(ICML-W, 2018) Text to image synthesis, by distilling concepts from multiple captions.

Winner (Titan Xp as awards), 2019 Just Model It Event: freely making a model on dgx system and backend.ai

Research for text-to-image synthesis via modified auxiliary classifier GANs. Incremental modification of model architecture for improved results, fully documented.

A CLI tool/python module for generating images from text using guided diffusion and CLIP from OpenAI.

A Simple Android Client for Dall-E Mini Targeting API 23 and above (Android 6+)

Official code of "StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis" (CVPR 2022)

✭ MAGNETRON ™ ✭: Implementation of Google's Dreambooth (with Stable Diffusion model) instead of IMAGEN (for making MAGNETRON ™ TECHNOLOGY IMAGINATION PROXIA). In particular this can be used to mimic the appearance of subjects in a given reference set and synthesize novel renditions of them in different contexts.

The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020 (oral)

TISE: Bag of Metrics for Text-to-Image Synthesis Evaluation (ECCV 2022)

Modular image generation library

This repository illustrates how to use the Hotpot.ai API. Our API provides Stable Diffusion, image generator, text-to-image generator, background removal, image upscaler, photo restoration, and picture colorization.

Styled text-to-drawing synthesis method. Featured at IJCAI 2022 and the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

PyTorch Implementation of Generative Adversarial Text to Image Synthesis

A library of images generated using Stable Diffusion

Implementation of StackGANs for text-to-image generation in Tensorflow

Course materials, tutorials, and project works from the Neural Networks (EEE443) course at Bilkent University.

The code for "Text-to-image synthesis with self-supervised bi-stage generative adversarial network"

The code for "Text-to-image synthesis with self-supervised learning"

Developed a text-to-image synthesizer system and compared its performances when using VAEs, GANs and Diffusion Models