So thoughts<p>Positives<p>• 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.<p>• 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.<p>• It's fast using QwenImage2.1 convrot, a 1MP image took around ~5 seconds on an RTX4090.<p>Negatives<p>• 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.<p>Qwen-Image 1.0, released about a year ago, only scored 4/15 on my <i>GenAI Showdown Benchmarks</i>. 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.<p><a href="https://genai-showdown.specr.net" rel="nofollow">https://genai-showdown.specr.net</a>
Well, the results are in, at least for text-to-image (the editing bench will come later).<p>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.<p>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.<p>I've also added a progress dropdown under Model Performance so you can see how cloud vs. local models have been trending since 2024. <i>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).</i><p>Downsides:<p>- 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.<p>- They've moved away from the permissive Apache license. Commercial usage is only allowed by request.<p>Comparisons:<p><a href="https://genai-showdown.specr.net" rel="nofollow">https://genai-showdown.specr.net</a><p>If you just want to compare local models only:<p><a href="http://genai-showdown.specr.net/?models=local" rel="nofollow">http://genai-showdown.specr.net/?models=local</a>
What's to stop somebody using the output at scale on local hardware to distill their own model and then making that available open weights?
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):<p>* 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. <i>The little guy's status quo remains, the frontier labs' work slows down massively.</i><p>* 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 <i>huge</i> threat of copyrightwashing. But on the other hand, there's also zero legal moat around the models. <i>The little guy is eviscerated, but so are the frontier labs.</i><p>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<p>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.<p>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.
Yeah but here is the thing. Say you train a Lora for a model. You then merge those weights into the open weight model.<p>Now prompt it for an original image, it will pretty much be able to reproduce that exact image.<p>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<p>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
> Models don't take the content as-is, they take measurements.<p>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.<p>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.
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.<p>My point is that they're <i>also</i> screwed in the opposite scenario, because they rely on the same legal protection (against deriving works) as the works they trained on.<p>That's why I don't understand how any of this can be sustained.
> and the kind of slept-on Boogu-Image 0.1<p>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.
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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.<p>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.<p>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. :)<p>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).
It would make more sense to compare with Qwen Image 2, since that was the last open weights Qwen model.<p>Edit: This is wrong.
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.
Could you add new OAI 2.5 image models?
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.<p>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.
They're trying to cash in but this is just sad
A lot of the previous Qwen models seem to have used Apache licenses, among others:<p><a href="https://en.wikipedia.org/wiki/Qwen#List_of_models" rel="nofollow">https://en.wikipedia.org/wiki/Qwen#List_of_models</a><p>Unfortunately, it looks like this model is using a much more restrictive license:<p><a href="https://github.com/QwenLM/Qwen-Image-2.1/blob/main/LICENSE" rel="nofollow">https://github.com/QwenLM/Qwen-Image-2.1/blob/main/LICENSE</a>
Calling open-weights as open-source in marketing materials is the usual misrepresentation. But now with the restriction on commercial use (which is against opensource definition) it is not even open-weights, technically it would be more accurate to call it weights-available.
I'm willing to bet a nonzero amount of its training material is GPL, so I'll treat it as GPL licensed instead and use it however the fuck I want.<p>If AI labs get to ignore licenses, so do we.
> I'm willing to bet a nonzero amount of its training material is GPL<p>Image data?<p>> GPL licensed instead and use it however the fuck I want<p>GPL is not a "use it however the fuck I want" license. Maybe you're thinking of the WTFPL?
It's not going to matter unless you plan to commercially deploy the model, as far as I see.<p>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?<p>That said, I am disappointed that the model is not actually open-weights as I expected based on the headline.
It could have an attempt at steganographic watermarking trained into the model.
<i>It's not going to matter unless you plan to commercially deploy the model, as far as I see.</i><p>It's not going to matter then, either. What are they going to do, sue me for copyright infringement?
That seems quite important. Definitely requires any grown up company to have to do a bunch of legal paperwork to use it.
You are not wrong, but will a judge and jury be competent enough to understand the difference after you've spent several hundred thousand in litigation?
Agreed.
I have seen comments on X that they will consider a revenue cap for the non-commercial restriction.<p>A researcher commented among the lines that they have no interest in restricting creators from using it for monetized YT content.<p>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.<p>So it sounds like one could likely receive a free commercial license if needed.
Was going to post about this: the last image models with Apache 2.0 license seem to be from 2025, recent Qwen models are "non-commercial use".
Companies can use llm to license-wash open source code regardless of license.<p>How difficult would it be to use this model to create a second model without licensing issues?
Why would you even do that? Just... use it? There hasn't been any legal precedent on if models can even be copyright restricted. Labs just keep publishing license documents as if they matter.
Well, it is an indication that it matters to the lab, so if you don't want legal fees to be the first one to set precedent, then it does matter a great deal.
No one really cares about licenses, especially now. Just use it, the odds of consequences are unfathomably low.
What.
I love the non-commercial clauses because of how many people are using these for deceptive ads and “virtual staging” and fake social media accounts. Anything that makes those guys lives harder while still letting me make silly pictures for my kids and tapestries for my D&D campaign feel fine by me.
This achieves absolutely nothing to that end.<p>People can continue to use closed SOTA models to generate outputs for commercial or malicious purposes.<p>What this research license achieves is that we cannot use this model in applications we publish.
You think they care about the probably unenforceable license terms?
What are the top image models that still use a less restrictive license today?
Boogu-Image has the Apache 2.0 License [1] (good coherence, but outputs can look synthetic).<p>And Krea 2 has a community license [2] that is fairly permissive - I think commercial usage is allowed under $1 million.<p>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.<p>[1] - <a href="https://github.com/Boogu-Project/Boogu-Image" rel="nofollow">https://github.com/Boogu-Project/Boogu-Image</a><p>[2] - <a href="https://www.krea.ai/krea-2-licensing" rel="nofollow">https://www.krea.ai/krea-2-licensing</a><p>[3] - <a href="https://genai-showdown.specr.net/?models=fd,hd,kd,qi,f2d,zt,id4,k2,sxj,bg1" rel="nofollow">https://genai-showdown.specr.net/?models=fd,hd,kd,qi,f2d,zt,...</a>
Tune the weights a bit and call them derivative work.
I run a prompt-to-ui design site that uses image models for the design process[1]. The text rendering especially makes this model deeply interesting to me, despite the license. Here are some tests using my harness comparing the outputs of gpt-image-2 and qwen 2.1:<p><a href="https://html.non.io/qwen-comparison/" rel="nofollow">https://html.non.io/qwen-comparison/</a><p>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.<p>I'll be trying a post-training run on this for web design, it has some serious potential.<p>[1] diffui.ai
Those simple prompts produce nearly the exact same layout in the 2 different models?
My harness expands the prompt into a json representation that specifies layout much more rigorously, which is why you see such that amount of alignment between the two.<p>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.
Private harness?
That's so cool!
Totally normal for modern models due to training on the same datasets supplied by third parties, dataset contamination, and mode collapse, especially for simple prompts that don't have enough semantic capacity. -isms are often very similar even without distillation, and tend to come and go in waves along with model generations.
Equally confused with this. They must be using a lot more guidance than just the provided prompt.
Qwen is trained off of gpt’s outputs. This is both a positive and negative
Qwen's latest image models have a ton of distillation from gpt-image, same with Grok Imagine.<p>Even the artifacts are getting picked up.
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.<p>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.
> The text rendering definitely is much, much better than anything else on the open weights market right now<p>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.
The capabilities of local LLM text-to-image is honestly pretty damn impressive. IMO, I think local image generation is currently ahead of local code generation. I can get an image in seconds locally with the quality being way higher than what I'd expect from a local model. However with coding it's much slower and much less impressive. I'm sure there's a reason for this and I'm not an AI expert so I'll let the smarter folks tell me why, but that's just been my observation thus far.
I've played with diffusion models on and off since the first release of Stable Diffusion - just for amusement, without a particular goal.<p>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.<p>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.
I think there was a lot more brainpower invested in the media generation side of things. The noise-based diffusion technique is further developed. It had a discovery of applying a physics-based understanding of Brownian motion to guide it. Image generation has comparatively simple training process - this is an image with dog, and without dog (contrastive learning).<p>Might be worth to watch the diffusion based LLMs.
Remember that quality output is a necessary but insufficient property of a generative model.<p>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.<p>So, as others have said, the artist knows better.
I mean I’m sure it’s the reverse for an artist. They would be less impressed with the image and more impressed with the code quality
The point I think is interesting is that this is just 7B. The current SOTA 7B LLMs are barely usable for quite simple coding.
Text is in a sense way harder to do than images because of radical nonlocality. A word at the start of one paragraph can directly influence the meaning of a word five paragraphs away. Whereas images typically represent the real world, or at least a spatial domain, which gives you a lot of structure 'for free'. If you are drawing a human, you can make a reasonable guess where their hands go in relation to their face. If someone hands you the first half of an essay, finishing it is not trivial.
To generalize, LLMs are great at what you are not skilled at.
That's a fair statement, I agree. I'm quite an abysmal artist so I could be a victim of my own bias here
How do you use this model locally, similarly to using `llama-server -m <model>`?<p>(I mean: outside direct or substantial use of Python, and running the Neural Network in the most efficient way.)
I've set it up on my local machine just now, as my first local image diffuser. I can confirm it's very easy.<p>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.<p>[0] <a href="https://github.com/leejet/stable-diffusion.cpp/blob/master/docs/build.md" rel="nofollow">https://github.com/leejet/stable-diffusion.cpp/blob/master/d...</a><p>[1] <a href="https://github.com/leejet/stable-diffusion.cpp/blob/master/docs/qwen_image_2.1.md" rel="nofollow">https://github.com/leejet/stable-diffusion.cpp/blob/master/d...</a><p>[2] <a href="https://i.ibb.co/yMknC2K/output.png" rel="nofollow">https://i.ibb.co/yMknC2K/output.png</a>
Additional question is what kind of local hardware would be required for this? 7B parameters <i>sounds</i> very light weight, but I'm not sure. (Edit: The download is 33 GB).<p>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.
It's about 16 GiB at Q8 quants (combining both the image and language parts). (Meaning, community quantized models from HuggingFace).<p>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).<p>> <i>"I really wish people didn't use Python for this stuff. A simple C/C++ program would be so much better."</i><p>You mean besides stable-diffusion.cpp ?
There is difussion.cpp which is intended for those types of models. I set up krea-2-turbo with the help of ChatGPT 2 months ago, if you have a capable computer that's what I would suggest once it becomes supported.
I am using sd.cpp, which is the cousin of llama.cpp: <a href="https://github.com/leejet/stable-diffusion.cpp" rel="nofollow">https://github.com/leejet/stable-diffusion.cpp</a><p>it already has day-0 qwen image 2.1 support!
I am on AI max 395, comfyUI+qwen models is all you technically need. With today's release, I just built a quick and dirty html that allow simpler prompt use and edits ( via headless comfyui ).. its not bad for a day's work, but a little too unpolished to publish. I would say, try comfyUI first ( complex, but it worked OOTB ).
on the linked GitHub page they list support Diffusers, ComfyUI, vLLM-Omni, SGLang, and LightX2V with links to each
> <i>Diffusers, ComfyUI, vLLM-Omni, SGLang, and LightX2V</i><p>I think that's all Python (not a direct executable).<p>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 use opencode + <a decent saas llm> to set up all this new ai generation stuff. GLM-5.3 is my current gun. Safely inside podman containers too because I dont trust this fast moving python eco system at all. Never do I want this running on my main OS.<p>I have FLUX.2 klein and dev, Ideogram, LaDA-Image and SenseNova locally. Works great. Ive never touched a file.<p>The days of making container yamls myself is over. I read them but I dont edit anymore.
Probably ComfyUI is one of the easiest way to get started with local image/video models. Or perhaps vLLM, if they have support for it already, would be something like `vllm serve <model> --omni --port 9080`
Unsloth Desktop is the easiest way imo. There are already gguf quants of this model, or simply wait until the official one comes out.
why not just as you suggested i.e. <a href="https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html#llama-server" rel="nofollow">https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp....</a> then get the result either via a UI or wget/curl it back?
A 7B diffusion model can now render CJK text better than Microsoft Windows.
Ideogram 4 has been around for a while haha
Just think about how recently we got that feature in the official ChatGPT image gen. And now we have that running locally — assuming that is, I can figure out how to get this running on my Mac — blows my mind.
Is ChatGPT really that good?<p>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?
I want to say that they released a version since then before 2.5, but I'm not entirely sure. I should also note that I do not know a single Chinese character, so it's possible they are broken in such a way that I wouldn't necessarily notice. I do remember, however, that English text used to be broken; now it's completely readable.
Pretty sure comfyui has a mac executable
Anyone get success editing videos, frame by frame, using image editing AI? Which model works well?<p>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.
God I love the Qwen team. Easily the most diverse set of models from all the Chinese labs. Only Gemini/DeepMind comes close.
They finally fixed their VAE. It really held back their models over the last 2 years.<p>EDIT: It still produces artifacts it's better but unusable for production work. In midvalues you will see a slight dot pattern.
Its happy to see a new open image model from qwen. But the license is a let down. And it dosent even beat their closed qwen3 image wich is already a bit old.
The minute you start looking at other non-frontier models, you understand why Dario, Altman, and Musk are coming together to say we need regulation.<p>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)
I am really grateful to the Chinese Labs for open sourcing their best models. If it was left to the Americans, we would be forced to pay obscene API fees to use them.
Note that the license on this has this in it:<p>> You shall not use the Materials for any commercial purpose without obtaining a separate commercial license from us.<p>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.<p><a href="https://github.com/QwenLM/Qwen-Image-2.1/blob/main/LICENSE" rel="nofollow">https://github.com/QwenLM/Qwen-Image-2.1/blob/main/LICENSE</a>
Honestly, that's fine. The commercial license isn't that bad, and cloud providers selling API access to this can afford it.<p>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.
Good luck to them enforcing that license.
Which lab is open sourcing their model?
My first impression is that it's not so good at following prompt directions. I asked it to place a 3D text made of glass in a particular city. It instead gave me a broken 3D text on a white background. Maybe with different seeds it gets better, but it's more of a trial and error process than reliable results.
Try translating your prompt to Chinese first, it seems a lot better at understanding and following Chinese prompts even with the translation hop.
You could try attaching other images as references (I think you can attach a maximum of 10 images). If the attachments can be blurred or sketchy or generic enough, they could be used for generalization.
Curious how Qwen Image 2.1 performs on less common datasets compared to SDXL. Always good to see more open models.
Love that these models are releasing making it easier to get away from these stupid subscriptions.
Interesting in the example of assembling the Cheers team how the otherwise great result genericizes Shelley Long.
I'm actually looking for a model that is able to create old school pixel art (like 90s style, games like Sierra and so on). But to this date, I haven't seen anything yet. It all reeks of "AI slop". Maybe this is a good thing, I don't know. But if anyone has any tips, I'd appreciate. It's for my own personal use.
Qwen and Alibaba are the biggest competitor for basically every model out there. They're beating the benchmarks like top-frontier models, focused on open-source and much cheaper than the competitors.<p>Excited to see what the future holds for them!
People it renders look Asian. If you give it a reference image of a caucasian it will render an Asian. I wonder why?
I don't want only cherry picked examples. Show me failure modes too.
its opensource?
Very impressive, and kind of worrying a 7B model can have such capabilities. The implications are huge. And Qwen does no watermarking (yet) yeah?
Boy do I love waking up to find a new awesome toy from the Qwen team waiting for me to play with! Pulling it now
Are all the humans in those photos fake?
The uncanny valley cheers is really freaking me out.
What problem is this trying to solve?
is it possible to run on 8gb ?
While the license of this model is a shame it is still unenforceable.<p>I know a few friends of mine who are running models and are ignoring the licence.<p>Whether it is AGPL 3.0, or a completely restrictive license, it is going to get broken anyway and be used for commercial purposes.<p>I don't know anyone who looks at the licenses of the OSS software they are using.<p>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.<p>So it begs the question, why should we abide by <i>their</i> licenses of their models?
Woody Harrelson?
Not sure there's a better avatar for the absurdity of AI slop imagery than the "cowboy on horseback". That's a pony with a child's saddle on it, and they've composited a grown man on top of it.
I would call this a license trap: Qwen RESEARCH LICENSE AGREEMENT<p>Code on github, models on huggingface, nice intro text: "We are excited to open-source Qwen-Image-2.1 [...]".<p>meh...
Still fails to generate smoothly animated sprites, although the native RGBA transparency is nice. Anyone found one that can?
While I'm impressed with the Bluey example, the lack of Muffin disappoints me.
Is it just me or are Alibaba / qwen’s websites often appear broken / very slow?
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Image gen you eyeball one frame and stop, code needs hundreds of tokens all correct in sequence, one bad line and the whole thing fails.