Update README.md

README.md

@@ -11,14 +11,26 @@ 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 downloads 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 ```phito of a xxx```, where ```xxx``` 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. The text prompt can be ```photo of a xxx```, where ```xxx``` 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 xxx" 
+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 <xxx>" 
 ```
 
 I generate 8 images for regularization. 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
+
+```
+python main.py --base configs/stable-diffusion/v1-finetune_unfrozen.yaml 
+                -t 
+                --actual_resume /path/to/original/stable-diffusion/sd-v1-4-full-ema.ckpt  
+                -n <job name> 
+                --gpus 0, 
+                --data_root /root/to/training/images 
+                --reg_data_root /root/to/regularization/images 
+                --class_word <xxx>
+```
 
 ### Generation