# ColoDiffusion: Stable Diffusion with Colossal-AI

Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion).
<p id="diffusion_train" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20v2.png" width=800/>
</p>

- [Training](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).

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

- [DreamBooth Fine-tuning](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): Personalize your model using just 3-5 images of the desired subject.

<p id="inference" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
</p>

- [Inference](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion): Reduce inference GPU memory consumption by 2.5x.

More details can be found in our [blog of Stable Diffusion v1](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) and [blog of Stable Diffusion v2](https://www.hpc-ai.tech/blog/colossal-ai-0-2-0).

## Installation

### Option #1: install from source
#### Step 1: 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 .
```

##### Step 2: install lightning

Install Lightning version later than 2022.01.04. We suggest you install lightning from source.

https://github.com/Lightning-AI/lightning.git


##### Step 3:Install [Colossal-AI](https://colossalai.org/download/) From Our Official Website

For example, you can install  v0.1.12 from our official website.

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

### Option #2: Use Docker

To use the stable diffusion Docker image, you can either build using the provided the [Dockerfile](./docker/Dockerfile) or pull a Docker image from our Docker hub.

```
# 1. build from dockerfile
cd docker
docker build -t hpcaitech/diffusion:0.2.0  .

# 2. pull from our docker hub
docker pull hpcaitech/diffusion:0.2.0
```

Once you have the image ready, you can launch the image with the following command:

```bash
########################
# On Your Host Machine #
########################
# make sure you start your image in the repository root directory
cd Colossal-AI

# run the docker container
docker run --rm \
  -it --gpus all \
  -v $PWD:/workspace \
  -v <your-data-dir>:/data/scratch \
  -v <hf-cache-dir>:/root/.cache/huggingface \
  hpcaitech/diffusion:0.2.0 \
  /bin/bash

########################
#  Insider Container   #
########################
# Once you have entered the docker container, go to the stable diffusion directory for training
cd examples/images/diffusion/

# start training with colossalai
bash train_colossalai.sh
```

It is important for you to configure your volume mapping in order to get the best training experience.
1. **Mandatory**, mount your prepared data to `/data/scratch` via `-v <your-data-dir>:/data/scratch`, where you need to replace `<your-data-dir>` with the actual data path on your machine.
2. **Recommended**, store the downloaded model weights to your host machine instead of the container directory via `-v <hf-cache-dir>:/root/.cache/huggingface`, where you need to repliace the `<hf-cache-dir>` with the actual path. In this way, you don't have to repeatedly download the pretrained weights for every `docker run`.
3. **Optional**, if you encounter any problem stating that shared memory is insufficient inside container, please add `-v /dev/shm:/dev/shm` to your `docker run` command.



## 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_colossalai.sh` to run the training task with colossalai,
and can also use `train_ddp.sh` to run the training task with ddp to compare.

In `train_colossalai.sh` the main command is:
```
python main.py --logdir /tmp/ -t -b configs/train_colossalai.yaml
```

- you can change the `--logdir` to decide where to save the log information and the last checkpoint.

### Training config

You can change the trainging config in the yaml file

- devices: device number used for training, default 8
- max_epochs: max training epochs, default 2
- precision: the precision type used in training, default 16 (fp16), you must use fp16 if you want to apply colossalai
- more information about the configuration of ColossalAIStrategy can be found [here](https://pytorch-lightning.readthedocs.io/en/latest/advanced/model_parallel.html#colossal-ai)

## 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)
  --use_int8            whether to use quantization method
  --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}
}
```
