README.md

# ColoDiffusion: Stable Diffusion with Colossal-AI

*[Colosssal-AI](https://github.com/hpcaitech/ColossalAI) provides a faster and lower cost solution for pretraining and
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/).*

We take advantage of [Colosssal-AI](https://github.com/hpcaitech/ColossalAI) 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](https://huggingface.co/CompVis/stable-diffusion) is a latent text-to-image diffusion
model.
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.
Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.

<p id="diffusion_train" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_train.png" width=800/>
</p>

[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).

<p id="diffusion_demo" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/diffusion_demo.png" width=800/>
</p>

## Requirements

A suitable [conda](https://conda.io/) 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](https://github.com/CompVis/latent-diffusion) environment by running

```
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
pip install transformers==4.19.2 diffusers invisible-watermark
pip install -e .
```

### install lightning

```
git clone https://github.com/1SAA/lightning.git
cd lightning
git checkout strategy/colossalai
export PACKAGE_NAME=pytorch
pip install .
```

### Install [Colossal-AI v0.1.10](https://colossalai.org/download/) From Our Official Website

```
pip install colossalai==0.1.12+torch1.12cu11.3 -f https://release.colossalai.org
```

> 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.

## Download the model checkpoint from pretrained

### stable-diffusion-v1-4

Our default model config use the weight from [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4?text=A+mecha+robot+in+a+favela+in+expressionist+style)

```
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](https://huggingface.co/runwayml/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](https://laion.ai/blog/laion-5b/), the subset of [LAION](https://laion.ai/),
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` and `train_ddp.yaml`

For example, you can run the training from colossalai by
```
python main.py --logdir /tmp/ -t -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

## Finetune Example
### Training on Teyvat Datasets

We provide the finetuning example on [Teyvat](https://huggingface.co/datasets/Fazzie/Teyvat) dataset, which is create by BLIP generated captions.

You can run by config `configs/Teyvat/train_colossalai_teyvat.yaml`
```
python main.py --logdir /tmp/ -t -b configs/Teyvat/train_colossalai_teyvat.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  \
```

```commandline
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](https://github.com/openai/guided-diffusion)
, [lucidrains](https://github.com/lucidrains/denoising-diffusion-pytorch),
[Stable Diffusion](https://github.com/CompVis/stable-diffusion), [Lightning](https://github.com/Lightning-AI/lightning) and [Hugging Face](https://huggingface.co/CompVis/stable-diffusion).
Thanks for open-sourcing!

- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).

- The implementation of [flash attention](https://github.com/HazyResearch/flash-attention) is from [HazyResearch](https://github.com/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}
}
```