Stars
Lumina-T2X is a unified framework for Text to Any Modality Generation
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning
An Open-source Toolkit for LLM Development
This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.
An open source implementation of "Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning", an all-new multi modal AI that uses just a decoder to generate both text and images
Official implementation of the paper "Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.
GIT: A Generative Image-to-text Transformer for Vision and Language
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
Refine high-quality datasets and visual AI models
High-Resolution Image Synthesis with Latent Diffusion Models
Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)
Code for the NeurIPS19 paper "Meta-Learning Representations for Continual Learning"
Data augmentation for NLP, presented at EMNLP 2019
The framework to deal with ctr problem。The project contains FNN,PNN,DEEPFM, NFM etc
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"
Data Science Workspace containing sample recipes, datasets and notebooks
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
A curated list of network embedding techniques.
Virtual Adversarial Training (VAT) implementation for PyTorch
Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset