vqgan

[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer

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

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

PyTorch codes for "Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors", ACM MM2022 (Oral)

Official Implement of Multi-Stage Multi-Codebook (MSMC) TTS

Start here

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.

🇰🇷 Text to Image in Korean

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

NTIRE 2022 - Image Inpainting Challenge

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

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

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

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

Docker for VQGAN+CLIP (z+quantize method)

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.

Colabs for text prompt steered image generators

Text-to-Image Synthesis using Multimodal (VQGAN + CLIP) Architectures

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