photo of a <class>" 
 ```
 
-I generate 8 images for regularization. After that, save the generated images (separately, one image per ```.png``` file) at ```/root/to/regularization/images```.
+I generate 8 images for regularization, but more regularization images may lead to stronger regularization and better editability. After that, save the generated images (separately, one image per ```.png``` file) at ```/root/to/regularization/images```.
 
 ### Training
 Training can be done by running the following command