I’m interested to see what this model can do, but also kinda annoyed at the use of a Studio Ghibli style image as one of the first examples. Miyazaki has said over and over that he hates AI image generation. Is it really so much to ask that people not deliberately train LoRAs and finetunes specifically on his work and use them in official documentation?
It reminds me of how CivitAI is full of “sexy Emma Watson” LoRAs, presumably because she very notably has said she doesn’t want to be portrayed in ways that objectify her body. There’s a really rotten vein of “anti-consent” pulsing through this community, where people deliberately seek out people who have asked to be left out of this and go “Oh yeah? Well there’s nothing you can do to stop us, here’s several terabytes of exactly what you didn’t want to happen”.
rushingcreek · 2h ago
Not sure why this isn’t a bigger deal —- it seems like this is the first open-source model to beat gpt-image-1 in all respects while also beating Flux Kontext in terms of editing ability. This seems huge.
jug · 38m ago
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.
jacooper · 18m ago
Not to mention, flux models are for non-commercial use only.
doctorpangloss · 5m ago
the license for flux models is $1,000/mo, hardly an obstacle to any serious commercial usage
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.
tetraodonpuffer · 1h ago
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.
cma · 59m ago
If 40GB you can lightly quantize and fit it on a 5090.
TacticalCoder · 1h ago
> I think the fact that, as far as I understand, it takes 40GB of VRAM to run, is probably dampening some of the enthusiasm.
40 GB of VRAM? So two GPU with 24 GB each? That's pretty reasonable compared to the kind of machine to run the latest Qwen coder (which btw are close to SOTA: they do also beat proprietary models on several benchmarks).
zamadatix · 1h ago
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!
rushingcreek · 1h ago
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.
rwmj · 2h ago
This may be obvious to people who do this regularly, but what kind of machine is required to run this? I downloaded & tried it on my Linux machine that has a 16GB GPU and 64GB of RAM. This machine can run SD easily. But Qwen-image ran out of space both when I tried it on the GPU and on the CPU, so that's obviously not enough. But am I off by a factor of two? An order of magnitude? Do I need some crazy hardware?
icelancer · 25m ago
> This may be obvious to people who do this regularly
This is not that obvious. Calculating VRAM usage for VLMs/LLMs is something of an arcane art. There are about 10 calculators online you can use and none of them work. Quantization, KV caching, activation, layers, etc all play a role. It's annoying.
But anyway, for this model, you need 40+ GB of VRAM. System RAM isn't going to cut it unless it's unified RAM on Apple Silicon, and even then, memory bandwidth is shot, so inference is much much slower than GPU/TPU.
liuliu · 7m ago
16GiB RAM with 8-bit quantization.
This is a slightly scaled up SD3 Large model.
mortsnort · 1h ago
I believe it's roughly the same size as the model files. If you look in the transformers folder you can see there are around 9 5gb files, so I would expect you need ~45gb vram on your GPU. Usually quantized versions of models are eventually released/created that can run on much less vram but with some quality loss.
halJordan · 3m ago
Model size = file for fp8, so if this was released at fp16 then 40-ish, if it's quantized to fp4 then 10ish
foobarqux · 1h ago
Why doesn't huggingface list the aggregate model size?
simonw · 6m ago
I've been bugging them about this for a while. There are repos that contain multiple model weights in a single repo which means adding up the file sizes won't work universally, but I'd still find it useful to have a "repo size" indicator somewhere.
Huggingface is just a git hosting service, like github. You can add up the sizes of all the files in the directory yourself
zippothrowaway · 1h ago
You're probably going to have to wait a couple of days for 4 bit quantized versions to pop up. It's 20B parameters.
TacticalCoder · 1h ago
> I think the fact that, as far as I understand, it takes 40GB of VRAM to run, is probably dampening some of the enthusiasm.
For PCs I take it one that has two PCIe 4.0 x16 or more recent slots? As in: quite some consumers motherboards. You then put two GPU with 24 GB of VRAM each.
A friend runs this (don't know if the tried this Qwen-Image yet): it's not an "out of this world" machine.
oceanplexian · 45m ago
Does anyone know how they actually trained text rendering into these models?
To me they all seem to suffer from the same artifacts, that the text looks sort of unnatural and doesn't have the correct shadows/reflections as the rest of the image. This applies to all the models I have tried, from OpenAI to Flux. Presumably they are all using the same trick?
yorwba · 35m ago
It's on page 14 of the technical report. They generate synthetic data by putting text on top of an image, apparently without taking the original lighting into account. So that's the look the model reproduces. Garbage in, garbage out.
Maybe in the future someone will come up with a method for putting realistic text into images so that they can generate data to train a model for putting realistic text into images.
halJordan · 1m ago
[delayed]
doctorpangloss · 4m ago
i'm not sure if that's such garbage as you suggest, surely it is helpful for generalization yes? kind of the point of self-supervised models
nickandbro · 3h ago
The fact that it doesn’t change the images like 4o image gen is incredible. Often when I try to tweak someone’s clothing using 4o, it also tweaks their face. This only seems to apply those recognizable AI artifacts to only the elements needing to be edited.
vunderba · 1h ago
That's why Flux Kontext was such a huge deal - it gave you the power of img2img inpainting without needing to manually mask the content.
You can select the area you want edited on 4o, and it’ll keep the rest unchanged
barefootford · 46m ago
gpt doesn't respect masks
icelancer · 27m ago
Correct. Have tried this without much success despite OpenAI's claims.
Destiner · 10m ago
The text rendering is impressive, but I don't understand the value — wouldn't it be easier to add any text that you like in Figma?
doctorpangloss · 6m ago
the value is: the absence of text where you expect it, and the presence of garbled text, are dead giveaways of AI generation. i'm not sure why you are being downvoted, compositing text seems like a legitimate alternative.
artninja1988 · 3h ago
Insane how many good Chinese open source models they've been releasing. This really gives me hope
It's also kind of interesting that no other languages than English and Chinese are named or shown...
sampton · 19m ago
Short canva.
yjftsjthsd-h · 3h ago
Wow, the text/writing is amazing! Also the editing in general, but the text really stands out
anon191928 · 3h ago
It will take years for people to use these but Adobe is not alone.
herval · 1h ago
Adobe has never been alone. Photoshop’s AI stuff is consistently behind OSS models and workflows. It’s just way more convenient
dvt · 1h ago
I think Adobe is also very careful with copyrighted content not being a part of their models, which inherently makes them of lower quality.
doctorpangloss · 2m ago
as long as you don't consider the part of the model which understands text as part of the model, and as long as you don't consider copyrighted text content copyrighted :)
herval · 19m ago
They have a much better and cleaner dataset than Stable Diffusion & others, so I’d expect it to be better with some kinds of images (photos in particular)
esafak · 18m ago
Team Qwen: Please stop ripping off Studio Ghibli to demo your product.
It reminds me of how CivitAI is full of “sexy Emma Watson” LoRAs, presumably because she very notably has said she doesn’t want to be portrayed in ways that objectify her body. There’s a really rotten vein of “anti-consent” pulsing through this community, where people deliberately seek out people who have asked to be left out of this and go “Oh yeah? Well there’s nothing you can do to stop us, here’s several terabytes of exactly what you didn’t want to happen”.
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.
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.
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.
40 GB of VRAM? So two GPU with 24 GB each? That's pretty reasonable compared to the kind of machine to run the latest Qwen coder (which btw are close to SOTA: they do also beat proprietary models on several benchmarks).
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!
This is not that obvious. Calculating VRAM usage for VLMs/LLMs is something of an arcane art. There are about 10 calculators online you can use and none of them work. Quantization, KV caching, activation, layers, etc all play a role. It's annoying.
But anyway, for this model, you need 40+ GB of VRAM. System RAM isn't going to cut it unless it's unified RAM on Apple Silicon, and even then, memory bandwidth is shot, so inference is much much slower than GPU/TPU.
This is a slightly scaled up SD3 Large model.
I ended up building my own tool for that: https://tools.simonwillison.net/huggingface-storage
For PCs I take it one that has two PCIe 4.0 x16 or more recent slots? As in: quite some consumers motherboards. You then put two GPU with 24 GB of VRAM each.
A friend runs this (don't know if the tried this Qwen-Image yet): it's not an "out of this world" machine.
To me they all seem to suffer from the same artifacts, that the text looks sort of unnatural and doesn't have the correct shadows/reflections as the rest of the image. This applies to all the models I have tried, from OpenAI to Flux. Presumably they are all using the same trick?
Maybe in the future someone will come up with a method for putting realistic text into images so that they can generate data to train a model for putting realistic text into images.
https://mordenstar.com/blog/edits-with-kontext
https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Q...