A list of Machine Learning Art Colabs

Overview

ML Visual Art Colabs

A list of cool Colabs on Machine Learning Imagemaking or other artistic purposes

3D Ken Burns Effect

Ken Burns Effect by Manuel RomeroDemo Video by Lia Coleman

3D Photo Inpainting

3D Photography using Context-aware Layered Depth InpaintingDemo Video

BigBiGAN

BigBiGAN by Tensorflow

BigGan

BigGan by Tensorflow

Colorization

Image Colorizer by DeOldify

Video Colorizer by DeOldify

Coltran by Google Brain

DCGAN

TF-GAN on TPUs by Tensorflow

DeepDream

DeepDream by Alex Mordvintsev • Demo Video

Minimal DeepDream Implementation by Tensorflow

First Order Motion Model

First Order Motion by Aliaksandr Siarohin

FUNIT

FUNIT by shaoanlu

Image/Data Processing

Process WikiArt Dataset by Peter Baylies

Image Generators

Looking Glass 1.1 by bearsharktopusTutorial

Image-GPT by Jonathan Fly

Lucid

Lucid visualizes the networks of many convolutional neural nets

Lucid

Lucent - Lucent is a PyTorch variation of Lucid.

Next Frame Prediction

Next Frame Prediction with Pix2PixHDTraining Demo VideoVideo Generation Demo

Object Detection

YOLO-v5 by Ultralytics

Object Mask Generation

U Square Net by Derrick Schultz

Shape Matching GAN

Shape Matching GAN by Derrick Schultz

SinGAN

SinGAN by Derrick Schultz • Demo Video

SinGAN Distortions by duskvirkus, inspired by the Yuma Kishi's Studies of Collage of Paintings for Humanity

StyleGAN

Flesh Digressions Loops of the constant and style layers • Demo Video

GanSpace Feature detection using PCA • Demo Video

Barycentric Cross-Network Interpolation with different layer interpolation rates by @arfafax

Network BendingDemo Video

Network Blending by Justin PinkneyDemo Video

StyleGAN Paintings (StyleGAN1)

StyleGAN Encoder Tutorial by Peter Baylies

StyleGAN2 by Derrick Schultz

StyleGAN2 by Mikael Christensen

SWA Playground by @arfafax

WikiArt Example Generation Peter Baylies

StyleGAN2 Activations and PCA Projection by duskvirkus, Look at lower network levels of SG2 generator.

Style Transfer

Lucid 2D Style Transfer by Google

Neural Style TF by Derrick Schultz • Demo Video

Superresolution

ESRGAN by Derrick Schultz

Image Superresolution by Erdene-Ochir Tuguldur

SRFBN by Derrick Schultz

SR Zoo ported to Colab by Derrick Schultz

Slow Motion

RIFE by Derrick Schultz (modified from Towards Data Science article)

Super Slomo by Erdene-Ochir Tuguldur

Text-to-Image Generation

Aphantasia by Vadim EpsteinTutorial

Attn-GAN The OG text-to-image generator • notebook by Derrick Schultz

Big Sleep (BigGAN controlled by CLIP) by Ryan Murdock • Demo Video

Disco Diffusion 4.1 by SOMNAIDemo/Tutorial

IllusTrip by Vadim EpsteinTutorial

Quick CLIP Guided Diffusion Fast CLIP/Guided Diffusion image generation • by Katherine Crowson, Daniel Russell, et al.

S2ML Art Generator by Justin Bennington

Zoetrope 5.5 CLIP-VQGAN tool by bearsharktopus

Texture Synthesis

Neural Cellular Automata by Alex Mordvitsev

Texturize: Grass DemoDemo Video

Texturize: Gravel Demo

TwinGAN

TwinGAN by Manuel Romero

Unpaired Image to Image Translation

CUT by Derrick Schultz

CycleGAN by Tensorflow

MUNIT by Derrick Schultz

StarGAN v2 PyTorch

ML Text Colabs

GPT-2

GPT-2 by Martin Woolf

ML Audio Colabs

Magenta

Generating Piano Music with Transformer by Magenta

Jukebox

Sampling and Co-Composing with Prompts by Anthony Matos

Music Source Separation

DemucsDemo video by Lia Coleman

Open Unmix

Other Helpful Repositories

dl-colab-notebooks by Erdene-Ochir Tuguldur

shared_colab_notebooks by Manuel Romero

Owner
Derrick Schultz (he/him)
Artists who uses code. Most of this stuff isn’t production level—I’m an artist first, programmer second.
Derrick Schultz (he/him)
A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

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This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

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A PyTorch Library for Accelerating 3D Deep Learning Research

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Immortal tracker

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Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation

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[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.

CoCLR: Self-supervised Co-Training for Video Representation Learning This repository contains the implementation of: InfoNCE (MoCo on videos) UberNCE

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PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility

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31 Apr 19, 2022
2021搜狐校园文本匹配算法大赛 分比我们低的都是帅哥队

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DiSECt: Differentiable Simulator for Robotic Cutting

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CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

CLIP-GEN [简体中文][English] 本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-F

75 Dec 29, 2022
U-Time: A Fully Convolutional Network for Time Series Segmentation

U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig

Mathias Perslev 176 Dec 19, 2022
Material del curso IIC2233 Programación Avanzada 📚

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IIC2233 @ UC 72 Dec 23, 2022
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD

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Proof of concept GnuCash Webinterface

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Learning Time-Critical Responses for Interactive Character Control

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"SOLQ: Segmenting Objects by Learning Queries", SOLQ is an end-to-end instance segmentation framework with Transformer.

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MEGVII Research 179 Jan 02, 2023
(EI 2022) Controllable Confidence-Based Image Denoising

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Images and Visual Representation Laboratory (IVRL) at EPFL 5 Dec 18, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks

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