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# DreamBooth training example for Stable Diffusion 3 (SD3) |
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[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. |
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The `train_dreambooth_sd3.py` script shows how to implement the training procedure and adapt it for [Stable Diffusion 3](https://huggingface.co/papers/2403.03206). We also provide a LoRA implementation in the `train_dreambooth_lora_sd3.py` script. |
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> [!NOTE] |
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> As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in: |
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```bash |
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huggingface-cli login |
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``` |
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This will also allow us to push the trained model parameters to the Hugging Face Hub platform. |
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## Running locally with PyTorch |
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### Installing the dependencies |
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Before running the scripts, make sure to install the library's training dependencies: |
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**Important** |
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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: |
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```bash |
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git clone https://github.com/huggingface/diffusers |
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cd diffusers |
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pip install -e . |
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``` |
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Then cd in the `examples/dreambooth` folder and run |
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```bash |
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pip install -r requirements_sd3.txt |
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``` |
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And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with: |
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```bash |
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accelerate config |
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``` |
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Or for a default accelerate configuration without answering questions about your environment |
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```bash |
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accelerate config default |
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``` |
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Or if your environment doesn't support an interactive shell (e.g., a notebook) |
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```python |
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from accelerate.utils import write_basic_config |
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write_basic_config() |
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``` |
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When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. |
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### Dog toy example |
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Now let's get our dataset. For this example we will use some dog images: https://huggingface.co/datasets/diffusers/dog-example. |
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Let's first download it locally: |
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```python |
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from huggingface_hub import snapshot_download |
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local_dir = "./dog" |
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snapshot_download( |
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"diffusers/dog-example", |
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local_dir=local_dir, repo_type="dataset", |
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ignore_patterns=".gitattributes", |
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) |
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``` |
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Now, we can launch training using: |
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```bash |
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export MODEL_NAME="stabilityai/stable-diffusion-3-medium-diffusers" |
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export INSTANCE_DIR="dog" |
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export OUTPUT_DIR="trained-sd3" |
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accelerate launch train_dreambooth_sd3.py \ |
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--pretrained_model_name_or_path=$MODEL_NAME \ |
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--instance_data_dir=$INSTANCE_DIR \ |
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--output_dir=$OUTPUT_DIR \ |
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--mixed_precision="fp16" \ |
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--instance_prompt="a photo of sks dog" \ |
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--resolution=1024 \ |
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--train_batch_size=1 \ |
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--gradient_accumulation_steps=4 \ |
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--learning_rate=1e-4 \ |
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--report_to="wandb" \ |
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--lr_scheduler="constant" \ |
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--lr_warmup_steps=0 \ |
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--max_train_steps=500 \ |
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--validation_prompt="A photo of sks dog in a bucket" \ |
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--validation_epochs=25 \ |
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--seed="0" \ |
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--push_to_hub |
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``` |
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To better track our training experiments, we're using the following flags in the command above: |
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* `report_to="wandb` will ensure the training runs are tracked on Weights and Biases. To use it, be sure to install `wandb` with `pip install wandb`. |
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* `validation_prompt` and `validation_epochs` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected. |
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> [!NOTE] |
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> If you want to train using long prompts with the T5 text encoder, you can use `--max_sequence_length` to set the token limit. The default is 77, but it can be increased to as high as 512. Note that this will use more resources and may slow down the training in some cases. |
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> [!TIP] |
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> You can pass `--use_8bit_adam` to reduce the memory requirements of training. Make sure to install `bitsandbytes` if you want to do so. |
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## LoRA + DreamBooth |
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[LoRA](https://huggingface.co/docs/peft/conceptual_guides/adapter#low-rank-adaptation-lora) is a popular parameter-efficient fine-tuning technique that allows you to achieve full-finetuning like performance but with a fraction of learnable parameters. |
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Note also that we use PEFT library as backend for LoRA training, make sure to have `peft>=0.6.0` installed in your environment. |
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To perform DreamBooth with LoRA, run: |
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```bash |
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export MODEL_NAME="stabilityai/stable-diffusion-3-medium-diffusers" |
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export INSTANCE_DIR="dog" |
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export OUTPUT_DIR="trained-sd3-lora" |
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accelerate launch train_dreambooth_lora_sd3.py \ |
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--pretrained_model_name_or_path=$MODEL_NAME \ |
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--instance_data_dir=$INSTANCE_DIR \ |
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--output_dir=$OUTPUT_DIR \ |
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--mixed_precision="fp16" \ |
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--instance_prompt="a photo of sks dog" \ |
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--resolution=512 \ |
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--train_batch_size=1 \ |
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--gradient_accumulation_steps=4 \ |
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--learning_rate=1e-5 \ |
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--report_to="wandb" \ |
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--lr_scheduler="constant" \ |
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--lr_warmup_steps=0 \ |
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--max_train_steps=500 \ |
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--validation_prompt="A photo of sks dog in a bucket" \ |
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--validation_epochs=25 \ |
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--seed="0" \ |
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--push_to_hub |
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``` |
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## Other notes |
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We default to the "logit_normal" weighting scheme for the loss following the SD3 paper. Thanks to @bghira for helping us discover that for other weighting schemes supported from the training script, training may incur numerical instabilities. |