It’s the most basic of functions for a camera, that when you point it at a scene, it produces a photograph of what it sees. [Jasper van Loenen] has created a camera that does just that, but not perhaps in the way we might expect. Instead of committing pixels to memory it takes a picture, uses AI to generate a text description of what is in the picture, and then uses another AI to generate an image from that picture. It’s a curiously beautiful artwork as well as an ultimate expression of the current obsession with the technology, and we rather like it.
The camera itself is a black box with a simple twin-lens reflex viewfinder. Inside is a Raspberry Pi that takes the photo and sends it through the various AI services, and a Fuji Instax Mini printer. Of particular interest is the connection to the printer which we think may be of interest to quite a few others, he’s reverse engineered the Bluetooth protocols it uses and created Python code allowing easy printing. The images it produces are like so many such AI-generated pieces of content, pretty to look at but otherworldly, and weird parallels of the scenes they represent.
It’s inevitable that consumer cameras will before long offer AI augmentation features for less-competent photographers, meanwhile we’re pleased to see Jasper getting there first.
The media got their collective knickers in a twist this week with the news that Wyoming is banning the sale of electric vehicles in the state. Headlines like that certainly raise eyebrows, which is the intention, of course, but even a quick glance at the proposed legislation might have revealed that the “ban” was nothing more than a non-binding resolution, making this little more than a political stunt. The bill, which would only “encourage” the phase-out of EV sales in the state by 2035, is essentially meaningless, especially since it died in committee before ever coming close to a vote. But it does present a somewhat lengthy list of the authors’ beefs with EVs, which mainly focus on the importance of the fossil fuel industry in Wyoming. It’s all pretty boneheaded, but then again, outright bans on ICE vehicle sales by some arbitrary and unrealistically soon deadline don’t seem too smart either. Couldn’t people just decide what car works best for them?
Speaking of which, a man in neighboring Colorado might have some buyer’s regret when he learned that it would take five days to fully charge his brand-new electric Hummer at home. Granted, he bought the biggest battery pack possible — 250 kWh — and is using a standard 120-volt wall outlet and the stock Hummer charging dongle, which adds one mile (1.6 km) to the vehicle’s range every hour. The owner doesn’t actually seem all that surprised by the results, nor does he seem particularly upset by it; he appears to know enough about the realities of EVs to recognize the need for a Level 2 charger. That entails extra expense, of course, both to procure the charger and to run the 240-volt circuit needed to power it, not to mention paying for the electricity. It’s a problem that will only get worse as more chargers are added to our creaky grid; we’re not sure what the solution is, but we’re pretty sure it’ll be found closer to the engineering end of the spectrum than the political end.
Continue reading “Hackaday Links: January 22, 2023” →
You might not have heard about Stable Diffusion. As of writing this article, it’s less than a few weeks old. Perhaps you’ve heard about it and some of the hubbub around it. It is an AI model that can generate images based on a text prompt or an input image. Why is it important, how do you use it, and why should you care?
This year we have seen several image generation AIs such as Dall-e 2, Imagen, and even Craiyon. Nvidia’s Canvas AI allows someone to create a crude image with various colors representing different elements, such as mountains or water. Canvas can transform it into a beautiful landscape. What makes Stable Diffusion special? For starters, it is open source under the Creative ML OpenRAIL-M license, which is relatively permissive. Additionally, you can run Stable Diffusion (SD) on your computer rather than via the cloud, accessed by a website or API. They recommend a 3xxx series NVIDIA GPU with at least 6GB of RAM to get decent results. But due to its open-source nature, patches and tweaks enable it to be CPU only, AMD powered, or even Mac friendly.
This touches on the more important thing about SD. The community and energy around it. There are dozens of repos with different features, web UIs, and optimizations. People are training new models or fine-tuning models to generate different styles of content better. There are plugins to Photoshop and Krita. Other models are incorporated into the flow, such as image upscaling or face correction. The speed at which this has come into existence is dizzying. Right now, it’s a bit of the wild west. Continue reading “Stable Diffusion And Why It Matters” →
We don’t fully understand the appeal of asking an AI for a picture of a gorilla eating a waffle while wearing headphones. However, [Micael Widell] shows something in a recent video that might be the best use we’ve seen yet of DALL-E 2. Instead of concocting new photos, you can apparently use the same technology for cleaning up your own rotten pictures. You can see his video, below. The part about DALL-E 2 editing is at about the 4:45 mark.
[Nicholas Sherlock] fed the AI a picture of a fuzzy ladybug and asked it to focus the subject. It did. He also fed in some other pictures and asked it to make subtle variations of them. It did a pretty good job of that, too.
Continue reading “AI Image Generation Sharpens Your Bad Photos And Kills Photography?” →