LoRA training example for Stable Diffusion XL (SDXL)

Low-Rank Adaption of Large Language Models was first introduced by Microsoft in LoRA: Low-Rank Adaptation of Large Language Models by Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen.

In a nutshell, LoRA allows adapting pretrained models by adding pairs of rank-decomposition matrices to existing weights and only training those newly added weights. This has a couple of advantages:

Previous pretrained weights are kept frozen so that model is not prone to catastrophic forgetting.
Rank-decomposition matrices have significantly fewer parameters than original model, which means that trained LoRA weights are easily portable.
LoRA attention layers allow to control to which extent the model is adapted toward new training images via a scale parameter.

cloneofsimo was the first to try out LoRA training for Stable Diffusion in the popular lora GitHub repository.

With LoRA, it's possible to fine-tune Stable Diffusion on a custom image-caption pair dataset on consumer GPUs like Tesla T4, Tesla V100.

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:

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .

Then cd in the examples/text_to_image folder and run

pip install -r requirements_sdxl.txt

And initialize an 🤗Accelerate environment with:

accelerate config

Or for a default accelerate configuration without answering questions about your environment

accelerate config default

Or if your environment doesn't support an interactive shell (e.g., a notebook)

from accelerate.utils import write_basic_config
write_basic_config()

When running accelerate config, if we specify torch compile mode to True there can be dramatic speedups.

Training

First, you need to set up your development environment as is explained in the installation section. Make sure to set the MODEL_NAME and DATASET_NAME environment variables. Here, we will use Stable Diffusion XL 1.0-base and the Pokemons dataset.

Note: It is quite useful to monitor the training progress by regularly generating sample images during training. Weights and Biases is a nice solution to easily see generating images during training. All you need to do is to run pip install wandb before training to automatically log images.

export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
export DATASET_NAME="lambdalabs/pokemon-blip-captions"

For this example we want to directly store the trained LoRA embeddings on the Hub, so we need to be logged in and add the --push_to_hub flag.

huggingface-cli login

Now we can start training!

accelerate launch train_text_to_image_lora_sdxl.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$DATASET_NAME --caption_column="text" \
  --resolution=1024 --random_flip \
  --train_batch_size=1 \
  --num_train_epochs=2 --checkpointing_steps=500 \
  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
  --seed=42 \
  --output_dir="sd-pokemon-model-lora-sdxl" \
  --validation_prompt="cute dragon creature" --report_to="wandb" \
  --push_to_hub

The above command will also run inference as fine-tuning progresses and log the results to Weights and Biases.

Finetuning the text encoder and UNet

The script also allows you to finetune the text_encoder along with the unet.

🚨 Training the text encoder requires additional memory.

Pass the --train_text_encoder argument to the training script to enable finetuning the text_encoder and unet:

accelerate launch train_text_to_image_lora_sdxl.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$DATASET_NAME --caption_column="text" \
  --resolution=1024 --random_flip \
  --train_batch_size=1 \
  --num_train_epochs=2 --checkpointing_steps=500 \
  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
  --seed=42 \
  --output_dir="sd-pokemon-model-lora-sdxl-txt" \
  --train_text_encoder \
  --validation_prompt="cute dragon creature" --report_to="wandb" \
  --push_to_hub

Inference

Once you have trained a model using above command, the inference can be done simply using the DiffusionPipeline after loading the trained LoRA weights. You need to pass the output_dir for loading the LoRA weights which, in this case, is sd-pokemon-model-lora-sdxl.

from diffusers import DiffusionPipeline
import torch

model_path = "takuoko/sd-pokemon-model-lora-sdxl"
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.load_lora_weights(model_path)

prompt = "A pokemon with green eyes and red legs."
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5).images[0]
image.save("pokemon.png")