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▲ mdp2021 9 hours ago

How do you use this model locally, similarly to using `llama-server -m <model>`?

(I mean: outside direct or substantial use of Python, and running the Neural Network in the most efficient way.)

▲peri-cl 6 hours ago | parent | next [-]

I've set it up on my local machine just now, as my first local image diffuser. I can confirm it's very easy.

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...

[2] https://i.ibb.co/yMknC2K/output.png

▲mdp2021 3 hours ago | parent [-]

Thank you! Can you please check how much RAM does it consume (and require)?

▲peri-cl 2 hours ago | parent [-]

This is what the runtime reports, at Q8:

    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)
▲mdp2021 2 hours ago | parent [-]

That suggests that 16GB RAM will not be enough.

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.

▲rwmj 7 hours ago | parent | prev | next [-]

Additional question is what kind of local hardware would be required for this? 7B parameters sounds very light weight, but I'm not sure. (Edit: The download is 33 GB).

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.

▲peri-cl 6 hours ago | parent [-]

It's about 16 GiB at Q8 quants (combining both the image and language parts). (Meaning, community quantized models from HuggingFace).

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 ?

▲rwmj 5 hours ago | parent [-]

> You mean besides stable-diffusion.cpp ?

Yes, thanks, I didn't know about that. Will try it.

▲iugtmkbdfil834 2 hours ago | parent | prev | next [-]

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 ).

▲Iolaum 8 hours ago | parent | prev | next [-]

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.

▲nkhgfugjk 7 hours ago | parent | prev | next [-]

I am using sd.cpp, which is the cousin of llama.cpp: https://github.com/leejet/stable-diffusion.cpp

it already has day-0 qwen image 2.1 support!

▲finnjohnsen2 4 hours ago | parent | prev | next [-]

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.

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.

▲fp64 9 hours ago | parent | prev | next [-]

on the linked GitHub page they list support Diffusers, ComfyUI, vLLM-Omni, SGLang, and LightX2V with links to each

▲mdp2021 8 hours ago | parent [-]

> Diffusers, ComfyUI, vLLM-Omni, SGLang, and LightX2V

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.

▲wgd 7 hours ago | parent [-]

"just"

I don't think I have ever once run "pip install transformers" and had it work without three rounds of fiddling

▲mdp2021 2 hours ago | parent [-]

Or trying to install the whole of CUDA on machines that do not even have a GPU (not Nvidia, not anything past the embedded)...

Yep, that's (also) what I meant ;)

Lean, efficient... Also sensible and trouble-less.

▲iugtmkbdfil834 an hour ago | parent [-]

The 'not nvidia' is no longer a deal breaker by itself.

▲leumon 5 hours ago | parent | prev | next [-]

Unsloth Desktop is the easiest way imo. There are already gguf quants of this model, or simply wait until the official one comes out.

▲embedding-shape 9 hours ago | parent | prev | next [-]

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`

▲utopiah 9 hours ago | parent | prev | next [-]

why not just as you suggested i.e. https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.... then get the result either via a UI or wget/curl it back?

▲mdp2021 9 hours ago | parent [-]

I am not sure that llama.cpp also supports image generation models.

▲utopiah 8 hours ago | parent [-]

it's multimodal, see https://github.com/ggml-org/llama.cpp/blob/master/docs/multi...

▲exe34 8 hours ago | parent [-]

Multimodal doesn't guarantee input and output.

> Currently, we support image, audio and video input.

▲utopiah 8 hours ago | parent [-]

Seems I'm missing something. Does this model support other inputs?

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?

▲ 8 hours ago | parent | next [-]
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▲mdp2021 7 hours ago | parent | prev | next [-]

Not all architectures are supported by llama.cpp . The GGUF format encodes the NN in a standardized way, but then you need code that can use that NN structure.

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 {
...
▲utopiah 6 hours ago | parent [-]

I haven't used llama.cpp for image generation either but I recall an issue about it. Unfortunately I can't pinpoint it now and there is the older closed issue https://github.com/ggml-org/llama.cpp/issues/4408 so unless mtmd supports also multimodal outputs out of the box safe to assume output is still limited to text.

▲exe34 7 hours ago | parent | prev [-]

It's a diffusion model, completely different from autoregressive attention models.

▲utopiah 6 hours ago | parent [-]

like https://github.com/ggml-org/llama.cpp/tree/master/examples/d... ?

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.

▲exe34 5 hours ago | parent [-]

Aha I was wrong. Thanks for sharing that!

▲utopiah 5 hours ago | parent [-]

no worries, I was wrong too, it is multimodal but only for inputs apparently, so for now there seem to only be text as output but no image as output

▲ 7 hours ago | parent | prev [-]
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