Implementation of Taming Transformers for High-Resolution Image Synthesis (https://arxiv.org/abs/2012.09841) in PyTorch

Docker for VQGAN+CLIP (z+quantize method)

Experiments with Baudelaire and a text-to-image GAN.

Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized

🇰🇷 Text to Image in Korean

Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

VQGAN and CLIP are actually two separate machine learning algorithms that can be used together to generate images based on a text prompt. VQGAN is a generative adversarial neural network that is good at generating images that look similar to others (but not from a prompt), and CLIP is another neural network that is able to determine how well a c…

Traditional deepdream with VQGAN+CLIP and optical flow. Ready to use in Google Colab.

yet another VQGAN-CLIP variation

NTIRE 2022 - Image Inpainting Challenge

Art generation using VQGAN + CLIP using docker containers. A simplified, updated, and expanded upon version of Kevin Costa's work. This project tries to make generating art as easy as possible for anyone with a GPU by providing a simple web UI.

Pytorch implementation of Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors

Video generation of anime content based on the first and last frame

Pytorch implementation of "Taming transformer for high resolution image synthesis (VQGAN)"

Pipeline to create Paper2Fig dataset, a dataset for text-to-image generation from research papers and figures (e.g., diagrams of architectures or methods in fields like Machine Learning or Computer Vision)

OCR-VQGAN, a discrete image encoder (tokenizer and detokenizer) for figure images in Paper2Fig100k dataset. Implementation of OCR Perceptual loss for clear text-within-image generation. Fork from VQGAN in CompVis/taming-transformers

Streamlit Tutorial (ex: stock price dashboard, cartoon-stylegan, vqgan-clip, stylemixing, styleclip, sefa)

Multi-Modal Image Generation for News Stories

[CVPR 2023] | RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors