[docs] [dreambooth training] num_class_images clarification (#1508)

examples/dreambooth/README.md

@@ -70,7 +70,7 @@ accelerate launch train_dreambooth.py \
 ### Training with prior-preservation loss
 
 Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
-According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases.
+According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. The `num_class_images` flag sets the number of images to generate with the class prompt. You can place existing images in `class_data_dir`, and the training script will generate any additional images so that `num_class_images` are present in `class_data_dir` during training time.
 
 ```bash
 export MODEL_NAME="CompVis/stable-diffusion-v1-4"

examples/dreambooth/train_dreambooth.py

@@ -107,8 +107,8 @@ def parse_args(input_args=None):
         type=int,
         default=100,
         help=(
-            "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
-            " sampled with class_prompt."
+            "Minimal class images for prior preservation loss. If there are not enough images already present in"
+            " class_data_dir, additional images will be sampled with class_prompt."
         ),
     )
     parser.add_argument(

examples/dreambooth/train_dreambooth_flax.py

@@ -89,8 +89,8 @@ def parse_args():
         type=int,
         default=100,
         help=(
-            "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
-            " sampled with class_prompt."
+            "Minimal class images for prior preservation loss. If there are not enough images already present in"
+            " class_data_dir, additional images will be sampled with class_prompt."
         ),
     )
     parser.add_argument(