DarthAffe/stable-diffusion.cpp
1
0 Fork 0
mirror of https://github.com/leejet/stable-diffusion.cpp.git synced 2026-11-11 11:26:34 +00:00
Code Issues Packages Projects Releases Wiki Activity

ci: align MUSA Docker image with upstream SDK 5.2.0 and mp_31

Browse Source
This commit is contained in:
leejet committed 2026-10-17 14:14:29 +08:00
1 parent 68285df319
commit f0aac524d3
2 files changed
+9 -8
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

docker/Dockerfile.musa
+5 -6
View File
@@ -1,7 +1,6 @@
ARG MUSA_VERSION=rc4.2.0 ARG MUSA_IMAGE=registry.mthreads.com/mcconline/inference/pytorch:2.9.1.post1-py3.10-musa5.2.0-mp31-devel-ubuntu22.04-amd64
ARG UBUNTU_VERSION=22.04
FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 as build FROM ${MUSA_IMAGE} AS build
# sd-server embeds the web UI at build time, so the build image needs Node/pnpm. # sd-server embeds the web UI at build time, so the build image needs Node/pnpm.
RUN apt-get update && apt-get install -y --no-install-recommends ccache cmake git ca-certificates curl gnupg && \ RUN apt-get update && apt-get install -y --no-install-recommends ccache cmake git ca-certificates curl gnupg && \
@@ -22,9 +21,8 @@ COPY . .
RUN mkdir build && cd build && \ RUN mkdir build && cd build && \
cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ \ cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ \
-DCMAKE_C_FLAGS="${CMAKE_C_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
-DCMAKE_CXX_FLAGS="${CMAKE_CXX_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
-DSD_MUSA=ON \ -DSD_MUSA=ON \
-DMUSA_ARCHITECTURES=31 \
-DSD_BUILD_SHARED_LIBS=ON \ -DSD_BUILD_SHARED_LIBS=ON \
-DGGML_NATIVE=OFF \ -DGGML_NATIVE=OFF \
-DSD_BUILD_SHARED_GGML_LIB=ON \ -DSD_BUILD_SHARED_GGML_LIB=ON \
@@ -35,7 +33,7 @@ RUN mkdir build && cd build && \
-DCMAKE_BUILD_TYPE=Release && \ -DCMAKE_BUILD_TYPE=Release && \
cmake --build . --config Release cmake --build . --config Release
FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runtime FROM ${MUSA_IMAGE} AS runtime
COPY --from=build /sd.cpp/build/bin /sd.cpp/bin COPY --from=build /sd.cpp/build/bin /sd.cpp/bin
RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \ RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
@@ -45,3 +43,4 @@ RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
WORKDIR /sd.cpp WORKDIR /sd.cpp
ENTRYPOINT [ "/sd-cli" ] ENTRYPOINT [ "/sd-cli" ]
CMD []
docs/build.md
+4 -2
View File
@@ -119,14 +119,16 @@ cmake --build . --config Release
## Build with MUSA ## Build with MUSA
This provides GPU acceleration using Moore Threads GPU. Make sure to have the MUSA toolkit installed. This provides GPU acceleration using Moore Threads GPUs. The configuration below follows upstream validation with MUSA SDK 5.2.0 and MTT S5000 (PH1, `mp_31`). Install that toolkit before building. Older SDKs such as 4.2 may lack the CUB headers required by the current backend.
```shell ```shell
mkdir build && cd build mkdir build && cd build
cmake .. -DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release cmake .. -DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DMUSA_ARCHITECTURES=31 -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release cmake --build . --config Release
``` ```
The MUSA Docker image uses the same SDK 5.2.0 development image as upstream for both build and runtime stages, and targets `mp_31` only. It includes the development tools and PyTorch from that base image, so it is larger than a minimal runtime image. Other GPU architectures require separate validation.
## Build with Metal ## Build with Metal
Using Metal makes the computation run on the GPU. Currently, there are some issues with Metal when performing operations on very large matrices, making it highly inefficient at the moment. Performance improvements are expected in the near future. Using Metal makes the computation run on the GPU. Currently, there are some issues with Metal when performing operations on very large matrices, making it highly inefficient at the moment. Performance improvements are expected in the near future.