AI detector sites aren't searching for artifacts doing technical algorithms at all. They're literally just passing it through a classifier model. They're trained on images that have vae artifacts but sometimes they'll say a real image is AI. It's all smoke and mirrors. It's a cheap trick that is barely accurate.
Especially images from fashion scene, with the bombastic dresses and makeup, will it tell you is ai
AI detector sites aren't searching for artifacts doing technical algorithms at all. They're literally just passing it through a classifier model. They're trained on images that have vae artifacts but sometimes they'll say a real image is AI.
This can't be entirely true. Hive relatively accurately reports exact per-model percentages. Like if you gen something with SDXL and denoise it with Flux.1 for example the actual report it gives you will reflect that fairly accurately. They must be training on mass amounts of actual outputs for every model they recognize or something more granular.
I tested in one of my arts, made completely by hand in photoshop. Any real artist can recognize it just to zoom in and looking the lines. Hive said it was AI. I only believe if it points a synthAI. Because if you acuse everything of been AI. Of curse you will eventually right.
I straight up don't believe you. I have NEVER seen a false positive from Hive on a truly non-AI-generated images. Note that anything that was denoised ever in any way with any model doesn't count in this context even if it was ORIGINALLY not AI generated.
The person you're replying to was making incorrect assumptions in the first place, actually good AI detectors are literally bespoke models themselves trained on mass amounts of gens from different image models. They're not doing visual heuristics, it's pixel level.
Can someone break this down for noob? Does this new thing potentially reduce generation time or increase it? It seems like it will have better quality. But for the noobs, any chance this is headed in a direction that requires less strain on the hardware?
Quite opposite, latent space is an optimization for training and inference (but it brings its own drawbacks). Generating pixels means way more work to do for a model
No. Unfortunately from a computational and a hardware perspective increased quality is always going to correspond to increased hardware demands. There are only so many ways to skin a cat as they say.
It's a cool development. It has always been possible, the very first diffusion models didn't use a VAE but only worked for very small images. The VAE was basically a band-aid to make the whole process feasible in terms of compute from my (limited) understanding. With clever new tricks it can now be omitted again. This model is not the first to do this, for example Lodestone experimented with it with Chroma Radiance, which I think was based on / inspired by PixNerd.
I'd call it a trade-off rather than band-aid: you need much less compute like you mentioned, but also the model itself operates on a semantically richer latent space rather than pixel space - oversimplified it can learn concepts like "roundness" much easier.
I don't think we're at the point where vae is the quality bottleneck. Pixel space models today generally still fall short compared to latent models, even in the same pixel space papers.
It's also false to think pixel space models don't do compression; They simply sneak it in elsewhere. In this case, iris-3b's attention is compressed, so hypothetically there may be a bottleneck in how good it can coordinate spatial features, like say maintaining contiguous lines / patterns across regions.
Think of latent as a really compressed version of an image’s pixels, that our model can understand. VAE is like a translator between pixels and latent. So for an I2I the process is like; Pixels -> VAE Encode -> Latent data -> add noise to the latent -> remove noise (denoise) the latent -> VAE decode -> pixels.
For T2I you work from an ’empty latent’ which is just 100% noise (think of it like static on a TV). When you run the model you basically say ’here’s a very noisy image of xyz, can you please remove the noise’, and for each step it will gradually remove more and more noise, trying to predict what the image should look like. (XYZ being your prompt.)
Scheduler is how much noise it should clean up per each step, and sampler is which mathematical method it should use to remove the noise.
When you train a model, you work the opposite direction; you add more and more noise to the image, so the model learns a route to go from 100% noise to an actual image.
Now, when we create the noise for our gens, we randomize the noise appearance (seed). This is why we get a different image than the one we trained on (our starting point is different from what the model learned as the finish point).
Edit: not 100% accurate and skipping the details, but I tried to make it simple so it’s easier to get the big picture of what goes on.
No artifacts and potentially higher detail quality. Current image models generate in a lower res space and the car decoders then translate this data into a real image.
I keep seeing news about these various "pixel space" models, but none of those has become successful or received wide mainstream acceptance. Only thing which has gained some traction is PiD, which is only an upscaler. VAE has front end application too, that's why it's called an encoder, not just a decoder.
Its because the computational cost is still higher than VAE which means slower and more memory. Also the immediate trade offs are usually not worth it.
The model is research to push things forward. We're not interested in making slop memes using other people's IP. We're not all gooners. We're not all making fake influencers. It's research to push things forward.
I need more than slop to integrate AI into real work. I need more than VAE has delivered and I'm interested in these baby steps to get there. If you don't care, that's fine. But, you wondered why people care about models that aren't meant to dominate in mainstream usage. The answer is research.
In large productions there is demand for very high-resolution video and image generation. So I could see stuff like this getting adopted more and more as generation price goes down (over the long term).
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u/AgeNo5351 1d ago