mirror of https://github.com/hpcaitech/ColossalAI
150 lines
6.2 KiB
Markdown
150 lines
6.2 KiB
Markdown
# Stable Diffusion with Colossal-AI
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*[Colosssal-AI](https://github.com/hpcaitech/ColossalAI) provides a faster and lower cost solution for pretraining and
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fine-tuning for AIGC (AI-Generated Content) applications such as the model [stable-diffusion](https://github.com/CompVis/stable-diffusion) from [Stability AI](https://stability.ai/).*
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We take advantage of [Colosssal-AI](https://github.com/hpcaitech/ColossalAI) to exploit multiple optimization strategies
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, e.g. data parallelism, tensor parallelism, mixed precision & ZeRO, to scale the training to multiple GPUs.
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## 🚀Quick Start
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1. Create a new environment for diffusion
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```bash
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conda env create -f environment.yaml
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conda activate ldm
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```
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2. Install Colossal-AI from our official page
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```bash
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pip install colossalai==0.1.10+torch1.11cu11.3 -f https://release.colossalai.org
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```
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3. Install PyTorch Lightning compatible commit
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```bash
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git clone https://github.com/Lightning-AI/lightning && cd lightning && git reset --hard b04a7aa
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pip install -r requirements.txt && pip install .
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cd ..
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```
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4. Comment out the `from_pretrained` field in the `train_colossalai_cifar10.yaml`.
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5. Run training with CIFAR10.
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```bash
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python main.py -logdir /tmp -t true -postfix test -b configs/train_colossalai_cifar10.yaml
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```
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## Stable Diffusion
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[Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) is a latent text-to-image diffusion
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model.
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Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
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Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
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this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
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<p id="diffusion_train" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_train.png" width=800/>
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</p>
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[Stable Diffusion with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion) 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).
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<p id="diffusion_demo" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_demo.png" width=800/>
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</p>
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## Requirements
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A suitable [conda](https://conda.io/) environment named `ldm` can be created
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and activated with:
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```
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conda env create -f environment.yaml
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conda activate ldm
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```
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You can also update an existing [latent diffusion](https://github.com/CompVis/latent-diffusion) environment by running
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```
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conda install pytorch torchvision -c pytorch
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pip install transformers==4.19.2 diffusers invisible-watermark
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pip install -e .
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```
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### Install [Colossal-AI v0.1.10](https://colossalai.org/download/) From Our Official Website
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```
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pip install colossalai==0.1.10+torch1.11cu11.3 -f https://release.colossalai.org
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```
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### Install [Lightning](https://github.com/Lightning-AI/lightning)
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We use the Sep. 2022 version with commit id as `b04a7aa`.
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```
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git clone https://github.com/Lightning-AI/lightning && cd lightning && git reset --hard b04a7aa
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pip install -r requirements.txt && pip install .
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```
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> The specified version is due to the interface incompatibility caused by the latest update of [Lightning](https://github.com/Lightning-AI/lightning), which will be fixed in the near future.
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## Dataset
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The dataSet is from [LAION-5B](https://laion.ai/blog/laion-5b/), the subset of [LAION](https://laion.ai/),
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you should the change the `data.file_path` in the `config/train_colossalai.yaml`
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## Training
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We provide the script `train.sh` to run the training task , and two Stategy in `configs`:`train_colossalai.yaml`
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For example, you can run the training from colossalai by
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```
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python main.py --logdir /tmp -t --postfix test -b configs/train_colossalai.yaml
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```
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- you can change the `--logdir` the save the log information and the last checkpoint
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### Training config
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You can change the trainging config in the yaml file
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- accelerator: acceleratortype, default 'gpu'
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- devices: device number used for training, default 4
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- max_epochs: max training epochs
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- precision: usefp16 for training or not, default 16, you must use fp16 if you want to apply colossalai
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## Example
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### Training on cifar10
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We provide the finetuning example on CIFAR10 dataset
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You can run by config `train_colossalai_cifar10.yaml`
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```
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python main.py --logdir /tmp -t --postfix test -b configs/train_colossalai_cifar10.yaml
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```
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## Comments
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- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
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, [lucidrains](https://github.com/lucidrains/denoising-diffusion-pytorch),
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[Stable Diffusion](https://github.com/CompVis/stable-diffusion), [Lightning](https://github.com/Lightning-AI/lightning) and [Hugging Face](https://huggingface.co/CompVis/stable-diffusion).
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Thanks for open-sourcing!
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- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).
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- The implementation of [flash attention](https://github.com/HazyResearch/flash-attention) is from [HazyResearch](https://github.com/HazyResearch).
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## BibTeX
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```
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@article{bian2021colossal,
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title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
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author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
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journal={arXiv preprint arXiv:2110.14883},
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year={2021}
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}
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@misc{rombach2021highresolution,
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title={High-Resolution Image Synthesis with Latent Diffusion Models},
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author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
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year={2021},
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eprint={2112.10752},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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@article{dao2022flashattention,
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title={FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness},
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author={Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
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journal={arXiv preprint arXiv:2205.14135},
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year={2022}
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}
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```
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