I think the fact that, as far as I understand, it takes 40GB of VRAM to run, is probably dampening some of the enthusiasm.

As an aside, I am not sure why for LLM models the technology to spread among multiple cards is quite mature, while for image models, despite also using GGUFs, this has not been the case. Maybe as image models become bigger there will be more of a push to implement it.

If 40GB you can lightly quantize and fit it on a 5090.

Which very few people have, comparatively.

Training it will also be out of reach for most. I’m sure I’ll be able to handle it on my own 5090 at some point but it’ll be slow going.

A 3090 + 2xTitanXP? technically i have 48, but i don't think you can "split it" over multiple cards. At least with Flux, it would OOM the Titans and allocate the full 3090

You can’t split image models over 2 GPUs like you can LLMs.

They also released an inference server for their models. Wan and qwen-image can be split without problems. https://github.com/modelscope/DiffSynth-Engine

Unless I missed something just from skimming their tutorial it looks like they can do parallelism to speed things up with some models, not actually split the model (apart from the usual chunk offloading techniques).

40GB is small IMO: you can run it on a mid-tier Macbook Pro... or the smallest M3 Ultra Mac Studio! You don't need Nvidia if you're doing at-home inference, Nvidia only becomes economical at very high throughput: i.e. dedicated inference companies. Apple Silicon is much more cost effective for single-user for the small-to-medium-sized models. The M3 Ultra is ~roughly on par with a 4090 in terms of memory bandwidth, so it won't be much slower, although it won't match a 5090.

Also for a 20B model, you only really need 20GB of VRAM: FP8 is near-identical to FP16, it's only below FP8 that you start to see dramatic drop-offs in quality. So literally any Mac Studio available for purchase will do, and even a fairly low-end Macbook Pro would work as well. And a 5090 should be able to handle it with room to spare as well.

Does M3 Ultra or later have hardware FP8 support on the CPU cores?

Ah, you're right: it doesn't have dedicated FP8 cores, so you'd get significantly worse performance (a quick Google search implies 5x worse). Although you could still run the model, just slowly.

Any M3 Ultra Mac Studio, or midrange-or-better Macbook Pro, would handle FP16 with no issues though. A 5090 would handle FP8 like a champ and a 4090 could probably squeeze it in as well, although it'd be tight.

Memory bandwidth is only relevant for comparing LLM performance. For image generation, the limiting factor is compute, and Apple sucks with it.

If you want to wait 20 minutes for one image you can certainly run it on a macbook pro.

The quality doesn't have to get much higher for that to be a great deal. For humans the wait time is typically measured in days.

Tell me you have no experience with generative ai image models nor with human artists.

What experience do you want to point too? I've never seen an artist streaming where they can draw something equivalent to a good piece of AI artwork in 20 minutes. Their advantage right now comes from a higher overall cap on quality of the work. Minute for minute, AIs are much better. It is just that it is pointless giving a typical AI more than a a little time on a GPU because current models can't consistently improve their own work.

"a good piece of AI artwork"

You really don't understand art. At all.

If you need a hug, I suspect unfortunately I am on the wrong continent. Try thinking some positive thoughts.

It's only been a few hours and the demo is constantly erroring out, people need more time to actually play with it before getting excited. Some quantized GGUFs + various comfy workflows will also likely be a big factor for this one since people will want to run it locally but it's pretty large compared to other models. Funnily enough, the main comparison to draw might be between Alibaba and Alibaba. I.e. using Wan 2.2 for image generation has been an extremely popular choice, so most will want to know how big a leap Qwen-Image is from that rather than Flux.

The best time to judge how good a new image model actually is seems to be about a week from launch. That's when enough pieces have fallen into place that people have had a chance to really mess with it and come out with 3rd party pros/cons of the models. Looking hopeful for this one though!

I spun up an H100 on Voltage Park to give it a try in an isolated environment. It's really, really good. The only area where it seems less strong than gpt-image-1 is in generating images of UI (e.g. make me a landing page for Product Hunt in the style of Studio Ghibli), but other than that, I am impressed.

It's not clear from their page but the editing model is not released yet: https://github.com/QwenLM/Qwen-Image/issues/3#issuecomment-3...

I think it does way more than gpt-image-1 too?

Besides style transfer, object additions and removals, text editing, manipulation of human poses, it also supports object detection, semantic segmentation, depth/edge estimation, super-resolution and novel view synthesis (NVS) i.e. synthesizing new perspectives from a base image. It’s quite a smorgasbord!

Early results indicate to me that gpt-image-1 has a bit better sharpness and clarity but I’m honestly not sure if OpenAI doesn’t simply do some basic unsharp mask or something as a post-processing step? I’ve always felt suspicious about that, because the sharpness seems oddly uniform even in out-of-focus areas? And sometimes a bit much, even.

Otherwise, yeah this one looks about as good.

Which is impressive! I thought OpenAI had a lead here from their unique image generation solution that’d last them this year at least.

Oh, and Flux Krea has lasted four days since announcement! In case this one is truly similar in quality to gpt-image-1.

Not to mention, flux models are for non-commercial use only.

the license for flux models is $1,000/mo, hardly an obstacle to any serious commercial usage

Per 100k image. And it is additionally $0.01 per image. Considering H100 is $1.5 per hour and you can get 1 image per 5s, we are talking about bare-metal cost of ~$0.002 per image + $0.01 license cost.

The pricing seems reasonable for a SOTA class model that needs to be commercially viable or it dies.

Yeah, not trying to argue either way other than saying contrary to the parent comment, licensing cost is a significant component of the cost equation.

With the notable exception of gpt-image-1, discussion about AI image generation has become much less popular. I suspect it's a function of a) AI discourse being dominated by AI agents/vibe coding and b) the increasing social stigma of AI image generation.

Flux Kontext was a gamechanger release for image editing and it can do some absurd things, but it's still relatively unknown. Qwen-Image, with its more permissive license, could lead to much more innovation once the editing model is released.

There's no social stigma to using AI image generation.

There is what's probably better described as a bullying campaign. People tried the same thing when synthesizers and cameras were invented. But nobody takes it seriously unless you're already in the angry person fandom.

In practice AI image generation is ubiquitous at this point. AI image editing is also built into all major phones.

There absolutely is - everytime someone uses an AI image in a presentation slide, or in an article to illustrate the point, everybody just rolls their eyes - in my opinion a stock photo or even nothing is preferable to a low effort AI image.

Who is everybody? How do you know? What is your personal bubble? Could it be you're presenting your opinion for the thing that commonly happens?

Responding to myself, as I realized that my post above feels too dismissive. Being a long time privacy advocate for non-tech-adjacent people, I'm perfectly aware about my bubble and biases. For any normal person, anything I say about digital privacy sounds absolutely abstract and detached from real life, where convenience and low effort dominates everything else. Even in 2025 with all political shenanigans, they just fail to see the link and how it applies to their life. AI imagegen is the same from my observations, most concerns are contained in a tiny bubble of perpetually online people. Not even all artists share the loud opinions (for reference, I used to manage a couple hundred artists), especially not VFX and 3D folks. And that tiny bubble only really exists in the anglosphere - you'll see a completely different picture in other cultural bubbles. There's absolutely no stigma of any kind outside of it.

Useless AI art (which is almost all of it) is not like the camera or the synthesizer, it's closer to when 50-60yo moms were sharing Minion memes on facebook: cringe and tasteless. It getting better won't make it more accepted, it will simply make people suspect of actual art until no one really gives a chance to any of it.

Your argument might actually be suggesting that you don't like art in general more than that there is a stigma against AI. If there is no value in artisanal art that differentiates it from AI-produced works and therefore both will be discarded as the quality converges, what was supposed to be the value in art to start with?

So far, the times had allowed artworks to be proxies for the artistry behind them; the artwork itself conveyed enough information to appreciate it. But as forgery of the art process itself spreads, that signal disappears and artworks, out of context, simply are. The artwork is still necessary, but now insufficient, to understand and appreciate its artistry, because there might not be any, or at least not any intentional one.

I think it’s revolutionary. My use case has been creating visuals for use in various VDMX workflows. One cool trick I’ve found has been generating starter images with green screens and then putting those into my local LTX video creation workflow, then using VDMX built a chroma layer with the green screen video and go from there, lots and lots of creative fun. So no not useless AI art.

I've qualified with "useless" for a reason. It's cool if you've got a novel use case, but so far I think most uses of AI art are either uncanny filler for blogs and slides; or a driver for the deprofessionalization and commoditization of artworks, with AI art producers flooding art sites to fight regular artists for attention, and industry forcing artists to paint over AI generated works (already common in mobile games) until their cheaper substitutes can replace them, and their next job forces them to set art aside.

Social stigma? Only if you listen to mentally ill Twitter users.

It's more that the novelty just wore off. Mainstream image generation in online services is "good enough" for most casual users - and power users are few, and already knee deep in custom workflows. They aren't about to switch to the shiny new thing unless they see a lot of benefits to it.

gpt-image-1 is the League of Legends of image generation. It is a tool in front of like 30 million DAUs...

how can it beat gpt-image-1 if there is no image editor?

Slightly hyperbolic, gpt-image-1 is better on at least a couple of the text metrics.

I've been playing around with it for the past hour. It's really good but from my preliminary testing it definitely falls short of gpt-image-1 (or even Imagen 3/4) where reasonably complex strict prompt adherence is concerned. Scored around ~50% where gpt-image-1 scored ~75%. Couldn't handle the maze, Schrödinger's equation, etc.

https://genai-showdown.specr.net

Is it fair to call the OpenAI octopus “real”?

Those are the wrong images - the CDN was caching older media - I've since purged it so the right ones should show up now. Thanks for the call out!

No problem. I am seeing the correct image now.

Fantastic comparisons! Great to see the limitations of the latest models.

interesting to see how many still can't handle the nine pointed star correctly

Great work, thanks.

Midjourneys images are the only ones which don’t make me uncomfortable (most of the time), hopefully they can fix their prompt adherence.

I too have have been underwhelmed by these pelican on a bicycle creators. We used to use them for company blog art, by lately we've switched to using attributed images from Wikimedia commons.

Prompt idea: "A person holding a wooden Penrose triangle." Only GPT-4o image generation is able to make Penrose triangles, as far as I can tell.

Thanks for that comparison/overview/benchmark!

However, you have mistakenly marked some answers as correct ones in the octopus prompt: only 1 generated image has octopus have sock puppets on all of its tentacles. And you marked that one image as an incorrect one due to sock looking more like gloves.

Do try it. The image quality and diversity is pretty shocking and not in a good way.

Considering they have not released their image, editor weights, I’m not sure how you could make a conclusion that it is better than Flux Kontext aside from the graphs they put out.

But, obviously you wouldn’t do that. Right? Did you look at the scaling on their graphs?

It’s either a shill or just an ai bot

Have you tried the image editing?