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High-Resolution Image Synthesis with Latent Diffusion Models

Auto_Jobs_Applier_AI_Agent aims to easy job hunt process by automating the job application process. Utilizing artificial intelligence, it enables users to apply for multiple jobs in an automated an…

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Mamba SSM architecture

A generative world for general-purpose robotics & embodied AI learning.

Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

An Autonomous LLM Agent for Complex Task Solving

A Collection of Variational Autoencoders (VAE) in PyTorch.

Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"

A concise but complete full-attention transformer with a set of promising experimental features from various papers

AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

A library for scientific machine learning and physics-informed learning

《大模型白盒子构建指南》：一个全手搓的Tiny-Universe

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

Recipe for a General, Powerful, Scalable Graph Transformer

Using graph network to solve PDEs

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax for Partial Differential Equations

[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting

PyTorch implementations of Learning Mesh-based Simulation With Graph Networks

A large-scale benchmark for machine learning methods in fluid dynamics

Solvers for NP-hard and NP-complete problems with an emphasis on high-performance GPU computing.

Code for the paper "Poseidon: Efficient Foundation Models for PDEs"