3.9 KiB

Raw Blame History

ColoDiffusion

ColoDiffusion is a Faster Train implementation of the model stable-diffusion from Stability AI

We take advantage of Colosssal-AI to exploit multiple optimization strategies , e.g. data parallelism, tensor parallelism, mixed precision & ZeRO, to scale the training to multiple GPUs.

Stable Diffusion is a latent text-to-image diffusion model. Thanks to a generous compute donation from Stability AI and support from LAION, we were able to train a Latent Diffusion Model on 512x512 images from a subset of the LAION-5B database. Similar to Google's Imagen, this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM. See this section below and the model card.

Requirements

A suitable conda environment named ldm can be created and activated with:

conda env create -f environment.yaml
conda activate ldm

You can also update an existing latent diffusion environment by running

conda install pytorch torchvision -c pytorch
pip install transformers==4.19.2 diffusers invisible-watermark
pip install -e .

Install ColossalAI

git clone https://github.com/hpcaitech/ColossalAI.git
git checkout v0.1.10
pip install .

Install colossalai lightning

git clone -b colossalai https://github.com/Fazziekey/lightning.git
pip install .

Dataset

The DataSet is from LAION-5B, the subset of LAION, you should the change the data.file_path in the config/train_colossalai.yaml

Training

we provide the script train.sh to run the training task , and three Stategy in configs:train_colossalai.yaml, train_ddp.yaml, train_deepspeed.yaml

for example, you can run the training from colossalai by

python main.py --logdir /tmp -t --postfix test -b config/train_colossalai.yaml

you can change the --logdir the save the log information and the last checkpoint

Training config

you can change the trainging config in the yaml file

accelerator: acceleratortype, default 'gpu'
devices: device number used for training, default 4
max_epochs: max training epochs
precision: usefp16 for training or not, default 16, you must use fp16 if you want to apply colossalai

Comments

Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase and https://github.com/lucidrains/denoising-diffusion-pytorch. Thanks for open-sourcing!
The implementation of the transformer encoder is from x-transformers by lucidrains.
the implementation of flash attention is from HazyResearch

BibTeX

@misc{rombach2021highresolution,
      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
      year={2021},
      eprint={2112.10752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@article{dao2022flashattention,
  title={FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness},
  author={Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
  journal={arXiv preprint arXiv:2205.14135},
  year={2022}
}

3.9 KiB Raw Blame History