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Author SHA1 Message Date
Robert Oswald 7e3e20a50b ci: add ROCm Dockerfile (#2094) 2026-10-13 21:09:13 +08:00
leejet d637cfb935 feat: add Qwen Image 2.1 Turbo sigma preset (#2130) 2026-10-12 23:09:42 +08:00
5 changed files with 100 additions and 6 deletions
+50
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@@ -0,0 +1,50 @@
FROM rocm/dev-ubuntu-24.04 AS build
# 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 \
build-essential git cmake ninja-build ccache ca-certificates curl gnupg && \
mkdir -p /etc/apt/keyrings && \
curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource-repo.gpg.key && \
gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource-repo.gpg.key && \
rm /tmp/nodesource-repo.gpg.key && \
echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
apt-get update && apt-get install -y --no-install-recommends nodejs && \
npm install -g pnpm@10.15.1 && \
apt-get clean && rm -rf /var/lib/apt/lists/*
WORKDIR /sd.cpp
# ROCm's clang is not on PATH in Docker RUN shells (profile scripts are not loaded).
ENV PATH="/opt/rocm/llvm/bin:$PATH"
COPY . .
ARG AMDGPU_TARGETS=""
RUN cmake . -B ./build -G Ninja \
-DCMAKE_C_COMPILER=clang \
-DCMAKE_CXX_COMPILER=clang++ \
-DSD_HIPBLAS=ON \
-DGGML_NATIVE=OFF \
-DAMDGPU_TARGETS="${AMDGPU_TARGETS}" \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
RUN cmake --build ./build --config Release --parallel
FROM rocm/dev-ubuntu-24.04 AS runtime
# ROCm clang links against its own OpenMP runtime in /opt/rocm/llvm/lib,
# and the ROCm runtime libs (hipblas, rocblas, amdhip64) live in /opt/rocm/lib.
ENV LD_LIBRARY_PATH="/opt/rocm/llvm/lib:/opt/rocm/lib:$LD_LIBRARY_PATH"
RUN apt-get update && \
apt-get install --yes --no-install-recommends libgomp1 && \
apt-get clean && rm -rf /var/lib/apt/lists/*
COPY --from=build /sd.cpp/build/bin /sd.cpp/bin
RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
printf '#!/bin/sh\nexec /sd.cpp/bin/sd-server "$@"\n' > /sd-server && \
chmod +x /sd-cli /sd-server
ENTRYPOINT [ "/sd-cli" ]
+32 -1
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@@ -24,12 +24,25 @@ docker build -f docker/Dockerfile -t sd .
## Building variants using Docker
Vulkan:
### Vulkan:
```shell
docker build -f docker/Dockerfile.vulkan -t sd .
```
### ROCm (AMD GPU):
Defining one or multiple targets with `--build-arg AMDGPU_TARGETS` is strongly recommended.
Building the image will otherwise likely fail or build for a target that does not fit your
hardware.
```shell
# Build for one target
docker build -f docker/Dockerfile.rocm --build-arg "AMDGPU_TARGETS=gfx1100" -t sd .
# Build for multiple targets
docker build -f docker/Dockerfile.rocm --build-arg "AMDGPU_TARGETS=gfx1030;gfx1100" -t sd .
```
## Run locally built image's CLI
```shell
@@ -37,3 +50,21 @@ docker run --rm -v /path/to/models:/models -v /path/to/output/:/output sd [args.
# For example
# docker run --rm -v ./models:/models -v ./build:/output sd -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
```
### ROCm (AMD GPU)
The user that executes this command needs to be in the `video` group and have read and write
access to `/dev/dri` and `/dev/kfd`.
`--device` and `--group-add` allow the container access to the GPU.
```shell
docker run --rm --device=/dev/dri --device=/dev/kfd --group-add video \
-v /path/to/models:/models -v /path/to/output/:/output \
sd [args...]
# For example
# docker run --rm --device=/dev/dri --device=/dev/kfd --group-add video \
# -v ./models:/models -v ./build:/output \
# sd -m /models/sd-v1-4.ckpt -v -o /output/output.png \
# -p "a lovely cat"
```
+10
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@@ -32,6 +32,16 @@ Run the following commands from the build directory. Use image dimensions divisi
To use GGUF diffusion weights, set `--diffusion-model` to the path of a file such as `qwen_image_2.1-Q4_K.gguf`.
### Turbo
For [Qwen Image 2.1 Turbo](https://huggingface.co/Qwen/Qwen-Image-2.1-Turbo), use the Turbo diffusion weights and select its fixed 8-step sigma schedule with `--sigmas qwen-image-2.1-turbo`. For example, add these sampling options to the command above:
```text
--sigmas qwen-image-2.1-turbo --steps 8 --cfg-scale 1.0 --sampling-method euler
```
The preset is equivalent to `--sigmas 1.0,0.978453,0.95418,0.926626,0.89508,0.845148,0.704534,0.414568,0.0`. It only supplies the sigma values; CFG scale and sampling method remain independently configurable. As with an explicit sigma list, the sigma count determines the actual number of sampling steps. The same preset name is also accepted by `--hires-sigmas` for the highres fix second pass.
### Image editing
Pass the reference image with `-r` and describe the edit in `-p`. Vision weights are required; the example below loads them separately with `--llm_vision`.
+5 -2
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@@ -1475,6 +1475,9 @@ ArgOptions SDGenerationParams::get_options() {
return -1;
}
std::string sigmas_str = value;
if (sigmas_str == "qwen-image-2.1-turbo") {
sigmas_str = "1.0,0.978453,0.95418,0.926626,0.89508,0.845148,0.704534,0.414568,0.0";
}
if (!sigmas_str.empty() && sigmas_str.front() == '[') {
sigmas_str.erase(0, 1);
}
@@ -1721,11 +1724,11 @@ ArgOptions SDGenerationParams::get_options() {
on_scheduler_arg},
{"",
"--sigmas",
"custom sigma values for the sampler, comma-separated (e.g., \"14.61,7.8,3.5,0.0\").",
"custom sigma values for the sampler, comma-separated (e.g., \"14.61,7.8,3.5,0.0\") or preset: qwen-image-2.1-turbo.",
on_sigmas_arg},
{"",
"--hires-sigmas",
"custom sigma values for the highres fix second pass, comma-separated (e.g., \"0.85,0.725,0.421875,0.0\").",
"custom sigma values for the highres fix second pass, comma-separated (e.g., \"0.85,0.725,0.421875,0.0\") or preset: qwen-image-2.1-turbo.",
on_hires_sigmas_arg},
{"",
"--skip-layers",
+3 -3
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@@ -2735,9 +2735,9 @@ sd::Tensor<float> StableDiffusionGGML::sample(const std::shared_ptr<DiffusionMod
if (batch_cfg_ok) {
sd::Tensor<float> batched_out = run_batched_condition(*positive_condition, c_concat_override);
if (!batched_out.empty() && batched_out.dim() >= 4 && batched_out.shape()[3] == 2) {
auto parts = sd::ops::chunk(batched_out, 2, 3);
cond_out = std::move(parts[0]);
uncond_out = std::move(parts[1]);
auto parts = sd::ops::chunk(batched_out, 2, 3);
cond_out = std::move(parts[0]);
uncond_out = std::move(parts[1]);
}
}