paarthneekhara/text-to-image

Health v2 · maintenance only

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Adoption not part of the score

2163 stars · 398 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity abandoned

deep-learning machine-learning image-processing deep-learning machine-learning image-processing python gan text-to-image tensorflow skip-thought-vectors generative-models

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markdown · JSON · MCP: product_card(name="paarthneekhara/text-to-image")