https://github.com/overshard/ai-art

Art generation using VQGAN + CLIP using docker containers. A simplified, updated, and expanded upon version of Kevin Costa's work. This project tries to make generating art as easy as possible for anyone with a GPU by providing a simple web UI.
https://github.com/overshard/ai-art

ai ai-art clip docker docker-compose imagenet python pytorch torch torchvision vqgan vqgan-clip

Last synced: about 1 month ago
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Art generation using VQGAN + CLIP using docker containers. A simplified, updated, and expanded upon version of Kevin Costa's work. This project tries to make generating art as easy as possible for anyone with a GPU by providing a simple web UI.

• Host: GitHub
• URL: https://github.com/overshard/ai-art
• Owner: overshard
• License: bsd-2-clause
• Created: 2022-06-29T19:06:45.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2022-07-04T00:11:01.000Z (over 2 years ago)
• Last Synced: 2023-06-16T20:17:50.957Z (over 1 year ago)
• Topics: ai, ai-art, clip, docker, docker-compose, imagenet, python, pytorch, torch, torchvision, vqgan, vqgan-clip
• Language: Python
• Homepage: https://hub.docker.com/r/overshard/ai-art
• Size: 2.23 MB
• Stars: 15
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# AI-Art

![AI-Art Screenshot](./Screenshot.webp)

Art generation using VQGAN + CLIP using docker containers. A simplified,

updated, and expanded upon version of

[Kevin Costa's work](https://github.com/kcosta42/VQGAN-CLIP-Docker). This

project tries to make generating art as easy as possible for anyone with a GPU

by providing a simple web UI.

## Samples

For samples check out the [AI Generated](https://isaacbythewood.com/art) section

on the art page on my website.

## Using ai-art

This works best if you have an NVIDIA GPU however there is a fallback CPU mode

included. I've found the CPU mode to take significantly longer than even the

most basic of GPUs though.

Install [Docker Desktop](https://www.docker.com/products/docker-desktop/) for

your OS.

### Quick start usage

Note that this creates a new directory in your current directory called ai-art

for all output and model storage. Make sure it's where you want it to be.

    docker run -it --rm --gpus all -p 3000:3000 -v ${pwd}/ai-art:/data overshard/ai-art

Once it's running you can access AI-Art in your browser at:

    http://localhost:3000/

### Development usage

Get the latest version of this project from GitHub:

    git clone https://github.com/overshard/ai-art.git

Then run it with:

    docker build --tag overshard/ai-art .

    docker run -it --rm --gpus all -p 3000:3000 -v ${pwd}/data:/data overshard/ai-art

If you are using a docker container on Windows to develop this project like I am

then you can use something like this to mount a directory on the host system

from your development container:

    docker run -it --rm --gpus all -p 3000:3000 -v "/C/Users/Isaac Bythewood/Documents/AI-Art:/data" overshard/ai-art

## Image sizes

The larger the image the more VRAM your graphics card needs:

- 6 GB of VRAM is required to generate 256x256 images.

- 12 GB of VRAM is required to generate 512x512 images.

- 24 GB of VRAM is required to generate 1024x1024 images.

If you don't know how much VRAM your graphics card has you probably have 6 GB

or less so stick with smaller images.

That being said you can do non-square images if you want as long as you don't

go above the number of pixels your GPU's VRAM supports, for example you could

do ultrawide images with 6 GB of ram at "384x128" or do tall images at "128x384"

and so on. You do not have to use numbers with a power of 2, "300x100" is also

perfectly valid.