(I mean: outside direct or substantial use of Python, and running the Neural Network in the most efficient way.)
Back in Apr, ChatGPT Images 2.0 has some broken Chinese texts in its featured examples, and they later removed that from blog post. Is 2.5 better now?
> You shall not use the Materials for any commercial purpose without obtaining a separate commercial license from us.
It probably will be much cheaper to use than other image models, but it seems that will be up to the whims of Qwen/Alibaba rather than just being the cost of putting it in a cloud provider.
I am just happy I can run these models on my own hardware. Hopefully in 10 years, self-hosted models far exceeding what's currently available will run comfortable on commodity hardware.
https://en.wikipedia.org/wiki/Qwen#List_of_models
Unfortunately, it looks like this model is using a much more restrictive license:
People can continue to use closed SOTA models to generate outputs for commercial or malicious purposes.
What this research license achieves is that we cannot use this model in applications we publish.
EDIT: It still produces artifacts it's better but unusable for production work. In midvalues you will see a slight dot pattern.
Can you share the sources?
latents go from 16ch @ 8x compression to 64ch @ 16x, so roughly the same total latent budget but much more channel heavy. It’s also deeper/wider, and the old 2x2 transformer patching is gone.
On some images it still produces artifacts but can't say if it's the transformer or the VAE yet.
Is this not simply some sort of watermark instead of an artifact?
Excited to see what the future holds for them!
https://html.non.io/qwen-comparison/
The text rendering definitely is much, much better than anything else on the open weights market right now. Small text fidelity is quite good. It seems like the text encoder however gets a little bit overloaded with larger prompts - note the presence of hex codes in the design output, those were inputs from the expanded prompt.
I'll be trying a post-training run on this for web design, it has some serious potential.
[1] diffui.ai
Really? Because basically everything in those screenshots is completely garbled. I didn't follow it super closely but I thought Ideogram or whatever was really good for this particular use, with actual clear text.
I know a few friends of mine who are running models and are ignoring the licence.
Whether it is AGPL 3.0, or a completely restrictive license, it is going to get broken anyway and be used for commercial purposes.
I don't know anyone who looks at the licenses of the OSS software they are using.
In today’s world OSS is synonymous with "Free" and the AI model providers are proof of that with their training of code, datasets, etc.
So it begs the question, why should we abide by their licenses of their models?
Positives
• 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.
Code on github, models on huggingface, nice intro text: "We are excited to open-source Qwen-Image-2.1 [...]".
meh...
(https://www.shutterstock.com/blog/shirley-card-racial-photog...).
None are as good yet, but what everyone said is coming true - models are not moats. And these folks need an exit (even Msuk whose shares are still locked)
In my experience, video models generate videos pretty well but are mid at editing. They actually regenerate the entire video along with the edit. So these models being non-deterministic tweak the rest of the video as well, the parts you hoped would be left not edited. It gets exponentially worse when there are humans in the videos, annoying face distortions and for some reason these models just don't understand fingers.
(Or does this model has those capabilities natively ?)
Through a combination of careful initial prompting (e.g. requesting line drawings, requesting limited colours ["posterization" works] if you need colour at all, requesting background removal) and then using the settings in VTracer to limit the colours used for the resulting SVG to the minimum needed, you can end up with an SVG that doesn't take too long to clean up in Inkscape. It usually takes a few minutes of deletions and path unions to produce something usable.
Diffusion models outputting SVGs would obviously be very interesting for this, but this is probably a small niche compared to everything that diffusion models can do with raster images, and I've not found anything usable online so far. LLMs (understandably, given their core technology) are still pretty bad at creating SVG art.
It runs VTracer under the hood too. Most of the cleanup you're doing in Inkscape could possibly be pushed to before and after the trace?
Skechworks picks the palette before the trace and forces it again after. I quantize to 8 colors with pngquant, trace, then snap every fill back to the nearest palette color. VTracer fills each region with its own average color, otherwise you get hundreds of nearly identical grays. One design went from 279 fill colors to 3.
I upscale 2x before I quantize. The anti-aliasing is what tells the tracer where an edge really sits. Quantize at 1x and you throw that away, and small text comes out lumpy. Related gotcha: filter_speckle is an area, so don't double it when you double the image.
I also flatten transparency onto white first. VTracer counts every alpha value as a different color. I had a 5 color picture that it saw as 688 colors, and the trace took 6 GB of memory. Flattened, the same picture took 300 MB.
For the background I sample the whole edge of the picture. If 85% of it is one color, I delete every path in that color that touches the edge. White inside the drawing never touches the edge, so it stays.
For black and white I threshold with Otsu, then trace in color mode with hierarchical stacked instead of bw mode. bw mode hands you one giant black path full of holes. Stacked gives you every region as its own shape, which is much easier to edit. You have to pad the image with the background color first, or VTracer decides your subject is the canvas.
Your FLUX step is the same trick I use for photos and messy art. I have an image model redraw the picture as flat art, then trace the redraw. That came out about a quarter the file size of the best direct trace I could get. The catch is that the model moves and resizes the subject a little every run. I measure the box around the ink in both pictures and fit the trace back onto the original.
What I haven't solved is curve fitting. vectorizer.ai gives you real arcs and straight lines. VTracer gives you splines, so circles are never quite circles.
> Currently, we support image, audio and video input.
Image outputs are supported, videos I'm not sure but I don't think that's an output, just a preview of the equirectangular example, so, same question here, what does this model outputs that isn't supported?
At the risk of stating the obvious llama.cpp isn't just about LLaMa as https://github.com/ggml-org/llama.cpp/blob/master/src/llama-... someone else pointed out.
I understand that llama.cpp could only output text, last time I checked (I do not know how to find a good source for that though).
See https://github.com/ggml-org/llama.cpp/blob/master/src/llama-... , the
enum llm_arch {
I think that's all Python (not a direct executable).
You could just do (see the "Quick Start") four `pip install` and have a dozen lines script to generate the image. But `llama.cpp` and similar do not require e.g. installing Torch (or PyTorch) - you can use `llama.cpp` on a non-specialized machine.
I don't think I have ever once run "pip install transformers" and had it work without three rounds of fiddling
Yep, that's (also) what I meant ;)
Lean, efficient... Also sensible and trouble-less.
Edit x2: As usual I'm in a twisty maze of pip packages that don't work together, with obscure errors about missing modules, even though I followed the instructions on the page to the letter. I really wish people didn't use Python for this stuff. A simple C/C++ program would be so much better.
I think it will technically run on anything that has enough memory. I just tried it on a standard laptop (dual-channel DDR5), and it took about 3 minutes for a 512x512. If you'd want to run it at interactive speeds, you would want a GPU (one which fits this in VRAM).
> "I really wish people didn't use Python for this stuff. A simple C/C++ program would be so much better."
You mean besides stable-diffusion.cpp ?
Yes, thanks, I didn't know about that. Will try it.
it already has day-0 qwen image 2.1 support!
I tried stable-diffusion.cpp, following its compile guide here[0], and its Qwen Image-2.1 specific instructions here[1]. It works out of the box. I made a test pelican[2]. It took 3 minutes on a CPU.
[0] https://github.com/leejet/stable-diffusion.cpp/blob/master/d...
[1] https://github.com/leejet/stable-diffusion.cpp/blob/master/d...
total params memory size = 15645.19MB (VRAM 15645.19MB, RAM 0.00MB):
text_encoders 7669.77MB(VRAM),
diffusion_model 7331.05MB(VRAM),
vae 644.38MB(VRAM),
controlnet 0.00MB(N/A),
extensions 0.00MB(N/A)In fact, like it appears in the reports above, it is "7b" as in
> 7B parameters in its visual generation component
It seems they calibrated the size to fill a 16GB VRAM near the limit. RAM requirements will vary.
I have FLUX.2 klein and dev, Ideogram, LaDA-Image and SenseNova locally. Works great. Ive never touched a file.
The days of making container yamls myself is over. I read them but I dont edit anymore.
How difficult would it be to use this model to create a second model without licensing issues?
If AI labs get to ignore licenses, so do we.
If you were to generate outputs for commercial use, I think it would still violate this research license, but it's not like they are going to know, are they?
That said, I am disappointed that the model is not actually open-weights as I expected based on the headline.
It's not going to matter then, either. What are they going to do, sue me for copyright infringement?
Why even bother demanding such terms? Release the weights or don't release the weights, but this is silly. No one is going to pay them to run or host a 7B static image model.
It’s more of something to scare companies with legal teams. If you’re an individual or hobbyist doing a side project the risk is essentially zero.
Image data?
> GPL licensed instead and use it however the fuck I want
GPL is not a "use it however the fuck I want" license. Maybe you're thinking of the WTFPL?
No. GPL is a "use it however the fuck you want" license. The GPL has no restrictions on *use*, only on redistribution.
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.
[1] - https://github.com/Boogu-Project/Boogu-Image
[2] - https://www.krea.ai/krea-2-licensing
[3] - https://genai-showdown.specr.net/?models=fd,hd,kd,qi,f2d,zt,...
It's just freeware.
A researcher commented among the lines that they have no interest in restricting creators from using it for monetized YT content.
The way he put it, it suggested they are at this time looking to understand use cases rather than necessarily limit or charge for commercial use, and he recommended reaching out to the commercial team.
So it sounds like one could likely receive a free commercial license if needed.
Even the artifacts are getting picked up.
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.
That internal json backing helps significantly when you want to maintain consistent design system components/patterns across multiple pages. The aligned layout is it working as intended.
There is no single opinion, and clearly no single Chinese approach.
Also Chinese labs are in particular very careful about anything which be used to create pornographic content, which is highly illegal in the PRC.
MiniMax H3 is dominating AI porn right now, and they are Chinese.
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.
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.
Sorry, I'm not trying to nitpick. I'm just joining in because I'm interested in how the models dealt with the request.
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).
I'll likely be redoing that particular bench with added minimum passing criteria of an anvil.
[1] - https://huggingface.co/spaces/ArtificialAnalysis/Text-to-Ima...
Also "vintage" photography being corrected, where the original is clearly ai generated with unrealistic sharp focus everywhere
Edit: This is wrong.
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.
Comparisons:
https://genai-showdown.specr.net
If you just want to compare local models only:
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.
* 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.
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.
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.
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.
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
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Recently, I've been helping a friend's wife with some basic vector images for her sewing hobby (she has what is essentially a CNC sewing machine) and have been super-impressed with FLUX.1-Kontext, which I've been running on my Macbook Pro with mflux. Its ability to (for example) take a photo of a human or an animal and return a line drawing which is recognisably them (rather than just a generic similarish image as I've experienced with other models) is excellent.
It's an older model now, but (AIUI) has the text-handling features baked in, and in my various testing is very reliable at giving me the outputs that I want, without the randomness I've experienced previously. It's big and relatively slow (~3 mins per 512x512 image edit on my M1 Max Mac) but excellent to work with. It's also very straightforward to set up, without the harness complexity of e.g. comfyui.
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Prompt-adherence is really hit-or-miss—especially if one lacks the visual vocabulary. Likewise with coding, I find junior devs don't think to prompt re: respecting this-or-that interface, or refactoring to point-free style, etc.
So, as others have said, the artist knows better.
tarcon•8h ago
Might be worth to watch the diffusion based LLMs.