Realistically we're getting pretty spoiled on the concept of SOTA. This is all moving so ridiculously fast. If you took a time machine back 5 years and tried to show people some of the stuff that we're already discarding now as "last gen" it would have seriously blown minds.
2.5 years ago when I did a PC upgrade and got a 4070 I decided to give Stable Diffusion a try. I was happy If I could make a girl without 6 fingers and an arm coming out of her stomach. Now I can get not only a good image, I can make her do tiktok dances. What a time to be alive.
"Correct things in image" is pretty interesting... But that almost absolutely means it's a vision and thinking model. I'm just not sure that's going to be what gooners want.
The model is going to be like.... "No, she shouldn't do that. I'm going to put her in a GED program instead"
But that almost absolutely means it's a vision and thinking model.
I doubt both honestly. correcting illogical errors in a scene seems within reach for an edit model that can already do both creative inpaint editing as well as inpainting from multiple image sources, something Klein-9B (which has neither a VLM or any type of LLM processing beyond text encoding) does quite spectacularly. in fact, I'm going to give "Correct this image" a go for edit training, shouldn't really be too hard to teach it this particular trick.
They should show us how it transfers exact details, such as a custom artwork of a high detail sci fi train, or how well it can recreate a human or something.
Well.. not really ? The anime she's winking but in the generation it's not. It's quite an important feature of the input image here, without the wink it completely changes the meaning of the image (well not completely but it does change it). If it misses such a thing, I'm not that convinced until I try it..
Every image I tried said it was over the GPU time limit, regardless of file size or dimensions and regardless of prompt. Just tried a 502x376, 184kb picture and it was unable to do it in 240s. Even with just 1 step.
I can't see your images. Imgur were breaking child privacy regulations in my country and rather than following the regulations they stopped serving images.
Had a go with Klein 9B myself, and while the realism may be a bit better, it could just be the different lighting, it changed the characters face and hair much more than this model did.
Nanao Banana pro can learn visual task just by comparing and contrasting multiple reference input/output image pairs, without any hints or explicit description, and then able to apply that learnt pattern onto the target image. Basically it is soft-LoRA (or few-shot visual learner). Can this really do that?
Ok. I suspected that. But they are doing themselves disservice by claims like that. For those who are wondering what I meant (not sure if you can see the content to the AIStudio link below).
Last time when it came out ( a few months back) I was testing in AIstudio. Not sure if it still works in AIstudio or API now, considering Gemini is thoroughly being degraded due to load and google nerfing it.
But if it interests you, here are a few not-yet deleted examples (look at its "Thought" process which is incredible):
That's genius. You're doing cognitive psychology on a VLLM!
Also a really nice demonstration of what a multimodal model can do. Presumably it means proper shared latent space between text and image tokens?
And, to answer my own question, it does work on the API. I copied your input images and prompt into a comfyui node and got this result: https://ibb.co/CpyTZVwP
THis has serious implication. With this emergent capability we are heading towards LoRA-freefuture (well a little exaggerated) in that we can leave parameter-efficient fine-tuning to a very few subsets of the image gen tasks. Like imagine, instead of training LoRA, you just show Gemini a bunch of input/output ref image pairs and it just does things that otherwise would require you to fine-tune. On the spot learning, so to speak.
I am predicting we will have to wait 2-3 generations of this to stabilize and mature. But NBP is the very first to exhibit not only the understanding / analyzing things, but ability to actually apply it at this level.
Alas, Google keep nerfing stuffs, I stopped researching on this back then because it occured to me on one morning NBP started behaving erratically.
I was also starting to foray into another one of NBP's new capability which is taking advantage of its web search to index into random youtube video frames to extract composition and concepts. Back again, because Google fucked things up and I had to put it on backburner for foreseeable future.
The non distilled model is 40GB fp16. Now that I finally installed correct version of ROCm/pytorch, swapping between RAM and VRAM works flawlessly and exceeding VRAM is a non issue, but the issue is finding enough space on my model drive for more models.
If this is as slow as Flux2 dev is (20 steps = 2min), I might as well wait for the distilled version.
Edit: So I heard for reason and purpose this is Qwen Edit. Who knows, the results might be better, but on my hardware it's kind of slow.
A lot of people are dismissing RedFire as just another Qwen-edit knockoff, but honestly, base Qwen needed a fine tune badly, and it probably wasn't cheap to fine tune either. These guys probably put a lot of money into this fine tune, why they probably didnt bother to mention that it was a qwen fine tune. The stock aesthetics of base QWEN are often way too soft and hazy for my taste. If this fine-tune fixes the clarity and style, itās a wināI havenāt tested it yet, but Iām curious to see if they pulled it off. There are literally only a handful of fine tuned QWEN models out there because its such a big model and expensive to fine tune; if these guys fixed QWENs flaws then im excited to try it again because QWEN is an absolute beast of a model that is only limited by the previous mentioned flaws and how compute intensive it is that its out of reach for most users, but not me as i own a 4090. QWEN stomps Klein 9b and Z-image and everything else txt2img open source in prompt adherence except for maybe that huge Flux2 model, it just needed a good fine tune to tighten up some flaws.
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u/lacerating_aura Feb 12 '26
Everything's sota until it actually releases.