• 2024/10/22: LoRA support for Hyper SDXL.
• 2024/8/1: Orbax is the new default checkpointer. You can still use pipeline.save_pretrained after training to save in diffusers format.
• 2024/7/20: Dreambooth training for Stable Diffusion 1.x,2.x is now supported.

MaxDiffusion is a collection of reference implementations of various latent diffusion models written in pure Python/Jax that run on XLA devices including Cloud TPUs and GPUs. MaxDiffusion aims to be a launching off point for ambitious Diffusion projects both in research and production. We encourage you to start by experimenting with MaxDiffusion out of the box and then fork and modify MaxDiffusion to meet your needs.

The goal of this project is to provide reference implementations for latent diffusion models that help developers get started with training, tuning, and serving solutions on XLA devices including Cloud TPUs and GPUs. We started with Stable Diffusion inference on TPUs, but welcome code contributions to grow.

MaxDiffusion supports

• Stable Diffusion 2 base (training and inference)
• Stable Diffusion 2.1 (training and inference)
• Stable Diffusion XL (training and inference).
• Stable Diffusion Lightning (inference).
• Hyper-SD XL LoRA loading (inference).
• ControlNet inference (Stable Diffusion 1.4 & SDXL).
• Dreambooth training support for Stable Diffusion 1.x,2.x.

WARNING: The training code is purely experimental and is under development.

• Getting Started
  • Training
    • Dreambooth
  • Inference
    • Hyper-SD XL LoRA
    • SDXL Lightning
    • ControlNet
• Comparison To Alternatives
• Development

We recommend starting with a single TPU host and then moving to multihost.

Minimum requirements: Ubuntu Version 22.04, Python 3.10 and Tensorflow >= 2.12.0.

For your first time running Maxdiffusion, we provide specific instructions.

After installation completes, run the training script.

• Stable Diffusion XL

  export LIBTPU_INIT_ARGS=""
  python -m src.maxdiffusion.train_sdxl src/maxdiffusion/configs/base_xl.yml run_name="my_xl_run" output_dir="gs://your-bucket/" per_device_batch_size=1

  To generate images with a trained checkpoint, run:

  python -m src.maxdiffusion.generate src/maxdiffusion/configs/base_xl.yml run_name="my_run" pretrained_model_name_or_path=<your_saved_checkpoint_path> from_pt=False attention=dot_product

• Stable Diffusion 2 base

  export LIBTPU_INIT_ARGS=""
  python -m src.maxdiffusion.train src/maxdiffusion/configs/base_2_base.yml run_name="my_run" jax_cache_dir=gs://your-bucket/cache_dir activations_dtype=float32 weights_dtype=float32 per_device_batch_size=2 precision=DEFAULT dataset_save_location=/tmp/my_dataset/ output_dir=gs://your-bucket/ attention=flash

• Stable Diffusion 1.4

  export LIBTPU_INIT_ARGS=""
  python -m src.maxdiffusion.train src/maxdiffusion/configs/base14.yml run_name="my_run" jax_cache_dir=gs://your-bucket/cache_dir activations_dtype=float32 weights_dtype=float32 per_device_batch_size=2 precision=DEFAULT dataset_save_location=/tmp/my_dataset/ output_dir=gs://your-bucket/ attention=flash

  To generate images with a trained checkpoint, run:

  python -m src.maxdiffusion.generate src/maxdiffusion/configs/base_2_base.yml run_name="my_run" output_dir=gs://your-bucket/ from_pt=False attention=dot_product

  Dreambooth

  Stable Diffusion 1.x,2.x

  python src/maxdiffusion/dreambooth/train_dreambooth.py src/maxdiffusion/configs/base14.yml class_data_dir=<your-class-dir> instance_data_dir=<your-instance-dir> instance_prompt="a photo of ohwx dog" class_prompt="photo of a dog" max_train_steps=150 jax_cache_dir=<your-cache-dir> class_prompt="a photo of a dog" activations_dtype=bfloat16 weights_dtype=float32 per_device_batch_size=1 enable_profiler=False precision=DEFAULT cache_dreambooth_dataset=False learning_rate=4e-6 num_class_images=100 run_name=<your-run-name> output_dir=gs://<your-bucket-name>

To generate images, run the following command:

• Stable Diffusion XL

  Single and Multi host inference is supported with sharding annotations:

  python -m src.maxdiffusion.generate_sdxl src/maxdiffusion/configs/base_xl.yml run_name="my_run"

  Single host pmap version:

  python -m src.maxdiffusion.generate_sdxl_replicated

• Stable Diffusion 2 base

  python -m src.maxdiffusion.generate src/maxdiffusion/configs/base_2_base.yml run_name="my_run"
  

• Stable Diffusion 2.1

  python -m src.maxdiffusion.generate src/maxdiffusion/configs/base21.yml run_name="my_run"

  Hyper SDXL LoRA

  Supports Hyper-SDXL models from ByteDance

  python src/maxdiffusion/generate_sdxl.py src/maxdiffusion/configs/base_xl.yml run_name="test-lora" output_dir=/tmp/ jax_cache_dir=/tmp/cache_dir/ num_inference_steps=2 do_classifier_free_guidance=False prompt="a photograph of a cat wearing a hat riding a skateboard in a park." per_device_batch_size=1 pretrained_model_name_or_path="Lykon/AAM_XL_AnimeMix" from_pt=True revision=main diffusion_scheduler_config='{"_class_name" : "FlaxDDIMScheduler", "timestep_spacing" : "trailing"}' lora_config='{"lora_model_name_or_path" : ["ByteDance/Hyper-SD"], "weight_name" : ["Hyper-SDXL-2steps-lora.safetensors"], "adapter_name" : ["hyper-sdxl"], "scale": [0.7], "from_pt": ["true"]}'

  SDXL Lightning

  Single and Multi host inference is supported with sharding annotations:

  python -m src.maxdiffusion.generate_sdxl src/maxdiffusion/configs/base_xl_lightning.yml run_name="my_run" lightning_repo="ByteDance/SDXL-Lightning" lightning_ckpt="sdxl_lightning_4step_unet.safetensors"

  ControlNet

  Might require installing extra libraries for opencv: apt-get update && apt-get install ffmpeg libsm6 libxext6 -y

  • Stable Diffusion 1.4

    python src/maxdiffusion/controlnet/generate_controlnet_replicated.py

  • Stable Diffusion XL

    python src/maxdiffusion/controlnet/generate_controlnet_sdxl_replicated.py

Multihost training for Stable Diffusion 2 base can be run using the following command:

TPU_NAME=<your-tpu-name>
ZONE=<your-zone>
PROJECT_ID=<your-project-id>
gcloud compute tpus tpu-vm ssh $TPU_NAME --zone=$ZONE --project $PROJECT_ID --worker=all --command="
export LIBTPU_INIT_ARGS=""
git clone https://github.com/google/maxdiffusion
cd maxdiffusion
pip3 install jax[tpu] -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
pip3 install -r requirements.txt
pip3 install .
python -m src.maxdiffusion.train src/maxdiffusion/configs/base_2_base.yml run_name=my_run output_dir=gs://your-bucket/"

MaxDiffusion started as a fork of Diffusers, a Hugging Face diffusion library written in Python, Pytorch and Jax. MaxDiffusion is compatible with Hugging Face Jax models. MaxDiffusion is more complex and was designed to run distributed across TPU Pods.

Whether you are forking MaxDiffusion for your own needs or intending to contribute back to the community, a full suite of tests can be found in tests and src/maxdiffusion/tests.

To run unit tests, simply run:

python -m pytest

This project uses pylint and pyink to enforce code style. Before submitting a pull request, please ensure your code passes these checks by running:

bash code_style.sh

This script will automatically format your code with pyink and help you identify any remaining style issues.

The full suite of -end-to end tests is in tests and src/maxdiffusion/tests. We run them with a nightly cadance.