# lucidrains/deep-daze

Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun

Repository: https://github.com/lucidrains/deep-daze
Canonical: https://ross.abutalabs.com/products/deep-daze
Language: Python
License: MIT
License Family: permissive
Topics: artificial-intelligence, deep-learning, transformers, siren, implicit-neural-representation, text-to-image, multi-modality
Last push: 2022-03-13T19:09:13+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2054, "days_push": 1634, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4315, forks 310 (observed 2026-08-28T04:08:41.822570+00:00)

## What it is
Deep Daze is a simple command line tool for text-to-image generation that combines OpenAI's CLIP with a Siren implicit neural representation network. It renders images from a text prompt by optimizing a neural network to match the CLIP embedding of the phrase.

## Use cases
- generate an image from a text prompt
- create generative art from a phrase
- visualize a sentence as an image
- run CLIP-guided text-to-image synthesis locally
- experiment with implicit neural representations for image generation

## When to choose
- you want a simple CLI to turn text prompts into images
- you have an Nvidia or AMD GPU with at least 4GB VRAM
- you want to experiment with CLIP + Siren generative techniques

## When to avoid
- you need state-of-the-art text-to-image quality like diffusion models
- you have no GPU available
- you need a maintained tool with active development

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, cli
- domain: artificial-intelligence, deep-learning, image-processing
- platform: python, cli, windows
- tags: text-to-image, clip, siren, implicit-neural-representation, generative-art, gpu, linux, macos

## Member repositories
- lucidrains/deep-daze (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:41.822570+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:46.509732+00:00, confidence not recorded.
  - readme: https://github.com/lucidrains/deep-daze (fetched 2026-08-28T04:08:41.822570+00:00, sha 9d282ec3d9b5)
  - registry_pypi: https://pypi.org/pypi/deep-daze/json (fetched 2026-08-29T09:10:54.837159+00:00, sha 2ead9dcc3444)
- Data as of 2026-08-30T08:39:29.467469+00:00.