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PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2"
MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications
Vector (and Scalar) Quantization, in Pytorch
Vector Quantized VAEs - PyTorch Implementation
A pytorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937)
Attention is all you need implementation
Minimal implementation of scalable rectified flow transformers, based on SD3's approach
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
All standford Cheatsheets: Artificial Intelligence, Transformers, LLMs, Deep Learning, Machine Learning, Probabilities, Statistics, Algebra and Calculus.
A repository of implementations of some nonlinear ICA methods
Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch
🦖Pytorch implementation of popular Attention Mechanisms, Vision Transformers, MLP-Like models and CNNs.🔥🔥🔥
An unofficial implementation of both ViT-VQGAN and RQ-VAE in Pytorch
The Missing Semester of Your CS Education 📚
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography.
Implementation of the convolutional module from the Conformer paper, for use in Transformers
Official code for Conformer: Local Features Coupling Global Representations for Visual Recognition
[Unofficial] PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)
LPIPS metric. pip install lpips
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 track…
Taming Transformers for High-Resolution Image Synthesis
A latent text-to-image diffusion model
Combination of transformers and diffusion models for flexible all-in-one simulation-based inference
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference (Chang et al., AISTATS 2025)