Showing 1-20 of 22 projects
An ultra-simple, state-of-the-art codebase for autoregressive image generation using advanced AI models.
An open-source implementation of large language models with a focus on model parallelism and efficiency.
NeuralProphet is a simple forecasting package that uses neural networks and time series analysis to make predictions.
An autoregressive character-level language model for generating text in a variety of styles.
MAGI-1 is a powerful autoregressive model for generating high-quality video at scale, built for vibe coders who work with AI tools.
Self Forcing: Bridging Training and Inference in Autoregressive Video Diffusion
An open autoregressive foundational video AI model for vibe coders building with AI tools.
A scalable image generation model based on the Llama language model, outperforming diffusion models.
A high-performance, autoregressive model for generating high-resolution images from text prompts.
A comprehensive Python library for time series forecasting using machine learning models.
An open-source library for automatically rigging diverse skeletons using a single neural network model.
Unified compression methods for KV caching in autoregressive language models like GPT-3.
Introductory examples and tutorials on deep generative modeling techniques like VAEs, GANs, and score-based models.
Matcha-TTS is a fast and efficient text-to-speech (TTS) architecture using a conditional flow matching approach.
An all-in-one model for offline and simultaneous speech recognition, translation, and synthesis.
A CVPR 2025 video diffusion model that enables fast autoregressive video generation from slow bidirectional models.
Variational autoencoder implementation in TensorFlow and PyTorch, including inverse autoregressive flow.
An open-source text-to-speech library built using Transformer-based neural networks for high-quality speech synthesis.
Lumina-mGPT 2.0 is a stand-alone autoregressive image modeling tool powered by Python.
An extensive benchmark for scientific machine learning, focused on physics-informed neural networks and partial differential equations.
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