Sync with upstream master-968-8cd731e (FLUX.3 Image Klein)

.github/workflows/build.yml

@@ -450,7 +450,7 @@ jobs:
     runs-on: windows-2022
 
     env:
-      ROCM_VERSION: "7.14.0"
+      ROCM_VERSION: "10.1.0"
       GPU_TARGETS: "gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"
       # rocm-sdk re-expands clang from the wheel every run, so its mtime always
       # differs and the default compiler_check=mtime misses every object.
@@ -502,7 +502,7 @@ jobs:
 
           write-host "Installing ROCm wheels for multi-arch support"
           # Install ROCm wheels for multi-arch support (this may take several minutes)
-          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{env.ROCM_VERSION}}"
+          python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{env.ROCM_VERSION}}"
 
           # Pre-expand the devel tree so it is included in the cache
           write-host "Initializing ROCm devel tree"
@@ -599,9 +599,9 @@ jobs:
     strategy:
       matrix:
         include:
-          - ROCM_VERSION: "7.14.0"
+          - ROCM_VERSION: "10.1.0"
             gpu_targets: "gfx900;gfx906;gfx908;gfx90a;gfx90c;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"
-            build: x64
+            build: 'x64'
 
     steps:
       - name: Clone
@@ -618,6 +618,15 @@ jobs:
           # Plateaus at the 500M default (88.19% full, 142 misses/run).
           max-size: 2G
 
+      - name: Tune ccache for reinstalled ROCm toolchain
+        run: |
+          # ROCm is pip-installed fresh each run, so the clang binary's mtime
+          # changes every time. With the default compiler_check=mtime that
+          # invalidates the cache; hash compiler contents instead so warm
+          # builds hit.
+          ccache --set-config=compiler_check=content
+          ccache --set-config=sloppiness=time_macros,include_file_mtime,include_file_ctime
+
       - name: Dependencies
         id: depends
         run: |
@@ -653,7 +662,7 @@ jobs:
           # libraries = HIP runtime and CMake configs needed for linking
           # devel = compilers, headers, static libs
           python -m pip install --upgrade pip
-          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{matrix.ROCM_VERSION}}"
+          python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{matrix.ROCM_VERSION}}"
 
           # Get ROCm installation paths using the rocm-sdk CLI tool
           ROCM_PATH=$(rocm-sdk path --root)

CMakeLists.txt

@@ -225,6 +225,8 @@ file(GLOB SD_LIB_SOURCES CONFIGURE_DEPENDS
     "src/core/*.h"
     "src/core/*.cpp"
     "src/core/*.hpp"
+    "src/core/probe/*.h"
+    "src/core/probe/*.cpp"
     "src/extensions/*.h"
     "src/extensions/*.cpp"
     "src/extensions/*.hpp"
@@ -342,6 +344,14 @@ if (SD_CUDA)
     target_link_libraries(${SD_LIB} PUBLIC CUDA::cuda_driver)
     set_property(SOURCE src/core/ggml_extend_backend.cpp APPEND PROPERTY COMPILE_DEFINITIONS SD_USE_CUDA)
 endif()
+if (SD_HIPBLAS)
+    find_package(hip REQUIRED CONFIG HINTS "$ENV{ROCM_PATH}" /opt/rocm)
+    target_link_libraries(${SD_LIB} PRIVATE hip::host)
+    set_property(SOURCE src/core/ggml_extend_backend.cpp APPEND PROPERTY COMPILE_DEFINITIONS SD_USE_HIP)
+    if (GGML_HIP_MMQ_MFMA)
+        set_property(SOURCE src/core/ggml_extend_backend.cpp APPEND PROPERTY COMPILE_DEFINITIONS SD_HIP_MFMA)
+    endif()
+endif()
 target_include_directories(${SD_LIB} PUBLIC . src include)
 target_include_directories(${SD_LIB} PRIVATE src/core)
 target_include_directories(${SD_LIB} PUBLIC . thirdparty)

CONTRIBUTING.md

@@ -29,6 +29,10 @@ PRs should include:
 - Linked issue/PR context when applicable.
 - Screenshots or sample outputs for UI/visual behavior changes.
 
+## README UI Listings
+
+The [UIs list in the README](README.md#uis) only includes open-source projects that use `stable-diffusion.cpp` as a backend. Do not submit PRs to add closed-source UIs.
+
 ## Code Style
 
 Format code according to the repository style before submitting changes.

README.md

@@ -15,6 +15,7 @@ API and command-line option may change frequently.***
 
 ## 🔥Important News
 
+* **2026/10/16** 🚀 stable-diffusion.cpp adds **Day-0 support for FLUX.3 Image Klein / Klein Base**
 * **2026/09/20** 🚀 stable-diffusion.cpp adds **Day-0 support for Qwen-Image-2.1**
 * **2026/08/20** 🚀 stable-diffusion.cpp now supports **LTX-2.5**
 * **2026/08/04** 🚀 stable-diffusion.cpp adds **Day-1 support for MiniMax-H3**
@@ -44,6 +45,7 @@ API and command-line option may change frequently.***
     - [SD3/SD3.5](./docs/sd3.md)
     - [FLUX.1-dev/FLUX.1-schnell](./docs/flux.md)
     - [FLUX.2-dev/FLUX.2-klein](./docs/flux2.md)
+    - [FLUX.3 Image Klein / Klein Base](./docs/flux3.md)
     - [Lens](./docs/lens.md)
     - [Chroma](./docs/chroma.md)
     - [Chroma1-Radiance](./docs/chroma_radiance.md)
@@ -183,14 +185,14 @@ These projects wrap `stable-diffusion.cpp` for easier use in other languages/fra
 These projects use `stable-diffusion.cpp` as a backend for their image generation.
 
 - [GIMP Plugins](https://github.com/themanyone/gimp-plugins)
-- [Jellybox](https://jellybox.com)
 - [Stable Diffusion GUI](https://github.com/fszontagh/sd.cpp.gui.wx)
 - [Stable Diffusion CLI-GUI](https://github.com/piallai/stable-diffusion.cpp)
 - [Local Diffusion](https://github.com/rmatif/Local-Diffusion)
 - [sd.cpp-webui](https://github.com/daniandtheweb/sd.cpp-webui)
 - [LocalAI](https://github.com/mudler/LocalAI)
 - [Neural-Pixel](https://github.com/Luiz-Alcantara/Neural-Pixel)
 - [KoboldCpp](https://github.com/LostRuins/koboldcpp)
+- [Kokon](https://github.com/GabrieleLozupone/kokon)
 
 ## Contributors
 

docker/Dockerfile.rocm

@@ -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" ]

docs/anima.md

@@ -15,7 +15,7 @@
 ## Examples
 
 ```sh
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\anima-preview.safetensors --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\anima-preview.safetensors --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu
 ```
 
 <img alt="anima image example" src="../assets/anima/example.png" />

docs/animatediff.md

@@ -51,7 +51,7 @@ SD 1.5 defaults (euler_a, low CFG) produces noise-like output.
 .\bin\Release\sd-cli.exe -M vid_gen \
     --model ..\models\checkpoints\realisticVisionV60B1.safetensors \
     --motion-module ..\models\animatediff\mm_sd15_v3.safetensors \
-    --offload-to-cpu --diffusion-fa \
+    --offload-to-cpu \
     -p "a red apple on a wooden table" \
     --cfg-scale 8.0 --sampling-method euler --scheduler discrete \
     -H 512 -W 512 --video-frames 8 --fps 8 --steps 20 -s 42 \
@@ -66,7 +66,7 @@ use layer streaming:
 .\bin\Release\sd-cli.exe -M vid_gen \
     --model ..\models\checkpoints\realisticVisionV60B1.safetensors \
     --motion-module ..\models\animatediff\mm_sd15_v3.safetensors \
-    --offload-to-cpu --diffusion-fa \
+    --offload-to-cpu \
     -p "photo of coastline, rocks, storm weather, wind, waves, lightning" \
     --cfg-scale 8.0 --sampling-method euler --scheduler discrete \
     -H 384 -W 384 --video-frames 16 --fps 8 --steps 20 -s 42 \
@@ -79,7 +79,7 @@ Low-VRAM streaming (verified with a 2 GiB cap on RTX 3060):
 .\bin\Release\sd-cli.exe -M vid_gen \
     --model ..\models\checkpoints\realisticVisionV60B1.safetensors \
     --motion-module ..\models\animatediff\mm_sd15_v3.safetensors \
-    --max-vram 2.0 --diffusion-fa \
+    --max-vram 2.0 \
     -p "photo of coastline, rocks, storm weather, wind, waves, lightning" \
     --cfg-scale 8.0 --sampling-method euler --scheduler discrete \
     -H 384 -W 384 --video-frames 8 --fps 8 --steps 20 -s 42 \

docs/backend.md

@@ -219,6 +219,123 @@ If a VAE decode fails, decoding retries with spatial tiling even when `--auto-fi
 is off; supported video decoders try temporal tiling first and can then add
 spatial tiling. Spatial retries use half-size tiles along each latent dimension.
 
+## Direct convolution (`--conv-direct on|off|auto`)
+
+`--conv-direct` defaults to `auto` and accepts module assignments for
+`diffusion`, `vae` (including preview VAE), and `controlnet`, using the same
+aliases and precedence rules as `--flash-attn`:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --conv-direct auto,vae=off
+sd-cli -m model.safetensors -p "a cat" --conv-direct off,diffusion=on
+```
+
+- `on` requests direct 2D and 3D convolution.
+- `off` uses the existing im2col-based convolution paths.
+- `auto` first checks support for a representative F16-weight/F32-input direct
+  convolution graph. Vulkan then benchmarks direct convolution against im2col
+  using two small shapes per dimension, with warmup, output validation, and five
+  interleaved measurements per shape. Direct convolution must improve the sum of
+  median times by at least 5%; a failed probe defaults to `off`.
+  Other backends retain their implicit GEMM checks without benchmarking:
+  CUDA 2D requires a compiled Turing-or-newer MMA architecture; HIP 2D requires
+  the supported CDNA MFMA or RDNA WMMA architectures described below for flash
+  attention. MUSA 2D has a SIMT implementation. CUDA, HIP, and MUSA 3D also have
+  a SIMT implementation, so they do not require MMA. Other backends default to
+  `off`. Results and decision logs are cached per compute backend and dimension
+  in the context, as with flash attention. Each dimension is selected independently,
+  and all compute devices in a module must qualify. Parameter-storage devices do
+  not affect this choice.
+
+These are backend-level defaults. GGML still chooses the actual kernel based on
+weight type, shape, and hardware; enabling direct convolution does not guarantee
+that every operation uses implicit GEMM. The CUDA-family implicit GEMM paths
+require F16 weights and can use ordinary direct kernels for other cases.
+This option does not change the separately created image upscaler context.
+
+Repeating `--conv-direct` replaces the previous assignment string. Existing
+`--diffusion-conv-direct` (diffusion and ControlNet) and `--vae-conv-direct`
+flags are deprecated and emit a warning, but remain supported when
+`--conv-direct` is omitted. Use `--conv-direct auto,diffusion=on,controlnet=on`
+and `--conv-direct auto,vae=on`, respectively. An explicit
+`--conv-direct` takes precedence over both legacy flags regardless of order.
+Library callers can set the appended `sd_ctx_params_t::conv_direct_mode` field
+to the same string; null or empty selects `auto` while honoring legacy true
+flags. The context copies the string.
+
+## Flash attention (`--flash-attn on|off|auto`)
+
+`--flash-attn` defaults to `auto`. Selection happens while creating the model
+context, before inference, and is cached once per compute backend in that context.
+Parameter-storage backends are not benchmarked.
+
+The option also accepts module assignments, using the same module aliases as
+`--backend`:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --flash-attn auto,te=off,vae=on
+sd-cli -m model.safetensors -p "a cat" --flash-attn off,diffusion=auto
+```
+
+Supported modules are `diffusion`, `te`, `clip_vision`, `vae`, `controlnet`, and
+`audio_encoder`. Modules without an assignment use the default (`auto` if no
+default is supplied). A bare mode or `all=`, `default=`, or `*=` sets the default;
+module-specific settings take precedence regardless of their position. Within
+one assignment string, the last setting for the same module or default wins.
+Repeating `--flash-attn` replaces the entire previous string.
+
+Only modules set to `auto` need backend probing. An explicit `on` or `off` for
+one module does not change another module's decision, even when they share a
+backend. Modules without configurable attention are rejected instead of silently
+ignoring their settings.
+
+- `on` requests flash attention for all models. Unsupported attention operations
+  still fall back to ordinary attention.
+- `off` disables flash attention, including the flash-attention fallback from
+  SageAttention. SageAttention itself remains controlled by `--sage-attn`.
+- `auto` enables CPU and supported CUDA MMA configurations without timing a
+  benchmark. CUDA uses both the device's capability and the compiled GGML
+  architectures. HIP also skips timing on known CDNA MFMA architectures
+  (`gfx908`, `gfx90a`, `gfx940`/`gfx941`/`gfx942`, `gfx950`) and RDNA 3/3.5/4
+  WMMA architectures (`gfx11xx`/`gfx12xx`). The CDNA shortcut requires
+  `GGML_HIP_MMQ_MFMA` to be enabled at build time. All shortcuts still check
+  support for the FA probe graph. Vulkan devices whose description identifies
+  NVIDIA RTX hardware (GeForce RTX, TITAN RTX, Quadro RTX and professional RTX)
+  also skip timing. This is a device-name heuristic and needs no CUDA runtime.
+  Unsupported graphs are disabled; other configurations, including older or
+  unknown HIP architectures and other Vulkan devices, are benchmarked.
+
+The probe compares ordinary and flash attention through the same attention
+builder used by models, including layout conversion and F16 conversion. It uses
+two small self-attention graphs (512 tokens with head size 64, and 1024 tokens
+with head size 128, both with four heads), checks their outputs, warms up each
+path, then alternates five synchronized measurements. Flash attention is enabled
+when the sum of its median times is at least 5% lower. These representative
+shapes are a startup heuristic, not a guarantee for every model or resolution.
+Allocation/execution failures and inconclusive probes select `off`. Timing stops
+starting new measurement pairs after one second of measured computation;
+backend initialization, warmup and an in-flight pair can take longer.
+
+Different modules can select different results. A multi-device model enables
+flash attention only if all of its compute devices select `on`, preserving the
+model's existing single boolean setting. This conservative rule also covers
+row-split configurations, which can fall back to layer split during initialization.
+
+`--fa` and `--diffusion-fa` are deprecated and emit a warning when used.
+Use `--flash-attn on` and `--flash-attn off,diffusion=on`, respectively.
+Both aliases write the same mode string as `--flash-attn`; the last occurrence
+of any of these options wins. Library callers
+can set `sd_ctx_params_t::flash_attn_mode` to the same string, including
+`"on"`, `"off"`, `"auto"`, or `"auto,te=off,vae=on"`.
+A null or empty string uses automatic selection unless a
+legacy `flash_attn` or `diffusion_flash_attn` boolean is set. The context copies
+the string; no new enum is required.
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --flash-attn auto
+sd-cli -m model.safetensors -p "a cat" --flash-attn off
+```
+
 ## Modules
 
 | Module | Purpose | Accepted names |