discoart VS stylegan2-projecting-images

Just look at basic Python syntax and read through the discoart readme and it should all make sense :). If you still get lost, me and other people are always here to help more

just install latest Python from official page, then PyTorch from here (make sure it's some of the CUDA versions) and then follow the steps here and you should be good to go

discoart is a python library that significantly simplifies interacting with disco diffusion. Install the library with pip, a single import statement, and call the create() function and you're running. I've used it with a Google Colab sheet set to use a GPU runtime and had a good time generating until I hit Big G's limits.

try out discoart! Takes a bit of practice though

If you don't want to set up TensorFlow locally, you can use Google Colab, which comes with a GPU by default. You can access it via this link.

Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link.

Google Colab Documentation Beginner-friendly documentation to get started with Google Colab: https://colab.research.google.com/

If you don't want to install PyTorch locally, you can use Google Colab, which provides a free cloud-based environment with PyTorch pre-installed. This allows you to run PyTorch code without any setup on your local machine. Simply go to Google Colab and create a new notebook.

Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to test and build interfaces.

To begin, you need to set up a Google Colab environment. Google Colab allows you to run Python code in the cloud without the need for any setup on your local machine. You will also need to install the yt-dlp library, which is an open-source tool used for downloading YouTube videos and other media from various websites.

Both are designed to be self-contained and easy to share. Online environments like Google Colab and JupyterHub abstract away the often-complex Python setup process.

My opinion: Many Meta tools seem like they were created by former Googlers that sought to recreate something they previously had at Google, but also changed aspects of the tool that were annoying or diverged from their needs.

Since Bento doesn't appear to be usable by the public, aparallel version of this that people can get a feel for cross-tool integration would be Google's Colaboratory / Colab notebooks (https://colab.research.google.com/) that have many baked-in integrations driven by actual internal use (i.e. dogfooding).

(I checked there doesn’t seem to be a straightforward way to do C in swift playgrounds. Bonus content: https://github.com/uraimo/Awesome-Swift-Playgrounds )

What I do for most of my coding on iPad is use Google Colab. https://colab.research.google.com/

It’s a Jupyter Notebook, but you can get most things including working with C. The below link confirms C can be bound to and how.

https://blog.jupyter.org/interactive-workflows-for-c-with-ju...

As a matter of point, iPads aren’t really set up tool wise for programming. You can force something into this niche, but you probably want a computer for coding, because a significant portion of programming is massaging the system into doing what you need.