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docs: clarify application-owned operator extensions

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leejet committed 2026-11-01 03:08:02 +08:00
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docs/build.md
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@@ -32,8 +32,10 @@ cmake -S . -B build-upstream -DSD_USE_UPSTREAM_GGML=ON -DSD_GGML_SOURCE_DIR=../g
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.
Upstream compatibility mode uses the selected library and matching private
headers. The patched GGML build uses public headers, including `ggml-ext.h` and
`ggml-graph.h`. 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
@@ -50,10 +52,12 @@ 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.
with `ggml_DIR` or `CMAKE_PREFIX_PATH`. An installed patched package must include
the extension and graph APIs; its source tree is not required for private
headers. The installed library must use the same ABI settings as sd.cpp,
including `GGML_MAX_NAME`. For an upstream package, also set
`SD_USE_UPSTREAM_GGML=ON` and point `SD_GGML_SOURCE_DIR` to its matching source
tree for the compatibility layer's private headers.
## WebP and WebM Support in Examples
@@ -95,8 +99,8 @@ cmake --build . --config Release
## Build with CUDA
Native SageAttention is included when using CUDA with patched GGML
(`SD_USE_UPSTREAM_GGML=OFF`).
The sd.cpp SageAttention extension is included when using CUDA with patched
GGML (`SD_USE_UPSTREAM_GGML=OFF`).
See [SageAttention](sage_attention.md) 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](https://developer.nvidia.com/cuda-downloads). Recommended to have at least 4 GB of VRAM.