A latent text-to-image diffusion model

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.

High-Resolution Image Synthesis with Latent Diffusion Models

PyTorch code and models for the DINOv2 self-supervised learning method.

A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.

[ICLR24] Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

Code for the paper "Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models"

Code for "Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed", CVPR 2024

Doppelgangers: Learning to Disambiguate Images of Similar Structures

[ECCV 2022] PlaneFormers: From Sparse View Planes to 3D Reconstruction