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decntai/
sd-cli

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sd-cli — multi-arch stable-diffusion.cpp builds (incl. Blackwell)

Prebuilt sd-cli binaries from leejet/stable-diffusion.cpp, plus the build recipe. Two CUDA variants, each a fat binary covering every NVIDIA GPU architecture from its floor up through Blackwell.

Supported GPU architectures

SM	Architecture	Example GPUs	cu12	cu13
sm_70	Volta	Tesla V100, Titan V	yes	no
sm_75	Turing	RTX 20-series, GTX 16-series, T4	yes	yes
sm_80	Ampere (DC)	A100, A30	yes	yes
sm_86	Ampere	RTX 30-series, A40, A10, A2000	yes	yes
sm_89	Ada Lovelace	RTX 40-series, L4, L40S	yes	yes
sm_90	Hopper	H100, H200, GH200	yes	yes
sm_100	Blackwell (DC)	B100, B200, GB200	yes	yes
sm_120	Blackwell	RTX 50-series, RTX PRO 6000 Blackwell	yes	yes

Both binaries also embed sm_120 PTX (virtual arch), so they JIT-forward onto future architectures. Use cu12 for older drivers / Volta; cu13 for CUDA-13 hosts (Volta was dropped upstream in CUDA 13).

Usage

Download from the Files tab (here) or Releases (GitHub). Dynamically linked against the CUDA runtime (cudart/cublas/nccl) — same as upstream — so run on a box where those libs are on the loader path (a PyTorch/CUDA image, or pip install nvidia-cuda-runtime-cu12 nvidia-cublas-cu12 nvidia-nccl-cu12 + LD_LIBRARY_PATH).

sd-cli-cu12 -m model.gguf -p "a lovely cat" -o out.png

Build it yourself via the included Dockerfiles + build-and-validate-sd-cli.sh.

License

Build recipe: MIT (see LICENSE). Binaries derive from stable-diffusion.cpp and ggml, both MIT — see NOTICE.

decntai/sd-cli

sd-cli — multi-arch stable-diffusion.cpp builds (incl. Blackwell)

Supported GPU architectures

Usage

License

decntai/
sd-cli