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A latent text-to-image diffusion model
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Neural Networks: Zero to Hero
Examples and guides for using the Gemini API
A multi-voice TTS system trained with an emphasis on quality
This repository contains implementations and illustrative code to accompany DeepMind publications
High-Resolution Image Synthesis with Latent Diffusion Models
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body
Taming Transformers for High-Resolution Image Synthesis
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Reference models and tools for Cloud TPUs.
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
Language-Agnostic SEntence Representations
Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.
Massively parallel rigidbody physics simulation on accelerator hardware.
Kandinsky 2 — multilingual text2image latent diffusion model
Self hosted alternative to Google Photos
A simple notebook demonstrating prompt-based music generation via Mubert API
Easily compute clip embeddings and build a clip retrieval system with them
DeepFashion2 Dataset https://arxiv.org/pdf/1901.07973.pdf
Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the came…