Sean Sube 6c932e2ee8 | ||
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api | ||
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README.md |
README.md
ONNX Web UI
This is a rudimentary web UI for ONNX models, providing a way to run GPU-accelerated models on Windows and even AMD with a remote web interface.
This is still fairly early and instructions are a little rough, but it works on my machine.
Based on work 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
Contents
Setup
This is a very similar process to what harishanand95 and averad's gists recommend, split up into a few steps:
- Install Git and Python, if you have not already
- Create a virtual environment
- Install AI packages
- Install ORT Nightly package
- Download and convert models
Install Git and Python
Install Git and Python 3.10 for your environment:
The latest version of git should be fine. Python must be 3.10 or earlier, 3.10 seems to work well.
Create a Virtual Environment
Make sure you have Python 3.10 or earlier, then create a virtual environment:
> python --version
Python 3.10
> 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.
Every time you start using ONNX web, activate the virtual environment:
# on linux:
> ./onnx_env/bin/activate
# on windows:
> .\onnx_env\Scripts\Activate.bat
Install AI packages
Update pip itself:
> python -m pip install --upgrade pip
Install the following packages for AI:
> pip install diffusers transformers ftfy spacy scipy
> pip install onnx onnxruntime torch
> pip install onnxruntime-directml --force-reinstall # TODO: is this one necessary?
Install the following packages for the web UI:
> pip install flask stringcase
Install ORT Nightly package
Download the latest DirectML ORT nightly package for your version of Python and install it with pip.
Downloads can be found at https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly. If you are using
Python 3.10, download the cp310
package. For Python 3.9, download the cp39
package, and so on.
> wget https://aiinfra.pkgs.visualstudio.com/PublicPackages/_apis/packaging/feeds/7982ae20-ed19-4a35-a362-a96ac99897b7/pypi/packages/ort-nightly-directml/versions/1.14.dev20221214001/ort_nightly_directml-1.14.0.dev20221214001-cp310-cp310-win_amd64.whl/content
> pip install ~/Downloads/ort_nightly_directml-1.14.0.dev20221214001-cp310-cp310-win_amd64.whl --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.
Download and Convert Models
Sign up for an account at https://huggingface.co and find the models you want to use. Popular options include:
Download the conversion script from the huggingface/diffusers
repository:
> wget https://raw.githubusercontent.com/huggingface/diffusers/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py
Run the conversion script with your desired model(s):
> python convert_stable_diffusion_checkpoint_to_onnx.py --model_path="runwayml/stable-diffusion-v1-5" --output_path="./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 can verify that all of the steps up to this point worked correctly by attempting to run the basic txt2img
script
provided with diffusers
and included here as api/setup-test.py
.
Usage
Configuring and running the server
In the api/
directory, run the server with Flask:
> flask --app 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:
> flask --app 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:
{
"api": {
"root": "http://127.0.0.1:5000"
}
}
Still in the gui/
directory, run the dev server with Node:
> 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.
The txt2img tab will be active by default, with an example prompt. You can press the Generate
button and an image
should appear on the page 10-15 seconds later (depending on your GPU and other hardware).