🦜🔗 Build context-aware reasoning applications

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

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

High-Resolution Image Synthesis with Latent Diffusion Models

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

Inpaint anything using Segment Anything and inpainting models.

A unified framework for 3D content generation.

Metric depth estimation from a single image

🪼 a python library for doing approximate and phonetic matching of strings.

Python class that generates pixel art from images

Collection of algorithms for online portfolio selection

ADE20K Dataset

A wrapper of LLMs that biases its behaviour using prompts and contexts in a transparent manner to the end-users

Implementation of Toolformer: Language Models Can Teach Themselves to Use Tools

code for CLIPDraw

ComfyUI nodes based on the paper "FABRIC: Personalizing Diffusion Models with Iterative Feedback" (Feedback via Attention-Based Reference Image Conditioning)

Building Spotify playlists based on vibes using LangChain and GPT