# paarthneekhara/text-to-image

Text to image synthesis using thought vectors

Repository: https://github.com/paarthneekhara/text-to-image
Canonical: https://ross.abutalabs.com/products/text-to-image
Language: Python
License: MIT
License Family: permissive
Topics: deep-learning, tensorflow, generative-adversarial-network, skip-thought-vectors
Last push: 2018-01-30T16:29:57+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3669, "days_push": 3137, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2163, forks 398 (observed 2026-08-28T04:06:21.172225+00:00)

## What it is
An experimental TensorFlow implementation of text-to-image synthesis using Skip Thought Vectors and the GAN-CLS algorithm from the Generative Adversarial Text-to-Image Synthesis paper. It is built on top of a DCGAN implementation and trained on the flowers dataset.

## Use cases
- generate images from text captions
- train a GAN to synthesize images from sentence embeddings
- experiment with GAN-CLS text-to-image synthesis
- reproduce generative adversarial text-to-image research
- learn how skip thought vectors can condition image generation

## When to choose
- you want a simple, readable reference implementation of GAN-CLS text-to-image synthesis
- you are studying how caption embeddings can condition GAN generators
- you need a small educational codebase to modify for research experiments

## When to avoid
- you need production-quality or modern text-to-image generation like diffusion models
- you require Python 3 support or actively maintained dependencies
- you want high-resolution image synthesis beyond 64x64

## Facets
- artifact type: library
- maturity: abandoned
- function: deep-learning, machine-learning, image-processing
- domain: deep-learning, machine-learning, image-processing
- platform: python
- tags: gan, text-to-image, tensorflow, skip-thought-vectors, generative-models

## Member repositories
- paarthneekhara/text-to-image (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:21.172225+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 2026-08-30T02:50:12.702026+00:00, confidence not recorded.
  - readme: https://github.com/paarthneekhara/text-to-image (fetched 2026-08-28T04:06:21.172225+00:00, sha bf8a8587adb7)
- Data as of 2026-08-30T08:39:29.467469+00:00.