A latent text-to-image diffusion model

12 Lessons to Get Started Building AI Agents

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

Instruct-tune LLaMA on consumer hardware

StableLM: Stability AI Language Models

This repository contains implementations and illustrative code to accompany DeepMind publications

High-Resolution Image Synthesis with Latent Diffusion Models

LAVIS - A One-stop Library for Language-Vision Intelligence

Code release for NeRF (Neural Radiance Fields)

[NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". A…

Inpaint anything using Segment Anything and inpainting models.

Taming Transformers for High-Resolution Image Synthesis

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

PyTorch implementation of AnimeGANv2

Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.

Let us democratise high-resolution generation! (CVPR 2024)

[CVPR 2023] OneFormer: One Transformer to Rule Universal Image Segmentation

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

[CVPR 2022] Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer

Code for the Lovász-Softmax loss (CVPR 2018)

This repo contains the code for 1D tokenizer and generator

A PyTorch implementation of the paper "All are Worth Words: A ViT Backbone for Diffusion Models".

EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation

Official implementation of Diffusion Autoencoders

VOLO: Vision Outlooker for Visual Recognition

A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.