discoart 0.0.7.dev3

Create Disco Diffusion artworks in one line

Create compelling Disco Diffusion artworks in one line

DiscoArt is an elegant way of creating compelling Disco Diffusion^[*] artworks for generative artists, AI enthusiasts and hard-core developers. DiscoArt has a modern & professional API with a beautiful codebase, ensuring high usability and maintainability. It introduces handy features such as result recovery and persistence, gRPC/HTTP serving w/o TLS, post-analysis, easing the integration to larger cross-modal or multi-modal applications.

_{^{[*] Disco Diffusion is a Google Colab Notebook that leverages CLIP-Guided Diffusion to allow one to create compelling and beautiful images from text prompts.}}

👼 Available to all: fully optimized for Google Colab free tier! Perfect for those who don't own GPU by themselves.

🎨 Focus on creating not coding: one-liner create() with a Pythonic interface and powerful features. Fetch real-time results anywhere anytime, no more worry on session outrage on Google Colab free tier.

🏭 Ready for integration & production: built on top of DocArray data structure, enjoy smooth integration with Jina, CLIP-as-service and other cross-/multi-modal applications.

Install

pip install discoart

If you are not using DiscoArt under Google Colab, then other dependencies might be required.

Get Started

Create artworks

from discoart import create

da = create()

That's it! It will create with the default text prompts and parameters.

Set prompts and parameters

DiscoArt support parameters are listed here. You can specify them directly in create():

from discoart import create

create(text_prompts='A painting of sea cliffs in a tumultuous storm, Trending on ArtStation.',
       init_image='https://d2vyhzeko0lke5.cloudfront.net/2f4f6dfa5a05e078469ebe57e77b72f0.png',
       skip_steps=100)

This docs explains those parameters in very details.

Fetch results

create() returns da, a DocumentArray-type object. It contains the following information:

• All arguments passed to create() function, including seed, text prompts and model parameters.
• The generated image and its intermediate images.

This allows you to further post-process, analyze, export the results with powerful DocArray API.

For example, you can display all final images in a grid:

da.plot_image_sprites(skip_empty=True, fig_size=(10, 10), show_index=True)

Or the final image of one particular run:

da[0].display()

You can zoom into that run and display images on the intermediate steps:

da[0].chunks.plot_image_sprites(skip_empty=True, fig_size=(10, 10), show_index=True)

Finally, you can review its parameters via:

da[0].tags

Pull results

If you are a free-tier Google Colab user, one annoy thing is the lost of sessions from time to time. With DiscoArt, you can easily recover the results by pulling the last session ID.

1. Find the session ID. It appears on top of the image.

2. Pull the result via that ID on any machine at any time, not necessarily on Google Colab:

   from docarray import DocumentArray
   
   da = DocumentArray.pull('discoart-3205998582')
   

Verbose logs

You can also get verbose logs by setting the following lines before import discoart:

import os

os.environ['DISCOART_LOG_LEVEL'] = 'DEBUG'

What's next?

😎 If you are already a DD user: you are ready to go! There is no extra learning, DiscoArt respects the same parameter semantics as DD5.2. So just unleash your creativity!

• Note that DiscoArt does not support animation generation and image_prompt (which was marked as uneffective? in original DD 5.2).

👶 If you are a DALL·E Flow user: you may want to take step by step, as Disco Diffusion works in a very different way than DALL·E. It is much more advanced and powerful: e.g. Disco Diffusion can take weighted & structured text prompts; it can initialize from a image with controlled noise; and there are way more parameters one can tweak. Impatient prompt like "armchair avocado" will give you nothing but confusion and frustration. I highly recommend you to check out the following resources before trying your own prompt:

• Zippy's Disco Diffusion Cheatsheet v0.3
• EZ Charts - Diffusion Parameter Studies
• Disco Diffusion 70+ Artist Studies
• A Traveler’s Guide to the Latent Space

Support

• Join our Slack community and chat with other community members about ideas.
• Join our Engineering All Hands meet-up to discuss your use case and learn Jina's new features.
  • When? The second Tuesday of every month
  • Where? Zoom (see our public events calendar/.ical) and live stream on YouTube
• Subscribe to the latest video tutorials on our YouTube channel

Join Us

DiscoArt is backed by Jina AI and licensed under MIT License. We are actively hiring AI engineers, solution engineers to build the next neural search ecosystem in open-source.