OpenJourney-discord

This is the official repository for the OpenJourney Discord bot.

What is OpenJourney?

OpenJourney is a Discord bot that allows you to create your own generated pictures by using StableDiffusion

How to use?

To use OpenJourney, you need to have a Discord account. If you don't have one, you can create one here.

Once you have a Discord account, you can invite OpenJourney to your server by clicking here (Works for all servers for 1 week from publishing)

How to use the bot?

After adding bot just type /help to get started.
Or just to official guide page in Notion: OpenJourney Guide

How to contribute?

If you want to contribute to OpenJourney, you can fork this repository and make a pull request. If you want to add a new feature, please open an issue first. Name your forked repository OpenJourney-discord-<feature>. For example, if you want to add a new command, name your forked repository OpenJourney-discord-new-command.

Installation

Index

1. Create a Discord Application & Bot, Invite to your server
2. Clone the repository
3. Install NVIDIA Runtime
4. Install Docker and docker-compose (optional)
5. Install Nvidia Docker
6. Setup the environment
7. Build the image
8. Run the container

0. Create a Discord Application & Bot, Invite to your server

1. Go to Discord Developer Portal and create a new application
2. Go to the Bot tab and create a new bot
3. Go to the OAuth2 tab and select bot scope
4. Select the permissions you want to give to the bot
5. Copy the link and paste it in your browser
6. Select the server you want to add the bot to

1. Clone the repository

Install git, curl, if you don't have them

sudo apt install git curl

git clone https://github.com/Ar4ikov/OpenJourney-discord.git
cd OpenJourney-discord

2. Install NVIDIA Runtime

Do it like you would do it for any other project (for Windows, Linux, MacOS)
There is a simple example how to install it with Conda for Linux

conda install cudatoolkit=11.6 -c nvidia

3. Install Docker and docker-compose (optional)

sudo apt update && sudo apt install docker.io docker-compose

Or install docker and docker-ce that way:

curl https://get.docker.com | sh \
  && sudo systemctl --now enable docker

4. Install Nvidia Docker

Link to: NVIDIA Docker

1. Setup a package repository and the GPG key

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

2. Update the package repository

sudo apt-get update

3. Install the nvidia-docker-2

sudo apt-get install -y nvidia-docker2

4. Change the default runtime to nvidia Link

sudo nano /etc/docker/daemon.json

It should looks like:

{
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
         } 
    },
    "default-runtime": "nvidia" 
}

5. Restart the Docker daemon

sudo systemctl restart docker

At this point, you should be able to run the nvidia-docker2 container.

sudo docker run --rm --gpus all nvidia/cuda:11.6.0-base-ubuntu20.04 nvidia-smi

This should output the following:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06    Driver Version: 450.51.06    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            On   | 00000000:00:1E.0 Off |                    0 |
| N/A   34C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

5. Setup the environment

Environment variable	Description	Default value
DISCORD_TOKEN	Discord bot token	None
GUILD_ID	Discord server ID, -1 if sync commans globally	-1
SD_MODEL_ID_1	StableDiffusion model ID	dreamlike-art/dreamlike-photoreal-2.0
GPT_MODEL_ID	GPT-2 model ID for Magic Prompt generate	Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator
NUM_GPUS	Number of GPUs to use	1
NUM_THREADS_PER_GPU	Number of threads per GPU	2
NSFW_GENERATE	Allow NSFW generate images content	True

You can use multiple models for StableDiffusion, just add SD_MODEL_ID_2, SD_MODEL_ID_3 and so on

cp .env_example .env
nano .env

6. Build the image

source .env && docker-compose build

7.0. Run the container

source .env && docker-compose up -d

7.1. Stop the container

docker-compose down

Technical details

For every GPU that use FP16, there are some calculations:

• ±16-20 GB of RAM (per 1 GPU & 2 threads)
• ±2 GB of VRAM in background (per 1 GPU & 2 threads)
• ±6 GB of VRAM per thread in active stage (per 1 GPU & 1 threads)
• ±12 GB of VRAM per thread in active stage (per 1 GPU & 2 threads)

License

MIT

Credits

• Dreamlike Art Model
• StableDiffusion Prompt Generator
• CodeFormer Face Upsampler