prompt-engineering VS ml-stable-diffusion

An agent is a style of prompt that lets LLMs act as reasoning engines. It's also known as the ReAct pattern (which engineers are avoiding using for namespace collision reasions).

You can read a good intro example here: https://github.com/brexhq/prompt-engineering#react

Well, if developers get replaced by AI, then who are the managers going to manage :). I personally don't think AI is just going to replace us. The way we work will continue to change as new AI tools come out. I'm taking time to tinker with new tools and seeing how others do as well (e.g., I found Brex's tips and tricks for working with LLMs very insightful: https://github.com/brexhq/prompt-engineering).

I recognize there's plenty of catnip here when it comes to calling this "engineering" or not, however, whatever you want to call it (prompt fiddling?), the techniques are crucial if you want to achieve reasonably consistent output from current-state LLMs. As models improve concerns about context window limitations will be reduced and it will be easier to discern user intent.

These are good straight-to-the-point guides:

- Prompt Engineering by BrexHQ: https://github.com/brexhq/prompt-engineering

- OpenAI guidance: https://help.openai.com/en/articles/6654000-best-practices-f...

- https://devblogs.microsoft.com/dotnet/gpt-prompt-engineering...

- (great examples): https://www.deeplearning.ai/short-courses/chatgpt-prompt-eng...

tl;dr:

Brex's Prompt Engineering Guide (https://github.com/brexhq/prompt-engineering)

Not sure how that got in here. Apple released CoreML Stable Diffusion library a little over a year ago [1]. Hugging Face released their version of the example app for the CoreML Stable Diffusion library [2].

The app should be able to run on iPhone 14 Pro, I believe the requirements is about 6-8Gb of RAM. And I was not able to run it on iPhone 13 Mini, because it has only 4Gb of RAM.

- [1] https://github.com/apple/ml-stable-diffusion

Where are you seeing a Stable Diffusion example? I'm familiar with Apple's CoreML Implementation of StableDiffusion, but is there something else in the SD world available for download now as part of MLX?

Other features and improvements to the repo https://github.com/apple/ml-stable-diffusion

M1! https://github.com/apple/ml-stable-diffusion

M2 MBA can run Stable Diffusion and LLaMa comfortably, which means generating your potential game/image asset locally. They're pretty much impractical in 7340.

Interestingly these are OpenCL kernels so in theory some of the optimizations might run out-of-the-box on CPUs.

It would be instructive to compare their speedups on the iPhone to the Apple CoreML implementation: https://github.com/apple/ml-stable-diffusion

Stable Diffusion runs great on my M1 Macs. The Draw Things app makes it really easy to run too. You also can’t disregard that Apple’s M chips actually have dedicated neural processing for ML/AI. This actual makes a Mac more affordable in this category because you don’t need to purchase a beefy graphics card. Not to mention that Apple has even optimized their software specifically for Stable Diffusion (related GitHub). Draw Things can take advantage of this. There’s a few guides to running the web UI on M1 too. I prefer the Draw Things app because of how easy it is to use, but the web UI is also nice because of all of the plugins and workflows that the community has built over time.

LINKS: ml-stable-diffusion: https://github.com/apple/ml-stable-diffusion Diffusers (HuggingFace Mac App): https://apps.apple.com/app/diffusers/id1666309574?mt=12

So, is Stable Diffusion working finally on TPU or not? DiffusionBee uses GPU and running this https://github.com/apple/ml-stable-diffusion with CPU_AND_NE just segfaults