ColossalAI/examples/images/diffusion

Stable Diffusion with Colossal-AI

Colosssal-AI provides a faster and lower cost solution for pretraining and fine-tuning for AIGC (AI-Generated Content) applications such as 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

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.

Stable Diffusion with Colossal-AI provides 6.5x faster training and pretraining cost saving, the hardware cost of fine-tuning can be almost 7X cheaper (from RTX3090/4090 24GB to RTX3050/2070 8GB).

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 Colossal-AI v0.1.10 From Our Official Website

pip install colossalai==0.1.10+torch1.11cu11.3 -f https://release.colossalai.org

The specified version is due to the interface incompatibility caused by the latest update of Lightning, which will be fixed in the near future.

Download the model checkpoint from pretrained

stable-diffusion-v1-4

Our default model config use the weight from CompVis/stable-diffusion-v1-4

git lfs install
git clone https://huggingface.co/CompVis/stable-diffusion-v1-4

stable-diffusion-v1-5 from runway

If you want to useed the Last stable-diffusion-v1-5 wiegh from runwayml

git lfs install
git clone https://huggingface.co/runwayml/stable-diffusion-v1-5

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 two Stategy in configs:train_colossalai.yaml

For example, you can run the training from colossalai by

python main.py --logdir /tmp -t --postfix test -b configs/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

Example

Training on cifar10

We provide the finetuning example on CIFAR10 dataset

You can run by config train_colossalai_cifar10.yaml

python main.py --logdir /tmp -t --postfix test -b configs/train_colossalai_cifar10.yaml 

Inference

you can get yout training last.ckpt and train config.yaml in your --logdir, and run by

python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms 
    --outdir ./output \
    --config path/to/logdir/checkpoints/last.ckpt \
    --ckpt /path/to/logdir/configs/project.yaml  \

usage: txt2img.py [-h] [--prompt [PROMPT]] [--outdir [OUTDIR]] [--skip_grid] [--skip_save] [--ddim_steps DDIM_STEPS] [--plms] [--laion400m] [--fixed_code] [--ddim_eta DDIM_ETA]
                  [--n_iter N_ITER] [--H H] [--W W] [--C C] [--f F] [--n_samples N_SAMPLES] [--n_rows N_ROWS] [--scale SCALE] [--from-file FROM_FILE] [--config CONFIG] [--ckpt CKPT]
                  [--seed SEED] [--precision {full,autocast}]

optional arguments:
  -h, --help            show this help message and exit
  --prompt [PROMPT]     the prompt to render
  --outdir [OUTDIR]     dir to write results to
  --skip_grid           do not save a grid, only individual samples. Helpful when evaluating lots of samples
  --skip_save           do not save individual samples. For speed measurements.
  --ddim_steps DDIM_STEPS
                        number of ddim sampling steps
  --plms                use plms sampling
  --laion400m           uses the LAION400M model
  --fixed_code          if enabled, uses the same starting code across samples
  --ddim_eta DDIM_ETA   ddim eta (eta=0.0 corresponds to deterministic sampling
  --n_iter N_ITER       sample this often
  --H H                 image height, in pixel space
  --W W                 image width, in pixel space
  --C C                 latent channels
  --f F                 downsampling factor
  --n_samples N_SAMPLES
                        how many samples to produce for each given prompt. A.k.a. batch size
  --n_rows N_ROWS       rows in the grid (default: n_samples)
  --scale SCALE         unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))
  --from-file FROM_FILE
                        if specified, load prompts from this file
  --config CONFIG       path to config which constructs model
  --ckpt CKPT           path to checkpoint of model
  --seed SEED           the seed (for reproducible sampling)
  --precision {full,autocast}
                        evaluate at this precision

Comments

• Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. 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

@article{bian2021colossal,
  title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
  author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
  journal={arXiv preprint arXiv:2110.14883},
  year={2021}
}
@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}
}