stable-diffusion.cpp/docs/build.md

Build from scratch

Get the Code

git clone --recursive https://github.com/leejet/stable-diffusion.cpp
cd stable-diffusion.cpp

• If you have already cloned the repository, you can use the following command to update the repository to the latest code.

cd stable-diffusion.cpp
git pull origin master
git submodule init
git submodule update

Selecting a GGML source tree

By default, sd.cpp builds the patched GGML submodule in ggml/. To build with an upstream GGML checkout instead, enable SD_USE_UPSTREAM_GGML and set SD_GGML_SOURCE_DIR:

cmake -S . -B build-upstream -DSD_USE_UPSTREAM_GGML=ON -DSD_GGML_SOURCE_DIR=../ggml-upstream
cmake --build build-upstream --config Release

The selected source tree supplies both the library and its private headers. Backend options such as -DSD_CUDA=ON apply to the selected tree as usual.

SD_USE_UPSTREAM_GGML defaults to OFF, which enables the patched GGML extensions. Set it to ON when using upstream GGML; it selects the compatibility mode and does not download or replace the GGML source tree. Upstream mode keeps the original FP8 safetensors handling: FP8 tensors are converted to F16 at load time (one byte per element in the file, two in RAM and VRAM). INT8 tensorwise/convrot is disabled and its model files are rejected with an explicit error. FP8 GGUF files, FP8 weight type requests and tensor type rules are also rejected; no automatic conversion is performed.

Upstream GGML may lack some operators and performance optimizations provided by the patched version. A warning is emitted during CMake configuration and when creating an inference context. Ordinary floating-point and shared GGML quantization types remain available, subject to backend operator support.

SD_USE_SYSTEM_GGML=ON instead links an installed GGML CMake package, located with ggml_DIR or CMAKE_PREFIX_PATH. In that mode, SD_GGML_SOURCE_DIR must point to the matching source tree for private headers. The installed library must use the same ABI settings as sd.cpp, including GGML_MAX_NAME. Set SD_USE_UPSTREAM_GGML=ON as well if the installed package is upstream GGML.

WebP and WebM Support in Examples

The example applications (examples/cli and examples/server) use libwebp to support WebP image I/O, and examples/cli can also use libwebm for .webm video output. Both are enabled by default. WebM output currently reuses libwebp to encode each frame as VP8 before muxing with libwebm.

If you do not want WebP/WebM support, you can disable them at configure time:

mkdir build && cd build
cmake .. -DSD_WEBP=OFF -DSD_WEBM=OFF
cmake --build . --config Release

If the submodules are not available, you can also link against system packages instead:

mkdir build && cd build
cmake .. -DSD_USE_SYSTEM_WEBP=ON -DSD_USE_SYSTEM_WEBM=ON
cmake --build . --config Release

Build (CPU only)

If you don't have a GPU or CUDA installed, you can build a CPU-only version.

mkdir build && cd build
cmake ..
cmake --build . --config Release

Build with OpenBLAS

mkdir build && cd build
cmake .. -DGGML_OPENBLAS=ON
cmake --build . --config Release

Build with CUDA

Native SageAttention is included when using CUDA with patched GGML (SD_USE_UPSTREAM_GGML=OFF). See SageAttention for GPU requirements and --sage-attn usage.

This provides GPU acceleration using NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. apt install nvidia-cuda-toolkit) or from here: CUDA Toolkit. Recommended to have at least 4 GB of VRAM.

mkdir build && cd build
cmake .. -DSD_CUDA=ON
cmake --build . --config Release

Build with HipBLAS

This provides GPU acceleration using AMD GPU. Make sure to have the ROCm toolkit installed. To build for another GPU architecture than installed in your system, set $GFX_NAME manually to the desired architecture (replace first command). This is also necessary if your GPU is not officially supported by ROCm, for example you have to set $GFX_NAME manually to gfx1030 for consumer RDNA2 cards.

Windows User Refer to docs/hipBLAS_on_Windows.md for a comprehensive guide.

mkdir build && cd build
if command -v rocminfo; then export GFX_NAME=$(rocminfo | awk '/ *Name: +gfx[1-9]/ {print $2; exit}'); else echo "rocminfo missing!"; fi
if [ -z "${GFX_NAME}" ]; then echo "Error: Couldn't detect GPU!"; else echo "Building for GPU: ${GFX_NAME}"; fi
cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGPU_TARGETS=$GFX_NAME -DAMDGPU_TARGETS=$GFX_NAME -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON
cmake --build . --config Release

Build with MUSA

This provides GPU acceleration using Moore Threads GPU. Make sure to have the MUSA toolkit installed.

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 --build . --config Release

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.

mkdir build && cd build
cmake .. -DSD_METAL=ON
cmake --build . --config Release

Build with Vulkan

Install Vulkan SDK from https://www.lunarg.com/vulkan-sdk/. On Ubuntu, install the Vulkan development packages and SPIR-V headers:

sudo apt-get install build-essential libvulkan-dev glslc spirv-headers

mkdir build && cd build
cmake .. -DSD_VULKAN=ON
cmake --build . --config Release

Build with OpenCL (for Adreno GPU)

Currently, it supports only Adreno GPUs and is primarily optimized for Q4_0 type

To build for Windows ARM please refers to Windows 11 Arm64

Building for Android:

Android NDK: Download and install the Android NDK from the official Android developer site.

Setup OpenCL Dependencies for NDK:

You need to provide OpenCL headers and the ICD loader library to your NDK sysroot.

• OpenCL Headers:

  # In a temporary working directory
  git clone https://github.com/KhronosGroup/OpenCL-Headers
  cd OpenCL-Headers
  # Replace <YOUR_NDK_PATH> with your actual NDK installation path
  # e.g., cp -r CL /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
  sudo cp -r CL <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
  cd ..
  

• OpenCL ICD Loader:

  # In the same temporary working directory
  git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
  cd OpenCL-ICD-Loader
  mkdir build_ndk && cd build_ndk
  
  # Replace <YOUR_NDK_PATH> in the CMAKE_TOOLCHAIN_FILE and OPENCL_ICD_LOADER_HEADERS_DIR
  cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
    -DOPENCL_ICD_LOADER_HEADERS_DIR=<YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
    -DANDROID_ABI=arm64-v8a \
    -DANDROID_PLATFORM=24 \
    -DANDROID_STL=c++_shared
  
  ninja
  # Replace <YOUR_NDK_PATH>
  # e.g., cp libOpenCL.so /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
  sudo cp libOpenCL.so <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
  cd ../..
  

Build stable-diffusion.cpp for Android with OpenCL:

mkdir build-android && cd build-android

# Replace <YOUR_NDK_PATH> with your actual NDK installation path
# e.g., -DCMAKE_TOOLCHAIN_FILE=/path/to/android-ndk-r26c/build/cmake/android.toolchain.cmake
cmake .. -G Ninja \
  -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
  -DANDROID_ABI=arm64-v8a \
  -DANDROID_PLATFORM=android-28 \
  -DGGML_OPENMP=OFF \
  -DSD_OPENCL=ON

ninja

(Note: Don't forget to include LD_LIBRARY_PATH=/vendor/lib64 in your command line before running the binary)

Build with SYCL

Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and Intel® oneAPI Base toolkit before start. More details and steps can refer to llama.cpp SYCL backend.

# Export relevant ENV variables
source /opt/intel/oneapi/setvars.sh

# Option 1: Use FP32 (recommended for better performance in most cases)
cmake .. -DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

# Option 2: Use FP16
cmake .. -DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON

cmake --build . --config Release