NeuralVFX

NeuralVFX's Stars

• CompVis/stable-diffusion

  A latent text-to-image diffusion model

  Language:Jupyter Notebook70.1k57472210.4k

• NVIDIA/nvidia-docker

  Build and run Docker containers leveraging NVIDIA GPUs

  17.3k4531.6k2k

• k4yt3x/video2x

  A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018.

  Language:C++12.8k1381.1k1.2k

• CompVis/latent-diffusion

  High-Resolution Image Synthesis with Latent Diffusion Models

  Language:Jupyter Notebook12.6k983561.6k

• NielsRogge/Transformers-Tutorials

  This repository contains demos I made with the Transformers library by HuggingFace.

  Language:Jupyter Notebook10.2k1434621.5k

• open-mmlab/Amphion

  Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.

  Language:Python8.9k81256692

• cloneofsimo/lora

  Using Low-rank adaptation to quickly fine-tune diffusion models.

  Language:Jupyter Notebook7.3k55142488

• pytorch/serve

  Serve, optimize and scale PyTorch models in production

  Language:Java4.3k531.7k879

• huggingface/setfit

  Efficient few-shot learning with Sentence Transformers

  Language:Jupyter Notebook2.4k20332237

• crowsonkb/k-diffusion

  Karras et al. (2022) diffusion models for PyTorch

  Language:Python2.4k4267383

• NVlabs/nvdiffrec

  Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

  Language:Python2.2k52161230

• Linaqruf/kohya-trainer

  Adapted from https://note.com/kohya_ss/n/nbf7ce8d80f29 for easier cloning

  Language:Jupyter Notebook1.9k23286318

• xinntao/EDVR

  Winning Solution in NTIRE19 Challenges on Video Restoration and Enhancement (CVPR19 Workshops) - Video Restoration with Enhanced Deformable Convolutional Networks. EDVR has been merged into BasicSR and this repo is a mirror of BasicSR.

  Language:Python1.5k56199314

• rotemtzaban/STIT

  Language:Python1.2k5651169

• csxmli2016/DFDNet

  Blind Face Restoration via Deep Multi-scale Component Dictionaries (ECCV 2020)

  Language:Python9313791215

• microsoft/FaceSynthetics

  Face synthetics datasets

  84829059

• googleinterns/IBRNet

  Language:Python506112151

• VCL3D/VolumetricCapture

  A multi-sensor capture system for free viewpoint video.

  Language:Python4984010466

• GAP-LAB-CUHK-SZ/MVImgNet

  CVPR2023 | MVImgNet: A Large-scale Dataset of Multi-view Images

  Language:Python4298438

• PeizhuoLi/ganimator

  A motion generation model learned from a single example [SIGGRAPH 2022]

  Language:Python400151745

• andresprados/SPIGA

  SPIGA: Shape Preserving Facial Landmarks with Graph Attention Networks.

  Language:Jupyter Notebook321162935

• cxgincsu/SemanticGuidedHumanMatting

  Robust Human Matting via Semantic Guidance, ACCV 2022.

  Language:Python257132136

• nkolkin13/NeuralNeighborStyleTransfer

  Optimization based style transfer

  Language:Python255101232

• KunyuLin/Multi-view-Datasets

  Multi-view datasets for classification or clustering

  710120

• mshahbazi72/NeRF-GAN-Distillation

  20511

• RasLillebo/HighFrequencyEconometrics-HAR-vs.-Neural-Networks

  Inspired by Hillebrand & Medeiros (2009) and Corsi (2009), I put neural networks in a High frequency environment, and tested the performance of the two models (HAR & Neural Networks). - The data used in this project is 2 years worth of intraday 5-minute realized volatility (See: Sheppard, Patton, Liu, 2012) from 20 Dow Jones stocks, that has been scrutinized using bivariate analysis and manipulation into a single dimension.

  Language:R18103

• johnowhitaker/miniminiai

  Language:Jupyter Notebook16212

• artefactory/deploy_stable_difusion

  Language:Python11104

• infil00p/pytorch_android_cpp_example

  A sample application of how to use the NDK and OpenCV to make a basic classifier using PyTorch Mobile models.

  Language:C++6401

• kythanh/pytube

  A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.

  Language:Python2000