Hello,
I want to use diffusers/train_dreambooth_lora.py at main · huggingface/diffusers · GitHub . However I am not sure what ‘instance_prompt’ and ‘class_prompt’ is. What is their difference and how is the logic of a ‘class’ is used in this context?
Also do I have to name the images in my dataset like ‘dog in the style of xyz.png’? I have seen some tutorials do this and some skip this. Would naming them help the training?
hey @demegire !
the instance prompt and class prompt is documented in the README
# DreamBooth training example
[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
The `train_dreambooth.py` script shows how to implement the training procedure and adapt it for stable diffusion.
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
```
show original
# DreamBooth training example
[DreamBooth](https://arxiv.org/abs/2208.12242) is a method to personalize text2image models like stable diffusion given just a few(3~5) images of a subject.
The `train_dreambooth.py` script shows how to implement the training procedure and adapt it for stable diffusion.
## Running locally with PyTorch
### Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
**Important**
To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
```
show original
I don’t know off the top of my head if giving additional captions to your dataset will help or not. We don’t have additional prompts for our example dataset. Afaik, there’s not consensus rules on it. I recomend experimenting