Taming Transformers for High-Resolution Image Synthesis

Pytorch implementation of VQGAN (Taming Transformers for High-Resolution Image Synthesis) (https://arxiv.org/pdf/2012.09841.pdf)

[ICCV 2025] This is the official PyTorch codes for the paper: "DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution"

X-Transformer: Taming Pretrained Transformers for eXtreme Multi-label Text Classification

"SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation", TPAMI 2024

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

Code for INTERSPEECH 2023 paper "mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectra"

Refactoring dalle-pytorch and taming-transformers for TPU VM

This repo contains the implementation of VQGAN, Taming Transformers for High-Resolution Image Synthesis in PyTorch from scratch. I have added support for custom datasets, testings, experiment tracking etc.

Official implementation for AAAI 2025 paper: TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition

VQ-GAN for Various Data Modality based on Taming Transformers for High-Resolution Image Synthesis

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

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

[TGRS2023]MFormer: Taming Masked Transformer for Unsupervised Spectral Reconstruction

Gradio Web app for running VQGAN-CLIP locally

VQGAN from LDM without hell of dependencies

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

Keras implementation of "Taming Transformers for High-Resolution Image Synthesis", https://arxiv.org/pdf/2012.09841.pdf

This is a simplified implementation of VQ-GANs written in PyTorch. The architecture is borrowed from the paper "Taming Transformers for High-Resolution Image Synthesis".

Scripts and trained models from our paper: M. Ntrougkas, N. Gkalelis, V. Mezaris, "T-TAME: Trainable Attention Mechanism for Explaining Convolutional Networks and Vision Transformers", IEEE Access, 2024. DOI:10.1109/ACCESS.2024.3405788.

Taming Transformers for High-Resolution Image Synthesis

Homework for deep generative models at PKU Spring 2021. My pytorch implement of taming transformer.

A Jittor implementation for AAAI 2025 paper: TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition

Implementation of an image generation process using a combination of the CLIP (Contrastive Language-Image Pre-training) model and a VQ-VAE-2 based generator from the "taming-transformers" repository. The generated images are optimized based on input prompts, including both positive and negative textual cues.

VQGAN Taming transformer

woo

fork of taming transformers for AI jobs

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