This link opens a Dreambooth training & inference notebook in an A6000 machine

Check out the full guide here: blog.paperspace.com/dreambooth-stable-diffusion-tutorial-1/

After seeing the video of the fellow in the base jumping incident last week, this feels extra poignant. Glad this guy survived to talk about it.

This repo has all the code for the project, which is based on Whisper and MoviePy. It has a version that can run in an IPython Notebook, and I have also released an application version compatible with Gradient Deployments. This was only my second time working on front end with HTML, so it's all still a little rudimentary for now.

The way it works is by first generating the translated speech to text values at labeled timestamps with Whisper. Then, MoviePy scales the captions to the size of the inputted video and overlays them at the (mostly) correct timeslots.

Thanks to OpenAI for the incredible free model.

It will be fascinating to see what other objects hubble captured over the years look like in higher definition

In this tutorial, i walk through how Whisper can be used with MoviePy to automatically generate and overlay translated subtitles from any video sample. Be sure to check out the Github repo as well, linked at the bottom.

This project was based on Whisper, MoviePy, and Flask.

The intersection of architecture and 3d design with ML is an intersect I haven't seen much work in, but it would be fascinating to see what comes from this

Damn that's impressive. Its hard to imagine being up that high

In our newest article, we discuss autoencoders and convolutional autoencoders in the context of image data. We then show how to write custom autoencoders of our own with PyTorch, train them, and view our results in a Gradient Notebook.

I'm all here for the roll out of a more positive outlook towards the impending climate hell as we try to fix it before the storm is too big.

production code + keys = no bueno. I feel for them.