leejet

Due to the structural differences between this model and the original one, using the weights directly could lead to errors. I'll take some time to see if it's necessary to...

> Could you answer my other question, please? https://github.com/leejet/stable-diffusion.cpp/issues/28#issuecomment-1694486320 I have responded on corresponding issue

@walking-octopus SDXL Turbo is now supported.

> It is possible to use cuBLAS by enabling it when compiling: `-DGGML_CUBLAS=ON` > > Maybe add this to the readme? GPU support is already in my TODO list, and...

> Time for decode_first_stage stays at about 56s in all tests. This is expected as ggml conv 2d will not be optimized by blas and will not run on GPU,...

> It may be possible to find an optimal setting by testing different combinations of openblas threads and --threads. I guess this also applies to llama.cpp when using openblas. Because...

Discussions is indeed a good place for discussions and sharing. I've set up Discussions for this project. Would you mind moving this issue to Discussions?

By the way, I took a look at these two projects, and they seem to be fine-tuning weights on official models. So, they should be applicable to this project.

Quantization doesn't have any special effects when lowering the resolution. Quantization mainly involves sacrificing some computational precision in exchange for lower memory and storage usage.

> P.s: I tried to run f32 model but got this error: [ERROR] stable-diffusion.cpp:2893 - tensor 'model.diffusion_model.input_blocks.1.1.proj_in.weight' has wrong shape in model file: got [320, 320, 1, 1], expected [1,...