It's also kind of interesting that no other languages than English and Chinese are named or shown...

You can select the area you want edited on 4o, and it’ll keep the rest unchanged

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.

https://mordenstar.com/blog/edits-with-kontext

Adobe has never been alone. Photoshop’s AI stuff is consistently behind OSS models and workflows. It’s just way more convenient

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!

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 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.

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.

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.

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

You're probably going to have to wait a couple of days for 4 bit quantized versions to pop up. It's 20B parameters.

   # Configure NF4 quantization
   quant_config = PipelineQuantizationConfig(
       quant_backend="bitsandbytes_4bit",
       quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16},
       components_to_quantize=["transformer", "text_encoder"],
   )

   # Load the pipeline with NF4 quantization
   pipe = DiffusionPipeline.from_pretrained(
       model_name,
       quantization_config=quant_config,
       torch_dtype=torch.bfloat16,
       use_safetensors=True,
       low_cpu_mem_usage=True
   ).to(device)

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.

> 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.

> 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.

16GiB RAM with 8-bit quantization.

This is a slightly scaled up SD3 Large model (38 layers -> 60 layers).

For prod inference, 1xH100 is working well.

Qwen-Image requires at least 24GB VRAM for the full model, but you can run the 4-bit quantized version with ~8GB VRAM using libraries like AutoGPTQ.

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.

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.

Seems a bit drastic to compare Ghibli style transfer to revenge porn, but you do you I guess.

I mean, did you really expect anything more from the internet? Maybe I'm wrong, but hentai, erotic roleplay, and nudify applications seem to still represent a massive portion of AI use cases. At least in the case of ero RP, perhaps the exploitation of people for pornography might be lessened....

Welcome to the internet, which is for porn (and cat pictures).

I love that this is the only thing the community wants to know at every announce of a new model, but no organization wants to face the crude reality of human nature.

That, and the weird prudishness of most american people and companies.