feat: add per-module automatic flash attention selection (#2132)

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)

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

@@ -215,6 +215,79 @@ 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.
 
+## 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 |

docs/backend_probe.md

@@ -0,0 +1,46 @@
+# Adding backend performance probes
+
+`src/core/probe/backend_probe.h` provides the shared measurement machinery. Operator
+policy stays separate: `src/core/probe/flash_attn.cpp`, for example, defines attention
+shapes and its CPU/CUDA/HIP/Vulkan shortcuts, then calls `sd_run_backend_probe` for unknown
+configurations. A future convolution probe can use the same runner without
+changing the attention implementation.
+
+For each representative shape, create a `BackendProbeCase`:
+
+- `build(graph, candidate)` creates inputs and either the reference or candidate
+  graph in `graph.ctx`, registers input tensors in `graph.inputs`, and assigns
+  `graph.output`. Leave the output null if the requested implementation is
+  unavailable; do not silently benchmark its fallback as the candidate.
+- By default, inputs receive deterministic F32 data, with the same seed for both
+  variants. Supply `initialize` for F16 convolution weights, integer inputs, or
+  operator-specific values. It runs after allocation, outside timed execution.
+  Both variants must receive equivalent logical inputs.
+- Default output validation requires matching shapes and contiguous F32 outputs,
+  checks finiteness, and applies absolute and relative tolerances. Supply
+  `compare` for other output types or validation rules.
+
+Graphs must be safe to execute repeatedly without modifying their inputs.
+`BackendProbeGraph` owns its GGML context and buffer, so all return paths release
+probe resources. Constructors allocate metadata only; `supported()` can also be
+used before a known-architecture shortcut, without allocating device tensors.
+
+`BackendProbeOptions` controls graph metadata capacity, warmup count, measurement
+count, measured-time budget, minimum free device memory, comparison tolerances,
+and minimum improvement. Warmup and allocation are excluded from timing. The
+runner alternates the two variants, synchronizes the backend around execution,
+and compares sums of per-case medians. The time budget is checked before each
+measurement pair; it cannot interrupt an in-flight backend operation.
+
+Only use `candidate_faster` when `completed` is true. On incomplete probes,
+`error` reports why comparison failed and the timing totals may be partial.
+The caller handles logging, known architecture rules, fallback policy, and
+context-lifetime caching. Keep caches separate by operation (or key a shared
+cache by both operation and backend); an attention result must not select a
+convolution implementation. If shapes or settings vary within one context,
+include them in the corresponding cache key as well.
+
+No convolution policy or CLI option is added by this infrastructure.
+
+Module assignment parsing remains in `src/core/ggml_extend_backend.cpp`, next
+to backend assignment parsing. The probe directory does not parse CLI/API options.

docs/boogu_image.md

@@ -17,7 +17,7 @@ Boogu Image uses a Boogu diffusion transformer, the FLUX VAE, and Qwen3-VL as th
 ### Boogu Image Base
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_base_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\ae.sft -p "a lovely cat" --diffusion-fa -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_base_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\ae.sft -p "a lovely cat" -v --offload-to-cpu
 ```
 
 <img width="256" alt="Boogu Image Base example" src="../assets/boogu/example.png" />
@@ -25,7 +25,7 @@ Boogu Image uses a Boogu diffusion transformer, the FLUX VAE, and Qwen3-VL as th
 ### Boogu Image Edit
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_edit_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\mmproj-Qwen3VL-8B-Instruct-F16.gguf --vae ..\models\vae\ae.sft --diffusion-fa -v --offload-to-cpu -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'boogu.cpp'"
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_edit_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\mmproj-Qwen3VL-8B-Instruct-F16.gguf --vae ..\models\vae\ae.sft -v --offload-to-cpu -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'boogu.cpp'"
 ```
 
 <img width="256" alt="Boogu Image Edit example" src="../assets/boogu/edit_example.png" />

docs/docker.md

@@ -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"
+```

docs/ernie_image.md

@@ -21,7 +21,7 @@ You can run ERNIE-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — o
 ### ERNIE-Image-Turbo
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-turbo.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 8 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-turbo.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 8 -v --offload-to-cpu
 ```
 
 <img width="256" alt="ERNIE-Image Turbo example" src="../assets/ernie_image/turbo_example.png" />
@@ -29,7 +29,7 @@ You can run ERNIE-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — o
 ### ERNIE-Image
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-UD-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-UD-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu
 ```
 
 <img width="256" alt="ERNIE-Image example" src="../assets/ernie_image/example.png" />

docs/flux2.md

@@ -16,7 +16,7 @@
 ### Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --offload-to-cpu
 ```
 
 <img alt="flux2 example" src="../assets/flux2/example.png" />
@@ -42,19 +42,19 @@
 ### Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu
 ```
 
 <img alt="flux2-klein-4b" src="../assets/flux2/flux2-klein-4b.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --offload-to-cpu --steps 4
 ```
 
 <img alt="flux2-klein-4b-edit" src="../assets/flux2/flux2-klein-4b-edit.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu
 ```
 
 <img alt="flux2-klein-base-4b" src="../assets/flux2/flux2-klein-base-4b.png" />
@@ -78,19 +78,19 @@
 ### Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu
 ```
 
 <img alt="flux2-klein-9b" src="../assets/flux2/flux2-klein-9b.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --offload-to-cpu --steps 4
 ```
 
 <img alt="flux2-klein-9b-edit" src="../assets/flux2/flux2-klein-9b-edit.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu
 ```
 
 <img alt="flux2-klein-base-9b" src="../assets/flux2/flux2-klein-base-9b.png" />

docs/hunyuan_video.md

@@ -18,7 +18,7 @@ and ByT5 Small GlyphXL for glyph-aware text conditioning.
 ## Text-to-video example
 
 ```shell
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\hunyuanvideo1.5_720p_t2v_fp16.safetensors --vae ..\models\vae\hunyuanvideo15_vae_fp16.safetensors --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --t5xxl ..\models\text_encoders\byt5_small_glyphxl_fp16.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -W 1280 -H 720 --offload-to-cpu --diffusion-fa --video-frames 33 --vae-tiling
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\hunyuanvideo1.5_720p_t2v_fp16.safetensors --vae ..\models\vae\hunyuanvideo15_vae_fp16.safetensors --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --t5xxl ..\models\text_encoders\byt5_small_glyphxl_fp16.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -W 1280 -H 720 --offload-to-cpu --video-frames 33 --vae-tiling
 ```
 
 <video src=../assets/hunyuan_video/hy1.5_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>

docs/ideogram4.md

@@ -49,7 +49,7 @@ If you want lower VRAM usage, you can change the quantization from q8_0 to a low
 ## Examples
 
 ```sh
-.\bin\Release\sd-cli.exe --diffusion-model ideogram4-Q8_0.gguf --uncond-diffusion-model ideogram4_uncond-Q8_0.gguf --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors -p '{"high_level_description":"A square 1024 x 1024 luxury fashion magazine cover featuring exactly one short chubby fluffy cat as the main model. The cat sits on a soft ivory studio floor, facing the viewer with a stylish calm expression, wearing tiny black sunglasses, a red silk scarf, and a small gold collar charm. In front of the cat on the floor is a wide horizontal luxury nameplate that clearly reads ideogram4.cpp. The whole design feels premium, fashionable, clean, and editorial.","style_description":{"aesthetics":"luxury fashion magazine cover, high-end pet couture campaign, minimalist editorial design, elegant studio photography, soft paper texture, refined typography, fashionable and polished","lighting":"Soft diffused studio lighting, gentle spotlight on the cat, subtle floor shadow, warm ivory highlights, clean separation between subject and background","photo":"high-resolution fashion editorial photography look, front-facing cat portrait, crisp fur details, glossy sunglasses, clear readable nameplate text, shallow depth of field","medium":"mixed media fashion photography and premium editorial graphic design","color_palette":["#F4EFE7","#111111","#D8B56D","#B73A3A","#FFFFFF","#8A7A6A"]},"compositional_deconstruction":{"canvas":"Square 1024 x 1024 canvas with a normal upright orientation. Do not rotate the poster or any text. Use a clean fashion magazine cover layout.","background":"Warm ivory studio backdrop with subtle paper grain, a soft spotlight gradient, faint floor shadow, and a few minimal gold editorial lines. The background is spacious, premium, and uncluttered.","layout":"Top center has a small elegant headline. Center area features one cat as the main fashion model. Lower foreground has a wide horizontal luxury nameplate placed on the floor in front of the cat. Bottom center has a small footer. All text is horizontal, upright, and readable left to right.","elements":[{"type":"text","desc":"Top center headline reading LOOK WHAT I FOUND in a refined high-fashion serif font. The headline is horizontal, centered, elegant, and secondary to the nameplate text."},{"type":"obj","desc":"Exactly one short chubby fluffy cat sitting in the center like a luxury fashion model. The cat has a large round head, compact body, short legs, soft detailed fur, expressive eyes, and a calm confident pose. The cat is cute and rounded, not tall, not stretched, not duplicated."},{"type":"obj","desc":"Tiny glossy black sunglasses worn naturally by the cat, slightly oversized but still showing the cat face clearly. The sunglasses add a chic fashion-editorial attitude."},{"type":"obj","desc":"A red silk scarf tied neatly around the cat neck, with soft folds and a couture feeling. The scarf must not cover the cat face or the nameplate."},{"type":"obj","desc":"A small gold collar charm or fashion accessory under the scarf, subtle and premium, adding a luxury campaign detail."},{"type":"obj","desc":"In the lower foreground, place a wide horizontal luxury nameplate on the floor in front of the cat. The nameplate is low, flat, landscape-oriented, much wider than tall, like a fashion show seat card or premium display plaque. It is centered, front-facing, level, and fully visible. It must not become vertical, tall, standing, rotated, or side-facing."},{"type":"text","desc":"Print the exact text ideogram4.cpp only on the wide horizontal nameplate. Use clean bold black lettering, perfectly spelled, lowercase, with the number 4 and .cpp extension. The text must fit completely inside the nameplate, stay horizontal, and be readable from left to right."},{"type":"obj","desc":"Add sparse premium editorial accents around the edges: thin gold lines, small code brackets, tiny cursor marks, subtle dots, and minimal geometric details. No extra cats, no stickers, no animal faces, no busy decorations."},{"type":"text","desc":"Bottom center footer reading tiny paws, big compile energy in a small refined monospace or editorial font. The footer is horizontal, centered, understated, and much smaller than the nameplate text."}]}}'  --diffusion-fa -v --offload-to-cpu -H 1024 -W 1024
+.\bin\Release\sd-cli.exe --diffusion-model ideogram4-Q8_0.gguf --uncond-diffusion-model ideogram4_uncond-Q8_0.gguf --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors -p '{"high_level_description":"A square 1024 x 1024 luxury fashion magazine cover featuring exactly one short chubby fluffy cat as the main model. The cat sits on a soft ivory studio floor, facing the viewer with a stylish calm expression, wearing tiny black sunglasses, a red silk scarf, and a small gold collar charm. In front of the cat on the floor is a wide horizontal luxury nameplate that clearly reads ideogram4.cpp. The whole design feels premium, fashionable, clean, and editorial.","style_description":{"aesthetics":"luxury fashion magazine cover, high-end pet couture campaign, minimalist editorial design, elegant studio photography, soft paper texture, refined typography, fashionable and polished","lighting":"Soft diffused studio lighting, gentle spotlight on the cat, subtle floor shadow, warm ivory highlights, clean separation between subject and background","photo":"high-resolution fashion editorial photography look, front-facing cat portrait, crisp fur details, glossy sunglasses, clear readable nameplate text, shallow depth of field","medium":"mixed media fashion photography and premium editorial graphic design","color_palette":["#F4EFE7","#111111","#D8B56D","#B73A3A","#FFFFFF","#8A7A6A"]},"compositional_deconstruction":{"canvas":"Square 1024 x 1024 canvas with a normal upright orientation. Do not rotate the poster or any text. Use a clean fashion magazine cover layout.","background":"Warm ivory studio backdrop with subtle paper grain, a soft spotlight gradient, faint floor shadow, and a few minimal gold editorial lines. The background is spacious, premium, and uncluttered.","layout":"Top center has a small elegant headline. Center area features one cat as the main fashion model. Lower foreground has a wide horizontal luxury nameplate placed on the floor in front of the cat. Bottom center has a small footer. All text is horizontal, upright, and readable left to right.","elements":[{"type":"text","desc":"Top center headline reading LOOK WHAT I FOUND in a refined high-fashion serif font. The headline is horizontal, centered, elegant, and secondary to the nameplate text."},{"type":"obj","desc":"Exactly one short chubby fluffy cat sitting in the center like a luxury fashion model. The cat has a large round head, compact body, short legs, soft detailed fur, expressive eyes, and a calm confident pose. The cat is cute and rounded, not tall, not stretched, not duplicated."},{"type":"obj","desc":"Tiny glossy black sunglasses worn naturally by the cat, slightly oversized but still showing the cat face clearly. The sunglasses add a chic fashion-editorial attitude."},{"type":"obj","desc":"A red silk scarf tied neatly around the cat neck, with soft folds and a couture feeling. The scarf must not cover the cat face or the nameplate."},{"type":"obj","desc":"A small gold collar charm or fashion accessory under the scarf, subtle and premium, adding a luxury campaign detail."},{"type":"obj","desc":"In the lower foreground, place a wide horizontal luxury nameplate on the floor in front of the cat. The nameplate is low, flat, landscape-oriented, much wider than tall, like a fashion show seat card or premium display plaque. It is centered, front-facing, level, and fully visible. It must not become vertical, tall, standing, rotated, or side-facing."},{"type":"text","desc":"Print the exact text ideogram4.cpp only on the wide horizontal nameplate. Use clean bold black lettering, perfectly spelled, lowercase, with the number 4 and .cpp extension. The text must fit completely inside the nameplate, stay horizontal, and be readable from left to right."},{"type":"obj","desc":"Add sparse premium editorial accents around the edges: thin gold lines, small code brackets, tiny cursor marks, subtle dots, and minimal geometric details. No extra cats, no stickers, no animal faces, no busy decorations."},{"type":"text","desc":"Bottom center footer reading tiny paws, big compile energy in a small refined monospace or editorial font. The footer is horizontal, centered, understated, and much smaller than the nameplate text."}]}}' -v --offload-to-cpu -H 1024 -W 1024
 ```
 
 <img alt="ideogram4 image example" src="../assets/ideogram4/example.png" />

docs/int8_convrot.md

@@ -102,5 +102,5 @@ The dedicated CUDA and HIP convrot activation paths currently require a group si
 ComfyUI INT8 convrot safetensors can be passed to `--diffusion-model` without conversion:
 
 ```powershell
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\krea2_turbo_int8_convrot.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'" --steps 8 --cfg-scale 1 --diffusion-fa -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\krea2_turbo_int8_convrot.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'" --steps 8 --cfg-scale 1 -v --offload-to-cpu
 ```

docs/ip_adapter.md

@@ -48,7 +48,7 @@ sd-cli -m ..\models\sd_v1.5.safetensors --clip_vision ..\models\clip_vision_h.sa
 ## Example (SDXL)
 
 ```
-sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\reference.png --ip-adapter-strength 0.8 -p "a woman, best quality" -n "lowres, bad anatomy" --cfg-scale 6 --steps 25 --sampling-method dpm++2m --scheduler karras -W 1024 -H 1024 --diffusion-fa --vae-tiling
+sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\reference.png --ip-adapter-strength 0.8 -p "a woman, best quality" -n "lowres, bad anatomy" --cfg-scale 6 --steps 25 --sampling-method dpm++2m --scheduler karras -W 1024 -H 1024 --vae-tiling
 ```
 
 The SDXL VAE decode at 1024x1024 is memory heavy; add `--vae-tiling` (and
@@ -83,5 +83,5 @@ Add the usual Control Net options to keep the reference appearance while
 controlling the pose:
 
 ```
-sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\character.png --ip-adapter-strength 0.9 --control-net ..\models\OpenPoseXL2.safetensors --control-image ..\assets\pose.png --control-strength 0.8 -p "a character, side view" --cfg-scale 6 --steps 25 -W 1024 -H 1024 --diffusion-fa --vae-tiling
+sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\character.png --ip-adapter-strength 0.9 --control-net ..\models\OpenPoseXL2.safetensors --control-image ..\assets\pose.png --control-strength 0.8 -p "a character, side view" --cfg-scale 6 --steps 25 -W 1024 -H 1024 --vae-tiling
 ```

docs/iris.md

@@ -13,7 +13,7 @@ Iris-3B generates images directly in pixel space and uses Qwen3-VL-4B-Instruct a
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\iris-3b.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf -p "a lovely cat" --cfg-scale 3 -H 1024 -W 1024 --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\iris-3b.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf -p "a lovely cat" --cfg-scale 3 -H 1024 -W 1024
 ```
 
 <img width="256" alt="iris-3b example" src="../assets/iris/example.png" />

docs/krea2.md

@@ -21,7 +21,7 @@ Krea2 uses a Krea2 diffusion transformer, the Wan2.1 VAE, and Qwen3-VL 4B as the
 ### Krea2
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Krea-2-Raw-Q8_0.gguf --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'"  --diffusion-fa -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Krea-2-Raw-Q8_0.gguf --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'" -v --offload-to-cpu
 ```
 
 <img width="256" alt="Krea2 Raw example" src="../assets/krea2/example.png" />

docs/lens.md

@@ -22,7 +22,7 @@ Lens and Lens Turbo require an external GPT-OSS `tokenizer.json` matching the te
 ### Lens
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 5.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 5.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" -v
 ```
 
 <img width="256" alt="Lens example" src="../assets/lens/example.png" />
@@ -30,7 +30,7 @@ Lens and Lens Turbo require an external GPT-OSS `tokenizer.json` matching the te
 ### Lens Turbo
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_turbo_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 1.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_turbo_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 1.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" -v --steps 4
 ```
 
 <img width="256" alt="Lens Turbo example" src="../assets/lens/turbo_example.png" />

docs/lingbot_video.md

@@ -28,5 +28,5 @@ Lingbot Video uses a Lingbot diffusion transformer, the Wan2.1 VAE, and Qwen3-VL
 ### LingBot-Video T2V
 
 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\lingbot-video-dens-1.3b.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p '{"caption":{"comprehensive_description":"A lovely cat sits comfortably on a soft cushion near a sunlit window, looking calm, gentle, and adorable. The cat has soft fluffy fur, bright expressive eyes, small rounded ears, delicate whiskers, and a relaxed posture. Warm daylight falls across the cat from one side, creating soft highlights on the fur and a cozy glow around the scene. The background is softly blurred, showing hints of a peaceful indoor home environment with warm tones and gentle natural light. The overall atmosphere is cute, tender, serene, and photorealistic, emphasizing the cat''s charming appearance, soft texture, and affectionate presence.","camera_info":{"color":"Warm","frame_size":"Close Up","shot_type_angle":"Eye level","lens_size":"Medium Lens","composition":"Centered balanced","lighting":"Soft light","lighting_type":"Daylight"},"world_knowledge":[],"prominent_elements":[{"name":"lovely cat","description":"A cute and gentle domestic cat with soft fluffy fur, expressive eyes, and a calm relaxed presence.","location":"center of the frame","relative_size":"large","shape_and_color":"Small animal body with rounded face, triangular ears, bright eyes, and soft fur in warm natural tones","texture":"soft, fluffy, silky","appearance_details":"The cat has clean well-groomed fur, delicate whiskers, small ears, a cute nose, and bright attentive eyes. Its expression appears calm, affectionate, and slightly curious.","relationship":"Acts as the main subject and emotional focal point of the scene.","orientation":"facing the camera","pose":"sitting comfortably with a relaxed posture","expression":"gentle, adorable, calm, slightly curious","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"cat eyes","description":"Bright expressive eyes that give the cat a sweet and affectionate appearance.","location":"upper center of the cat face","relative_size":"small","shape_and_color":"Round almond-like eyes with glossy reflections","texture":"clear, glossy, reflective","appearance_details":"The eyes catch the soft daylight, creating small natural highlights that make the cat look vivid and alive.","relationship":"Enhance the emotional charm and cuteness of the cat.","orientation":"looking toward the camera","pose":"","expression":"soft and attentive","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"soft cushion","description":"A comfortable cushion or blanket where the cat is resting.","location":"bottom portion of the frame","relative_size":"medium","shape_and_color":"Soft rounded fabric surface in light warm neutral tones","texture":"plush, fabric, cozy","appearance_details":"The cushion gently supports the cat and adds a comfortable home-like feeling to the scene.","relationship":"Provides a cozy resting place for the cat.","orientation":"horizontal","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"sunlit indoor background","description":"A softly blurred indoor background with warm daylight and peaceful home atmosphere.","location":"behind the cat, filling the upper and side areas of the frame","relative_size":"large","shape_and_color":"Soft abstract shapes in warm beige, cream, and pale golden tones","texture":"soft, blurry, bokeh-like","appearance_details":"The background is intentionally out of focus, keeping attention on the cat while creating a cozy and serene mood.","relationship":"Provides a warm and gentle environment that supports the cute domestic scene.","orientation":"upright","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":"","is_cluster":true,"number_of_objects":"numerous"}]}}' -n '{"universal_negative":{"visual_quality":["low quality","worst quality","blurry","pixelated","jpeg artifacts","low resolution","unstable color","color flicker","underexposed","overexposed","invisible subject","subject hidden in darkness"],"artistic_style":["painting","illustration","drawing","cartoon","3d render","cgi","sketch","digital art"],"composition_and_content":["text","watermark","signature","logo","subtitles","pillarboxed","side bars","portrait image in landscape frame"],"temporal_and_motion_stability":["flickering","jittery","motion blur","temporal inconsistency","warping","morphing","incoherent motion","unnatural movement","static object with sudden jump","frame-to-frame inconsistency"],"material_and_structure":["plastic-like glass","unrealistic texture","deformed bottle","liquid freezing improperly","distorted reflections"]}}' --diffusion-fa --offload-to-cpu --cfg-scale 3 --video-frames 33 -v
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\lingbot-video-dens-1.3b.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p '{"caption":{"comprehensive_description":"A lovely cat sits comfortably on a soft cushion near a sunlit window, looking calm, gentle, and adorable. The cat has soft fluffy fur, bright expressive eyes, small rounded ears, delicate whiskers, and a relaxed posture. Warm daylight falls across the cat from one side, creating soft highlights on the fur and a cozy glow around the scene. The background is softly blurred, showing hints of a peaceful indoor home environment with warm tones and gentle natural light. The overall atmosphere is cute, tender, serene, and photorealistic, emphasizing the cat''s charming appearance, soft texture, and affectionate presence.","camera_info":{"color":"Warm","frame_size":"Close Up","shot_type_angle":"Eye level","lens_size":"Medium Lens","composition":"Centered balanced","lighting":"Soft light","lighting_type":"Daylight"},"world_knowledge":[],"prominent_elements":[{"name":"lovely cat","description":"A cute and gentle domestic cat with soft fluffy fur, expressive eyes, and a calm relaxed presence.","location":"center of the frame","relative_size":"large","shape_and_color":"Small animal body with rounded face, triangular ears, bright eyes, and soft fur in warm natural tones","texture":"soft, fluffy, silky","appearance_details":"The cat has clean well-groomed fur, delicate whiskers, small ears, a cute nose, and bright attentive eyes. Its expression appears calm, affectionate, and slightly curious.","relationship":"Acts as the main subject and emotional focal point of the scene.","orientation":"facing the camera","pose":"sitting comfortably with a relaxed posture","expression":"gentle, adorable, calm, slightly curious","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"cat eyes","description":"Bright expressive eyes that give the cat a sweet and affectionate appearance.","location":"upper center of the cat face","relative_size":"small","shape_and_color":"Round almond-like eyes with glossy reflections","texture":"clear, glossy, reflective","appearance_details":"The eyes catch the soft daylight, creating small natural highlights that make the cat look vivid and alive.","relationship":"Enhance the emotional charm and cuteness of the cat.","orientation":"looking toward the camera","pose":"","expression":"soft and attentive","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"soft cushion","description":"A comfortable cushion or blanket where the cat is resting.","location":"bottom portion of the frame","relative_size":"medium","shape_and_color":"Soft rounded fabric surface in light warm neutral tones","texture":"plush, fabric, cozy","appearance_details":"The cushion gently supports the cat and adds a comfortable home-like feeling to the scene.","relationship":"Provides a cozy resting place for the cat.","orientation":"horizontal","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"sunlit indoor background","description":"A softly blurred indoor background with warm daylight and peaceful home atmosphere.","location":"behind the cat, filling the upper and side areas of the frame","relative_size":"large","shape_and_color":"Soft abstract shapes in warm beige, cream, and pale golden tones","texture":"soft, blurry, bokeh-like","appearance_details":"The background is intentionally out of focus, keeping attention on the cat while creating a cozy and serene mood.","relationship":"Provides a warm and gentle environment that supports the cute domestic scene.","orientation":"upright","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":"","is_cluster":true,"number_of_objects":"numerous"}]}}' -n '{"universal_negative":{"visual_quality":["low quality","worst quality","blurry","pixelated","jpeg artifacts","low resolution","unstable color","color flicker","underexposed","overexposed","invisible subject","subject hidden in darkness"],"artistic_style":["painting","illustration","drawing","cartoon","3d render","cgi","sketch","digital art"],"composition_and_content":["text","watermark","signature","logo","subtitles","pillarboxed","side bars","portrait image in landscape frame"],"temporal_and_motion_stability":["flickering","jittery","motion blur","temporal inconsistency","warping","morphing","incoherent motion","unnatural movement","static object with sudden jump","frame-to-frame inconsistency"],"material_and_structure":["plastic-like glass","unrealistic texture","deformed bottle","liquid freezing improperly","distorted reflections"]}}' --offload-to-cpu --cfg-scale 3 --video-frames 33 -v
 ```

docs/llada_image.md

@@ -122,7 +122,6 @@ save_file(merged, "llada_connectors.safetensors")
   --height 1024 \
   --steps 4 \
   --cfg-scale 1.0 \
-  --diffusion-fa \
   --output output.png
 ```
 
@@ -145,7 +144,7 @@ only in the positive branch. Editing requires connectors that include the SigVQ
   unchanged instead of applying the instruction; LLaDA-Image-Turbo edits correctly at both.
 - Editing runs the reference and the target in one sequence, so it needs roughly twice the
   tokens of text to image at the same size. On 12 GB, editing at 1024x1024 needs
-  `--diffusion-fa`; without it the diffusion graph does not fit.
+  flash attention; without it the diffusion graph does not fit.
 - The weights total about 16 GB, but segmented execution streams them, so a much smaller
   budget works. At 512x512, `--max-vram 6` costs almost nothing over unconstrained execution,
   and `--max-vram 3` still produces byte-identical output at roughly 2.5x the time.

docs/longcat_image.md

@@ -24,7 +24,7 @@ LongCat uses quoted text for character-level text rendering. Put target text ins
 ### LongCat Image
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\LongCat-Image-Q4_K_M.gguf --vae ..\models\vae\ae.sft --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf -p "a lovely cat holding a sign says 'longcat.cpp'" --cfg-scale 5.0 --sampling-method euler --flow-shift 3 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\LongCat-Image-Q4_K_M.gguf --vae ..\models\vae\ae.sft --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf -p "a lovely cat holding a sign says 'longcat.cpp'" --cfg-scale 5.0 --sampling-method euler --flow-shift 3 -v --offload-to-cpu
 ```
 
 <img alt="longcat example" src="../assets/longcat/example.png" />

docs/ltx2.md

@@ -55,7 +55,7 @@ To run the text encoder quantized, convert it once with sd-cli:
 ### LTX-2.3 dev T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --fps 24 -o t2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --offload-to-cpu --video-frames 33 --fps 24 -o t2v.webm
 ```
 
 <video
@@ -67,7 +67,7 @@ To run the text encoder quantized, convert it once with sd-cli:
 ### LTX-2.3 dev I2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
 ```
 
 <video
@@ -79,7 +79,7 @@ To run the text encoder quantized, convert it once with sd-cli:
 ### LTX-2.3 dev FLF2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png -o flf2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --offload-to-cpu --video-frames 33 --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png -o flf2v.webm
 ```
 
 <video
@@ -95,7 +95,7 @@ LTX spatial latent upscale runs a model-backed x2 latent upsampler between the l
 Put `ltx-2.3-spatial-upscaler-x2-1.1.safetensors` under the directory passed to `--hires-upscalers-dir`, then use the model name without path or extension in `--hires-upscaler`.
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.3-spatial-upscaler-x2-1.1 --hires --hires-steps 4 -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 640 -H 360 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o hires_i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.3-spatial-upscaler-x2-1.1 --hires --hires-steps 4 -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 640 -H 360 --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o hires_i2v.webm
 ```
 
 By default, the hires refine pass uses the main sampler and scheduler, then trims the second-pass sigma schedule by `--hires-denoising-strength` (`0.7` by default). To reproduce a ComfyUI-style explicit refine schedule, pass custom hires sigmas:
@@ -113,13 +113,13 @@ By default, the hires refine pass uses the main sampler and scheduler, then trim
 ### LTX-2.5 dev T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "A wide aerial shot of a red vintage convertible driving along a coastal cliff road at sunset, waves crashing below" --cfg-scale 3.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 121 --fps 24 -o t2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "A wide aerial shot of a red vintage convertible driving along a coastal cliff road at sunset, waves crashing below" --cfg-scale 3.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --offload-to-cpu --video-frames 121 --fps 24 -o t2v.webm
 ```
 
 ### LTX-2.5 dev I2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "a lovely cat blinking slowly, gentle camera push in" --cfg-scale 3.0 --sampling-method euler -v -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 121 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "a lovely cat blinking slowly, gentle camera push in" --cfg-scale 3.0 --sampling-method euler -v -W 1280 -H 720 --offload-to-cpu --video-frames 121 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
 ```
 
 ### LTX-2.5 spatial latent upscale
@@ -129,7 +129,7 @@ Works exactly like the LTX-2.3 upscaler described below; put
 pass its name without path or extension to `--hires-upscaler`.
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0 --hires --hires-steps 6 -p "a lovely cat" --cfg-scale 3.0 --sampling-method euler -v -W 640 -H 360 --diffusion-fa --offload-to-cpu --video-frames 121 -o hires_t2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0 --hires --hires-steps 6 -p "a lovely cat" --cfg-scale 3.0 --sampling-method euler -v -W 640 -H 360 --offload-to-cpu --video-frames 121 -o hires_t2v.webm
 ```
 
 ## Not implemented

docs/mage_flow.md

@@ -15,7 +15,7 @@
 Use 30 steps for Base models and 4 steps with `--cfg-scale 1` for Turbo models. Image dimensions must be multiples of 16; the official checkpoints are trained for native resolutions from 512 to 2048 pixels.
 
 ```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Turbo.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\mage_vae.safetensors -p "a lovely cat holding a sign says 'mage.cpp'" --cfg-scale 1.0 --steps 4 --diffusion-fa -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Turbo.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\mage_vae.safetensors -p "a lovely cat holding a sign says 'mage.cpp'" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu
 ```
 
 <img width="256" alt="Mage-Flow example" src="../assets/mage_flow/example.png" />
@@ -27,7 +27,7 @@ Mage-Flow-Edit accepts one or more reference images. The default `mage_flow` ref
 For the Turbo edit checkpoint, use 4 steps and `--cfg-scale 1`.
 
 ```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Edit.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3-VL-4B-Instruct-mmproj-BF16.gguf --vae ..\models\vae\mage_vae.safetensors -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'mage.cpp'" --cfg-scale 4.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Edit.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3-VL-4B-Instruct-mmproj-BF16.gguf --vae ..\models\vae\mage_vae.safetensors -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'mage.cpp'" --cfg-scale 4.0 --sampling-method euler -v --offload-to-cpu
 ```
 
 <img width="256" alt="Mage-Flow-Edit example" src="../assets/mage_flow/edit_example.png" />

docs/ming_image.md

@@ -20,5 +20,5 @@ The example below uses `ming_image_0.1_design_bf16.safetensors` for the DiT. You
 Pass the Ling `tokenizer.json` with `--tokenizer` and use the matching Ming-Image VAE. Image dimensions must be multiples of 16. Save the output as PNG to preserve the alpha channel.
 
 ```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\ming_image_0.1_design_bf16.safetensors --llm ..\models\text_encoders\ming_image_0.1_ling_mini_2.0_bf16.safetensors --vae ..\models\vae\ming_image_vae_bf16.safetensors --tokenizer ..\models\text_encoders\tokenizer.json -p "A cheerful orange cat sticker, transparent background" --width 1024 --height 1024 --steps 12 --cfg-scale 1 --sampling-method euler --diffusion-fa -v --offload-to-cpu -o ming_image.png
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\ming_image_0.1_design_bf16.safetensors --llm ..\models\text_encoders\ming_image_0.1_ling_mini_2.0_bf16.safetensors --vae ..\models\vae\ming_image_vae_bf16.safetensors --tokenizer ..\models\text_encoders\tokenizer.json -p "A cheerful orange cat sticker, transparent background" --width 1024 --height 1024 --steps 12 --cfg-scale 1 --sampling-method euler -v --offload-to-cpu -o ming_image.png
 ```

docs/minimax_h3.md

@@ -38,7 +38,7 @@ are detected from their weights.
 ## Text-to-audio-video
 
 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "A cute American Shorthair silver tabby kitten surfs on a tropical ocean wave, riding a white surfboard with the clear text 'sd.cpp' on it. Cinematic tracking shot, realistic water, bright sunlight, smooth motion, and consistent character appearance. Add upbeat tropical surf-rock background music with cheerful drums and guitar, synchronized with the kitten’s energetic surfing." --cfg-scale 1.0 -v -W 864 -H 480 --diffusion-fa --offload-to-cpu --rng cpu --fps 24 --video-frames 56
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "A cute American Shorthair silver tabby kitten surfs on a tropical ocean wave, riding a white surfboard with the clear text 'sd.cpp' on it. Cinematic tracking shot, realistic water, bright sunlight, smooth motion, and consistent character appearance. Add upbeat tropical surf-rock background music with cheerful drums and guitar, synchronized with the kitten’s energetic surfing." --cfg-scale 1.0 -v -W 864 -H 480 --offload-to-cpu --rng cpu --fps 24 --video-frames 56
 ```
 
 <video src=../assets/minimax-h3/t2av.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -51,7 +51,7 @@ decoded audio track.
 Add `--init-img` for I2VA, or both `--init-img` and `--end-img` for FL2VA:
 
 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "a lovely cat" -i ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --diffusion-fa --offload-to-cpu --rng cpu --fps 24 --video-frames 56
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "a lovely cat" -i ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --offload-to-cpu --rng cpu --fps 24 --video-frames 56
 ```
 
 <video src=../assets/minimax-h3/i2av.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -62,7 +62,7 @@ Ref2VA accepts any combination of reference images, reference videos, paired
 video soundtracks, and standalone audio references:
 
 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_ref2va_pruned-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "Use the cat from <Picture 1> as the main character. Keep the cat’s appearance, fur color, facial features, and identity consistent with the reference image. Create a 2-second cinematic video: start with an extreme close-up shot of the cat’s face, focusing on its cute expression and detailed fur texture. The camera slowly rotates around the cat’s head, creating a dynamic reveal. Then smoothly pull back and zoom out to reveal the full scene: the cat is standing confidently on a surfboard, riding ocean waves. Water splashes around the board, sea breeze gently moves the cat’s fur, and the cat maintains a cute and fearless expression while surfing. Smooth camera movement, cinematic orbit shot, seamless zoom-out transition, low-angle wide shot, realistic ocean environment, golden sunlight, dynamic waves, high-quality realistic style, natural motion, no distortion, keep the cat’s identity unchanged." -r ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --diffusion-fa --offload-to-cpu --rng cpu --fps 24 --video-frames 56
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_ref2va_pruned-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "Use the cat from <Picture 1> as the main character. Keep the cat’s appearance, fur color, facial features, and identity consistent with the reference image. Create a 2-second cinematic video: start with an extreme close-up shot of the cat’s face, focusing on its cute expression and detailed fur texture. The camera slowly rotates around the cat’s head, creating a dynamic reveal. Then smoothly pull back and zoom out to reveal the full scene: the cat is standing confidently on a surfboard, riding ocean waves. Water splashes around the board, sea breeze gently moves the cat’s fur, and the cat maintains a cute and fearless expression while surfing. Smooth camera movement, cinematic orbit shot, seamless zoom-out transition, low-angle wide shot, realistic ocean environment, golden sunlight, dynamic waves, high-quality realistic style, natural motion, no distortion, keep the cat’s identity unchanged." -r ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --offload-to-cpu --rng cpu --fps 24 --video-frames 56
 ```
 
 <video src=../assets/minimax-h3/r2av.mp4 controls="controls" muted="muted" type="video/mp4"></video>

docs/minit2i.md

@@ -43,6 +43,5 @@ MiniT2I uses a MiniT2I diffusion transformer and `google/flan-t5-large` as the t
   --height 512 \
   --seed 42 \
   --sampling-method euler \
-  --diffusion-fa \
   --output minit2i_cuda.png
 ```

docs/ovis_image.md

@@ -13,7 +13,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu
 ```
 
 <img alt="ovis image example" src="../assets/ovis_image/example.png" />

docs/performance.md

@@ -8,7 +8,7 @@ eg.:
 For most backends, it slows things down, but for cuda it generally speeds it up too.
 At the moment, it is only supported for some models and some backends (like cpu, cuda/rocm, metal).
 
-Run by adding `--diffusion-fa` to the arguments and watch for:
+Watch for:
 ```
 [INFO ] stable-diffusion.cpp:312  - Using flash attention in the diffusion model
 ```

docs/pid.md

@@ -27,7 +27,7 @@ PiD and PiD 1.5 require an external Gemma 2 `tokenizer.json` matching the text e
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\pid_flux1_512_to_2048_4step_bf16.safetensors --llm "..\models\text_encoders\gemma_2_2b_it_elm_bf16.safetensors" --tokenizer ..\models\tokenizers\tokenizer_gemma2.json --vae ..\models\vae\ae.sft --vae-format flux --cfg-scale 1.0  -p "a lovely cat" -r ..\assets\ernie_image\turbo_example.png --diffusion-fa -v --steps 4 -H 2048 -W 2048 --rng cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\pid_flux1_512_to_2048_4step_bf16.safetensors --llm "..\models\text_encoders\gemma_2_2b_it_elm_bf16.safetensors" --tokenizer ..\models\tokenizers\tokenizer_gemma2.json --vae ..\models\vae\ae.sft --vae-format flux --cfg-scale 1.0  -p "a lovely cat" -r ..\assets\ernie_image\turbo_example.png -v --steps 4 -H 2048 -W 2048 --rng cpu
 ```
 
 Before:

docs/qwen_image.md

@@ -14,7 +14,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --flow-shift 3
 ```
 
 <img alt="qwen example" src="../assets/qwen/example.png" />

docs/qwen_image_2.1.md

@@ -25,19 +25,29 @@ Run the following commands from the build directory. Use image dimensions divisi
 ### Text to image
 
 ```powershell
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\qwen_image_2.1_int8_convrot.safetensors --vae ..\models\vae\qwen_image_2.1_vae_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf -p "a lovely cat holding a sign says 'qwen2.1.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --fa -o qwen_image_2.1.png
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\qwen_image_2.1_int8_convrot.safetensors --vae ..\models\vae\qwen_image_2.1_vae_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf -p "a lovely cat holding a sign says 'qwen2.1.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu -o qwen_image_2.1.png
 ```
 
 <img alt="Qwen Image 2.1 example" src="../assets/qwen/qwen_image_2.1.png" />
 
 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`.
 
 ```powershell
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\qwen_image_2.1_int8_convrot.safetensors --vae ..\models\vae\qwen_image_2.1_vae_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3VL-8B-Instruct-mmproj-BF16.gguf -r ..\assets\qwen\qwen_image_2.1.png -p "change 'qwen2.1.cpp' to 'sd.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --fa -o qwen_image_2.1_edit.png
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\qwen_image_2.1_int8_convrot.safetensors --vae ..\models\vae\qwen_image_2.1_vae_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3VL-8B-Instruct-mmproj-BF16.gguf -r ..\assets\qwen\qwen_image_2.1.png -p "change 'qwen2.1.cpp' to 'sd.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu -o qwen_image_2.1_edit.png
 ```
 
 For multiple reference images, repeat `-r` in the desired order, for example `-r first.png -r second.png`.

docs/qwen_image_edit.md

@@ -23,7 +23,7 @@
 ### Qwen Image Edit
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
 ```
 
 <img alt="qwen_image_edit" src="../assets/qwen/qwen_image_edit.png" />
@@ -32,7 +32,7 @@
 ### Qwen Image Edit 2509
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
 ```
 
 <img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
@@ -42,7 +42,7 @@
 To use the new Qwen Image Edit 2511 mode, `--model-args qwen_image_zero_cond_t=true` must be enabled; otherwise, image editing quality will degrade significantly.
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --model-args qwen_image_zero_cond_t=true
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --model-args qwen_image_zero_cond_t=true
 ```
 
 <img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2511.png" />

docs/sage_attention.md

@@ -37,12 +37,12 @@ rebuild with CUDA 12.8 or newer to use SageAttention2++.
 
 ## Use
 
-Replace `--diffusion-fa` with `--sage-attn` in an existing command. For example,
+Add `--sage-attn` to enable SageAttention. For example,
 from the build directory:
 
 ```powershell
 .\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，
-形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --sage-attn
+形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --offload-to-cpu --video-frames 33 --sage-attn
 ```
 
 SageAttention currently handles unmasked attention with head dimensions from
@@ -51,9 +51,7 @@ and multiple batches. Dimensions below 64 are zero-padded to 64; dimensions
 between 65 and 127 are zero-padded to 128. The original softmax scale is preserved,
 and the output is cropped back to the original dimension. Other attention
 operations fall back to FlashAttention when supported, then ordinary attention.
-SageAttention takes precedence in diffusion
-when combined with `--fa` or `--diffusion-fa`; `--fa` continues to control other
-modules. Existing attention scaling overrides remain effective.
+SageAttention takes precedence over FlashAttention in diffusion. Existing attention scaling overrides remain effective.
 
 Attention quantization changes numerical results. Compare image quality and
 end-to-end generation time using the same seed, dimensions, and sampling

docs/sefi_image.md

@@ -32,19 +32,19 @@ The dispatcher picks `alpha` from the filename (`turbo` substring => 1.0, otherw
 ### 1B / 2B turbo
 
 ```
-./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --diffusion-fa --offload-to-cpu -o out.png
+./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --offload-to-cpu -o out.png
 ```
 
 ### 1B / 2B base
 
 ```
-./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_base.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 4.0 --steps 50 -W 1024 -H 1024 -s 42 --diffusion-fa --offload-to-cpu -o out.png
+./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_base.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 4.0 --steps 50 -W 1024 -H 1024 -s 42 --offload-to-cpu -o out.png
 ```
 
 ### 5B (needs streaming on 12 GiB VRAM)
 
 ```
-./build/bin/sd-cli --diffusion-model /path/to/sefi_5b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_4b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --diffusion-fa --max-vram 8 --offload-to-cpu -o out.png
+./build/bin/sd-cli --diffusion-model /path/to/sefi_5b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_4b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --max-vram 8 --offload-to-cpu -o out.png
 ```
 
 <img alt="SeFi-Image 5B turbo example" src="../assets/sefi_image/example.png" />

docs/sensenova_u1.md

@@ -28,7 +28,6 @@ and the tokenizer files.
   --seed 42 \
   --sampling-method euler \
   --rng cuda \
-  --fa \
   --output output.png
 ```
 

docs/troubleshooting.md

@@ -40,8 +40,7 @@ the intended output scale:
 - `--linear-scale` scales Linear inputs before matrix multiplication and rescales
   the result.
 - `--attn-scale` scales attention keys and values (K/V). It takes effect only in
-  the Flash Attention path, where `--fa` or `--diffusion-fa` is enabled and the
-  backend supports it.
+  the Flash Attention path, when selected and supported by the backend.
 
 The two values can be set independently and apply across model components. The
 default `0` preserves each model's built-in settings; `1` explicitly disables the

docs/wan.md

@@ -67,7 +67,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.1 T2V 1.3B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -75,7 +75,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.1 T2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa  --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480  --offload-to-cpu --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -85,7 +85,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.1 I2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\models\clip_vision\clip_vision_h.safetensors -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\models\clip_vision\clip_vision_h.safetensors -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -93,7 +93,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.2 T2V A14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --offload-to-cpu --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -101,7 +101,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.2 I2V A14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --offload-to-cpu --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -114,7 +114,7 @@ wav2vec2 audio encoder. Wan2.2 S2V requires the wan_2.1 vae (16 channel), not
 the wan2.2 vae.
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_s2v-14B-Q8_0.gguf --audio-encoder ..\models\audio_encoders\wav2vec2_large_english_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a person is talking" --cfg-scale 6.0 --steps 20 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --vae-tiling --video-frames 81 -i ..\assets\cat_with_sd_cpp_42.png --audio .\input\speech.wav --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_s2v-14B-Q8_0.gguf --audio-encoder ..\models\audio_encoders\wav2vec2_large_english_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a person is talking" --cfg-scale 6.0 --steps 20 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --offload-to-cpu --vae-tiling --video-frames 81 -i ..\assets\cat_with_sd_cpp_42.png --audio .\input\speech.wav --flow-shift 3.0
 ```
 
 Notes:
@@ -151,7 +151,7 @@ Notes:
 ### Wan2.2 T2V A14B T2I
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --offload-to-cpu --flow-shift 3.0
 ```
 
 <img width="832" height="480" alt="Wan2 2_14B_t2i" src="../assets/wan/Wan2.2_14B_t2i.png" />
@@ -159,7 +159,7 @@ Notes:
 ### Wan2.2 T2V 14B with Lora
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat<lora:wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise:1><lora:|high_noise|wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise:1>" --cfg-scale 3.5 --sampling-method euler --steps 4 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 4 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --lora-model-dir ..\models\loras --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat<lora:wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise:1><lora:|high_noise|wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise:1>" --cfg-scale 3.5 --sampling-method euler --steps 4 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 4 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --offload-to-cpu --lora-model-dir ..\models\loras --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_t2v_lora.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -171,7 +171,7 @@ Notes:
 #### T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --offload-to-cpu --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_5B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -179,7 +179,7 @@ Notes:
 #### I2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_5B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -187,7 +187,7 @@ Notes:
 ### Wan2.1 FLF2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\models\clip_vision\clip_vision_h.safetensors -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\models\clip_vision\clip_vision_h.safetensors -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
 ```
 
 
@@ -196,7 +196,7 @@ Notes:
 ### Wan2.2 FLF2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -p "glass flower blossom" -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -p "glass flower blossom" -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_flf2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -206,7 +206,7 @@ Notes:
 #### T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 1 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --video-frames 1 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -215,7 +215,7 @@ Notes:
 #### R2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -226,7 +226,7 @@ Notes:
 ```
 mkdir post+depth
 ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\frame_%04d.jpg
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -236,7 +236,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -245,7 +245,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### R2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -255,7 +255,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### V2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -269,7 +269,7 @@ for the reference image, same as Wan2.1 VACE.
 #### R2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\Wan2.2-VACE-Fun-A14B-low-noise-Q8_0.gguf --high-noise-diffusion-model ..\models\diffusion_models\Wan2.2-VACE-Fun-A14B-high-noise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\Wan2.2-VACE-Fun-A14B-low-noise-Q8_0.gguf --high-noise-diffusion-model ..\models\diffusion_models\Wan2.2-VACE-Fun-A14B-high-noise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.2_A14B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>

docs/z_image.md

@@ -21,7 +21,7 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 ### Z-Image-Turbo
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512 --steps 8
+.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu -H 1024 -W 512 --steps 8
 ```
 
 <img width="256" alt="z-image example" src="../assets/z_image/q3_K.png" />
@@ -29,7 +29,7 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 ### Z-Image-Base
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\z_image_bf16.safetensors --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\z_image_bf16.safetensors --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 5.0 -v --offload-to-cpu -H 1024 -W 512
 ```
 
 <img width="256" alt="z-image example" src="../assets/z_image/base_bf16.png" />

docs/z_image_l2p.md

@@ -17,5 +17,5 @@ Do not pass `--vae`. Image dimensions must be multiples of 16. The reference pip
 The released checkpoint keeps part of the transformer in F32. Pass `--type bf16` to load it in BF16, as the reference pipeline runs it; this reduces the diffusion model from about 18.6 GB to 11.8 GB.
 
 ```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\model-1k-merge.safetensors --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "an origami pig on fire in the middle of a dark room with a pentagram on the floor" --cfg-scale 2.0 --steps 30 --sampling-method euler -W 1024 -H 1024 --diffusion-fa --type bf16 -v -o z_image_l2p.png
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\model-1k-merge.safetensors --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "an origami pig on fire in the middle of a dark room with a pentagram on the floor" --cfg-scale 2.0 --steps 30 --sampling-method euler -W 1024 -H 1024 --type bf16 -v -o z_image_l2p.png
 ```

examples/cli/README.md

@@ -11,6 +11,12 @@ Logging defaults to `info`. Use `--log-level <level>` to select `debug`, `verbos
 messages at that level and all less detailed levels. `-v` and `--verbose` are
 equivalent to `--log-level verbose`. If repeated, the last logging option wins.
 
+`--flash-attn on|off|auto` defaults to `auto`, selecting flash attention for each
+compute backend during context creation. See [Backend selection](../../docs/backend.md#flash-attention---flash-attn-onoffauto)
+for probing behavior.
+Use module assignments such as `--flash-attn auto,te=off,vae=on` to override
+individual modules.
+
 For direct image repair or automatic post-generation YOLOv8 detection followed by cropped inpainting, see
 [ADetailer](../../docs/adetailer.md).
 

examples/common/common.cpp

@@ -632,14 +632,6 @@ ArgOptions SDContextParams::get_options() {
          "--vae-on-cpu",
          "deprecated; use --backend vae=cpu",
          true, &vae_on_cpu},
-        {"",
-         "--fa",
-         "use flash attention",
-         true, &flash_attn},
-        {"",
-         "--diffusion-fa",
-         "use flash attention in the diffusion model only",
-         true, &diffusion_flash_attn},
         {"",
          "--sage-attn",
          "use native CUDA SageAttention in the diffusion model, with flash/default attention fallback",
@@ -733,10 +725,42 @@ ArgOptions SDContextParams::get_options() {
          }},
         {"",
          "--attn-scale",
-         "flash-attention K/V scale override (float, default: 0 = model default, 1 = no scaling); requires --fa or --diffusion-fa",
+         "flash-attention K/V scale override (float, default: 0 = model default, 1 = no scaling); applies when flash attention is selected",
          [this](int argc, const char** argv, int index) {
              return parse_scale_override(argc, argv, index, attn_scale);
          }},
+        {"",
+         "--flash-attn",
+         "on|off|auto or module assignments (default: auto), e.g. auto,te=off,vae=on. Last repeated option wins",
+         [this](int argc, const char** argv, int index) {
+             if (++index >= argc) {
+                 LOG_ERROR("--flash-attn requires on, off, or auto");
+                 return -1;
+             }
+             const std::string value = argv[index];
+             if (value.empty()) {
+                 LOG_ERROR("--flash-attn requires a nonempty value");
+                 return -1;
+             }
+             flash_attn_mode = value;
+             return 1;
+         }},
+        {"",
+         "--fa",
+         "deprecated; use --flash-attn on",
+         [this](int, const char**, int) {
+             LOG_WARN("--fa is deprecated; use --flash-attn on instead.");
+             flash_attn_mode = "on";
+             return 0;
+         }},
+        {"",
+         "--diffusion-fa",
+         "deprecated; use --flash-attn off,diffusion=on",
+         [this](int, const char**, int) {
+             LOG_WARN("--diffusion-fa is deprecated; use --flash-attn off,diffusion=on instead.");
+             flash_attn_mode = "off,diffusion=on";
+             return 0;
+         }},
         {"",
          "--auto-fit",
          "on|off (default: on). Preserve --backend (otherwise select one GPU) and place weights on the compute GPU, "
@@ -959,8 +983,7 @@ std::string SDContextParams::to_string() const {
         << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
         << "  clip_on_cpu: " << (clip_on_cpu ? "true" : "false") << ",\n"
         << "  vae_on_cpu: " << (vae_on_cpu ? "true" : "false") << ",\n"
-        << "  flash_attn: " << (flash_attn ? "true" : "false") << ",\n"
-        << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
+        << "  flash_attn_mode: " << flash_attn_mode << ",\n"
         << "  sage_attn: " << (sage_attn ? "true" : "false") << ",\n"
         << "  linear_scale: " << linear_scale << ",\n"
         << "  attn_scale: " << attn_scale << ",\n"
@@ -1018,8 +1041,7 @@ sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool taesd_preview) {
     sd_ctx_params.prediction                      = prediction;
     sd_ctx_params.lora_apply_mode                 = lora_apply_mode;
     sd_ctx_params.enable_mmap                     = enable_mmap;
-    sd_ctx_params.flash_attn                      = flash_attn;
-    sd_ctx_params.diffusion_flash_attn            = diffusion_flash_attn;
+    sd_ctx_params.flash_attn_mode                 = flash_attn_mode.c_str();
     sd_ctx_params.sage_attn                       = sage_attn;
     sd_ctx_params.linear_scale                    = linear_scale;
     sd_ctx_params.attn_scale                      = attn_scale;
@@ -1475,6 +1497,9 @@ 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 +1746,11 @@ 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",

examples/common/common.h

@@ -166,15 +166,14 @@ struct SDContextParams {
     std::string rpc_servers;
     std::string effective_backend;
     std::string effective_params_backend;
-    bool enable_mmap           = false;
-    bool control_net_cpu       = false;
-    bool clip_on_cpu           = false;
-    bool vae_on_cpu            = false;
-    bool flash_attn            = false;
-    bool diffusion_flash_attn  = false;
-    bool sage_attn             = false;
-    bool diffusion_conv_direct = false;
-    bool vae_conv_direct       = false;
+    bool enable_mmap            = false;
+    bool control_net_cpu        = false;
+    bool clip_on_cpu            = false;
+    bool vae_on_cpu             = false;
+    std::string flash_attn_mode = "auto";
+    bool sage_attn              = false;
+    bool diffusion_conv_direct  = false;
+    bool vae_conv_direct        = false;
 
     prediction_t prediction           = PREDICTION_COUNT;
     lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;

examples/server/README.md

@@ -3,7 +3,7 @@
 The following example starts `sd-server` with a standalone diffusion model, VAE, and LLM text encoder:
 
 ```
-.\bin\Release\sd-server.exe --diffusion-model  ..\models\diffusion_models\z_image_turbo_bf16.safetensors --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\qwen_3_4b.safetensors --diffusion-fa --offload-to-cpu -v --cfg-scale 1.0
+.\bin\Release\sd-server.exe --diffusion-model  ..\models\diffusion_models\z_image_turbo_bf16.safetensors --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\qwen_3_4b.safetensors --offload-to-cpu -v --cfg-scale 1.0
 ```
 
 What this example does:
@@ -11,7 +11,6 @@ What this example does:
 * `--diffusion-model` selects the standalone diffusion model
 * `--vae` selects the VAE decoder
 * `--llm` selects the text encoder / language model used by this pipeline
-* `--diffusion-fa` enables flash attention in the diffusion model
 * `--offload-to-cpu` reduces VRAM pressure by keeping weights in RAM when possible
 * `-v` enables verbose logging (equivalent to `--log-level verbose`)
 * `--cfg-scale 1.0` sets the default CFG scale for generation

include/stable-diffusion.h

@@ -251,6 +251,7 @@ typedef struct {
     const char* tokenizer;           // tokenizer.json path or main=FILE,clip-l=FILE,clip-g=FILE assignments; required for PiD and Lens
     bool sage_attn;
     int conditioning_cache_size;  // Maximum cached conditioning entries per context; 0 disables caching (default: 4)
+    const char* flash_attn_mode;  // on/off/auto or module assignments (e.g. "auto,te=off"); NULL/empty honors legacy flags.
 } sd_ctx_params_t;
 
 typedef struct {

scripts/format-code.ps1

@@ -8,6 +8,8 @@ $patterns = @(
     "src/core/*.cpp"
     "src/core/*.h"
     "src/core/*.hpp"
+    "src/core/probe/*.cpp"
+    "src/core/probe/*.h"
     "src/extensions/*.cpp"
     "src/extensions/*.h"
     "src/extensions/*.hpp"

scripts/format-code.sh

@@ -8,6 +8,7 @@ cd "$repo_root"
 for f in src/*.cpp src/*.h src/*.hpp \
          src/conditioning/*.cpp src/conditioning/*.h src/conditioning/*.hpp \
          src/core/*.cpp src/core/*.h src/core/*.hpp \
+         src/core/probe/*.cpp src/core/probe/*.h \
          src/extensions/*.cpp src/extensions/*.h src/extensions/*.hpp \
          src/pipeline/*.cpp src/pipeline/*.h \
          src/runtime/*.cpp src/runtime/*.h src/runtime/*.hpp \

src/core/ggml_extend.cpp

@@ -761,7 +761,7 @@ ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
     }
 #endif
 
-    if (kqv == nullptr && (flash_attn || sage_attn)) {
+    if (kqv == nullptr && flash_attn) {
         // LOG_VERBOSE("attention_ext L_q:%d L_k:%d n_head:%d C:%d d_head:%d N:%d", L_q, L_k, n_head, C, d_head, N);
         bool can_use_flash_attn = true;
         if (mask != nullptr) {

src/core/ggml_extend_backend.cpp

@@ -6,12 +6,17 @@
 #include <mutex>
 #include <sstream>
 #include <stdexcept>
+#include <utility>
 #include <vector>
 
 #ifdef SD_USE_CUDA
 #include <cuda.h>
 #endif
 
+#ifdef SD_USE_HIP
+#include <hip/hip_runtime_api.h>
+#endif
+
 #include "core/util.h"
 #include "ggml-backend-impl.h"
 #include "ggml-impl.h"
@@ -516,6 +521,49 @@ bool sd_backend_supports_cuda_mma(ggml_backend_t backend) {
 #endif
 }
 
+bool sd_backend_supports_hip_mma(ggml_backend_t backend) {
+#ifdef SD_USE_HIP
+    if (!sd_backend_is(backend, "ROCm")) {
+        return false;
+    }
+    auto dev = ggml_backend_get_device(backend);
+    if (dev == nullptr) {
+        return false;
+    }
+    ggml_backend_dev_props props{};
+    ggml_backend_dev_get_props(dev, &props);
+    int device = 0;
+    hipDeviceProp_t hip_props{};
+    if (props.device_id == nullptr ||
+        hipDeviceGetByPCIBusId(&device, props.device_id) != hipSuccess ||
+        hipGetDeviceProperties(&hip_props, device) != hipSuccess) {
+        return false;
+    }
+    const char* arch = hip_props.gcnArchName;
+    if (std::strncmp(arch, "gfx", 3) != 0) {
+        return false;
+    }
+    char* end        = nullptr;
+    const long value = std::strtol(arch + 3, &end, 16);
+    if (end == arch + 3 || (*end != '\0' && *end != ':')) {
+        return false;
+    }
+    // Match the HIP FA kernels; keep unknown architecture families on the probe path.
+    if (value >= 0x1100 && value < 0x1300) {
+        return true;
+    }
+#ifdef SD_HIP_MFMA
+    return value == 0x908 || value == 0x90a ||
+           value == 0x940 || value == 0x941 || value == 0x942 || value == 0x950;
+#else
+    return false;
+#endif
+#else
+    (void)backend;
+    return false;
+#endif
+}
+
 ggml_backend_t sd_backend_cpu_init() {
     ggml_backend_load_all_once();
     return ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
@@ -734,6 +782,59 @@ bool sd_parse_backend_assignment(const std::string& spec, SDBackendAssignment* a
     return true;
 }
 
+bool sd_parse_flash_attention_assignment(const std::string& spec, SDBackendAssignment* assignment, std::string* error) {
+    if (assignment == nullptr) {
+        return false;
+    }
+    SDBackendAssignment result;
+    result.set_default("auto");
+    std::istringstream stream(spec);
+    std::string part;
+    while (std::getline(stream, part, ',')) {
+        SDBackendAssignment entry;
+        if (!sd_parse_backend_assignment(part, &entry, error)) {
+            return false;
+        }
+        auto valid_mode = [&](const std::string& mode) {
+            if (mode == "on" || mode == "off" || mode == "auto") {
+                return true;
+            }
+            if (error != nullptr) {
+                *error = "invalid flash attention mode '" + mode + "': expected on, off, or auto";
+            }
+            return false;
+        };
+        if (!entry.default_name.empty()) {
+            if (!valid_mode(entry.default_name)) {
+                return false;
+            }
+            result.set_default(entry.default_name);
+        }
+        for (const auto& setting : entry.module_names) {
+            switch (setting.first) {
+                case SDBackendModule::DIFFUSION:
+                case SDBackendModule::TE:
+                case SDBackendModule::CLIP_VISION:
+                case SDBackendModule::VAE:
+                case SDBackendModule::CONTROL_NET:
+                case SDBackendModule::AUDIO_ENCODER:
+                    break;
+                default:
+                    if (error != nullptr) {
+                        *error = std::string("flash attention is not configurable for module '") + sd_backend_module_name(setting.first) + "'";
+                    }
+                    return false;
+            }
+            if (!valid_mode(setting.second)) {
+                return false;
+            }
+            result.set_module(setting.first, setting.second);
+        }
+    }
+    *assignment = std::move(result);
+    return true;
+}
+
 bool SDBackendAssignment::empty() const {
     return default_name.empty() && module_names.empty();
 }

src/core/ggml_extend_backend.h

@@ -88,6 +88,7 @@ private:
 bool sd_backend_is(ggml_backend_t backend, const std::string& name);
 bool sd_backend_is_cpu(ggml_backend_t backend);
 bool sd_backend_supports_cuda_mma(ggml_backend_t backend);
+bool sd_backend_supports_hip_mma(ggml_backend_t backend);
 ggml_backend_buffer_t sd_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device,
                                                           void* ptr,
                                                           size_t size,
@@ -100,6 +101,7 @@ ggml_status sd_backend_graph_compute_with_eval_callback(ggml_backend_t backend,
                                                         void* callback_eval_user_data);
 std::string sd_backend_resolve_name(const std::string& name);
 bool sd_parse_backend_assignment(const std::string& spec, SDBackendAssignment* assignment, std::string* error);
+bool sd_parse_flash_attention_assignment(const std::string& spec, SDBackendAssignment* assignment, std::string* error);
 const char* sd_backend_module_name(SDBackendModule module);
 void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value);
 bool add_rpc_devices(const std::string& servers);

src/core/probe/backend_probe.cpp

@@ -0,0 +1,193 @@
+#include "core/probe/backend_probe.h"
+
+#include <algorithm>
+#include <chrono>
+#include <cmath>
+#include <exception>
+
+#include "core/ggml_extend_backend.h"
+#include "core/util.h"
+#include "ggml-alloc.h"
+
+BackendProbeGraph::BackendProbeGraph(ggml_backend_t backend, size_t graph_size)
+    : backend_(backend), graph_size_(graph_size) {
+    ctx = ggml_init({graph_size * ggml_tensor_overhead() + ggml_graph_overhead_custom(graph_size, false), nullptr, true});
+}
+
+BackendProbeGraph::~BackendProbeGraph() {
+    ggml_backend_buffer_free(buffer_);
+    ggml_free(ctx);
+}
+
+bool BackendProbeGraph::supported() {
+    if (backend_ == nullptr || ctx == nullptr || output == nullptr) {
+        return false;
+    }
+    if (graph_ == nullptr) {
+        graph_ = ggml_new_graph_custom(ctx, graph_size_, false);
+        ggml_build_forward_expand(graph_, output);
+    }
+    for (int i = 0; i < ggml_graph_n_nodes(graph_); ++i) {
+        if (!ggml_backend_supports_op(backend_, ggml_graph_node(graph_, i))) {
+            return false;
+        }
+    }
+    return true;
+}
+
+bool BackendProbeGraph::allocate() {
+    if (ctx == nullptr || backend_ == nullptr) {
+        return false;
+    }
+    if (buffer_ == nullptr) {
+        buffer_ = ggml_backend_alloc_ctx_tensors(ctx, backend_);
+    }
+    return buffer_ != nullptr;
+}
+
+bool BackendProbeGraph::initialize_f32_inputs() {
+    uint32_t state = 42;
+    for (auto* tensor : inputs) {
+        if (tensor == nullptr || tensor->type != GGML_TYPE_F32 || !ggml_is_contiguous(tensor)) {
+            return false;
+        }
+        std::vector<float> values(static_cast<size_t>(ggml_nelements(tensor)));
+        for (float& value : values) {
+            state = state * 1664525u + 1013904223u;
+            value = static_cast<float>(state >> 8) / 16777216.f - 0.5f;
+        }
+        ggml_backend_tensor_set(tensor, values.data(), 0, ggml_nbytes(tensor));
+    }
+    return true;
+}
+
+double BackendProbeGraph::run() {
+    if (graph_ == nullptr || buffer_ == nullptr) {
+        return -1.;
+    }
+    ggml_backend_synchronize(backend_);
+    const auto start  = std::chrono::steady_clock::now();
+    const auto status = ggml_backend_graph_compute(backend_, graph_);
+    ggml_backend_synchronize(backend_);
+    if (status != GGML_STATUS_SUCCESS) {
+        return -1.;
+    }
+    return std::chrono::duration<double, std::milli>(std::chrono::steady_clock::now() - start).count();
+}
+
+static bool backend_probe_outputs_match(const BackendProbeGraph& reference,
+                                        const BackendProbeGraph& candidate,
+                                        const BackendProbeOptions& options) {
+    if (!ggml_are_same_shape(reference.output, candidate.output) ||
+        reference.output->type != GGML_TYPE_F32 || candidate.output->type != GGML_TYPE_F32 ||
+        !ggml_is_contiguous(reference.output) || !ggml_is_contiguous(candidate.output)) {
+        return false;
+    }
+    const size_t count = static_cast<size_t>(ggml_nelements(reference.output));
+    std::vector<float> expected(count), actual(count);
+    ggml_backend_tensor_get(reference.output, expected.data(), 0, count * sizeof(float));
+    ggml_backend_tensor_get(candidate.output, actual.data(), 0, count * sizeof(float));
+    for (size_t i = 0; i < count; ++i) {
+        if (!std::isfinite(expected[i]) || !std::isfinite(actual[i]) ||
+            std::abs(expected[i] - actual[i]) > options.absolute_tolerance + options.relative_tolerance * std::abs(expected[i])) {
+            return false;
+        }
+    }
+    return true;
+}
+
+BackendProbeResult sd_run_backend_probe(ggml_backend_t backend,
+                                        int n_threads,
+                                        const std::vector<BackendProbeCase>& cases,
+                                        const BackendProbeOptions& options) {
+    BackendProbeResult result;
+    if (backend == nullptr || cases.empty() || options.graph_size == 0 || options.warmup_runs < 1 || options.measurement_runs < 1 ||
+        !std::isfinite(options.time_budget_ms) || options.time_budget_ms < 0. ||
+        !std::isfinite(options.min_improvement) || options.min_improvement < 0. || options.min_improvement >= 1. ||
+        !std::isfinite(options.absolute_tolerance) || options.absolute_tolerance < 0.f ||
+        !std::isfinite(options.relative_tolerance) || options.relative_tolerance < 0.f) {
+        result.error = "invalid probe configuration";
+        return result;
+    }
+    try {
+        if (sd_backend_is_cpu(backend)) {
+            sd_backend_cpu_set_n_threads(backend, n_threads > 0 ? n_threads : sd_get_num_physical_cores());
+        }
+        auto dev = ggml_backend_get_device(backend);
+        if (dev != nullptr && !sd_backend_is_cpu(backend)) {
+            size_t free_bytes = 0, total_bytes = 0;
+            ggml_backend_dev_memory(dev, &free_bytes, &total_bytes);
+            if (free_bytes > total_bytes || free_bytes < options.min_free_device_bytes) {
+                result.error = "insufficient probe memory";
+                return result;
+            }
+        }
+        double timed_ms = 0.;
+        for (const auto& test : cases) {
+            BackendProbeGraph reference(backend, options.graph_size), candidate(backend, options.graph_size);
+            if (!test.build || reference.ctx == nullptr || candidate.ctx == nullptr) {
+                result.error = "probe graph unavailable";
+                return result;
+            }
+            test.build(reference, false);
+            test.build(candidate, true);
+            if (!reference.supported() || !candidate.supported()) {
+                result.error = "unsupported probe graph";
+                return result;
+            }
+            if (!reference.allocate() || !candidate.allocate()) {
+                result.error = "probe allocation failed";
+                return result;
+            }
+            auto initialize = [&](BackendProbeGraph& graph) {
+                return test.initialize ? test.initialize(graph) : graph.initialize_f32_inputs();
+            };
+            if (!initialize(reference) || !initialize(candidate)) {
+                result.error = "probe input initialization failed";
+                return result;
+            }
+            for (int i = 0; i < options.warmup_runs; ++i) {
+                if (reference.run() < 0. || candidate.run() < 0.) {
+                    result.error = "probe warmup failed";
+                    return result;
+                }
+            }
+            if (!(test.compare ? test.compare(reference, candidate) : backend_probe_outputs_match(reference, candidate, options))) {
+                result.error = "probe output check failed";
+                return result;
+            }
+            std::vector<double> reference_times(options.measurement_runs), candidate_times(options.measurement_runs);
+            for (int i = 0; i < options.measurement_runs; ++i) {
+                if (timed_ms >= options.time_budget_ms) {
+                    result.error = "probe time budget exhausted";
+                    return result;
+                }
+                // Alternate order so clock ramp-up does not consistently favor one path.
+                if (i % 2 == 0) {
+                    reference_times[i] = reference.run();
+                    candidate_times[i] = candidate.run();
+                } else {
+                    candidate_times[i] = candidate.run();
+                    reference_times[i] = reference.run();
+                }
+                if (reference_times[i] < 0. || candidate_times[i] < 0.) {
+                    result.error = "probe execution failed";
+                    return result;
+                }
+                timed_ms += reference_times[i] + candidate_times[i];
+            }
+            auto median = [](std::vector<double>& values) {
+                std::sort(values.begin(), values.end());
+                const size_t mid = values.size() / 2;
+                return values.size() % 2 ? values[mid] : (values[mid - 1] + values[mid]) * 0.5;
+            };
+            result.reference_ms += median(reference_times);
+            result.candidate_ms += median(candidate_times);
+        }
+        result.completed        = true;
+        result.candidate_faster = result.candidate_ms < result.reference_ms * (1. - options.min_improvement);
+    } catch (const std::exception& error) {
+        result.error = error.what();
+    }
+    return result;
+}

src/core/probe/backend_probe.h

@@ -0,0 +1,64 @@
+#ifndef __SD_CORE_PROBE_BACKEND_PROBE_H__
+#define __SD_CORE_PROBE_BACKEND_PROBE_H__
+
+#include <functional>
+#include <string>
+#include <vector>
+
+#include "ggml-backend.h"
+
+struct BackendProbeOptions {
+    size_t graph_size            = 128;
+    int warmup_runs              = 1;
+    int measurement_runs         = 5;
+    double time_budget_ms        = 1000.;
+    size_t min_free_device_bytes = 128ULL * 1024 * 1024;
+    double min_improvement       = 0.05;
+    float absolute_tolerance     = 0.001f;
+    float relative_tolerance     = 0.02f;
+};
+
+class BackendProbeGraph {
+    ggml_backend_t backend_;
+    size_t graph_size_;
+    ggml_backend_buffer_t buffer_ = nullptr;
+    ggml_cgraph* graph_           = nullptr;
+
+public:
+    ggml_context* ctx = nullptr;
+    std::vector<ggml_tensor*> inputs;
+    ggml_tensor* output = nullptr;
+
+    explicit BackendProbeGraph(ggml_backend_t backend, size_t graph_size = 128);
+    ~BackendProbeGraph();
+    BackendProbeGraph(const BackendProbeGraph&)            = delete;
+    BackendProbeGraph& operator=(const BackendProbeGraph&) = delete;
+
+    bool supported();
+    bool allocate();
+    bool initialize_f32_inputs();
+    double run();
+};
+
+struct BackendProbeCase {
+    // Variants must be repeatable without mutating inputs; leave output null if unavailable.
+    std::function<void(BackendProbeGraph&, bool candidate)> build;
+    // Optional overrides for non-F32 inputs/outputs or operation-specific data.
+    std::function<bool(BackendProbeGraph&)> initialize;
+    std::function<bool(const BackendProbeGraph&, const BackendProbeGraph&)> compare;
+};
+
+struct BackendProbeResult {
+    bool completed        = false;
+    bool candidate_faster = false;
+    double reference_ms   = 0.;
+    double candidate_ms   = 0.;
+    std::string error;
+};
+
+BackendProbeResult sd_run_backend_probe(ggml_backend_t backend,
+                                        int n_threads,
+                                        const std::vector<BackendProbeCase>& cases,
+                                        const BackendProbeOptions& options = {});
+
+#endif  // __SD_CORE_PROBE_BACKEND_PROBE_H__

src/core/probe/flash_attn.cpp

@@ -0,0 +1,101 @@
+#include "core/probe/flash_attn.h"
+
+#include <array>
+#include <cstring>
+#include <exception>
+#include <sstream>
+#include <utility>
+#include <vector>
+
+#include "core/ggml_extend.h"
+#include "core/ggml_extend_backend.h"
+#include "core/probe/backend_probe.h"
+#include "core/util.h"
+
+static bool is_vulkan_nvidia_rtx(ggml_backend_t backend) {
+    auto device = ggml_backend_get_device(backend);
+    if (device == nullptr) {
+        return false;
+    }
+    auto reg = ggml_backend_dev_backend_reg(device);
+    if (reg == nullptr || std::strcmp(ggml_backend_reg_name(reg), "Vulkan") != 0) {
+        return false;
+    }
+    const char* description = ggml_backend_dev_description(device);
+    if (description == nullptr) {
+        return false;
+    }
+    std::istringstream stream(description);
+    std::string token;
+    bool nvidia = false, rtx = false;
+    while (stream >> token) {
+        nvidia = nvidia || token == "NVIDIA";
+        rtx    = rtx || token == "RTX";
+    }
+    return nvidia && rtx;
+}
+
+static void build_flash_attention_probe(BackendProbeGraph& graph, ggml_backend_t backend, int length, int head_size, bool flash) {
+    constexpr int heads = 4;
+    auto q              = ggml_new_tensor_3d(graph.ctx, GGML_TYPE_F32, head_size * heads, length, 1);
+    auto k              = ggml_dup_tensor(graph.ctx, q);
+    auto v              = ggml_dup_tensor(graph.ctx, q);
+    graph.inputs        = {q, k, v};
+    bool used_flash     = false;
+    auto output         = ggml_ext_attention_ext(graph.ctx, backend, q, k, v, heads, nullptr, false, flash, 1.f, false, &used_flash);
+    graph.output        = flash && !used_flash ? nullptr : output;
+}
+
+bool sd_probe_flash_attention(ggml_backend_t backend, int n_threads) {
+    if (backend == nullptr) {
+        return false;
+    }
+    const char* name = ggml_backend_name(backend);
+    try {
+        BackendProbeGraph capability(backend);
+        if (capability.ctx == nullptr) {
+            LOG_INFO("flash attention auto: %s -> off (probe graph unavailable)", name);
+            return false;
+        }
+        build_flash_attention_probe(capability, backend, 512, 64, true);
+        if (!capability.supported()) {
+            LOG_INFO("flash attention auto: %s -> off (unsupported probe graph)", name);
+            return false;
+        }
+        if (sd_backend_is_cpu(backend)) {
+            LOG_INFO("flash attention auto: %s -> on (CPU)", name);
+            return true;
+        }
+        if (sd_backend_supports_cuda_mma(backend)) {
+            LOG_INFO("flash attention auto: %s -> on (CUDA MMA)", name);
+            return true;
+        }
+        if (sd_backend_supports_hip_mma(backend)) {
+            LOG_INFO("flash attention auto: %s -> on (HIP MMA)", name);
+            return true;
+        }
+        if (is_vulkan_nvidia_rtx(backend)) {
+            LOG_INFO("flash attention auto: %s -> on (Vulkan NVIDIA RTX)", name);
+            return true;
+        }
+        std::vector<BackendProbeCase> cases;
+        for (const auto& shape : {std::array<int, 2>{512, 64}, std::array<int, 2>{1024, 128}}) {
+            BackendProbeCase test;
+            test.build = [backend, shape](BackendProbeGraph& graph, bool candidate) {
+                build_flash_attention_probe(graph, backend, shape[0], shape[1], candidate);
+            };
+            cases.push_back(std::move(test));
+        }
+        const auto result = sd_run_backend_probe(backend, n_threads, cases);
+        if (!result.completed) {
+            LOG_WARN("flash attention auto: %s -> off (%s)", name, result.error.c_str());
+            return false;
+        }
+        LOG_INFO("flash attention auto: %s -> %s (probe: normal %.3f ms, flash %.3f ms)",
+                 name, result.candidate_faster ? "on" : "off", result.reference_ms, result.candidate_ms);
+        return result.candidate_faster;
+    } catch (const std::exception& error) {
+        LOG_WARN("flash attention auto: %s -> off (probe failed: %s)", name, error.what());
+        return false;
+    }
+}

src/core/probe/flash_attn.h

@@ -0,0 +1,8 @@
+#ifndef __SD_CORE_PROBE_FLASH_ATTN_H__
+#define __SD_CORE_PROBE_FLASH_ATTN_H__
+
+#include "ggml-backend.h"
+
+bool sd_probe_flash_attention(ggml_backend_t backend, int n_threads);
+
+#endif  // __SD_CORE_PROBE_FLASH_ATTN_H__

src/model_manager.cpp

@@ -1757,7 +1757,14 @@ ModelManager::CapacityCheck ModelManager::check_capacity(
         }
         // Vulkan's heap budget subtraction can underflow when usage exceeds the budget.
         if (total_bytes > 0 && free_bytes > total_bytes && sd_backend_is(backend, "Vulkan")) {
-            return size_t{0};
+            if (ggml_backend_dev_type(device) != GGML_BACKEND_DEVICE_TYPE_IGPU) {
+                return size_t{0};
+            }
+            // A wrapped report may reflect real pressure; reclaim before accepting the tracked estimate.
+            LOG_VERBOSE("ignoring impossible free memory report on %s (%.2f MB free / %.2f MB total), using tracked residency",
+                        ggml_backend_name(backend), free_bytes / (1024.0 * 1024.0), total_bytes / (1024.0 * 1024.0));
+            free_bytes                  = total_bytes;
+            result.requires_reclamation = true;
         }
         if (total_bytes > 0) {
             free_bytes = std::min(free_bytes, resident < total_bytes ? total_bytes - resident : 0);
@@ -1803,8 +1810,11 @@ bool ModelManager::fits_compute_backend_capacity(
     const DeviceMemoryRequest& request,
     const std::vector<ggml_tensor*>& required_params) const {
     std::vector<TensorState*> states;
-    return resolve_required_tensor_states(required_params, states, request.compute_backend) &&
-           check_capacity(request, states).fits();
+    if (!resolve_required_tensor_states(required_params, states, request.compute_backend)) {
+        return false;
+    }
+    const auto capacity = check_capacity(request, states);
+    return !capacity.requires_reclamation && capacity.fits();
 }
 
 bool ModelManager::ensure_compute_backend_capacity(
@@ -1826,7 +1836,10 @@ bool ModelManager::ensure_compute_backend_capacity(
         return true;
     }
 
-    auto fits = [&]() { return check_capacity(request, required_states).fits(); };
+    auto fits = [&]() {
+        const auto capacity = check_capacity(request, required_states);
+        return !capacity.requires_reclamation && capacity.fits();
+    };
     if (fits()) {
         return true;
     }
@@ -1866,7 +1879,12 @@ bool ModelManager::ensure_compute_backend_capacity(
             eviction_states.insert(state);
         }
     };
+    size_t last_eviction_count   = 0;
     auto release_eviction_states = [&]() {
+        if (eviction_states.size() == last_eviction_count) {
+            return false;
+        }
+        last_eviction_count = eviction_states.size();
         release_compute_staging_blocks(false, &eviction_states);
         release_params_storage_blocks(false, &eviction_states);
         return fits();
@@ -1947,6 +1965,9 @@ bool ModelManager::ensure_compute_backend_capacity(
     }
     revert_demotions();
 
+    if (check_capacity(request, required_states).fits()) {
+        return true;
+    }
     const auto capacity                = check_capacity(request, required_states, true);
     const std::string available_device = capacity.available_device_bytes == SIZE_MAX
                                              ? "unknown"

src/model_manager.h

@@ -152,6 +152,7 @@ private:
         size_t required_budget_bytes  = 0;
         size_t available_device_bytes = SIZE_MAX;
         size_t available_budget_bytes = SIZE_MAX;
+        bool requires_reclamation     = false;
         bool fits() const {
             return required_device_bytes <= available_device_bytes &&
                    required_budget_bytes <= available_budget_bytes;

src/pipeline/diffusion_engine.cpp

@@ -18,6 +18,7 @@
 #include "core/ggml_runner.h"
 #include "core/ggml_tensor_utils.h"
 #include "core/layer_split_partition.h"
+#include "core/probe/flash_attn.h"
 #include "model.h"
 
 #include "core/rng.hpp"
@@ -944,6 +945,13 @@ bool StableDiffusionGGML::init(const sd_ctx_params_t* sd_ctx_params) {
         LOG_ERROR("conditioning_cache_size must be non-negative");
         return false;
     }
+    const std::string flash_mode = SAFE_STR(sd_ctx_params->flash_attn_mode);
+    std::string flash_error;
+    if (!sd_parse_flash_attention_assignment(flash_mode, &flash_attn_assignment, &flash_error)) {
+        LOG_ERROR("invalid flash_attn_mode '%s': %s", flash_mode.c_str(), flash_error.c_str());
+        return false;
+    }
+    flash_attn_by_backend.clear();
     conditioning_cache_->set_capacity(static_cast<size_t>(sd_ctx_params->conditioning_cache_size));
     auto configuration = std::make_unique<ModelConfig>(*sd_ctx_params);
     n_threads          = sd_ctx_params->n_threads;
@@ -1182,30 +1190,73 @@ bool StableDiffusionGGML::build_extension_runners() {
     return true;
 }
 
+bool StableDiffusionGGML::flash_attention_for(SDBackendModule module) {
+    const auto& params     = config_->params;
+    const std::string mode = flash_attn_assignment.get(module);
+    if (mode == "on") {
+        return true;
+    }
+    if (mode == "off") {
+        return false;
+    }
+    if (strlen(SAFE_STR(params.flash_attn_mode)) == 0) {
+        if (params.flash_attn) {
+            return true;
+        }
+        if (params.diffusion_flash_attn) {
+            return module == SDBackendModule::DIFFUSION;
+        }
+    }
+    auto backends    = backend_manager.runtime_backends(module);
+    bool enabled     = !backends.empty();
+    bool any_enabled = false;
+    for (auto backend : backends) {
+        auto it = flash_attn_by_backend.find(backend);
+        if (it == flash_attn_by_backend.end()) {
+            it = flash_attn_by_backend.emplace(backend, sd_probe_flash_attention(backend, n_threads)).first;
+        }
+        enabled     = enabled && it->second;
+        any_enabled = any_enabled || it->second;
+    }
+    if (any_enabled && !enabled) {
+        LOG_INFO("flash attention auto: %s -> off (compute devices disagree)", sd_backend_module_name(module));
+    }
+    return enabled;
+}
+
 bool StableDiffusionGGML::validate_and_load_runners() {
     const auto* sd_ctx_params   = &config_->params;
     const bool use_tae          = uses_tae();
     const bool tae_preview_only = this->tae_preview_only();
-    if (sd_ctx_params->flash_attn) {
-        LOG_INFO("Using flash attention");
-        cond_stage_model->set_flash_attention_enabled(true);
-        if (clip_vision) {
-            clip_vision->set_flash_attention_enabled(true);
-        }
+    cond_stage_model->set_flash_attention_enabled(flash_attention_for(SDBackendModule::TE));
+    if (clip_vision) {
+        clip_vision->set_flash_attention_enabled(flash_attention_for(SDBackendModule::CLIP_VISION));
+    }
+    if (first_stage_model || preview_vae || audio_vae_model) {
+        const bool enabled = flash_attention_for(SDBackendModule::VAE);
         if (first_stage_model) {
-            first_stage_model->set_flash_attention_enabled(true);
+            first_stage_model->set_flash_attention_enabled(enabled);
         }
         if (preview_vae) {
-            preview_vae->set_flash_attention_enabled(true);
+            preview_vae->set_flash_attention_enabled(enabled);
+        }
+        if (audio_vae_model) {
+            audio_vae_model->set_flash_attention_enabled(enabled);
         }
     }
-
-    if (sd_ctx_params->flash_attn || sd_ctx_params->diffusion_flash_attn) {
-        LOG_INFO("Using flash attention in the diffusion model");
-        diffusion_model->set_flash_attention_enabled(true);
-        if (high_noise_diffusion_model) {
-            high_noise_diffusion_model->set_flash_attention_enabled(true);
-        }
+    const bool diffusion_fa = flash_attention_for(SDBackendModule::DIFFUSION);
+    diffusion_model->set_flash_attention_enabled(diffusion_fa);
+    if (high_noise_diffusion_model) {
+        high_noise_diffusion_model->set_flash_attention_enabled(diffusion_fa);
+    }
+    if (ip_adapter) {
+        ip_adapter->set_flash_attention_enabled(diffusion_fa);
+    }
+    if (control_net) {
+        control_net->set_flash_attention_enabled(flash_attention_for(SDBackendModule::CONTROL_NET));
+    }
+    if (audio_encoder) {
+        audio_encoder->set_flash_attention_enabled(flash_attention_for(SDBackendModule::AUDIO_ENCODER));
     }
     if (sd_ctx_params->sage_attn && !set_sage_attention_enabled(true)) {
         return false;
@@ -2735,9 +2786,9 @@ 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]);
             }
         }
 

src/pipeline/diffusion_engine.h

@@ -11,6 +11,7 @@
 #include <optional>
 #include <set>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include "core/ggml_extend_backend.h"
@@ -48,6 +49,8 @@ static inline bool sd_version_supports_ref_latent_img_cfg(SDVersion version) {
 class StableDiffusionGGML {
 public:
     SDBackendManager backend_manager;
+    std::unordered_map<ggml_backend_t, bool> flash_attn_by_backend;
+    SDBackendAssignment flash_attn_assignment;
 
     SDVersion version;
     bool external_vae_is_invalid = false;
@@ -131,7 +134,7 @@ public:
                                 &sd_ctx_params_t::clip_g_path, &sd_ctx_params_t::clip_vision_path,
                                 &sd_ctx_params_t::t5xxl_path, &sd_ctx_params_t::llm_path,
                                 &sd_ctx_params_t::llm_vision_path, &sd_ctx_params_t::diffusion_model_path,
-                                &sd_ctx_params_t::tokenizer,
+                                &sd_ctx_params_t::tokenizer, &sd_ctx_params_t::flash_attn_mode,
                                 &sd_ctx_params_t::high_noise_diffusion_model_path, &sd_ctx_params_t::uncond_diffusion_model_path,
                                 &sd_ctx_params_t::embeddings_connectors_path, &sd_ctx_params_t::vae_path,
                                 &sd_ctx_params_t::audio_vae_path, &sd_ctx_params_t::taesd_path,
@@ -334,6 +337,7 @@ public:
     bool build_extension_runners();
 
     bool validate_and_load_runners();
+    bool flash_attention_for(SDBackendModule module);
 
     bool build_denoiser();
 

src/stable-diffusion.cpp

@@ -338,6 +338,7 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     sd_ctx_params->batched_cfg               = true;
     sd_ctx_params->eager_load                = false;
     sd_ctx_params->enable_mmap               = false;
+    sd_ctx_params->flash_attn_mode           = nullptr;
     sd_ctx_params->diffusion_flash_attn      = false;
     sd_ctx_params->sage_attn                 = false;
     sd_ctx_params->linear_scale              = 0.f;
@@ -395,6 +396,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "model_args: %s\n"
              "auto_fit: %s\n"
              "flash_attn: %s\n"
+             "flash_attn_mode: %s\n"
              "diffusion_flash_attn: %s\n"
              "sage_attn: %s\n"
              "linear_scale: %g\n"
@@ -436,6 +438,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              SAFE_STR(sd_ctx_params->model_args),
              BOOL_STR(sd_ctx_params->auto_fit),
              BOOL_STR(sd_ctx_params->flash_attn),
+             sd_ctx_params->flash_attn_mode && sd_ctx_params->flash_attn_mode[0] ? sd_ctx_params->flash_attn_mode : "auto (legacy flags honored)",
              BOOL_STR(sd_ctx_params->diffusion_flash_attn),
              BOOL_STR(sd_ctx_params->sage_attn),
              sd_ctx_params->linear_scale,