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

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Will Berman
2022-12-02 03:12:28 -08:00
committed by GitHub
parent b25ae2e6ab
commit 25f850a23b
3 changed files with 5 additions and 5 deletions
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2
examples/dreambooth/README.md
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@@ -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"

4
examples/dreambooth/train_dreambooth.py
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@@ -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(

4
examples/dreambooth/train_dreambooth_flax.py
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@@ -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(