YHK - yet another hacker news clone

https://reddit.com/r/StableDiffusion/comments/1x79gth/why/

https://reddit.com/r/StableDiffusion/comments/1x7ktbu/enshit...

I gave Flux 3 Klein my typical human test that has never failed on newer image models and it did fail; in addition to lower quality, the fingers were definitely not the correct proportions.

Whether the cause is due to more safeguards against NSFW content is hard to confirm technically; it was more an intuition back in the Stable Diffusion XL days but I suspect modern training paradigms are better at correlative inference.

Flux 2 Klein 9b also had anatomical issues as well, but this one feels practically lobotomized.

https://imgpb.com/wDGGs

At high resolutions, structural integrity on people can definitely fall apart like older models, where you get one person turning into a long spaghetti spanning across the frame. It might be that you need to do grounding by generating some kind of scene reference at its stable resolutions first then having it use that for the larger resolution like older models too.

They say that their prompt "upscaler" model or system prompt are essentially necessary for the model, but my results have been hit or miss with that claim. Some basic prompts are better, some upscaled prompts are better. I think that if you gain an intuition for how the model understands things, you might get better results by default by hand crafting prompts specifically for it.

Some of the stylized art I'm getting out of it at higher resolutions are really nice and I prefer them over other models.

All of these models seem to have their own character in terms of system requirements, performance, prompt adherence, artistic outputs and so on. None of the models I have tried are a complete homerun, so maybe in this case the model feels a little inconsistent in some ways, but it's a model I will probably keep around.

This example is a rough comparison to what actually happens, but it does attempt to demonstrate what compression and knowledge are, and how patterns, symmetries, and extrapolation/interpolation can make describing/modeling a complex system much more efficient.

Models can even go a step further, offloading entropy to the input. An efficient model exploits predictable structure; the remaining details can be expensive to compress further. The input can supply whatever additional information is necessary to produce the desired output within some margin of error. The information required depends on the model’s efficiency over a given domain, the structure of that domain, and the required precision of the output.

Consider generating an image of a pebble driveway. Most models today will generate a plausible arrangement of pebbles using their learned distributions. Reproducing a particular arrangement requires the input to convey the details that distinguish it from other plausible arrangements. The more closely you want to reproduce it, the more informative the input needs to be—potentially including a reference image of the arrangement itself.

This certainly raises a lot of open questions that are fun to pursue, such as the smallest possible model that can recover a complete description of our universe from minimal input.

The get a proper LLM they have to model all of human knowledge regarding patterns, physics, biology, social interaction, skills, history, etc, etc. For images they have to recall what a dog looks like in a still photo. The image models aren't even necessarily good at a wide range of novel situations; usually they get used for stock scenes.

Natural evolution has also had a much easier time coming up with vision models (everything with eyeballs) than complex language models (as far as we know only a smallish family of monkeys).

Then I considered whether the universe of imagery is simply smaller than the universe of ideas—which must, itself, include the universe of images, too.

But finally, I'm thinking maybe images are just harder to benchmark.

Or maybe they're _not_ actually worth 1,000 words.

Image models are essentially a mapping of text embedding space -> image. So the clearer and cleaner the text embedding space is the better model you get as you need less weights trying to interpret a text prompt, recent image models even use multimodal versions of LLMs (Flux uses Qwen3 VL) so all the visual separation was done beforehand making the model even more efficient.

edit: Maybe a better analogy is that the image models are only half the model in a encoder-decoder sense, where the LLM is a pretrained frozen encoder and the image model is the decoder.