When's the last time a piece of tech blew your mind?
After tweaking some settings … wow— Swizec Teller (@Swizec) November 20, 2022
takes 3min on a friggen ipad 🤯 pic.twitter.com/rYacgzcVGF
For me it's the new M2 iPad Pro. I got one this weekend to replace the old iPad from when I was looking for the perfect light work device. The iPad + pencil + magic keyboard combo has become my main daily driver. It's where I do all my writing, emails, light doomscrolling, business management, and gentle examples coding.
The new iPad has more processing power than my work laptop 🤯
8-core CPU, 10-core GPU, and 16-core "neural engine" which I think is a GPU optimized for neural networks. In a 494 gram package with no active cooling, half the thickness of my pinky finger, and a battery that feels like it lasts forever.
Sure, the big fat laptop has more GPU and bigger RAM, but the fans make lots of noise and the laptop weighs 2177 grams. Cosplaying as a desktop machine most of its days.
The iPad's biggest holdback is its operating system. Optimized for mobile devices and machines with far less power, the OS won't let you push the limits.
There might be better apps to do on-device AI image generation but this is the one I’ve been playing with when I’m bored. https://t.co/SvRhSch0DU— Thomas Shaddox (@tshddx) November 20, 2022
And ... wow. For 2 hours, I couldn't stop. The iPad even started getting warm 😁
First I tried a silly prompt: "young racing driver winning the championship by van gogh"
Amazing pic.twitter.com/R8jj9Q0mqt— Swizec Teller (@Swizec) November 20, 2022
Pretty impressive. Then the prompt engineering started. You have to tell these models what you want in a lot of detail. Simple english, lots of adjectives, strong descriptions. Vibes don't work. The more explicit you can be, the better.
Eventually I got a decent picture of a Porsche 911 racing in the desert.
The trick is to iterate fast on low settings – this is 30 "iterations" – then when you like the prompt, punch up the values.
The pre-trained Stable Diffusion 1.5 model that I'm using is great with cars. You can try any make and model and get a recognizable result. Descriptive car names don't work. Better to say "BMW M3" than "sporty saloon car".
I think that's down to the training data.
Animals are more of a struggle. Species don't work, describing the features you want is better.
Playing with different styles is nice. This one is a "by rembrandt", but when I tried another artist, the AI instead decided to replace the bowl of food with another bird 🤨
But that's to be expected. The AI has no idea what it's doing. These images are created by using an image of pure noise then removing noise step by step and saying "Does this look like what I want?".
The very essence of Michelangelo's "Oh statue of david? Easy. Take a block of marble and remove anything that isn't the statue of david"
I don't know. But here's a passable bowl of chinese food
Right now the big problem is that these images require lots of cherry picking and iteration. You can't trust the AI to run on its own. And that's limiting.
PS: I wanna try running this on an AWS Lambda, that would be fun
I write articles with real insight into the career and skills of a modern software engineer. "Raw and honest from the heart!" as one reader described them. Fueled by lessons learned over 20 years of building production code for side-projects, small businesses, and hyper growth startups. Both successful and not.
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