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https://github.com/dribnet/pixray

neural image generation
https://github.com/dribnet/pixray

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neural image generation

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# pixray

## NOTICE: This is an archived version of this repository. The [latest versions are here](https://github.com/pixray/pixray).

![Alt text](https://user-images.githubusercontent.com/945979/132954388-1986e4c6-6996-48fd-9e91-91ec97963781.png "deep ocean monsters #pixelart")

Pixray is an image generation system. It combines previous ideas including:

* [Perception Engines](https://github.com/dribnet/perceptionengines) which uses image augmentation and iteratively optimises images against an ensemble of classifiers
* [CLIP guided GAN imagery](https://alexasteinbruck.medium.com/vqgan-clip-how-does-it-work-210a5dca5e52) from [Ryan Murdoch](https://twitter.com/advadnoun) and [Katherine Crowson](https://github.com/crowsonkb) as well as modifictions such as [CLIPDraw](https://twitter.com/kvfrans/status/1409933704856674304) from Kevin Frans
* Useful ways of navigating latent space from [Sampling Generative Networks](https://github.com/dribnet/plat)
* (more to come)

pixray it itself a python library and command line utility, but is also friendly to running on line in Google Colab notebooks.

There is currently [some documentation on options](https://dazhizhong.gitbook.io/pixray-docs/docs). Also checkout [THE DEMO NOTEBOOKS](https://github.com/dribnet/clipit/tree/master/demos) - especially the super simple "Start Here" colab or join in the [discussion on discord](https://discord.gg/x2g9TWrNKe).

## Usage

Pixray can be run in Docker using [Cog](https://github.com/replicate/cog).

First, [install Docker and Cog](https://github.com/replicate/cog#install), then you can use `cog run` to run Pixray inside Docker. For example:

cog run python pixray.py --drawer=pixel --prompt=sunrise --output myfile.png