Xiuyu-Li/q-diffusion
[ICCV 2023] Q-Diffusion: Quantizing Diffusion Models.
In the long run we are all dead
[ICCV 2023] Q-Diffusion: Quantizing Diffusion Models.
Stitch your rollouts together across clouds and regions
Official code for "Refocusing Is Key to Transfer Learning"
Post-training with Tinker
SGLang is a fast serving framework for large language models and vision language models.
Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024
Accelerating the development of large multimodal models (LMMs) with lmms-eval
VILA - a multi-image visual language model with training, inference and evaluation recipe, deployable from cloud to edge (Jetson Orin and laptops)
A high-throughput and memory-efficient inference and serving engine for LLMs
The code for the paper ROUTERBENCH: A Benchmark for Multi-LLM Routing System
Adapting the "Radioactive Data" paper to work for text models
Freefood is an IOS app that can recommend surrounding Facebook events that offer free food at Cornell.
Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".
StyleGAN2-ADA - Official PyTorch implementation
[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
ArtGAN: This work presents a series of new approaches to improve Generative Adversarial Network (GAN) for conditional image synthesis and we name the proposed model as “ArtGAN”. Implementations are in Caffe/Tensorflow.
3D Perception toolbox.
The official implementation for Pseudo Numerical Methods for Diffusion Models on Manifolds (ICLR 2022) and a generic framework for DDIM-like models
Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)
[CVPR 2022] Styleformer - Official Pytorch Implementation
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
Denoising Diffusion Implicit Models
[NeurIPS'21] Projected GANs Converge Faster
Not All Low-Pass Filters are Robust in Graph Convolutional Networks
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QLTH is a 2-step model compression scheme that applies Post-Training Quantization on the ”winning tickets” or ”matching” derived by iterative magnitude prunning.
This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).
Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"