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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.
🔊 Text-Prompted Generative Audio Model
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
A guidance language for controlling large language models.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
StableLM: Stability AI Language Models
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
High-Resolution Image Synthesis with Latent Diffusion Models
QLoRA: Efficient Finetuning of Quantized LLMs
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
LAVIS - A One-stop Library for Language-Vision Intelligence
Official inference library for Mistral models
PyTorch code and models for the DINOv2 self-supervised learning method.
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
A collection of pre-trained, state-of-the-art models in the ONNX format
Zero-Shot Speech Editing and Text-to-Speech in the Wild
Using Low-rank adaptation to quickly fine-tune diffusion models.
Tacotron 2 - PyTorch implementation with faster-than-realtime inference
Create delightful software with Jupyter Notebooks
This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Text recognition (optical character recognition) with deep learning methods, ICCV 2019
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.