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!
Note: It should be called out that BFL seems to support a more formalized JSON structure for more granular edits so I'm wondering if accuracy would improve using it.
The comparison are very useful but also quite limited in terms of styles. Models tend to have extremely diverse abilities in following a given style against steering to its own.
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.
Style is mostly irrelevant for editing, since the goal is to integrate seamlessly with the existing image. The focus is on performing relatively surgical edits or modifications to existing imagery while minimizing changes to the rest of the image. It is also primarily concerned with realism, though there are some illustrative examples (the JAWS poster, Great Wave off Kanagawa).
This contrasts with the generative section though even then the emphasis is on prompt adherence, and style/fidelity take a backseat (which is honestly what 99% of existing generative benchmarks already focus on).
How much energy does BFL have to keep playing this game against Google and ByteDance (SeeDream)?
If their new fancy model is only middle of the pack, and they're not as open source as the Chinese Qwen image models (or ByteDance / Alibaba / Lightricks video models), what's the point?
It's not just prompt adherence, the image quality of Flux models has been pretty bad. Plastic skin, inhumanely chiseled chins, that general faux "AI" aura.
Indeed, the Flux samples in your test suite that "pass" look God-awful. It might "pass" from a technical standpoint, but there's no way I'd choose Flux to solve my workflows. It looks bad.
(I wonder if they lack people on their data team with good aesthetic taste. It may be as simple as that.)
I think this company is struggling. They're pinned between Google and the Chinese. It's a tough, unenviable spot to be in.
I think a lot of the foundation model companies in media are having a really hard time: RunwayML, PikaLabs, LumaLabs. Some of them have pivoted hard away from solving media for everyone. I don't think they can beat the deep-pocketed hyperscalers or the Chinese ecosystem.
BFL just raised a massive round, so what do I know? I just can't help but feel that even though Runway raised similar money, they're struggling really hard now. And I would really not want to be fighting against Google who is already ahead in the game.
Sadly, I tend to agree. I'm rooting for BFL, but the results from this latest model (the Pro version, of all things) have just been a bit disappointing. Google’s release of NB Pro last week certainly didn’t help either, since it set the bar so incredibly high.
Flux 2 Pro only scored a single point higher than the Kontext models they released over half a year ago.
The text-to-image side was even more frustrating. It often felt like it was actively fighting me, as evidenced by the high number of re-rolls required before it passed some of the tests (Cubed⁵, for example).
I think the margin isn't that large to be honest. If we compare available resources and data it is quite tiny and perhaps should be larger.
Also it doesn't feel solved to me at all. There is no general model, perhaps it cannot reasonably exist. I think these tests are benchmarks are smart, but they don't show the whole picture.
Domain specific image generation tasks still require a domain specific models. For art purposes SD1.5 with specialized and finely tuned checkpoints will still provide the best results by far. It is also limited, but I think it dampened the hype for new image generators significantly.
Prompt understanding will only ever be as good as the language embeddings that are fed into the model’s input. Google’s hardware can host massive models that will never be run on your desktop GPU. By contrast, Flux and its kin have to make do with relatively tiny LLMs (Qwen Image uses a 7B-param LLM).
Hey I hope you see this. The scoring needs to be a 0-10 or something with a range rather than pass or fail. Flux one getting the same score for the surfer as Gemini pro 3 reduces the quality of the benchmark.
Hi bn-l, yeah as mentioned above and in the Release Notes - we'll be adding a more nuanced numerical score in the next week.
I don't know if I'm going to get as granular as 1-10 only because the finer the scoring - the more potential for subjectivity. That's why it was initially set up as a "Minimum Passing Criteria Rule Set" along with a Pass/Fail grade.
A suggestion from a previous HN post was something along the lines of (0 Fail, 0.5 Technical Pass, 1.0 Proficient Pass).
Great, especially that they still have an open-weight variant of this new model too.
But what happened to their work on their unreleased SOTA video model? did it stop being SOTA, others got ahead, and they folded the project, or what?
YT video about it: https://youtu.be/svIHNnM1Pa0?t=208
They even removed the page of that: https://bfl.ai/up-next/
Image models are more fundamentally important at this stage than video models.
Almost all of the control in image-to-video comes through an image. And image models still needs a lot of work and innovation.
On a real physical movie set, think about all of the work that goes into setting the stage. The set dec, the makeup, the lighting, the framing, the blocking. All the work before calling "action". That's what image models do and must do in the starting frame.
We can get way more influence out of manipulating images than video. There are lots of great video models and it's highly competitive. We still have so much need on the image side.
When you do image-to-video, yes you control evolution over time. But the direction is actually lower in terms of degrees of freedom. You expect your actors or explosions to do certain reasonable things. But those 1024x1024xRGB pixels (or higher) have way more degrees of freedom.
Image models have more control surface area. You exercise control over more parameters. In video, staying on rails or certain evolutionary paths is fine. Mistakes can not just be okay, they can be welcome.
It also makes sense that most of the work and iteration goes into generating images. It's a faster workflow with more immediate feedback and productivity. Video is expensive and takes much longer. Images are where the designer or director can influence more of the outcomes with rapidity.
Image models still need way more stylistic control, pose control (not just ControlNets for limbs, but facial expressions, eyebrows, hair - everything), sets, props, consistent characters and locations and outfits. Text layout, fonts, kerning, logos, design elements, ...
We still don't have models that look as good as Midjourney. Midjourney is 100x more beautiful than anything else - it's like a magazine photoshoot or dreamy Instagram feed. But it has the most lackluster and awful control of any model. It's a 2021-era model with 2030-level aesthetics. You can't place anything where you want it, you can't reuse elements, you can't have consistent sets... But it looks amazing. Flux looks like plastic, Imagen looks cartoony, and OpenAI GPT Image looks sepia and stuck in the 90's. These models need to compete on aesthetics and control and reproducibility.
That's a lot of work. Video is a distraction from this work.
Hot take: text-to-image models should be biased toward photorealism. This is because if I type in "a cat playing piano", I want to see something that looks like a 100% real cat playing a 100% real piano. Because, unless specified otherwise, a "cat" is trivially something that looks like an actual cat. And a real cat looks photorealistic. Not like a painting, or cartoon, or 3D render, or some fake almost-realistic-but-cleary-wrong "AI style".
As a startup, they pivoted and focused on image models (they are model providers, and image models often have more use cases than video models, not to mention they continue to have bigger image dataset moat, not video).
If they have so much data, then why do Flux model outputs look so God-awful bad?
They have plastic skin, weird chins, and have that "AI" aura. Not the good AI aura, mind you. The cheap automated YouTube video kind that you immediately skip.
Flux 2 seems to suffer from the exact same problems.
Midjourney is ancient. Their CEO is off trying to build a 3D volume and dating companion or some nonsense and leaving the product without guidance and much change. It almost feels abandoned. But even so, Midjourney has 10,000x better aesthetics despite having terrible prompt adherence and control. Midjourney images are dripping with magazine spread or Pulitzer aesthetics. It's why Zuckerberg went to them to license their model instead of quasi "open source" BFL.
Even SDXL looks better, and that's a literal dinosaur.
Most of the amazing things you see on social media either come from Midjourney or SDXL. To this day.
Makes no sense since they should have checkpoints earlier in the run that they could restart from and they should have regular checks that keep track if a model has exploded etc.
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 market has exploded in size this year, and will do video later
I just finished my Flux 2 testing (focusing on the Pro variant here: https://replicate.com/black-forest-labs/flux-2-pro). Overall, it's a tough sell to use Flux 2 over Nano Banana for the same use cases, but even if Nano Banana didn't exist it's only an iterative improvement over Flux 1.1 Pro.
Some notes:
- Running my nuanced Nano Banana prompts though Flux 2, Flux 2 definitely has better prompt adherence than Flux 1.1, but in all cases the image quality was worse/more obviously AI generated.
- The prompting guide for Flux 2 (https://docs.bfl.ai/guides/prompting_guide_flux2) encourages JSON prompting by default, which is new for an image generation model that has the text encoder to support it. It also encourages hex color prompting, which I've verified works.
- The Flux 2 API will flag anything tangently related to IP as sensentive even at its lowest sensitivity level, which is different from Flux 1.1 API. If you enable prompt upsampling, it won't get flagged, but the results are...unexpected. https://x.com/minimaxir/status/1993365968605864010
- Costwise and generation-speed-wise, Flux 2 Pro is on par with Nano Banana, and adding an image as an input pushes the cost of Flux 2 Pro higher than Nano Banana. The cost discrepancy increases if you try to utilize the advertised multi-image reference feature.
- Testing Flux 1.1 vs. Flux 2 generations does not result in objective winners, particularly around more abstract generations.
The fact that you have the possibility of running Flux locally might be enough of an argument to sway the balance for some cases. For example, if you've already set up a workflow and Google jacks up the price, or changes the API, you have no choice but to go along. If BFL does the same, you at least have the option of running locally.
Those cases imply commercial workflows that are prohibited with the open-weights model without purchasing a license.
I am curious to see how the Apache 2.0 distilled variant performs but it's still unlikely that the economics will favor it unless you have a specific niche use case: the engineering effort needed to scale up image inference for these large models isn't zero cost.
You can run Alibaba's Qwen(Edit) locally too, and the company isn't as weird with its license, weights, or training set.
I personally prefer Qwen's performance here. I'm waiting to see other folks' takes.
The Qwen folks are also a lot more transparent, spend time community building, and iterate on releases much more rapidly. In the open rather than behind closed doors.
I've re-run my benchmark with the Flux 2 Pro model and found that in some cases the higher resolution models (I believe Flux 2 Pro handles 4k) can actually backfire on some of the tests because it'll introduce the equivalent of an almost ESRGAN style upscale which may add in unwanted additional details. (See the Constanza test in particular).
Do you have generations contradicting that? The HF repo for the open-weights Flux 2 Dev says that IP filters are in place (and imply it's a violation of the license to do as such)
EDIT: Seeing a few generations on /r/StableDiffusion generating IP from the open weights model.
> Run FLUX.2 [dev] on GeForce RTX GPUs for local experimentation with an optimized fp8 reference implementation of FLUX.2 [dev], created in collaboration with NVIDIA and ComfyUI.
Glad to see that they're sticking with open weights.
That said, Flux 1.x was 12B params, right? So this is about 3x as large plus a 24B text encoder (unless I'm misunderstanding), so it might be a significant challenge for local use. I'll be looking forward to the distill version.
Looking at the file sizes on the open weights version (https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/mai...), the 24B text encoder is 48GB, the generation model itself is 64GB, which roughly tracks with it being the 32B parameters mentioned.
Downloading over 100GB of model weights is a tough sell for the local-only hobbyists.
100 GB is less than a game download, it's actually running it that's a tough sell. That said, the linked blog post seems to say the optimized model is both smaller and greatly improved the streaming approach from system RAM, so maybe it is actually reasonably usable on a single 4090/5090 type setup (I'm not at home to test).
Text encoder is Mistral-Small-3.2-24B-Instruct-2506 (which is multimodal) as opposed to the weird choice to use CLIP and T5 in the original FLUX, so that's a good start albeit kinda big for a model intended to be open weight. BFL likely should have held off the release until their Apache 2.0 distilled model was released in order to better differentiate from Nano Banana/Nano Banana Pro.
The pricing structure on the Pro variant is...weird:
> Input: We charge $0.015 for each megapixel on the input (i.e. reference images for editing)
> Output: The first megapixel is charged $0.03 and then each subsequent MP will be charged $0.015
> BFL likely should have held off the release until their Apache 2.0 distilled model was released in order to better differentiate from Nano Banana/Nano Banana Pro.
Qwen-Image-Edit-2511 is going to be released next week. And it will be Apache 2.0 licensed. I suspect that was one of the factors in the decision to release FLUX.2 this week.
Considering how little (and sometimes negative) benefit it provided in most of them compared to just using the biggest encoder model and having a null prompt on the rest (not just those using the specific combination Flux.1 did, but for most of the multi-encoder models), its actually pretty weird that people kept doing it.
> as opposed to the weird choice to use CLIP and T5 in the original FLUX
Yeah, CLIP here was essentially useless. You can even completely zero the weights through which the CLIP input is ingested by the model and it barely changes anything.
Nice catch. Looks like engineers tried to take care of the GTM part as well and (surprise!) messed it up. In any case, the biggest loser here is Europe once again.
It's nice as well for location that are banned to use private US models. Like here in Hong Kong, Google doesn't allow us to subscribe to Gemini Pro. (Same for OpenAI and Claude too actually).
I ran "family guy themed cyberpunk 2077 ingame screenshot, peter griffin as main character, third person view, view of character from the back" on both nano banana pro and bfl flux 2 pro. The results were staggering. The google model aligned better with the cyberpunk ingame scene, flux was too "realistic"
i think they focus their dataset on photography. flux 1 dev one was never really great at artistic style, mostly locking you into a somewhat generic style. my little flux 2 pro testing does seem to verify that. but with lora ecosystem and enough time to fiddle flux 1 dev is probably still the best if you want creative stylistic results.
Wow, the Krea relationship soured? These are both a16z companies and they've worked on private model development before. Krea.1 was supposed to be something to compete with Midjourney aesthetics and get away from the plastic-y Flux models with artificial skin tones, weird chins, etc.
This list of partners includes all of Krea's competitors: HiggsField (current aggregator leader), Freepik, "Open"Art, ElevenLabs (which now has an aggregator product), Leonardo.ai, Lightricks, etc. but Krea is absent. Really strange omission.
The model looks good for an open source model. I want to see how these models are trained. may be they have a base model from academic datasets and quickly fine-tune with models like nano banana pro or something? That could be the game for such models. But great to see an open source model competing with the big players.
great this is more on the techincal details. it is great but would be great to see the data. I know they will not expose such information but would be great to have a visibility onto the datasets and how the data was sourced.
Their published benchmarks leave a lot to be desired. I would be interested in seeing their multi-image performance vs. Nano Banana. I just finished up benchmarking Image Editing models and while Nano Banana is the clear winner for one-shot editing its not great at few-shot.
The issue with testing multi-image with Flux is that it's expensive due to its pricing scheme ($0.015 per input image for Flux 2 Pro, $0.06 per input image for Flux 2 Flex: https://bfl.ai/pricing?category=flux.2) while the cost of adding additional images is neligible in Nano Banana ($0.000387 per image).
In the case of Flux 2 Pro, adding just one image increases the total cost to be greater than a Nano Banana generation.
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'm not really familiar with image generators.) Would you care to share how well that works? Given the heavy censorship attitudes, I wouldn't expect that to be easy.
You can run it with a 5090 and the standard ComfyUI template, it just offloads some parts to RAM. Image generation takes about a minute for sizes like 1024x1024.
There is no reason to believe Gemini Image is not diffusion model. In fact, generated result suggests it at least have VAE and very likely is a diffusion model variant. (Most likely a transfusion model).
Oh, looks like someone had to release something very quickly after Google came for their lunch. Their little 15 mins is over already for BFL as it seems.
yeah except I can download this and run it on my computer, whereas Nano Banana is a service that Google will suddenly discontinue the instant they get bored with it
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 to how we evaluate model quality as they continue to improve.
If you're solely interested in seeing how Flux 2 Pro stacks up against the Nano Banana Pro, and another Black Forest model (Kontext), see here:
https://genai-showdown.specr.net/image-editing?models=km,nbp...
Note: It should be called out that BFL seems to support a more formalized JSON structure for more granular edits so I'm wondering if accuracy would improve using it.
The comparison are very useful but also quite limited in terms of styles. Models tend to have extremely diverse abilities in following a given style against steering to its own.
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.
The site is broken up into "Editing Comparison" and a "Generative Comparison" sections.
Generative: https://genai-showdown.specr.net
Editing: https://genai-showdown.specr.net/image-editing
Style is mostly irrelevant for editing, since the goal is to integrate seamlessly with the existing image. The focus is on performing relatively surgical edits or modifications to existing imagery while minimizing changes to the rest of the image. It is also primarily concerned with realism, though there are some illustrative examples (the JAWS poster, Great Wave off Kanagawa).
This contrasts with the generative section though even then the emphasis is on prompt adherence, and style/fidelity take a backseat (which is honestly what 99% of existing generative benchmarks already focus on).
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How much energy does BFL have to keep playing this game against Google and ByteDance (SeeDream)?
If their new fancy model is only middle of the pack, and they're not as open source as the Chinese Qwen image models (or ByteDance / Alibaba / Lightricks video models), what's the point?
It's not just prompt adherence, the image quality of Flux models has been pretty bad. Plastic skin, inhumanely chiseled chins, that general faux "AI" aura.
Indeed, the Flux samples in your test suite that "pass" look God-awful. It might "pass" from a technical standpoint, but there's no way I'd choose Flux to solve my workflows. It looks bad.
(I wonder if they lack people on their data team with good aesthetic taste. It may be as simple as that.)
I think this company is struggling. They're pinned between Google and the Chinese. It's a tough, unenviable spot to be in.
I think a lot of the foundation model companies in media are having a really hard time: RunwayML, PikaLabs, LumaLabs. Some of them have pivoted hard away from solving media for everyone. I don't think they can beat the deep-pocketed hyperscalers or the Chinese ecosystem.
BFL just raised a massive round, so what do I know? I just can't help but feel that even though Runway raised similar money, they're struggling really hard now. And I would really not want to be fighting against Google who is already ahead in the game.
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
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Sadly, I tend to agree. I'm rooting for BFL, but the results from this latest model (the Pro version, of all things) have just been a bit disappointing. Google’s release of NB Pro last week certainly didn’t help either, since it set the bar so incredibly high.
Flux 2 Pro only scored a single point higher than the Kontext models they released over half a year ago.
The text-to-image side was even more frustrating. It often felt like it was actively fighting me, as evidenced by the high number of re-rolls required before it passed some of the tests (Cubed⁵, for example).
> starting to feel a bit Sisyphean with all the new models coming out lately
You jinxed yourself: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
Clearly Google is winning this by some margin
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
I think the margin isn't that large to be honest. If we compare available resources and data it is quite tiny and perhaps should be larger.
Also it doesn't feel solved to me at all. There is no general model, perhaps it cannot reasonably exist. I think these tests are benchmarks are smart, but they don't show the whole picture.
Domain specific image generation tasks still require a domain specific models. For art purposes SD1.5 with specialized and finely tuned checkpoints will still provide the best results by far. It is also limited, but I think it dampened the hype for new image generators significantly.
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Prompt understanding will only ever be as good as the language embeddings that are fed into the model’s input. Google’s hardware can host massive models that will never be run on your desktop GPU. By contrast, Flux and its kin have to make do with relatively tiny LLMs (Qwen Image uses a 7B-param LLM).
Hey I hope you see this. The scoring needs to be a 0-10 or something with a range rather than pass or fail. Flux one getting the same score for the surfer as Gemini pro 3 reduces the quality of the benchmark.
Hi bn-l, yeah as mentioned above and in the Release Notes - we'll be adding a more nuanced numerical score in the next week.
I don't know if I'm going to get as granular as 1-10 only because the finer the scoring - the more potential for subjectivity. That's why it was initially set up as a "Minimum Passing Criteria Rule Set" along with a Pass/Fail grade.
A suggestion from a previous HN post was something along the lines of (0 Fail, 0.5 Technical Pass, 1.0 Proficient Pass).
On the site: s/sttae/state/g
Great, especially that they still have an open-weight variant of this new model too. But what happened to their work on their unreleased SOTA video model? did it stop being SOTA, others got ahead, and they folded the project, or what? YT video about it: https://youtu.be/svIHNnM1Pa0?t=208 They even removed the page of that: https://bfl.ai/up-next/
Image models are more fundamentally important at this stage than video models.
Almost all of the control in image-to-video comes through an image. And image models still needs a lot of work and innovation.
On a real physical movie set, think about all of the work that goes into setting the stage. The set dec, the makeup, the lighting, the framing, the blocking. All the work before calling "action". That's what image models do and must do in the starting frame.
We can get way more influence out of manipulating images than video. There are lots of great video models and it's highly competitive. We still have so much need on the image side.
When you do image-to-video, yes you control evolution over time. But the direction is actually lower in terms of degrees of freedom. You expect your actors or explosions to do certain reasonable things. But those 1024x1024xRGB pixels (or higher) have way more degrees of freedom.
Image models have more control surface area. You exercise control over more parameters. In video, staying on rails or certain evolutionary paths is fine. Mistakes can not just be okay, they can be welcome.
It also makes sense that most of the work and iteration goes into generating images. It's a faster workflow with more immediate feedback and productivity. Video is expensive and takes much longer. Images are where the designer or director can influence more of the outcomes with rapidity.
Image models still need way more stylistic control, pose control (not just ControlNets for limbs, but facial expressions, eyebrows, hair - everything), sets, props, consistent characters and locations and outfits. Text layout, fonts, kerning, logos, design elements, ...
We still don't have models that look as good as Midjourney. Midjourney is 100x more beautiful than anything else - it's like a magazine photoshoot or dreamy Instagram feed. But it has the most lackluster and awful control of any model. It's a 2021-era model with 2030-level aesthetics. You can't place anything where you want it, you can't reuse elements, you can't have consistent sets... But it looks amazing. Flux looks like plastic, Imagen looks cartoony, and OpenAI GPT Image looks sepia and stuck in the 90's. These models need to compete on aesthetics and control and reproducibility.
That's a lot of work. Video is a distraction from this work.
Hot take: text-to-image models should be biased toward photorealism. This is because if I type in "a cat playing piano", I want to see something that looks like a 100% real cat playing a 100% real piano. Because, unless specified otherwise, a "cat" is trivially something that looks like an actual cat. And a real cat looks photorealistic. Not like a painting, or cartoon, or 3D render, or some fake almost-realistic-but-cleary-wrong "AI style".
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As a startup, they pivoted and focused on image models (they are model providers, and image models often have more use cases than video models, not to mention they continue to have bigger image dataset moat, not video).
> bigger image dataset moat
If they have so much data, then why do Flux model outputs look so God-awful bad?
They have plastic skin, weird chins, and have that "AI" aura. Not the good AI aura, mind you. The cheap automated YouTube video kind that you immediately skip.
Flux 2 seems to suffer from the exact same problems.
Midjourney is ancient. Their CEO is off trying to build a 3D volume and dating companion or some nonsense and leaving the product without guidance and much change. It almost feels abandoned. But even so, Midjourney has 10,000x better aesthetics despite having terrible prompt adherence and control. Midjourney images are dripping with magazine spread or Pulitzer aesthetics. It's why Zuckerberg went to them to license their model instead of quasi "open source" BFL.
Even SDXL looks better, and that's a literal dinosaur.
Most of the amazing things you see on social media either come from Midjourney or SDXL. To this day.
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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.
Makes no sense since they should have checkpoints earlier in the run that they could restart from and they should have regular checks that keep track if a model has exploded etc.
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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 market has exploded in size this year, and will do video later
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FLUX.1 Pro Kontext was one of the best artistic model, still great at instruction following comparing to MidJourney V7.
See my third comparison in Nano Banana blog post: https://quesma.com/blog/nano-banana-pro-intelligence-with-to...
I just finished my Flux 2 testing (focusing on the Pro variant here: https://replicate.com/black-forest-labs/flux-2-pro). Overall, it's a tough sell to use Flux 2 over Nano Banana for the same use cases, but even if Nano Banana didn't exist it's only an iterative improvement over Flux 1.1 Pro.
Some notes:
- Running my nuanced Nano Banana prompts though Flux 2, Flux 2 definitely has better prompt adherence than Flux 1.1, but in all cases the image quality was worse/more obviously AI generated.
- The prompting guide for Flux 2 (https://docs.bfl.ai/guides/prompting_guide_flux2) encourages JSON prompting by default, which is new for an image generation model that has the text encoder to support it. It also encourages hex color prompting, which I've verified works.
- Prompt upsampling is an option, but it's one that's pushed in the documentation (https://github.com/black-forest-labs/flux2/blob/main/docs/fl...). This does allow the model to deductively reason, e.g. if asked to generate an image of a Fibonacci implementation in Python it will fail hilariously if prompt sampling is disabled, but get somewhere if it's enabled: https://x.com/minimaxir/status/1993361220595044793
- The Flux 2 API will flag anything tangently related to IP as sensentive even at its lowest sensitivity level, which is different from Flux 1.1 API. If you enable prompt upsampling, it won't get flagged, but the results are...unexpected. https://x.com/minimaxir/status/1993365968605864010
- Costwise and generation-speed-wise, Flux 2 Pro is on par with Nano Banana, and adding an image as an input pushes the cost of Flux 2 Pro higher than Nano Banana. The cost discrepancy increases if you try to utilize the advertised multi-image reference feature.
- Testing Flux 1.1 vs. Flux 2 generations does not result in objective winners, particularly around more abstract generations.
The fact that you have the possibility of running Flux locally might be enough of an argument to sway the balance for some cases. For example, if you've already set up a workflow and Google jacks up the price, or changes the API, you have no choice but to go along. If BFL does the same, you at least have the option of running locally.
Those cases imply commercial workflows that are prohibited with the open-weights model without purchasing a license.
I am curious to see how the Apache 2.0 distilled variant performs but it's still unlikely that the economics will favor it unless you have a specific niche use case: the engineering effort needed to scale up image inference for these large models isn't zero cost.
Their testing was for the Pro model, which you cannot host locally, and is already not price competitive with Google's offering for the capabilities.
You can run Alibaba's Qwen(Edit) locally too, and the company isn't as weird with its license, weights, or training set.
I personally prefer Qwen's performance here. I'm waiting to see other folks' takes.
The Qwen folks are also a lot more transparent, spend time community building, and iterate on releases much more rapidly. In the open rather than behind closed doors.
I don't like how secretive BFL is.
I've re-run my benchmark with the Flux 2 Pro model and found that in some cases the higher resolution models (I believe Flux 2 Pro handles 4k) can actually backfire on some of the tests because it'll introduce the equivalent of an almost ESRGAN style upscale which may add in unwanted additional details. (See the Constanza test in particular).
https://genai-showdown.specr.net/image-editing
That Constanza test result is baffling.
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Flux 2 Dev is not IP censored
Do you have generations contradicting that? The HF repo for the open-weights Flux 2 Dev says that IP filters are in place (and imply it's a violation of the license to do as such)
EDIT: Seeing a few generations on /r/StableDiffusion generating IP from the open weights model.
> Run FLUX.2 [dev] on GeForce RTX GPUs for local experimentation with an optimized fp8 reference implementation of FLUX.2 [dev], created in collaboration with NVIDIA and ComfyUI.
Glad to see that they're sticking with open weights.
That said, Flux 1.x was 12B params, right? So this is about 3x as large plus a 24B text encoder (unless I'm misunderstanding), so it might be a significant challenge for local use. I'll be looking forward to the distill version.
Looking at the file sizes on the open weights version (https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/mai...), the 24B text encoder is 48GB, the generation model itself is 64GB, which roughly tracks with it being the 32B parameters mentioned.
Downloading over 100GB of model weights is a tough sell for the local-only hobbyists.
100 GB is less than a game download, it's actually running it that's a tough sell. That said, the linked blog post seems to say the optimized model is both smaller and greatly improved the streaming approach from system RAM, so maybe it is actually reasonably usable on a single 4090/5090 type setup (I'm not at home to test).
Never mind the download size. Who has the VRAM to run it?
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The download is a trivial onetime cost and so is storing it on a direct attached NVMe SSD. The expensive part is getting a GPU with 64GB of memory.
Even a 5090 can handle that. You have to use multiple GPUs.
So the only option will be [klein] on a single GPU... maybe? Since we don't have much information.
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Text encoder is Mistral-Small-3.2-24B-Instruct-2506 (which is multimodal) as opposed to the weird choice to use CLIP and T5 in the original FLUX, so that's a good start albeit kinda big for a model intended to be open weight. BFL likely should have held off the release until their Apache 2.0 distilled model was released in order to better differentiate from Nano Banana/Nano Banana Pro.
The pricing structure on the Pro variant is...weird:
> Input: We charge $0.015 for each megapixel on the input (i.e. reference images for editing)
> Output: The first megapixel is charged $0.03 and then each subsequent MP will be charged $0.015
> BFL likely should have held off the release until their Apache 2.0 distilled model was released in order to better differentiate from Nano Banana/Nano Banana Pro.
Qwen-Image-Edit-2511 is going to be released next week. And it will be Apache 2.0 licensed. I suspect that was one of the factors in the decision to release FLUX.2 this week.
Fair point.
> as opposed to the weird choice to use CLIP and T5 in the original FLUX
This method was used in tons of image generation models. Not saying it's superior or even a good idea, but it definitely wasn't "weird".
Considering how little (and sometimes negative) benefit it provided in most of them compared to just using the biggest encoder model and having a null prompt on the rest (not just those using the specific combination Flux.1 did, but for most of the multi-encoder models), its actually pretty weird that people kept doing it.
> as opposed to the weird choice to use CLIP and T5 in the original FLUX
Yeah, CLIP here was essentially useless. You can even completely zero the weights through which the CLIP input is ingested by the model and it barely changes anything.
Nice catch. Looks like engineers tried to take care of the GTM part as well and (surprise!) messed it up. In any case, the biggest loser here is Europe once again.
Just an FYI, the open source version FLUX.2-DEV cannot be used commercially.
https://huggingface.co/black-forest-labs/FLUX.2-dev/blob/mai...
> open source version [...] cannot be used commercially
So, it’s not open source.
Good to see there's some competition to Nano Banana Pro. Other players are important for keeping the price of the leaders in check.
Also happy to see European players doing it.
It's nice as well for location that are banned to use private US models. Like here in Hong Kong, Google doesn't allow us to subscribe to Gemini Pro. (Same for OpenAI and Claude too actually).
18gb 4 bit quant via diffusers. "low vram setup" :)
I ran "family guy themed cyberpunk 2077 ingame screenshot, peter griffin as main character, third person view, view of character from the back" on both nano banana pro and bfl flux 2 pro. The results were staggering. The google model aligned better with the cyberpunk ingame scene, flux was too "realistic"
i think they focus their dataset on photography. flux 1 dev one was never really great at artistic style, mostly locking you into a somewhat generic style. my little flux 2 pro testing does seem to verify that. but with lora ecosystem and enough time to fiddle flux 1 dev is probably still the best if you want creative stylistic results.
> Launch Partners
Wow, the Krea relationship soured? These are both a16z companies and they've worked on private model development before. Krea.1 was supposed to be something to compete with Midjourney aesthetics and get away from the plastic-y Flux models with artificial skin tones, weird chins, etc.
This list of partners includes all of Krea's competitors: HiggsField (current aggregator leader), Freepik, "Open"Art, ElevenLabs (which now has an aggregator product), Leonardo.ai, Lightricks, etc. but Krea is absent. Really strange omission.
I wonder what happened.
They messed up. We (Krea) were also surprised.
They put our logo after we pointed it out.
Nice eye!
What?
The model looks good for an open source model. I want to see how these models are trained. may be they have a base model from academic datasets and quickly fine-tune with models like nano banana pro or something? That could be the game for such models. But great to see an open source model competing with the big players.
they released a research post on how the new model's VAE was trained here: https://bfl.ai/research/representation-comparison
Surprised there wasn't any mention of Equilibrium Matching [1] in the future work section
[1] https://raywang4.github.io/equilibrium_matching/
great this is more on the techincal details. it is great but would be great to see the data. I know they will not expose such information but would be great to have a visibility onto the datasets and how the data was sourced.
> The FLUX.2 - VAE is available on HF under an Apache 2.0 license.
anyone found this? To me the link doesn't lead to the model
There is no repo for the VAE on Hugging Face yet which implies it's not up yet: https://huggingface.co/black-forest-labs/models?sort=created
It's here: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/mai...
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Their published benchmarks leave a lot to be desired. I would be interested in seeing their multi-image performance vs. Nano Banana. I just finished up benchmarking Image Editing models and while Nano Banana is the clear winner for one-shot editing its not great at few-shot.
The issue with testing multi-image with Flux is that it's expensive due to its pricing scheme ($0.015 per input image for Flux 2 Pro, $0.06 per input image for Flux 2 Flex: https://bfl.ai/pricing?category=flux.2) while the cost of adding additional images is neligible in Nano Banana ($0.000387 per image).
In the case of Flux 2 Pro, adding just one image increases the total cost to be greater than a Nano Banana generation.
Been trying this and found it to be fantastic. Much more naturalist images than Gemini or ChatGPT and great level of understanding.
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
(I'm not really familiar with image generators.) Would you care to share how well that works? Given the heavy censorship attitudes, I wouldn't expect that to be easy.
We probably won't be able to run it on regular PCs, even with a 5090. So I am curious how good the results will be using a quntized version.
You can run it with a 5090 and the standard ComfyUI template, it just offloads some parts to RAM. Image generation takes about a minute for sizes like 1024x1024.
I spent 2 minutes on the website and I still don't know what it is. Generative AI? An image editor?
definitely gen. ai
If this is still a diffusion model, I wonder how well does it compare with NanoBanana.
There is no reason to believe Gemini Image is not diffusion model. In fact, generated result suggests it at least have VAE and very likely is a diffusion model variant. (Most likely a transfusion model).
Yes yes very impressive.
But can it still turn my screen orange?
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Oh, looks like someone had to release something very quickly after Google came for their lunch. Their little 15 mins is over already for BFL as it seems.
comparing a closed image model to an open one is like comparing a compiled closed source app to raw source code.
it's pointless to compare in pure output when one is set in stone and the other can be built upon.
Did you guys even check the licence? Not sure what is "open source" about that. Open weights at the very best, yet highly restrictive
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yeah except I can download this and run it on my computer, whereas Nano Banana is a service that Google will suddenly discontinue the instant they get bored with it