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license: mit
tags:
- stable-diffusion
- stable-diffusion-cpp
- cuda
- blackwell
- ggml
---
# 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.
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