OpenAI Parameter Golf: what 1,100 researchers built in six weeks
How 1,100 researchers beat OpenAI's own baseline with 16 megabytes and 10 minutes.
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Here's a quick and dirty guide to getting Huemin's JAX diffusion to run on runpod!
Today I figured I'd switch things up a bit and leave you a gif quick start instead of the usual article. It only takes a few minutes to get started with this and all the deps have been sorted out for you in the nb. If you need the notebook file that you should upload to your instance, you can download it from one of these two places: Link 1, Link 2
Please note that it will sit here for quite a few minutes while it downloads models and prepared for the first render cycle:
Be patient and it will start rendering when it's ready. I was getting around 1.5-2s/it on the demo example.
Enjoy!
Credits to: Huemin (@huemin_art), for the notebook, Alexander Redde (@alexanderredde3) for working out the deps, and nsheppherd (@nshepperd1) and Rivers Have Wings (@rivershavewings) for Jax Diffusion
Build an agentic AI safety pipeline with Runpod Flash and Granite Guardian 4.1
Learn how to set up a real-world agentic system with our new Flash framework.
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Announcing Runpod Flash
Flash is now generally available (GA) as a production-ready tool for running serverless GPU and CPU workloads in pure Python without needing Docker.
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Build what’s next.
The most cost-effective platform for building, training, and scaling machine learning models—ready when you are.
