# ONNX Web

This is a web UI for running [ONNX models](https://onnx.ai/) with GPU acceleration or in software, running locally or on a remote machine.

The API runs on both Linux and Windows and provides access to the major functionality of `diffusers`, along with
metadata about the available models and accelerators, and the output of previous runs. Hardware acceleration is
supported for AMD and experimental for Nvidia, with a CPU fallback capable of running on laptop-class machines.

The GUI runs in all major browsers, including on mobile devices, and allows you to select the model and accelerator
being used, along with the prompt and other image parameters. The last few output images are shown, making it easy to
refer back to previous parameters.

![txt2img with example astronaut prompt and image](./docs/readme-preview.png)

Based on guides by:

- https://gist.github.com/harishanand95/75f4515e6187a6aa3261af6ac6f61269
- https://gist.github.com/averad/256c507baa3dcc9464203dc14610d674
- https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs
- https://www.travelneil.com/stable-diffusion-updates.html

This is still an early project and the instructions are a little rough, but it works on my machine.

## Features

- REST API server capable of running ONNX models with DirectML acceleration
  - AMD hardware acceleration
  - CPU software fallback
  - multiple schedulers
- web app to generate and view images
  - can be hosted alongside API or on a CDN
  - built with React and MUI
- txt2img mode
  - image controls and scheduler selection
  - with recent image history
- img2img mode
  - image upload with preview
  - guided by prompt and negative prompt
- inpainting mode
  - mask painting
  - source to mask conversion tools

## Contents

- [ONNX Web](#onnx-web)
  - [Features](#features)
  - [Contents](#contents)
  - [Setup](#setup)
    - [Note about setup paths](#note-about-setup-paths)
    - [Install Git and Python](#install-git-and-python)
    - [Create a virtual environment](#create-a-virtual-environment)
    - [Install pip packages](#install-pip-packages)
      - [For AMD on Windows: Install ONNX DirectML](#for-amd-on-windows-install-onnx-directml)
      - [For CPU on Linux: Install PyTorch CPU](#for-cpu-on-linux-install-pytorch-cpu)
      - [For CPU on Windows: Install PyTorch CPU](#for-cpu-on-windows-install-pytorch-cpu)
      - [For Nvidia everywhere: Install PyTorch GPU and ONNX GPU](#for-nvidia-everywhere-install-pytorch-gpu-and-onnx-gpu)
    - [Download and convert models](#download-and-convert-models)
  - [Usage](#usage)
    - [Configuring and running the server](#configuring-and-running-the-server)
      - [Securing the server](#securing-the-server)
    - [Configuring and hosting the client](#configuring-and-hosting-the-client)
    - [Using the web interface](#using-the-web-interface)
    - [Running from containers](#running-from-containers)
    - [Customizing the config](#customizing-the-config)
    - [Known errors and solutions](#known-errors-and-solutions)

## Setup

This is a very similar process to what [harishanand95](https://gist.github.com/harishanand95/75f4515e6187a6aa3261af6ac6f61269)
and [averad's](https://gist.github.com/averad/256c507baa3dcc9464203dc14610d674) gists recommend, split up into a few
steps:

1. [Install Git and Python](#install-git-and-python), if you have not already
2. [Create a virtual environment](#create-a-virtual-environment)
3. [Install pip packages](#install-pip-packages)
   1. Install common packages
   2. Install platform-specific packages for your GPU (or CPU)
4. [Download and convert models](#download-and-convert-models)

### Note about setup paths

This project contains both Javascript and Python, for the client and server respectively. Make sure you are in the
correct directory when working with each part.

Most of these setup commands should be run in the Python environment and the `api/` directory:

```shell
> cd api
> pwd
/home/ssube/code/github/ssube/onnx-web/api
```

The Python virtual environment will be created within the `api/` directory.

The Javascript client can be built and run within the `gui/` directory.

### Install Git and Python

Install Git and Python 3.10 for your environment:

- https://www.python.org/downloads/
- https://gitforwindows.org/

The latest version of git should be fine. Python must be 3.10 or earlier, 3.10 seems to work well. If you already have
Python installed for another form of Stable Diffusion, that should work, but make sure to verify the version in the next
step.

Make sure you have Python 3.10 or earlier:

```shell
> python --version
Python 3.10
```

If your system uses `python3` and `pip3` for the Python 3.x tools, make sure to adjust the commands shown here.

Once you have those basic packages installed, clone this git repository:

```shell
> git clone https://github.com/ssube/onnx-web.git
```

### Create a virtual environment

Change into the `api/` directory, then create a virtual environment:

```shell
> pip install virtualenv

> python -m venv onnx_env
```

This will contain all of the pip libraries. If you update or reinstall Python, you will need to recreate the virtual
environment.

If you receive an error like `Error: name 'cmd' is not defined`, there may be [a bug in the `venv` module](https://www.mail-archive.com/debian-bugs-dist@lists.debian.org/msg1884072.html) on certain
Debian-based systems. You may need to install venv through apt instead:

```shell
> sudo apt install python3-venv   # only if you get an error
```

Every time you start using ONNX web, activate the virtual environment:

```shell
# on linux:
> source ./onnx_env/bin/activate

# on windows:
> .\onnx_env\Scripts\Activate.bat
```

Update pip itself:

```shell
> python -m pip install --upgrade pip
```

### Install pip packages

Install the following packages for AI:

```shell
> pip install "numpy>=1.20,<1.24"
> pip install "protobuf<4,>=3.20.2"
> pip install accelerate diffusers ftfy onnx onnxruntime spacy scipy transformers
```

Install the following packages for the web UI:

```shell
> pip install flask stringcase
```

_Or_ install all of these packages at once using [the `requirements.txt` file](./api/requirements.txt):

```shell
> pip install -r requirements.txt
```

At the moment, only `numpy` and `protobuf` seem to need a specific version. If you see an error about `np.float`, make
sure you are not using `numpy>=1.24`.
[This SO question](https://stackoverflow.com/questions/74844262/how-to-solve-error-numpy-has-no-attribute-float-in-python)
has more details.

#### For AMD on Windows: Install ONNX DirectML

If you are running on Windows, install the DirectML ONNX runtime as well:

```shell
> pip install onnxruntime-directml --force-reinstall
```

You can optionally install the latest DirectML ORT nightly package, which may provide a substantial performance increase
(on my machine, the stable version takes about 30sec/image vs 9sec/image for the nightly).

Downloads can be found at https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly. You can install
through pip or download the package file. If you are using Python 3.10, download the `cp310` package. For Python 3.9,
download the `cp39` package, and so on. Installing with pip will figure out the correct version:

```shell
> pip install --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ ort-nightly-directml --force-reinstall
```

Make sure to include the `--force-reinstall` flag, since it requires some older versions of other packages, and will
overwrite the versions you currently have installed.

#### For CPU on Linux: Install PyTorch CPU

If you are running with a CPU and no hardware acceleration, install `onnxruntime` and the CPU version of PyTorch:

```shell
> pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
```

#### For CPU on Windows: Install PyTorch CPU

If you are running with a CPU and no hardware acceleration, install `onnxruntime` and the CPU version of PyTorch:

```shell
> pip install torch
```

#### For Nvidia everywhere: Install PyTorch GPU and ONNX GPU

If you are running with an Nvidia GPU, install `onnxruntime-gpu`:

```shell
> pip install onnxruntime-gpu
> pip install torch --extra-index-url https://download.pytorch.org/whl/cu117
```

Make sure you have CUDA 11.x installed and that the version of PyTorch matches the version of CUDA
([see their documentation](https://pytorch.org/get-started/locally/) for more details).

### Download and convert models

Sign up for an account at https://huggingface.co and find the models you want to use. Popular options include:

- https://huggingface.co/runwayml/stable-diffusion-v1-5

Log into the HuggingFace CLI:

```shell
> huggingface-cli.exe login
```

Issue an API token from https://huggingface.co/settings/tokens, naming it something memorable like `onnx-web`, and then
paste it into the prompt.

Download the conversion script from the `huggingface/diffusers` repository to the root of this project:

- https://raw.githubusercontent.com/huggingface/diffusers/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py

Run the conversion script with your desired model(s):

```shell
# on linux:
> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-v1-5" --output_path="./models/stable-diffusion-onnx-v1-5"

# on windows:
> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-v1-5" --output_path=".\models\stable-diffusion-onnx-v1-5"
```

This will take a little while to convert each model. Stable diffusion v1.4 is about 6GB, v1.5 is at least 10GB or so.

You should verify that all of the steps up to this point have worked correctly by attempting to run the
`api/test-setup.py` script, which is a slight variation on the original txt2img script.

If you want to use inpainting, you will need a second model trained for that purpose:

```shell
# on linux:
> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-inpainting" --output_path="./models/stable-diffusion-inpainting"

# on windows:
> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-inpainting" --output_path=".\models\stable-diffusion-inpainting"
```

## Usage

### Configuring and running the server

The server relies mostly on two paths, the models and outputs. It will make sure both paths exist when it starts up,
and will exit with an error if the models path does not.

Both of those paths exist in the git repository, with placeholder files to make sure they exist. You should not have to
create them, if you are using the default settings. You can customize the paths by setting `ONNX_WEB_MODEL_PATH` and
`ONNX_WEB_OUTPUT_PATH`, if your models exist somewhere else or you want output written to another disk, for example.

In the `api/` directory, run the server with Flask:

```shell
> flask --app=onnx_web.serve run
```

Note the IP address this prints.

If you want to access the server from other machines on your local network, pass the `--host` argument:

```shell
> flask --app=onnx_web.serve run --host=0.0.0.0
```

This will listen for requests from your current local network and may be dangerous.

#### Securing the server

When making the server publicly visible, make sure to use appropriately restrictive firewall rules along with it, and
consider using a web application firewall to help prevent malicious requests.

### Configuring and hosting the client

From within the `gui/` directory, edit the `gui/examples/config.json` file so that `api.root` is the URL printed out by
the `flask run` command from earlier. It should look something like this:

```json
{
  "api": {
    "root": "http://127.0.0.1:5000"
  }
}
```

Still in the `gui/` directory, build the UI bundle and run the dev server with Node:

```shell
> make bundle

> node serve.js
```

### Using the web interface

You should be able to access the web interface at http://127.0.0.1:3000/index.html or your local machine's hostname.

- If you get a `Connection Refused` error, make sure you are using the correct address and the dev server is still running.
- If you get a `File not found` error, make sure you have built the UI bundle (`make bundle`) and are using the `/index.html` path

The txt2img tab will be active by default, with an example prompt. When you press the `Generate` button, an image should
appear on the page 10-15 seconds later (depending on your GPU and other hardware). Generating images on CPU will take
substantially longer, at least 2-3 minutes. The last four images will be shown, along with the parameters used to
generate them.

### Running from containers

OCI images are available for both the API and GUI, `ssube/onnx-web-api` and `ssube/onnx-web-gui`, respectively. These
are regularly built from the `main` branch and for all tags.

The `ssube/onnx-web-gui` image is available in both Debian and Alpine-based versions, but the `ssube/onnx-web-api`
image is only available as a Debian-based image, due to [this Github issue with `onnxruntime`](https://github.com/microsoft/onnxruntime/issues/2909#issuecomment-593591317).

When using the containers, make sure to mount the `models/` and `outputs/` directories. The models directory can be
read-only, but outputs should be read-write.

```shell
> podman run -p 5000:5000 --rm -v ../models:/models:ro -v ../outputs:/outputs:rw docker.io/ssube/onnx-web-api:main-buster

> podman run -p 8000:80 --rm docker.io/ssube/onnx-web-gui:main-nginx-bullseye
```

### Customizing the config

You can customize the config file if you want to change the default model, platform (hardware acceleration), scheduler,
and prompt. If you have a good base prompt or always want to use the CPU fallback, you can set that in the config file:

```json
{
  "default": {
    "model": "stable-diffusion-onnx-v1-5",
    "platform": "amd",
    "scheduler": "euler-a",
    "prompt": "an astronaut eating a hamburger"
  }
}
```

When running the dev server, `node serve.js`, the config file will be loaded from `out/config.json`. If you want to load
a different config file, save it to your home directory named `onnx-web-config.json` and copy it into the output
directory after building the bundle:

```shell
> make bundle && cp -v ~/onnx-web-config.json out/config.json
```

When running the container, the config will be loaded from `/usr/share/nginx/html/config.json` and you can mount a
custom config using:

```shell
> podman run -p 8000:80 --rm -v ~/onnx-web-config.json:/usr/share/nginx/html/config.json:ro docker.io/ssube/onnx-web-gui:main-nginx-bullseye
```

### Known errors and solutions

- `Error: name 'cmd' is not defined` while setting up the virtual environment:
  - install venv through apt: `sudo apt install python3-venv`
  - see https://www.mail-archive.com/debian-bugs-dist@lists.debian.org/msg1884072.html
- command not found `python` or `pip`:
  - some systems differentiate between Python 2 and 3, and reserve the `python` command for 2
- connection refused:
  - make sure the API and GUI are both running
  - make sure you are using the correct hostname or IP address
  - open the appropriate firewall ports:
    - TCP/5000 for the API dev server
    - TCP/3000 for the GUI dev server
    - TCP/80 for the GUI using nginx without a container
    - TCP/8000 for the GUI using the nginx container
- CUDA errors:
  - make sure you are using CUDA 11.x
  - https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements
- numpy invalid combination of arguments:
  - make sure to export ONNX models using the same packages and versions that you use while running the API
- numpy `np.float` missing
  - reinstall `pip install "numpy>=1.20,<1.24 --force-reinstall"`
  - another package may have upgraded numpy to 1.24, which removed that symbol