afruehstueck/tileGAN
Code for TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures (SIGGRAPH 2019)
This tool helps artists, game developers, or graphic designers generate extremely large, plausible texture maps from a collection of smaller input images. You provide various texture examples, and it outputs a high-resolution, seamless texture that can span hundreds of megapixels, ideal for backgrounds or large surfaces without noticeable tiling. It offers a user interface for creative control over the final output.
226 stars. No commits in the last 6 months.
Use this if you need to create vast, non-repetitive background textures or environmental maps for games, films, or architectural visualizations that need to look natural and detailed at a large scale.
Not ideal if you need to generate textures from a single input image or require fine-grained, pixel-level control over every detail of the generated output.
Stars
226
Forks
19
Language
Python
License
GPL-3.0
Category
Last pushed
Dec 02, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/afruehstueck/tileGAN"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Yutong-Zhou-cv/Awesome-Text-to-Image
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
tobran/DF-GAN
[CVPR2022 oral] A Simple and Effective Baseline for Text-to-Image Synthesis
aelnouby/Text-to-Image-Synthesis
Pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper
akanimax/T2F
T2F: text to face generation using Deep Learning
woozzu/tagan
An official PyTorch implementation of the paper "Text-Adaptive Generative Adversarial Networks:...