91–100 of 124 posts
Good to see there's some competition to Nano Banana Pro. Other players are important for keeping the price of the leaders in check.
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i may be wrong, but it doesn't seem like BFL is struggling to me. they were apparently founded in august 2024, and have already signed $100M+ revenue deals with customers like meta ( https://www.bloomberg.com/news/articles/2025-09-09/meta-to-p... ) in fact, it seems like BFL has benefited a lot by becoming the go-to alternative for big enterprise customers who don't want to be dependent on google
Wow, I didn't hear about this. That's impressive, and kudos to the team. That's why they raised the massive round, then. But this just leads to more questions - I have to wonder if and for how long this is just going to be to plug in a gap for Meta's own AI product offering. At some point they'll want to build their own in-house models or perhaps just acquire BFL. Zuckerberg would not be printing AI data centers if t…
I wonder if this architectural change makes it easier to use other vision models such as the ones in Llama 3 and 4, or possibly a future Llama 5.
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I heard a possibly unsubstantiated rumor that they had a major failed training run with the video model and canceled the project.
lol, unless I’m wrong, that is not how model development works a ‘major training run’ only becomes major after you sample from it iteratively every few thousand steps, check its good, fix your pipeline, then continue almost by design, major training runs don’t fail if I had to guess, like most labs. they’ve probably had to reallocate more time and energy to their image models than expected since the AI image editing…
If they found that their architecture worked better on static images then it is better to pivot to that than wasting the effort. Especially if you have a trained model that is good at producing static images and bad at generating video.
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Never mind the download size. Who has the VRAM to run it?
I do, 2x Strix Halo machines ready to go.
That's great, and I love the little laptop for the amount of x86 perf it can pack into so little cooling, but my used Epyc box of ~the same price is usually faster for AI (despite the complete lack of video card) and able to load models 3x the size (well, before RAM prices doubled this last month) because it has modular 12 channel RAM and memory speeds this low don't really need a GPU to keep up with the matrix math. Meanwhile, Flux is already slow when it's on actual real high bandwidth dedicated GPU memory VRAM.
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I didn't read "major failed training run" as in "the process crashed and we lost all data" but more like "After spending N weeks on training, we still didn't achieve our target(s)", which could be considered "failing" as well.
They could have done what Lightricks did with LTX-1 - build almost embarrassingly small models in the open and iteratively improve from learning. LTX's first model felt two years behind SOTA when it launched, but they viewed it as a success and kept going. The investment initially is low and can scale with confidence. BFL goes radio silent and then drops stuff. Now they're dropping stuff that is clearly middle of the…
I spent 2 minutes on the website and I still don't know what it is. Generative AI? An image editor?
Updating the GenAI comparison website is starting to feel a bit Sisyphean with all the new models coming out lately, but the results are in for the Flux 2 Pro Editing model! https://genai-showdown.specr.net/image-editing It scored slightly higher than BFL's Kontext model, coming in around the middle of the pack at 6 / 12 points. I’ll also be introducing an additional numerical metric soon, so we can add more nuance t…
It's pretty obvious that OpenAI is terrible at it -- it is known for its unmissable touch. However, for Flux it really depends on the style. They already posted at some point that they changed their training to avoid averaging different styles together, which is the ultimate AI look. But this is at odds with the goal to directly generate images that are visually appealing, so the style matching is going to be a problem for a while, at least.
Genuine question, does anyone use any of these text to image models regularly for non trivial tasks? I am curious to know how they get used. It literally seems like there is a new model reaching the top 3 every week
I use them to generate very niche porn
Updating the GenAI comparison website is starting to feel a bit Sisyphean with all the new models coming out lately, but the results are in for the Flux 2 Pro Editing model! https://genai-showdown.specr.net/image-editing It scored slightly higher than BFL's Kontext model, coming in around the middle of the pack at 6 / 12 points. I’ll also be introducing an additional numerical metric soon, so we can add more nuance t…
Seedream is also very good and makes me think the next version will challenge Google for SOTA image gen
Increasingly feels like image gen is a solved problem