Purpose
ml-art-colabs is a collection of Google Colab notebooks for machine learning art creation, hosted on GitHub. This repository gathers Colab notebooks that can be used for machine learning image generation or other artistic purposes, making it convenient for users to run experiments directly in the browser without configuring a local environment.
Main Capabilities
The repository organizes Colab notebooks into multiple categories by functional direction, including but not limited to:
- 3D Effects: 3D Ken Burns Effect, 3D Photo Inpainting
- Image Colorization: Image Colorizer by DeOldify, Video Colorizer by DeOldify
- Image Generation: BigBiGAN, BigGAN, Coltran, TF-GAN on TPUs, DeepDream, Looking Glass, Image-GPT, Lucid, Lucent, etc.
- Video and Motion: First Order Motion Model, Next Frame Prediction, FILM, RIFE, Super Slomo
- Object Detection and Segmentation: YOLO-v5, U Square Net
- GAN-related: Shape Matching GAN, SinGAN, GanSpace, Network Bending, Network Blending, StyleGAN Paintings, StyleGAN2, SWA Playground, Lucid 2D Style Transfer, Neural Style TF, etc.
- Super-resolution: ESRGAN, Image Superresolution, SRFBN, SR Zoo
- Text-to-image Generation: Aphantasia, Attn-GAN, Big Sleep, Disco Diffusion 4.1, IllusTrip, Quick CLIP Guided Diffusion, S2ML Art Generator, Zoetrope 5.5
- Texture Synthesis: Neural Cellular Automata, Texturize: Grass, Texturize: Gravel
- Image-to-image Translation: TwinGAN, CUT, CycleGAN, MUNIT, StarGAN v2
- Audio Generation: Generating Piano Music with Transformer, Sampling and Co-Composing with Prompts, Music Source Separation (Demucs)
- Other: GPT-2, Image/Data Processing, Object Mask Generation, etc.
How to Use
Users can access the links to each Colab notebook through the GitHub repository page, open them in Google Colab, and run them. Each notebook usually contains author information and related instructions, and some entries also include demo videos or tutorial links.
Use Cases
This repository is suitable for research and creative scenarios such as machine learning art creation, image and video generation experiments, style transfer, super-resolution, text-to-image generation, and audio generation. It is especially suitable for users who want to get started quickly without configuring a local environment.
Limitations
The repository does not provide detailed information about the specific runtime environment requirements, dependency versions, computing resource requirements, or usage restrictions of each notebook. Some notebooks may depend on specific versions of libraries or require GPU resources; please refer to each notebook's own instructions for details.
