I used to live down the street from a great big data center. It wasn’t a big deal. It’s basically just a building full of servers with extra AC units.

Inside? Loud AF (think: Jet engine. Wear hearing protection).

Outside: The hum of lots of industrial air conditioning units. Only marginally louder than a big office building.

A data center this big is going to have a lot more AC units than normal but they’ll be spread all around the building. It’s not like living next to an airport or busy train tracks (that’s like 100x worse).

Note that there’s more than one model to do pixel art and there’s pixel art LoRAs that do a decent job. There’s loads of flexibility when generating this kind of thing.

Also, you can just tell it to generate a thousand over like 10 minutes and pick the best one and use that as a base to improve upon. AI is just a single tool in the workflow.

I also want to point out that not everyone can just pay someone. Don’t be paternalistic: If people want to use AI in their workflow for any reason that’s their concern. To angrily throw your hands in the air and say, “I’m not touching it because AI!” is like giving free money to the big publishers.

You’re setting a completely unnecessary high bar, “you must be this rich to ride.”

This is my take at well, but not just for gaming… AI is changing the landscape for all sorts of things. For example, if you wanted a serious, professional grammar, consistency, and similar checks of your novel you had to pay thousands of dollars for a professional editor to go over it.

Now you can just paste a single chapter at a time into a FREE AI tool and get all that and more.

Yet here we are: Still seeing grammatical mistakes, copy & paste oversights, and similar in brand new books. It costs nothing! Just use the AI FFS.

Checking a book with an AI chat bot uses up as much power/water as like 1/100th of streaming a YouTube Short. It’s not a big deal.

The Nebula Awards recently banned books that used AI for grammar checking. My take: “OK, so only books from big publishers are allowed, then?”

If you use a pixel art export node in ComfyUI that won’t be a problem. There’s a whole guide about it here:

https://inzaniak.github.io/blog/articles/the-pixel-art-comfyui-workflow-guide.html

Every modern monitor has some memory in it. They have timing controllers and image processing chips that need DRAM to function. Not much, but it is standard DDR3/DDR4 or LPDDR RAM.

No shit. There’s easier ways to open the fridge.

unless you consider every single piece of software or code ever to be just “a way of giving instructions to computers”

Yes. Yes I do. That’s exactly what code is: instructions. That’s literally how computers work. That’s what people like me (software developers) do when we write software: We’re writing down instructions.

When you click or move your mouse, you’re giving the computer instructions (well, the driver is). When you type a key, that’s resulting in an instruction being executed (dozens to thousands, actually).

When I click “submit” on this comment, I’m giving a whole bunch of computers some instructions.

Insert meme of, “you mean computers are just running instructions?” “Always have been.”

In Kadrey v. Meta (court case) a group of authors sued Meta/Anthropic for copyright infringement but the case was thrown out by the judge because they couldn’t actually produce any evidence of infringement beyond, “Look! This passage is similar.” They asked for more time so they could keep trying thousands (millions?) of different prompts until they finally got one that matched enough that they might have some real evidence.

In Getty Images v. Stability AI (UK), the court threw out the case for the same reason: It was determined that even though it was possible to generate an image similar to something owned by Getty, that didn’t meet the legal definition of infringement.

Basically, the courts ruled in both cases, “AI models are not just lossy/lousy compression.”

IMHO: What we really need a ruling on is, “who is responsible?” When an AI model does output something that violate someone’s copyright, is it the owner/creator of the model that’s at fault or the person that instructed it to do so? Even then, does generating something for an individual even count as “distribution” under the law? I mean, I don’t think it does because to me that’s just like using a copier to copy a book. Anyone can do that (legally) for any book they own, but if they start selling/distributing that copy, then they’re violating copyright.

Even then, there’s differences between distributing an AI model that people can use on their PCs (like Stable Diffusion) VS using an AI service to do the same thing. Just because the model can be used for infringement should be meaningless because anything (e.g. a computer, Photoshop, etc) can be used for infringement. The actual act of infringement needs to be something someone does by distributing the work.

You know what? Copyright law is way too fucking complicated, LOL!

Hmmm… That’s all an interesting argument but it has nothing to do with my comparison to YouTube/Netflix (or any other kind of video) streaming.

If we were to compare a heavy user of ChatGPT to a teenager that spends a lot of time streaming videos, the ChatGPT side of the equation wouldn’t even amount to 1% of the power/water used by streaming. In fact, if you add up all the usage of all the popular AI services power/water usage that still doesn’t add up to much compared to video streaming.

Sell? Only “big AI” is selling it. Generative AI has infinite uses beyond ChatGPT, Claude, Gemini, etc.

Most genrative AI research/improvement is academic in nature and it’s being developed by a bunch of poor college students trying to earn graduate degrees. The discoveries of those people are being used by big AI to improve their services.

You seem to be making some argument from the standpoint that “AI” == “big AI” but this is not the case. Research and improvements will continue regardless of whether or not ChatGPT, Claude, etc continue to exist. Especially image AI where free, open source models are superior to the commercial products.

but we can reasonably assume that Stable Diffusion can render the image on the right partly because it has stored visual elements from the image on the left.

No, you cannot reasonably assume that. It absolutely did not store the visual elements. What it did, was store some floating point values related to some keywords that the source image had pre-classified. When training, it will increase or decrease those floating point values a small amount when it encounters further images that use those same keywords.

What the examples demonstrate is a lack of diversity in the training set for those very specific keywords. There’s a reason why they chose Stable Diffusion 1.4 and not Stable Diffusion 2.0 (or later versions)… Because they drastically improved the model after that. These sorts of problems (with not-diverse-enough training data) are considered flaws by the very AI researchers creating the models. It’s exactly the type of thing they don’t want to happen!

The article seems to be implying that this is a common problem that happens constantly and that the companies creating these AI models just don’t give a fuck. This is false. It’s flaws like this that leave your model open to attack (and letting competitors figure out your weights; not that it matters with Stable Diffusion since that version is open source), not just copyright lawsuits!

Here’s the part I don’t get: Clearly nobody is distributing copyrighted images by asking AI to do its best to recreate them. When you do this, you end up with severely shitty hack images that nobody wants to look at. Basically, if no one is actually using these images except to say, “aha! My academic research uncovered this tiny flaw in your model that represents an obscure area of AI research!” why TF should anyone care?

They shouldn’t! The only reason why articles like this get any attention at all is because it’s rage bait for AI haters. People who severely hate generative AI will grasp at anything to justify their position. Why? I don’t get it. If you don’t like it, just say you don’t like it! Why do you need to point to absolutely, ridiculously obscure shit like finding a flaw in Stable Diffusion 1.4 (from years ago, before 99% of the world had even heard of generative image AI)?

Generative AI is just the latest way of giving instructions to computers. That’s it! That’s all it is.

Nobody gave a shit about this kind of thing when Star Trek was pretending to do generative AI in the Holodeck. Now that we’ve got he pre-alpha version of that very thing, a lot of extremely vocal haters are freaking TF out.

Do you want the cool shit from Star Trek’s imaginary future or not? This is literally what computer scientists have been dreaming of for decades. It’s here! Have some fun with it!

Generative AI uses up less power/water than streaming YouTube or Netflix (yes, it’s true). So if you’re about to say it’s bad for the environment, I expect you’re just as vocal about streaming video, yeah?

Correction: Newer versions of ChatGPT (GPT-5.x) are failing in insidious ways. The article has no mention of the other popular services or the dozens of open source coding assist AI models (e.g. Qwen, gpt-oss, etc).

The open source stuff is amazing and gets better just as quickly as the big AI options. Yet they’re boring so they don’t make the news.

Well, the CSAM stuff is unforgivable but I seriously doubt even the soulless demon that is Elon Musk wants his AI tool generating that. I’m sure they’re working on it (it’s actually a hard computer science sort of problem because the tool is supposed to generate what the user asks for and there’s always going to be an infinite number of ways to trick it since LLMs aren’t actually intelligent).

Porn itself is not illegal.

I don’t know, man… Have you even seen Amber? It might be worth an alert 🤷

I don’t know how to tell you this but… Every body gives a shit. We’re born shitters.

The real problem here is that Xitter isn’t supposed to be a porn site (even though it’s hosted loads of porn since before Musk bought it). They basically deeply integrated a porn generator into their very publicly-accessible “short text posts” website. Anyone can ask it to generate porn inside of any post and it’ll happily do so.

It’s like showing up at Walmart and seeing everyone naked (and many fucking), all over the store. That’s not why you’re there (though: Why TF are you still using that shithole of a site‽).

The solution is simple: Everyone everywhere needs to classify Xitter as a porn site. It’ll get blocked by businesses and schools and the world will be a better place.

“To solve this puzzle, you have to get your dog to poop in the circle…”

Yep. Stadia also had a feature like this (that no one ever used).

Just another example of why software patents should not exist.

No, a .safetensors file is not a database. You can’t query a .safetensors file and there’s nothing like ACID compliance (it’s read-only).

Imagine a JSON file that has only keys and values in it where both the keys and the values are floating point numbers. It’s basically gibberish until you go through an inference process and start feeding random numbers through it (over and over again, whittling it all down until you get a result that matches the prompt to a specified degree).

How do the “turbo” models work to get a great result after one step? I have no idea. That’s like black magic to me haha.

Or, with AI image gen, it knows that when some one asks it for an image of a hand holding a pencil, it looks at all the artwork in it’s training database and says, “this collection of pixels is probably what they want”.

This is incorrect. Generative image models don’t contain databases of artwork. If they did, they would be the most amazing fucking compression technology, ever.

As an example model, FLUX.dev is 23.8GB:

https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main

It’s a general-use model that can generate basically anything you want. It’s not perfect and it’s not the latest & greatest AI image generation model, but it’s a great example because anyone can download it and run it locally on their own PC (and get vastly superior results than ChatGPT’s DALL-E model).

If you examine the data inside the model, you’ll see a bunch of metadata headers and then an enormous array of arrays of floating point values. Stuff like, [0.01645, 0.67235, ...]. That is what a generative image AI model uses to make images. There’s no database to speak of.

When training an image model, you need to download millions upon millions of public images from the Internet and run them through their paces against an actual database like ImageNET. ImageNET contains lots of metadata about millions of images such as their URL, bounding boxes around parts of the image, and keywords associated with those bounding boxes.

The training is mostly a linear process. So the images never really get loaded into an database, they just get read along with their metadata into a GPU where it performs some Machine Learning stuff to generate some arrays of floating point values. Those values ultimately will end up in the model file.

It’s actually a lot more complicated than that (there’s pretraining steps and classifiers and verification/safety stuff and more) but that’s the gist of it.

I see soooo many people who think image AI generation is literally pulling pixels out of existing images but that’s not how it works at all. It’s not even remotely how it works.

When an image model is being trained, any given image might modify one of those floating point values by like ±0.01. That’s it. That’s all it does when it trains on a specific image.

I often rant about where this process goes wrong and how it can result in images that look way too much like some specific images in training data but that’s a flaw, not a feature. It’s something that every image model has to deal with and will improve over time.

At the heart of every AI image generation is a random number generator. Sometimes you’ll get something similar to an original work. Especially if you generate thousands and thousands of images. That doesn’t mean the model itself was engineered to do that. Also: A lot of that kind of problem happens in the inference step but that’s a really complicated topic…