DreamBooth is a training technique that updates the entire diffusion model by training on just a few images of a subject or style. It works by associating a special word in the prompt with the example images.
If you’re training on a GPU with limited vRAM, you should try enabling the
mixed_precision parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with xFormers. JAX/Flax training is also supported for efficient training on TPUs and GPUs, but it doesn’t support gradient checkpointing or xFormers. You should have a GPU with >30GB of memory if you want to train faster with Flax.
This guide will explore the train_dreambooth.py script to help you become more familiar with it, and how you can adapt it for your own use-case.
Before running the script, make sure you install the library from source:
git clone https://github.com/huggingface/diffusers cd diffusers pip install .
Navigate to the example folder with the training script and install the required dependencies for the script you’re using:
cd examples/dreambooth pip install -r requirements.txt
🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It’ll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate Quick tour to learn more.
Initialize an 🤗 Accelerate environment:
To setup a default 🤗 Accelerate environment without choosing any configurations:
accelerate config default
Or if your environment doesn’t support an interactive shell, like a notebook, you can use:
from accelerate.utils import write_basic_config write_basic_config()
Lastly, if you want to train a model on your own dataset, take a look at the Create a dataset for training guide to learn how to create a dataset that works with the training script.
The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn’t cover every aspect of the script in detail. If you’re interested in learning more, feel free to read through the script and let us know if you have any questions or concerns.
DreamBooth is very sensitive to training hyperparameters, and it is easy to overfit. Read the Training Stable Diffusion with Dreambooth using 🧨 Diffusers blog post for recommended settings for different subjects to help you choose the appropriate hyperparameters.
The training script offers many parameters for customizing your training run. All of the parameters and their descriptions are found in the
parse_args() function. The parameters are set with default values that should work pretty well out-of-the-box, but you can also set your own values in the training command if you’d like.
For example, to train in the bf16 format:
accelerate launch train_dreambooth.py \ --mixed_precision="bf16"
Some basic and important parameters to know and specify are:
--pretrained_model_name_or_path: the name of the model on the Hub or a local path to the pretrained model
--instance_data_dir: path to a folder containing the training dataset (example images)
--instance_prompt: the text prompt that contains the special word for the example images
--train_text_encoder: whether to also train the text encoder
--output_dir: where to save the trained model
--push_to_hub: whether to push the trained model to the Hub
--checkpointing_steps: frequency of saving a checkpoint as the model trains; this is useful if for some reason training is interrupted, you can continue training from that checkpoint by adding
--resume_from_checkpointto your training command
The Min-SNR weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting
epsilon (noise) or
v_prediction, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script.
--snr_gamma parameter and set it to the recommended value of 5.0:
accelerate launch train_dreambooth.py \ --snr_gamma=5.0
Prior preservation loss is a method that uses a model’s own generated samples to help it learn how to generate more diverse images. Because these generated sample images belong to the same class as the images you provided, they help the model retain what it has learned about the class and how it can use what it already knows about the class to make new compositions.
--with_prior_preservation: whether to use prior preservation loss
--prior_loss_weight: controls the influence of the prior preservation loss on the model
--class_data_dir: path to a folder containing the generated class sample images
--class_prompt: the text prompt describing the class of the generated sample images
accelerate launch train_dreambooth.py \ --with_prior_preservation \ --prior_loss_weight=1.0 \ --class_data_dir="path/to/class/images" \ --class_prompt="text prompt describing class"
To improve the quality of the generated outputs, you can also train the text encoder in addition to the UNet. This requires additional memory and you’ll need a GPU with at least 24GB of vRAM. If you have the necessary hardware, then training the text encoder produces better results, especially when generating images of faces. Enable this option by:
accelerate launch train_dreambooth.py \ --train_text_encoder
DreamBooth comes with its own dataset classes:
DreamBoothDataset: preprocesses the images and class images, and tokenizes the prompts for training
PromptDataset: generates the prompt embeddings to generate the class images
If you enabled prior preservation loss, the class images are generated here:
sample_dataset = PromptDataset(args.class_prompt, num_new_images) sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) sample_dataloader = accelerator.prepare(sample_dataloader) pipeline.to(accelerator.device) for example in tqdm( sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process ): images = pipeline(example["prompt"]).images
# Load the tokenizer if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False) elif args.pretrained_model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision, use_fast=False, ) # Load scheduler and models noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") text_encoder = text_encoder_cls.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision ) if model_has_vae(args): vae = AutoencoderKL.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision ) else: vae = None unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision )
Then, it’s time to create the training dataset and DataLoader from
train_dataset = DreamBoothDataset( instance_data_root=args.instance_data_dir, instance_prompt=args.instance_prompt, class_data_root=args.class_data_dir if args.with_prior_preservation else None, class_prompt=args.class_prompt, class_num=args.num_class_images, tokenizer=tokenizer, size=args.resolution, center_crop=args.center_crop, encoder_hidden_states=pre_computed_encoder_hidden_states, class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states, tokenizer_max_length=args.tokenizer_max_length, ) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation), num_workers=args.dataloader_num_workers, )
Lastly, the training loop takes care of the remaining steps such as converting images to latent space, adding noise to the input, predicting the noise residual, and calculating the loss.
If you want to learn more about how the training loop works, check out the Understanding pipelines, models and schedulers tutorial which breaks down the basic pattern of the denoising process.
You’re now ready to launch the training script! 🚀
from huggingface_hub import snapshot_download local_dir = "./dog" snapshot_download( "diffusers/dog-example", local_dir=local_dir, repo_type="dataset", ignore_patterns=".gitattributes", )
Set the environment variable
MODEL_NAME to a model id on the Hub or a path to a local model,
INSTANCE_DIR to the path where you just downloaded the dog images to, and
OUTPUT_DIR to where you want to save the model. You’ll use
sks as the special word to tie the training to.
If you’re interested in following along with the training process, you can periodically save generated images as training progresses. Add the following parameters to the training command:
--validation_prompt="a photo of a sks dog" --num_validation_images=4 --validation_steps=100
One more thing before you launch the script! Depending on the GPU you have, you may need to enable certain optimizations to train DreamBooth.
On a 16GB GPU, you can use bitsandbytes 8-bit optimizer and gradient checkpointing to help you train a DreamBooth model. Install bitsandbytes:
pip install bitsandbytes
Then, add the following parameter to your training command:
accelerate launch train_dreambooth.py \ --gradient_checkpointing \ --use_8bit_adam \
export MODEL_NAME="runwayml/stable-diffusion-v1-5" export INSTANCE_DIR="./dog" export OUTPUT_DIR="path_to_saved_model" accelerate launch train_dreambooth.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_data_dir=$INSTANCE_DIR \ --output_dir=$OUTPUT_DIR \ --instance_prompt="a photo of sks dog" \ --resolution=512 \ --train_batch_size=1 \ --gradient_accumulation_steps=1 \ --learning_rate=5e-6 \ --lr_scheduler="constant" \ --lr_warmup_steps=0 \ --max_train_steps=400 \ --push_to_hub
Once training is complete, you can use your newly trained model for inference!
Can’t wait to try your model for inference before training is complete? 🤭 Make sure you have the latest version of 🤗 Accelerate installed.
from diffusers import DiffusionPipeline, UNet2DConditionModel from transformers import CLIPTextModel import torch unet = UNet2DConditionModel.from_pretrained("path/to/model/checkpoint-100/unet") # if you have trained with `--args.train_text_encoder` make sure to also load the text encoder text_encoder = CLIPTextModel.from_pretrained("path/to/model/checkpoint-100/checkpoint-100/text_encoder") pipeline = DiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", unet=unet, text_encoder=text_encoder, dtype=torch.float16, ).to("cuda") image = pipeline("A photo of sks dog in a bucket", num_inference_steps=50, guidance_scale=7.5).images image.save("dog-bucket.png")
from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("path_to_saved_model", torch_dtype=torch.float16, use_safetensors=True).to("cuda") image = pipeline("A photo of sks dog in a bucket", num_inference_steps=50, guidance_scale=7.5).images image.save("dog-bucket.png")
LoRA is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). Use the train_dreambooth_lora.py script to train with LoRA.
The LoRA training script is discussed in more detail in the LoRA training guide.
Stable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the train_dreambooth_lora_sdxl.py script to train a SDXL model with LoRA.
The SDXL training script is discussed in more detail in the SDXL training guide.
Congratulations on training your DreamBooth model! To learn more about how to use your new model, the following guide may be helpful:
- Learn how to load a DreamBooth model for inference if you trained your model with LoRA.