Stars
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
A curated list of awesome Machine Learning frameworks, libraries and software.
The world's simplest facial recognition api for Python and the command line
High-Resolution Image Synthesis with Latent Diffusion Models
Code and documentation to train Stanford's Alpaca models, and generate the data.
Generative Models by Stability AI
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep lear…
Data Apps & Dashboards for Python. No JavaScript Required.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
OCR, layout analysis, reading order, line detection in 90+ languages
Train transformer language models with reinforcement learning.
Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
Official repo for consistency models.
A concise but complete full-attention transformer with a set of promising experimental features from various papers
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
[ICCV 2023 Oral] Text-to-Image Diffusion Models are Zero-Shot Video Generators
Outpainting with Stable Diffusion on an infinite canvas
An open-source framework for training large multimodal models.
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV 2023)
A one-stop data processing system to make data higher-quality, juicier, and more digestible for (multimodal) LLMs! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷为大模型提供更高质量、更丰富、更易”消化“的数据！
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators