I've been running it through tests on my local machine. The Text-to-Image component is VERY poor. It's not a good sign when the stock ComfyUI Flux3 Klein template with built-in config/prompts produces SD 1.5 levels of half-melted faces and various other body horror.

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

https://imgpb.com/wDGGs

Some examples of the Reddit posts/memes:

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.

Yes, a prime example of when the performance vs "bias" tradeoff is handled anti-business

I've tried it some, only locally. I rarely use APIs, so can't say what the official inference API results look like. It correctly followed some prompts that almost every model I've ever tried performed poorly at, but failed in strange ways on prompts that other models handle with ease.

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.

Lame. Back to Chroma it is then.

There's a base version for LoRA training so a porn adapter is likely already being built.

Looks like the people over /r/SD seem to like MOSS (i'm not up to date on the image stuff) but what i notice is that it is Apache2 licensed when every other openweight model seems to have some weird license (from a quick check Qwen-Image even downright forbids any commercial use at all).

The smaller we go with good models, the more I wonder about tiny animals.

By memorizing the fact that the sun rises in the East and sets in the West, you no longer have to make complex predictions and draw arcs every day. By memorizing the fact that the Earth rotates, and that generally most bodies in solar systems do, you can extrapolate this fact to any planet or solar system.

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.

That's true. What's also true is that it cannot compete performance-wise.

These models are, in a sense, distillations of language models. They wouldn't be as good without a pre-trained language model, so it's unfair to compare them.

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.

To make a rather speculative argument - since an LLM should be able to describer an image losslessly, images are a subset of what an LLM needs to understand. It is possible that the statistical model being learned for images is just simpler.

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).

At first, I considered that diffusion models struggle with reasoning because they underweight 'prior beliefs', ie the propositions by which an argument advances.

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.

They can make good images but they all look the same. Guess it's not hard to replicate a small section of what exists.

There is a story that Stability.AI went under with their trash release of Stable Diffusion 3, and that their better people went on to form and work at Black Forest Labs… apparently “3” is viral infected for these people.