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I have one of my own. My reason for this is that you can get up to 2x performance compared to llama.cpp on the same model weights. It is optimized for single model to serve it well.

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I did, the sd.cpp take too much time on my laptop to generate image, the issue is known and it did not seem like it gonna be resolved, so I build my own, and that's why https://github.com/netdur/qwen-image-cplus

Have you considered contributing upstream?

llama.cpp is a generalized inference engine; therefore, model-specific optimizations should remain outside its core architecture. When developing a bespoke inference application, it is standard practice to fuse operations and heavily optimize the compute kernels for the specific target model.

llama.cpp has strict policies on AI-assisted contributions. Most of these specialization projects are done by coding agents. So llama.cpp won't take them (which is fair).

No, it doesn't. AI contributions are welcome if you understand the code. Ping me directly if you want to have something upstreamed.

> if you understand the code

Well, I don't mean to sound rude or anything but that can be a lot to expect from maintainers of those projects.

As I said, it's better for projects like llama.cpp to be selective in this regard.

Well even that is okay. I can use my agent to extract something useful from it.

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