People on reddit claim it's bad at generating images of people, apparently because they removed anything close to nsfw from the training set. Anyone already tried it?
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.
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.
i think its instructive how the ancient sculptors and artists used to have to draw nudes in order to understand the human body. perhaps our image models need this
It isn't just the ancient sculptors and artists (unless 20 years ago is ancient :-P), when i went to an art school there were nude drawing lessons there too.
It's editing capabilities have been really solid, but I've been disappointed with its ability to do finer text details (a niche use case for my own purposes). There's also a lot of reports of body horror outputs, but I haven't encountered any myself.
Still though, it's a lot like the porsche mission S vs the tesla roadster situation with the weights release. Moss Image dropped which looks far better at the moment, leading to a "why Flux?" question in the community.
It kind of blows me away how “unreasonably effective” these small image models seem for their size.
Meanwhile a decent LLM that can do tool use and code and reason tends to weigh in at a minimum of 30B parameters, and you need over 100B to get good performance.
Do LLMs just contain more information or are diffusion models more efficient at compression?
I saw some work on language diffusion models a while back but I think they tend to have problems with reasoning versus transformer type models.
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).
this is the right answer. ultimately text and general llm usage incl coding is far more internally complex than just memorizing the looks and composition of concepts placed in an image. dont forget that most of our reasoning benchmarks basically require the llm to have a search engine and calculator in its own weights.
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.
Very disappointing compared to Flux 2 Klein. I was excited to see what the next frontier of small local image gen might be like, but this model is absolutely atrocious. I can't believe they bothered releasing it.
Flux 2 Klein 9b also had anatomical issues as well, but this one feels practically lobotomized.
https://imgpb.com/wDGGs
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.
This seems like a very competitive price for image models if it can compete on quality, no?
For example, Krea 3 released yesterday starting at $0.06/image.
Still though, it's a lot like the porsche mission S vs the tesla roadster situation with the weights release. Moss Image dropped which looks far better at the moment, leading to a "why Flux?" question in the community.
I still think the edit capabilites are absolutely excellent, but for t2i I'd try out moss: https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base/tree/ma...
I've been VERY impressed with Moss' design capabilites out of the box. Some samples:
https://image.non.io/9a32f732-668e-4fb3-bfef-13f0f40a0972.we...
https://image.non.io/8b5ee315-1ffd-4985-8ff0-d62e19d33f29.we...
https://image.non.io/5130501a-5c07-4635-82f5-fa64d60e8871.we...
IMO the taste is lacking, but it has some of the best text reproductions I've seen with their new VAE. Here's a type ramp example:
https://image.non.io/57246870-a245-4875-983f-993656e09e3d.we...
https://news.ycombinator.com/item?id=50105648
Meanwhile a decent LLM that can do tool use and code and reason tends to weigh in at a minimum of 30B parameters, and you need over 100B to get good performance.
Do LLMs just contain more information or are diffusion models more efficient at compression?
I saw some work on language diffusion models a while back but I think they tend to have problems with reasoning versus transformer type models.
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.
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.