For the past 6 months I've also been working on a period-accurate QBASIC 1.1 simulator (and corresponding virtual hardware layer) that runs entirely in the browser.
I've got a stack of books on my desk from when I was a kid in the 90s that have literally been my religious texts (Undocumented DOS, The Programmer's PC Sourcebook, etc), so I know how hard it is to get this right.
I've actually posted about it a couple times here in HN.
Anyway small bit of feedback:
Technically if you don't clear the screen (using CLS, or set a SCREEN mode other than zero), the cursor retains its position even between program executions.
So basically if you have a program like this:
PRINT "WHY IS A RAVEN LIKE A WRITING DESK"
END
And then you run it again - the output screen should display:
WHY IS A RAVEN LIKE A WRITING DESK
WHY IS A RAVEN LIKE A WRITING DESK
There's a thousand other subtleties like this that make the proverbial "last mile" sometimes feel insurmountable. Fun though!
I haven't done enough tests on the editing capabilities but as far as the strict text to image goes, Alibaba is reaching to claim it is comparable to NB 2.
It's definitely a nice upgrade from the last open-weight version (Qwen-Image 1.0) at least in terms of strict coherence and prompt understanding... but a lot of the outputs seem distilled for lack of a better word - likely trained on poor synthetic data. There's also some elements of tinging that very much reminds me of early gpt-image outputs.
You can mitigate it a bit using better samplers (like res_2m paired with the beta_57 scheduler, etc). A lot of people have also seen better results using a higher CFG than what is recommended by the Qwen team.
On my GenAI Image Showdown bench which emphasizes adherence to prompts, Qwen-Image 2.1 clocked in at 7 out of 15 which is SOTA for an open-weight model (Ideogram 4 is the only other open-weight model that outscored it), but the quality is frustratingly inconsistent.
As avaer already mentioned it seems to be a thin wrapper around H3 Max Director, which generates real-time video and takes “natural language directions” that are then relatively seamlessly incorporated into the continuous video stream.
It's like a Choose-Your-Own-Adventure dialed up to 11.
I played around with it briefly on Fal.ai generating a cartoon, but it’s more of a technical curiosity for the time being since I don’t have a Scrooge McDuck vault of gold coins.
This is the way - 99% of my stuff is Caddy-proxied through subdomains to a single Debian VPS and works great even when deluged with the occasional HN-levels of traffic.
If you want to look at a company that does pixel art right for the modern era, Yacht Club Games is probably the gold standard with games like Shovel Knight and Mina the Hollower.
Agreed. There's also a lot of bad tinging/yellow saturation that very much reminds me of early gpt-image outputs on a lot of the non-cherry picked stuff I've been seeing on Twitter/Reddit.
A lot of people were putting ZiT as a refiner downstream in early Qwen-Image 1.0 workflows, so I'm wondering if we're going to see something similar with 2.1.
This is my experience as well. Ideogram4 (assuming you are willing to put in the work to use the proper structured JSON input) is very accurate when it comes to text rendering in an image.
• It's a heck of a lot smaller than Qwen-Image 1 (20b parameters) at only 7b, making it one of the smaller open-weight models available (Z-Image Turbo is one of the few that is smaller at 6b) when compared to Ideogram, Krea2, Flux2, etc.
• It supports native transparency (Qwen's team, as far as I know, is the only one attempting to tackle this). Even though it's relatively trivial to set up background removal postprocessors, it's also neat to see it natively supported.
• It's fast using QwenImage2.1 convrot, a 1MP image took around ~5 seconds on an RTX4090.
Negatives
• The license (assuming you respect it) is far more restrictive. The original Qwen Image 1 was released under the standard Apache license; this one explicitly forbids commercial usage without obtaining a separate license. On the other hand, a lot of us didn't expect the Qwen team to ever release "weights-available" ever again.
Qwen-Image 1.0, released about a year ago, only scored 4/15 on my GenAI Showdown Benchmarks. Since that time, they've been upstaged by Krea 2 (6/15) and Ideogram4 (8/15). I'll post the new results once I have some more time to run them.
Well, the results are in, at least for text-to-image (the editing bench will come later).
Qwen-Image 2.1 is definitely a pretty big leap over the last open-weight version, Qwen-Image 1.0, released back in August of last year and managed to score 7 out of 15 as opposed to its predecessor which scored 4 out of 15.
Even though it's significantly smaller, 7b vs 20b, it's multimodal (so you don't need a separate image-to-image model like you did with Qwen-Edit), more coherent, and significantly faster even when outputting at higher 2K resolutions. However, in my testing, I found that I had to play with dialing up the CFG depending on the complexity of the prompt.
I've also added a progress dropdown under Model Performance so you can see how cloud vs. local models have been trending since 2024. Spoiler: June of this year released some of the biggest bangers (Krea 2, Ideogram 4, and the kind of slept-on Boogu-Image 0.1).
Downsides:
- It was clearly trained on at least some level of synthetic training data, and it shows in some of the subpar outputs in terms of fidelity. Some of this you might be able to iron out with a refiner model downstream or a custom LoRA but time will tell.
- They've moved away from the permissive Apache license. Commercial usage is only allowed by request.
This is the fundamental problem with where the AI race is heading, IMHO. Broadly, there are two possible legal interpretations (to my layman's mind):
* A model is derived work of its training data. This seems sane to me. Open, but copyrighted, works (like FOSS) remain protected from abuse. There's some legal moat around AI models. But on the other hand it seems unlikely that there's enough liberally licensed (or public domain) training data to go around. The little guy's status quo remains, the frontier labs' work slows down massively.
* A model is not derived work of its training data. This seems to me insane, but a lot of the world seems to hold this view (including the frontier labs). Stuff like FOSS or indie art is under huge threat of copyrightwashing. But on the other hand, there's also zero legal moat around the models. The little guy is eviscerated, but so are the frontier labs.
Neither interpretation seems, to me, to be capable of sustaining the last couple of years' developments. But what do I know.
> A model is not derived work of its training data. This seems to me insane, but a lot of the world seems to hold this view
Why insane? Models don't take the content as-is, they take measurements. I don't owe you royalties just because I used your photo to get the proportions and coloring of a duck right. Go watch artist streams, you'll often see people to go Google Images for references. I've never seen that result in credit or payments.
The alternative is that we hand out lots of money to a few large companies specializing in content archives, and there's really no benefit to anyone else anyway. On the long term I would expect a few fat cats to get fatter, the small guy to get nothing, and AI still work but get there slowly. I don't see the point or the benefit.
> Models don't take the content as-is, they take measurements.
At some point, enough measurements constitute a copy. If I redistribute the average value of all the pixels in your photo, I'm obviously not in violation of your copyright. If I measure and redistribute 90% of its DCT coefficients (i.e. make a slightly compressed JPEG), I am.
The interesting stuff happens between those extremes. We cannot just take as a given that all LLMs always are on the safe side. It is not at all obvious.
Yeah but here is the thing. Say you train a Lora for a model. You then merge those weights into the open weight model.
Now prompt it for an original image, it will pretty much be able to reproduce that exact image.
You can say it is just measurements but at some point, it can just reproduce with high enough accuracy to just be seen as a copy
Given that corporations buy up any valuable IP, I personally think the answer is to abolish copyright because right now it is really only protecting the rich and corporations . Individuals have the illusion of protection but if Disney steals your shit, good luck with the pain and suffering you experience trying to win a court case against them
Abolishing copyright would make it much harder for smaller players to protect their interests. Their original work would be gobbled up by the big players, and made easily available under the umbrella of a large corporations pre-existing market share. Not a fun situation at all to be in. Especially for the little guys.
Lemme fix that for you in the style of Accelerando....
This is the fundamentally great thing about where the AI spend is heading, looks like there is no moat and will be a democratization of cognition simply by dint of the way the tech works. Which is awesome.
I shed 1x tear for the private investors and large existing monopolists who poured money into this, only because they might not make the same mistake with the next great technology, but given their greed, that's unlikely. They also might get government backing to mitigate their loss, which would be horrible and inflationary, but given the state of open-weights, that seems unlikely outside of the USA.
If a model is derived work of its training data, surely all the existing frontier models that have been training on copyrighted work have a big legal issue, and therefore so does anything produced with them?
Yes. Of course. They'd have to be rebuilt with acceptably licensed training data. And since there might not be a enough of it, the model owners are screwed.
My point is that they're also screwed in the opposite scenario, because they rely on the same legal protection (against deriving works) as the works they trained on.
That's why I don't understand how any of this can be sustained.
Not slept on at all. It was absolute trash, and I’m super curious why people pretend otherwise. There isn’t a single thing that model did better than any temporal peer.
That benchmark might have some issues. You prompted the models to generate an image of striking a ring against a crucible. Then you (presumably, manually?) scored the images that depicted an anvil higher than the ones striking something resembling a crucible.
That’s a good catch. Yes, all scoring is done through manual review since relying on a VL model for these kinds of meta-metrics is a sort of loose equivalent of gödel's second incompleteness theorem.
I’ll have to think about this one. When I crafted the prompt, I wasn’t really thinking about the differences between a crucible and an anvil. It was more the visual of an archangel smelting halos for newly arrived heavenly beings.
I'm not sure why one would even strike metal against a crucible! It's a container for liquid metal. One of the outputs shows it being smashed by the manoeuvre, which is probably the most realistic outcome of all of them.
Sorry, I'm not trying to nitpick. I'm just joining in because I'm interested in how the models dealt with the request.
Well this is HN - original home of the "ummm actually..." - so I appreciate when people pick all the nits. :)
Even though I prompted for a crucible in the prompt, I think the fact that the prompt also contained terms like “blacksmith” and “hammer,” caused it to lean towards anvils over crucibles in some of the pictures (which as you brought up makes more sense anyway).
Perhaps for the particular image you liked. But choosing something “typical” or average over what it was instructed to do is a massively common failure mode for AI. One that makes the difference between a useful model and one that makes you want to throw your laptop out a window.
I agree. In fact the entire reason I initially built GenAI Showdown was because many of the comparative tests on places like Image Arena Leaderboard [1] are not designed to challenge models on prompt adherence. Even when they are, a considerable number of amateur judges tend to prioritize aesthetics over adherence or instruction-following.
I'll likely be redoing that particular bench with added minimum passing criteria of an anvil.
ive been taking a look there. I think for some of the image to image ones you really ought to use real images as the base prompt, there are a lot of weird things happening where the base image has issues and then its hard to say if the model should be correcting the flaws or not. Things like "childrens drawing app where each crayon is clearly sized to be tapped on" but the base image has the rainbow cut off partway across with only half the red and black crayons visible.
Also "vintage" photography being corrected, where the original is clearly ai generated with unrealistic sharp focus everywhere
I promise I searched on my phone before posting that, but it looks like I misinterpreted an hf search result that was actually Qwen-Image-2512 but the url was truncated to Qwen-Image-2 due to my screen width (ugh). For some reason I was thinking Qwen Image 2.1 was the one that had been available for a while but not released, but you're right that was 2.0.
Native transparency isn’t so hard to do by the way, I made an image AE (I don’t say VAE deliberately as none of these are VAEs, I don’t know why they keep being called that since the variational part is completely absent) that supported this about two years ago as a hobby project. I haven’t really been following the space recently, I’m surprised it’s taken so long for this to come out if it’s a first.
It’s not hard architecturally, but it is hard to find or create good datasets of images on the magnitude you want. I suspect the qwen team heavily used synthetic data for this.
Not very, I had a tiny dataset compared to a production run, I used a bunch of 3D renderings to augment the images with transparency. It worked pretty well given the scale.
Can do! GPT-Image-2 already scored unsurprisingly very high: 12 out of 15 on text-to-image, and 10 out of 12 on image-to-image.
The three benchmarks it failed on (D20, Flat Earth, and Banded Snake) are pretty difficult, so I'd be surprised if 2.5 manages to pass them, but I’ll add it for completeness’ sake later this week.
That's because the users don't care about the licence. They did try to take down a porn Lora repo on hugging face. The trick is, don't say you're using minimax, and you can make porn with it.
It is a total wild west here (i live in China). No escape from AI, literally everything is AI, from AI toilet paper to AI companions. No one has money or jobs and when this bubble bursts we are so cooked.
Boogu-Image has the Apache 2.0 License [1] (good coherence, but outputs can look synthetic).
And Krea 2 has a community license [2] that is fairly permissive - I think commercial usage is allowed under $1 million.
Boogu-Image scored 6/15 and Krea 2 scored 7/15 on my GenAI Showdown benchmark [3] - only Ideogram4 eclipses them in terms of local models, but its got a far more restrictive license and the JSON structured inputs can be a pain to work with.
• Wish this was a web app though, I have a general policy of not isntalling unknown software on my computer.
• Website needs a demonstration video (or at the very least some screenshots)
If you're interested in improving your blindfold chess techniques, I actually put together a pretty in-depth blindfold chess trainer website about a year ago called Shah Kur. It lets you play Stockfish AI with different blindfold variations (Last N hidden, pieces reduced to color only, etc.), and it uses voice activation/TTS so you can play on BT headphones while you're out walking without having to physically look at your phone.
It also has a positional recall game constructed from historical games, so you’re building an actual practical “memory chunking” perspective instead of just random pieces on a board that would never correspond to an actual game.
For the past 6 months I've also been working on a period-accurate QBASIC 1.1 simulator (and corresponding virtual hardware layer) that runs entirely in the browser.
I've got a stack of books on my desk from when I was a kid in the 90s that have literally been my religious texts (Undocumented DOS, The Programmer's PC Sourcebook, etc), so I know how hard it is to get this right.
I've actually posted about it a couple times here in HN.
Anyway small bit of feedback:
Technically if you don't clear the screen (using CLS, or set a SCREEN mode other than zero), the cursor retains its position even between program executions.
So basically if you have a program like this:
And then you run it again - the output screen should display: There's a thousand other subtleties like this that make the proverbial "last mile" sometimes feel insurmountable. Fun though!* Your definition of "fun" may vary.
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