feat: reduce CLIP memory usage with no embeddings (#768)

* kontext: support  ref images indices

* lora: support x_embedder

* update help message

* Support for negative indices

* support for OmniControl (offsets at index 0)

* c++11 compat

* add --increase-ref-index option

* simplify the logic and fix some issues

* update README.md

* remove unused variable

---------

Co-authored-by: leejet <leejet714@gmail.com>

.github/workflows/build.yml

@@ -146,7 +146,7 @@ jobs:
             sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip
 
   windows-latest-cmake:
-    runs-on: windows-2019
+    runs-on: windows-2025
 
     env:
       VULKAN_VERSION: 1.3.261.1
@@ -155,17 +155,17 @@ jobs:
       matrix:
         include:
           - build: "noavx"
-            defines: "-DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx2"
-            defines: "-DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx"
-            defines: "-DGGML_AVX2=OFF -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX=ON -DGGML_AVX2=OFF -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx512"
-            defines: "-DGGML_AVX512=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
-            defines: "-DSD_CUBLAS=ON -DSD_BUILD_SHARED_LIBS=ON"
-          - build: "rocm5.5"
-            defines: '-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS="gfx1100;gfx1102;gfx1030" -DSD_BUILD_SHARED_LIBS=ON'
+            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES=90;89;86;80;75"
+          # - build: "rocm5.5"
+          #   defines: '-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS="gfx1100;gfx1102;gfx1030" -DSD_BUILD_SHARED_LIBS=ON'
           - build: 'vulkan'
             defines: "-DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON"
     steps:
@@ -178,9 +178,9 @@ jobs:
       - name: Install cuda-toolkit
         id: cuda-toolkit
         if: ${{ matrix.build == 'cuda12' }}
-        uses: Jimver/cuda-toolkit@v0.2.11
+        uses: Jimver/cuda-toolkit@v0.2.19
         with:
-          cuda: "12.2.0"
+          cuda: "12.6.2"
           method: "network"
           sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]'
 

.gitignore

@@ -1,13 +1,14 @@
 build*/
+cmake-build-*/
 test/
 .vscode/
+.idea/
 .cache/
 *.swp
-.vscode/
 *.bat
 *.bin
 *.exe
 *.gguf
 output*.png
 models*
-*.log
+*.log

.gitmodules

@@ -1,3 +1,3 @@
 [submodule "ggml"]
     path = ggml
-	url = https://github.com/ggerganov/ggml.git
+	url = https://github.com/ggml-org/ggml.git

CMakeLists.txt

@@ -24,19 +24,22 @@ endif()
 # general
 #option(SD_BUILD_TESTS                "sd: build tests"    ${SD_STANDALONE})
 option(SD_BUILD_EXAMPLES             "sd: build examples" ${SD_STANDALONE})
-option(SD_CUBLAS                     "sd: cuda backend" OFF)
+option(SD_CUDA                       "sd: cuda backend" OFF)
 option(SD_HIPBLAS                    "sd: rocm backend" OFF)
 option(SD_METAL                      "sd: metal backend" OFF)
 option(SD_VULKAN                     "sd: vulkan backend" OFF)
+option(SD_OPENCL                     "sd: opencl backend" OFF)
 option(SD_SYCL                       "sd: sycl backend" OFF)
+option(SD_MUSA                       "sd: musa backend" OFF)
 option(SD_FAST_SOFTMAX               "sd: x1.5 faster softmax, indeterministic (sometimes, same seed don't generate same image), cuda only" OFF)
 option(SD_BUILD_SHARED_LIBS          "sd: build shared libs" OFF)
+option(SD_USE_SYSTEM_GGML            "sd: use system-installed GGML library" OFF)
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)
 
-if(SD_CUBLAS)
-    message("-- Use CUBLAS as backend stable-diffusion")
+if(SD_CUDA)
+    message("-- Use CUDA as backend stable-diffusion")
     set(GGML_CUDA ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    add_definitions(-DSD_USE_CUDA)
 endif()
 
 if(SD_METAL)
@@ -51,18 +54,33 @@ if (SD_VULKAN)
     add_definitions(-DSD_USE_VULKAN)
 endif ()
 
+if (SD_OPENCL)
+    message("-- Use OpenCL as backend stable-diffusion")
+    set(GGML_OPENCL ON)
+    add_definitions(-DSD_USE_OPENCL)
+endif ()
+
 if (SD_HIPBLAS)
     message("-- Use HIPBLAS as backend stable-diffusion")
-    set(GGML_HIPBLAS ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    set(GGML_HIP ON)
+    add_definitions(-DSD_USE_CUDA)
     if(SD_FAST_SOFTMAX)
         set(GGML_CUDA_FAST_SOFTMAX ON)
     endif()
 endif ()
 
+if(SD_MUSA)
+    message("-- Use MUSA as backend stable-diffusion")
+    set(GGML_MUSA ON)
+    add_definitions(-DSD_USE_CUDA)
+    if(SD_FAST_SOFTMAX)
+        set(GGML_CUDA_FAST_SOFTMAX ON)
+    endif()
+endif()
+
 set(SD_LIB stable-diffusion)
 
-file(GLOB SD_LIB_SOURCES 
+file(GLOB SD_LIB_SOURCES
     "*.h"
     "*.cpp"
     "*.hpp"
@@ -86,6 +104,7 @@ endif()
 if(SD_SYCL)
     message("-- Use SYCL as backend stable-diffusion")
     set(GGML_SYCL ON)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl")
     add_definitions(-DSD_USE_SYCL)
     # disable fast-math on host, see:
     # https://www.intel.com/content/www/us/en/docs/cpp-compiler/developer-guide-reference/2021-10/fp-model-fp.html
@@ -100,23 +119,37 @@ endif()
 
 set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
-# see https://github.com/ggerganov/ggml/pull/682
-add_definitions(-DGGML_MAX_NAME=128)
+if (NOT SD_USE_SYSTEM_GGML)
+    # see https://github.com/ggerganov/ggml/pull/682
+    add_definitions(-DGGML_MAX_NAME=128)
+endif()
 
 # deps
 # Only add ggml if it hasn't been added yet
 if (NOT TARGET ggml)
-    add_subdirectory(ggml)
+    if (SD_USE_SYSTEM_GGML)
+        find_package(ggml REQUIRED)
+        if (NOT ggml_FOUND)
+            message(FATAL_ERROR "System-installed GGML library not found.")
+        endif()
+        add_library(ggml ALIAS ggml::ggml)
+    else()
+        add_subdirectory(ggml)
+    endif()
 endif()
 
 add_subdirectory(thirdparty)
 
 target_link_libraries(${SD_LIB} PUBLIC ggml zip)
 target_include_directories(${SD_LIB} PUBLIC . thirdparty)
-target_compile_features(${SD_LIB} PUBLIC cxx_std_11)
+target_compile_features(${SD_LIB} PUBLIC c_std_11 cxx_std_17)
 
 
 if (SD_BUILD_EXAMPLES)
     add_subdirectory(examples)
 endif()
 
+set(SD_PUBLIC_HEADERS stable-diffusion.h)
+set_target_properties(${SD_LIB} PROPERTIES PUBLIC_HEADER "${SD_PUBLIC_HEADERS}")
+
+install(TARGETS ${SD_LIB} LIBRARY PUBLIC_HEADER)

Dockerfile.musa

@@ -0,0 +1,23 @@
+ARG MUSA_VERSION=rc4.2.0
+ARG UBUNTU_VERSION=22.04
+
+FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 as build
+
+RUN apt-get update && apt-get install -y ccache cmake git
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+RUN mkdir build && cd build && \
+    cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ \
+        -DCMAKE_C_FLAGS="${CMAKE_C_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
+        -DCMAKE_CXX_FLAGS="${CMAKE_CXX_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
+        -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build . --config Release
+
+FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runtime
+
+COPY --from=build /sd.cpp/build/bin/sd /sd
+
+ENTRYPOINT [ "/sd" ]

README.md

@@ -4,36 +4,45 @@
 
 # stable-diffusion.cpp
 
-Inference of Stable Diffusion and Flux in pure C/C++
+Diffusion model(SD,Flux,Wan,...) inference in pure C/C++
+
+***Note that this project is under active development. \
+API and command-line option may change frequently.***
 
 ## Features
 
 - Plain C/C++ implementation based on [ggml](https://github.com/ggerganov/ggml), working in the same way as [llama.cpp](https://github.com/ggerganov/llama.cpp)
 - Super lightweight and without external dependencies
-- SD1.x, SD2.x, SDXL and [SD3/SD3.5](./docs/sd3.md) support
-    - !!!The VAE in SDXL encounters NaN issues under FP16, but unfortunately, the ggml_conv_2d only operates under FP16. Hence, a parameter is needed to specify the VAE that has fixed the FP16 NaN issue. You can find it here: [SDXL VAE FP16 Fix](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl_vae.safetensors).
-- [Flux-dev/Flux-schnell Support](./docs/flux.md)
-
-- [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo) and [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo) support
-- [PhotoMaker](https://github.com/TencentARC/PhotoMaker) support.
+- Supported models
+  - Image Models
+    - SD1.x, SD2.x, [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo)
+    - SDXL, [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo)
+      - !!!The VAE in SDXL encounters NaN issues under FP16, but unfortunately, the ggml_conv_2d only operates under FP16. Hence, a parameter is needed to specify the VAE that has fixed the FP16 NaN issue. You can find it here: [SDXL VAE FP16 Fix](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl_vae.safetensors).
+    - [SD3/SD3.5](./docs/sd3.md)
+    - [Flux-dev/Flux-schnell](./docs/flux.md)
+    - [Chroma](./docs/chroma.md)
+  - Image Edit Models
+    - [FLUX.1-Kontext-dev](./docs/kontext.md)
+  - Video Models
+    - [Wan2.1/Wan2.2](./docs/wan.md)
+  - [PhotoMaker](https://github.com/TencentARC/PhotoMaker) support.
+  - Control Net support with SD 1.5
+  - LoRA support, same as [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora)
+  - Latent Consistency Models support (LCM/LCM-LoRA)
+  - Faster and memory efficient latent decoding with [TAESD](https://github.com/madebyollin/taesd)
+  - Upscale images generated with [ESRGAN](https://github.com/xinntao/Real-ESRGAN)
 - 16-bit, 32-bit float support
 - 2-bit, 3-bit, 4-bit, 5-bit and 8-bit integer quantization support
 - Accelerated memory-efficient CPU inference
     - Only requires ~2.3GB when using txt2img with fp16 precision to generate a 512x512 image, enabling Flash Attention just requires ~1.8GB.
 - AVX, AVX2 and AVX512 support for x86 architectures
-- Full CUDA, Metal, Vulkan and SYCL backend for GPU acceleration.
+- Full CUDA, Metal, Vulkan, OpenCL and SYCL backend for GPU acceleration.
 - Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs models
     - No need to convert to `.ggml` or `.gguf` anymore!
 - Flash Attention for memory usage optimization
-- Original `txt2img` and `img2img` mode
 - Negative prompt
 - [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) style tokenizer (not all the features, only token weighting for now)
-- LoRA support, same as [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora)
-- Latent Consistency Models support (LCM/LCM-LoRA)
-- Faster and memory efficient latent decoding with [TAESD](https://github.com/madebyollin/taesd)
-- Upscale images generated with [ESRGAN](https://github.com/xinntao/Real-ESRGAN)
 - VAE tiling processing for reduce memory usage
-- Control Net support with SD 1.5
 - Sampling method
     - `Euler A`
     - `Euler`
@@ -49,7 +58,7 @@ Inference of Stable Diffusion and Flux in pure C/C++
     - Linux
     - Mac OS
     - Windows
-    - Android (via Termux)
+    - Android (via Termux, [Local Diffusion](https://github.com/rmatif/Local-Diffusion))
 
 ### TODO
 
@@ -113,12 +122,12 @@ cmake .. -DGGML_OPENBLAS=ON
 cmake --build . --config Release
 ```
 
-##### Using CUBLAS
+##### Using CUDA
 
 This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). Recommended to have at least 4 GB of VRAM.
 
 ```
-cmake .. -DSD_CUBLAS=ON
+cmake .. -DSD_CUDA=ON
 cmake --build . --config Release
 ```
 
@@ -128,10 +137,20 @@ This provides BLAS acceleration using the ROCm cores of your AMD GPU. Make sure
 Windows User Refer to [docs/hipBLAS_on_Windows.md](docs%2FhipBLAS_on_Windows.md) for a comprehensive guide.
 
 ```
-cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=gfx1100
+export GFX_NAME=$(rocminfo | grep -m 1 -E "gfx[^0]{1}" | sed -e 's/ *Name: *//' | awk '{$1=$1; print}' || echo "rocminfo missing")
+echo $GFX_NAME
+cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGPU_TARGETS=$GFX_NAME -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON
 cmake --build . --config Release
 ```
 
+##### Using MUSA
+
+This provides BLAS acceleration using the MUSA cores of your Moore Threads GPU. Make sure to have the MUSA toolkit installed.
+
+```bash
+cmake .. -DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release
+cmake --build . --config Release
+```
 
 ##### Using Metal
 
@@ -151,6 +170,73 @@ cmake .. -DSD_VULKAN=ON
 cmake --build . --config Release
 ```
 
+##### Using OpenCL (for Adreno GPU)
+
+Currently, it supports only Adreno GPUs and is primarily optimized for Q4_0 type
+
+To build for Windows ARM please refers to [Windows 11 Arm64
+](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/OPENCL.md#windows-11-arm64)
+
+Building for Android:
+
+  Android NDK:
+       Download and install the Android NDK from the [official Android developer site](https://developer.android.com/ndk/downloads).
+
+Setup OpenCL Dependencies for NDK:
+
+You need to provide OpenCL headers and the ICD loader library to your NDK sysroot.
+
+*   OpenCL Headers:
+    ```bash
+    # In a temporary working directory
+    git clone https://github.com/KhronosGroup/OpenCL-Headers
+    cd OpenCL-Headers
+    # Replace <YOUR_NDK_PATH> with your actual NDK installation path
+    # e.g., cp -r CL /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
+    sudo cp -r CL <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
+    cd ..
+    ```
+
+*   OpenCL ICD Loader:
+    ```bash
+    # In the same temporary working directory
+    git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
+    cd OpenCL-ICD-Loader
+    mkdir build_ndk && cd build_ndk
+
+    # Replace <YOUR_NDK_PATH> in the CMAKE_TOOLCHAIN_FILE and OPENCL_ICD_LOADER_HEADERS_DIR
+    cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
+      -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
+      -DOPENCL_ICD_LOADER_HEADERS_DIR=<YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
+      -DANDROID_ABI=arm64-v8a \
+      -DANDROID_PLATFORM=24 \
+      -DANDROID_STL=c++_shared
+
+    ninja
+    # Replace <YOUR_NDK_PATH>
+    # e.g., cp libOpenCL.so /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
+    sudo cp libOpenCL.so <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
+    cd ../..
+    ```
+
+Build `stable-diffusion.cpp` for Android with OpenCL:
+
+```bash
+mkdir build-android && cd build-android
+
+# Replace <YOUR_NDK_PATH> with your actual NDK installation path
+# e.g., -DCMAKE_TOOLCHAIN_FILE=/path/to/android-ndk-r26c/build/cmake/android.toolchain.cmake
+cmake .. -G Ninja \
+  -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
+  -DANDROID_ABI=arm64-v8a \
+  -DANDROID_PLATFORM=android-28 \
+  -DGGML_OPENMP=OFF \
+  -DSD_OPENCL=ON
+
+ninja
+```
+*(Note: Don't forget to include `LD_LIBRARY_PATH=/vendor/lib64` in your command line before running the binary)*
+
 ##### Using SYCL
 
 Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and [Intel® oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before start. More details and steps can refer to [llama.cpp SYCL backend](https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md#linux).
@@ -206,14 +292,17 @@ usage: ./bin/sd [arguments]
 
 arguments:
   -h, --help                         show this help message and exit
-  -M, --mode [MODEL]                 run mode (txt2img or img2img or convert, default: txt2img)
+  -M, --mode [MODE]                  run mode, one of: [img_gen, vid_gen, convert], default: img_gen
   -t, --threads N                    number of threads to use during computation (default: -1)
                                      If threads <= 0, then threads will be set to the number of CPU physical cores
+  --offload-to-cpu                   place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
   -m, --model [MODEL]                path to full model
   --diffusion-model                  path to the standalone diffusion model
+  --high-noise-diffusion-model       path to the standalone high noise diffusion model
   --clip_l                           path to the clip-l text encoder
-  --clip_g                           path to the clip-l text encoder
-  --t5xxl                            path to the the t5xxl text encoder
+  --clip_g                           path to the clip-g text encoder
+  --clip_vision                      path to the clip-vision encoder
+  --t5xxl                            path to the t5xxl text encoder
   --vae [VAE]                        path to vae
   --taesd [TAESD_PATH]               path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
   --control-net [CONTROL_PATH]       path to control net model
@@ -223,29 +312,56 @@ arguments:
   --normalize-input                  normalize PHOTOMAKER input id images
   --upscale-model [ESRGAN_PATH]      path to esrgan model. Upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now
   --upscale-repeats                  Run the ESRGAN upscaler this many times (default 1)
-  --type [TYPE]                      weight type (f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_k, q3_k, q4_k)
+  --type [TYPE]                      weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K)
                                      If not specified, the default is the type of the weight file
+  --tensor-type-rules [EXPRESSION]   weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
   --lora-model-dir [DIR]             lora model directory
-  -i, --init-img [IMAGE]             path to the input image, required by img2img
+  -i, --init-img [IMAGE]             path to the init image, required by img2img
+  --mask [MASK]                      path to the mask image, required by img2img with mask
+  -i, --end-img [IMAGE]              path to the end image, required by flf2v
   --control-image [IMAGE]            path to image condition, control net
+  -r, --ref-image [PATH]             reference image for Flux Kontext models (can be used multiple times)
+  --increase-ref-index               automatically increase the indices of references images based on the order they are listed (starting with 1).
   -o, --output OUTPUT                path to write result image to (default: ./output.png)
   -p, --prompt [PROMPT]              the prompt to render
   -n, --negative-prompt PROMPT       the negative prompt (default: "")
   --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
+  --img-cfg-scale SCALE              image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
+  --guidance SCALE                   distilled guidance scale for models with guidance input (default: 3.5)
+  --slg-scale SCALE                  skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+                                     0 means disabled, a value of 2.5 is nice for sd3.5 medium
+  --eta SCALE                        eta in DDIM, only for DDIM and TCD: (default: 0)
+  --skip-layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])
+  --skip-layer-start START           SLG enabling point: (default: 0.01)
+  --skip-layer-end END               SLG disabling point: (default: 0.2)
+  --scheduler {discrete, karras, exponential, ays, gits} Denoiser sigma scheduler (default: discrete)
+  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd}
+                                     sampling method (default: "euler_a")
+  --steps  STEPS                     number of sample steps (default: 20)
+  --high-noise-cfg-scale SCALE       (high noise) unconditional guidance scale: (default: 7.0)
+  --high-noise-img-cfg-scale SCALE   (high noise) image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
+  --high-noise-guidance SCALE        (high noise) distilled guidance scale for models with guidance input (default: 3.5)
+  --high-noise-slg-scale SCALE       (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+                                     0 means disabled, a value of 2.5 is nice for sd3.5 medium
+  --high-noise-eta SCALE             (high noise) eta in DDIM, only for DDIM and TCD: (default: 0)
+  --high-noise-skip-layers LAYERS    (high noise) Layers to skip for SLG steps: (default: [7,8,9])
+  --high-noise-skip-layer-start      (high noise) SLG enabling point: (default: 0.01)
+  --high-noise-skip-layer-end END    (high noise) SLG disabling point: (default: 0.2)
+  --high-noise-scheduler {discrete, karras, exponential, ays, gits} Denoiser sigma scheduler (default: discrete)
+  --high-noise-sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd}
+                                     (high noise) sampling method (default: "euler_a")
+  --high-noise-steps  STEPS          (high noise) number of sample steps (default: -1 = auto)
+                                     SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])
   --strength STRENGTH                strength for noising/unnoising (default: 0.75)
-  --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20%)
+  --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20)
   --control-strength STRENGTH        strength to apply Control Net (default: 0.9)
                                      1.0 corresponds to full destruction of information in init image
   -H, --height H                     image height, in pixel space (default: 512)
   -W, --width W                      image width, in pixel space (default: 512)
-  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm}
-                                     sampling method (default: "euler_a")
-  --steps  STEPS                     number of sample steps (default: 20)
   --rng {std_default, cuda}          RNG (default: cuda)
   -s SEED, --seed SEED               RNG seed (default: 42, use random seed for < 0)
   -b, --batch-count COUNT            number of images to generate
-  --schedule {discrete, karras, exponential, ays, gits} Denoiser sigma schedule (default: discrete)
-  --clip-skip N                      ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1)
+  --clip-skip N                      ignore last_dot_pos layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1)
                                      <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x
   --vae-tiling                       process vae in tiles to reduce memory usage
   --vae-on-cpu                       keep vae in cpu (for low vram)
@@ -253,9 +369,21 @@ arguments:
   --diffusion-fa                     use flash attention in the diffusion model (for low vram)
                                      Might lower quality, since it implies converting k and v to f16.
                                      This might crash if it is not supported by the backend.
+  --diffusion-conv-direct            use Conv2d direct in the diffusion model
+                                     This might crash if it is not supported by the backend.
+  --vae-conv-direct                  use Conv2d direct in the vae model (should improve the performance)
+                                     This might crash if it is not supported by the backend.
   --control-net-cpu                  keep controlnet in cpu (for low vram)
   --canny                            apply canny preprocessor (edge detection)
-  --color                            Colors the logging tags according to level
+  --color                            colors the logging tags according to level
+  --chroma-disable-dit-mask          disable dit mask for chroma
+  --chroma-enable-t5-mask            enable t5 mask for chroma
+  --chroma-t5-mask-pad  PAD_SIZE     t5 mask pad size of chroma
+  --video-frames                     video frames (default: 1)
+  --fps                              fps (default: 24)
+  --moe-boundary BOUNDARY            timestep boundary for Wan2.2 MoE model. (default: 0.875)
+                                     only enabled if `--high-noise-steps` is set to -1
+  --flow-shift SHIFT                 shift value for Flow models like SD3.x or WAN (default: auto)
   -v, --verbose                      print extra info
 ```
 
@@ -282,7 +410,7 @@ Using formats of different precisions will yield results of varying quality.
 
 
 ```
-./bin/sd --mode img2img -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
+./bin/sd -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
 ```
 
 <p align="center">
@@ -303,10 +431,12 @@ Using formats of different precisions will yield results of varying quality.
 
 These projects wrap `stable-diffusion.cpp` for easier use in other languages/frameworks.
 
-* Golang: [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (non-cgo): [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (cgo): [Binozo/GoStableDiffusion](https://github.com/Binozo/GoStableDiffusion)
 * C#: [DarthAffe/StableDiffusion.NET](https://github.com/DarthAffe/StableDiffusion.NET)
 * Python: [william-murray1204/stable-diffusion-cpp-python](https://github.com/william-murray1204/stable-diffusion-cpp-python)
 * Rust: [newfla/diffusion-rs](https://github.com/newfla/diffusion-rs)
+* Flutter/Dart: [rmatif/Local-Diffusion](https://github.com/rmatif/Local-Diffusion)
 
 ## UIs
 
@@ -314,6 +444,10 @@ These projects use `stable-diffusion.cpp` as a backend for their image generatio
 
 - [Jellybox](https://jellybox.com)
 - [Stable Diffusion GUI](https://github.com/fszontagh/sd.cpp.gui.wx)
+- [Stable Diffusion CLI-GUI](https://github.com/piallai/stable-diffusion.cpp)
+- [Local Diffusion](https://github.com/rmatif/Local-Diffusion)
+- [sd.cpp-webui](https://github.com/daniandtheweb/sd.cpp-webui)
+- [LocalAI](https://github.com/mudler/LocalAI)
 
 ## Contributors
 
@@ -337,3 +471,5 @@ Thank you to all the people who have already contributed to stable-diffusion.cpp
 - [latent-consistency-model](https://github.com/luosiallen/latent-consistency-model)
 - [generative-models](https://github.com/Stability-AI/generative-models/)
 - [PhotoMaker](https://github.com/TencentARC/PhotoMaker)
+- [Wan2.1](https://github.com/Wan-Video/Wan2.1)
+- [Wan2.2](https://github.com/Wan-Video/Wan2.2)

clip.hpp

@@ -179,9 +179,9 @@ public:
 
         auto it = encoder.find(utf8_to_utf32("img</w>"));
         if (it != encoder.end()) {
-            LOG_DEBUG(" trigger word img already in vocab");
+            LOG_DEBUG("trigger word img already in vocab");
         } else {
-            LOG_DEBUG(" trigger word img not in vocab yet");
+            LOG_DEBUG("trigger word img not in vocab yet");
         }
 
         int rank = 0;
@@ -488,14 +488,14 @@ public:
         blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, bool mask = true) {
+    struct ggml_tensor* forward(struct ggml_context* ctx, ggml_backend_t backend, struct ggml_tensor* x, bool mask = true) {
         // x: [N, n_token, d_model]
         auto self_attn   = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
         auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
         auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
         auto mlp         = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
 
-        x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
+        x = ggml_add(ctx, x, self_attn->forward(ctx, backend, layer_norm1->forward(ctx, x), mask));
         x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
         return x;
     }
@@ -517,7 +517,11 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, int clip_skip = -1, bool mask = true) {
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
+                                struct ggml_tensor* x,
+                                int clip_skip = -1,
+                                bool mask     = true) {
         // x: [N, n_token, d_model]
         int layer_idx = n_layer - 1;
         // LOG_DEBUG("clip_skip %d", clip_skip);
@@ -532,7 +536,7 @@ public:
             }
             std::string name = "layers." + std::to_string(i);
             auto layer       = std::dynamic_pointer_cast<CLIPLayer>(blocks[name]);
-            x                = layer->forward(ctx, x, mask);  // [N, n_token, d_model]
+            x                = layer->forward(ctx, backend, x, mask);  // [N, n_token, d_model]
             // LOG_DEBUG("layer %d", i);
         }
         return x;
@@ -544,19 +548,30 @@ protected:
     int64_t embed_dim;
     int64_t vocab_size;
     int64_t num_positions;
+    bool force_clip_f32;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["token_embedding.weight"]    = ggml_new_tensor_2d(ctx, wtype, embed_dim, vocab_size);
-        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, num_positions);
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+        enum ggml_type token_wtype = GGML_TYPE_F32;
+        if (!force_clip_f32) {
+            auto tensor_type = tensor_types.find(prefix + "token_embedding.weight");
+            if (tensor_type != tensor_types.end())
+                token_wtype = tensor_type->second;
+        }
+        enum ggml_type position_wtype = GGML_TYPE_F32;
+
+        params["token_embedding.weight"]    = ggml_new_tensor_2d(ctx, token_wtype, embed_dim, vocab_size);
+        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, position_wtype, embed_dim, num_positions);
     }
 
 public:
     CLIPEmbeddings(int64_t embed_dim,
                    int64_t vocab_size    = 49408,
-                   int64_t num_positions = 77)
+                   int64_t num_positions = 77,
+                   bool force_clip_f32   = false)
         : embed_dim(embed_dim),
           vocab_size(vocab_size),
-          num_positions(num_positions) {
+          num_positions(num_positions),
+          force_clip_f32(force_clip_f32) {
     }
 
     struct ggml_tensor* get_token_embed_weight() {
@@ -591,11 +606,14 @@ protected:
     int64_t image_size;
     int64_t num_patches;
     int64_t num_positions;
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+        enum ggml_type patch_wtype    = GGML_TYPE_F16;
+        enum ggml_type class_wtype    = GGML_TYPE_F32;
+        enum ggml_type position_wtype = GGML_TYPE_F32;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["patch_embedding.weight"]    = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, patch_size, patch_size, num_channels, embed_dim);
-        params["class_embedding"]           = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, embed_dim);
-        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, num_positions);
+        params["patch_embedding.weight"]    = ggml_new_tensor_4d(ctx, patch_wtype, patch_size, patch_size, num_channels, embed_dim);
+        params["class_embedding"]           = ggml_new_tensor_1d(ctx, class_wtype, embed_dim);
+        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, position_wtype, embed_dim, num_positions);
     }
 
 public:
@@ -651,9 +669,10 @@ enum CLIPVersion {
 
 class CLIPTextModel : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
         if (version == OPEN_CLIP_VIT_BIGG_14) {
-            params["text_projection"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, projection_dim, hidden_size);
+            enum ggml_type wtype      = GGML_TYPE_F32;
+            params["text_projection"] = ggml_new_tensor_2d(ctx, wtype, projection_dim, hidden_size);
         }
     }
 
@@ -667,12 +686,11 @@ public:
     int32_t n_head            = 12;
     int32_t n_layer           = 12;    // num_hidden_layers
     int32_t projection_dim    = 1280;  // only for OPEN_CLIP_VIT_BIGG_14
-    int32_t clip_skip         = -1;
     bool with_final_ln        = true;
 
     CLIPTextModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14,
-                  int clip_skip_value = -1,
-                  bool with_final_ln  = true)
+                  bool with_final_ln  = true,
+                  bool force_clip_f32 = false)
         : version(version), with_final_ln(with_final_ln) {
         if (version == OPEN_CLIP_VIT_H_14) {
             hidden_size       = 1024;
@@ -685,37 +703,31 @@ public:
             n_head            = 20;
             n_layer           = 32;
         }
-        set_clip_skip(clip_skip_value);
 
-        blocks["embeddings"]       = std::shared_ptr<GGMLBlock>(new CLIPEmbeddings(hidden_size, vocab_size, n_token));
+        blocks["embeddings"]       = std::shared_ptr<GGMLBlock>(new CLIPEmbeddings(hidden_size, vocab_size, n_token, force_clip_f32));
         blocks["encoder"]          = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size));
         blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
-    void set_clip_skip(int skip) {
-        if (skip <= 0) {
-            return;
-        }
-        clip_skip = skip;
-    }
-
     struct ggml_tensor* get_token_embed_weight() {
         auto embeddings = std::dynamic_pointer_cast<CLIPEmbeddings>(blocks["embeddings"]);
         return embeddings->get_token_embed_weight();
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* tkn_embeddings,
                                 size_t max_token_idx = 0,
-                                bool return_pooled   = false) {
+                                bool return_pooled   = false,
+                                int clip_skip        = -1) {
         // input_ids: [N, n_token]
         auto embeddings       = std::dynamic_pointer_cast<CLIPEmbeddings>(blocks["embeddings"]);
         auto encoder          = std::dynamic_pointer_cast<CLIPEncoder>(blocks["encoder"]);
         auto final_layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["final_layer_norm"]);
 
         auto x = embeddings->forward(ctx, input_ids, tkn_embeddings);  // [N, n_token, hidden_size]
-        x      = encoder->forward(ctx, x, return_pooled ? -1 : clip_skip, true);
+        x      = encoder->forward(ctx, backend, x, return_pooled ? -1 : clip_skip, true);
         if (return_pooled || with_final_ln) {
             x = final_layer_norm->forward(ctx, x);
         }
@@ -726,7 +738,7 @@ public:
             if (text_projection != NULL) {
                 pooled = ggml_nn_linear(ctx, pooled, text_projection, NULL);
             } else {
-                LOG_DEBUG("Missing text_projection matrix, assuming identity...");
+                LOG_DEBUG("identity projection");
             }
             return pooled;  // [hidden_size, 1, 1]
         }
@@ -767,7 +779,11 @@ public:
         blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values, bool return_pooled = true) {
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
+                                struct ggml_tensor* pixel_values,
+                                bool return_pooled = true,
+                                int clip_skip      = -1) {
         // pixel_values: [N, num_channels, image_size, image_size]
         auto embeddings     = std::dynamic_pointer_cast<CLIPVisionEmbeddings>(blocks["embeddings"]);
         auto pre_layernorm  = std::dynamic_pointer_cast<LayerNorm>(blocks["pre_layernorm"]);
@@ -776,7 +792,7 @@ public:
 
         auto x = embeddings->forward(ctx, pixel_values);  // [N, num_positions, embed_dim]
         x      = pre_layernorm->forward(ctx, x);
-        x      = encoder->forward(ctx, x, -1, false);
+        x      = encoder->forward(ctx, backend, x, clip_skip, false);
         // print_ggml_tensor(x, true, "ClipVisionModel x: ");
         auto last_hidden_state = x;
         x                      = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]
@@ -798,9 +814,9 @@ protected:
     int64_t out_features;
     bool transpose_weight;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+        enum ggml_type wtype = get_type(prefix + "weight", tensor_types, GGML_TYPE_F32);
         if (transpose_weight) {
-            LOG_ERROR("transpose_weight");
             params["weight"] = ggml_new_tensor_2d(ctx, wtype, out_features, in_features);
         } else {
             params["weight"] = ggml_new_tensor_2d(ctx, wtype, in_features, out_features);
@@ -844,16 +860,23 @@ public:
         blocks["visual_projection"] = std::shared_ptr<GGMLBlock>(new CLIPProjection(hidden_size, projection_dim, transpose_proj_w));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
+                                struct ggml_tensor* pixel_values,
+                                bool return_pooled = true,
+                                int clip_skip      = -1) {
         // pixel_values: [N, num_channels, image_size, image_size]
-        // return: [N, projection_dim]
+        // return: [N, projection_dim] if return_pooled else [N, n_token, hidden_size]
         auto vision_model      = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
         auto visual_projection = std::dynamic_pointer_cast<CLIPProjection>(blocks["visual_projection"]);
 
-        auto x = vision_model->forward(ctx, pixel_values);  // [N, hidden_size]
-        x      = visual_projection->forward(ctx, x);        // [N, projection_dim]
+        auto x = vision_model->forward(ctx, backend, pixel_values, return_pooled, clip_skip);  // [N, hidden_size] or [N, n_token, hidden_size]
 
-        return x;  // [N, projection_dim]
+        if (return_pooled) {
+            x = visual_projection->forward(ctx, x);  // [N, projection_dim]
+        }
+
+        return x;
     }
 };
 
@@ -861,31 +884,31 @@ struct CLIPTextModelRunner : public GGMLRunner {
     CLIPTextModel model;
 
     CLIPTextModelRunner(ggml_backend_t backend,
-                        ggml_type wtype,
+                        bool offload_params_to_cpu,
+                        const String2GGMLType& tensor_types,
+                        const std::string prefix,
                         CLIPVersion version = OPENAI_CLIP_VIT_L_14,
-                        int clip_skip_value = 1,
-                        bool with_final_ln  = true)
-        : GGMLRunner(backend, wtype), model(version, clip_skip_value, with_final_ln) {
-        model.init(params_ctx, wtype);
+                        bool with_final_ln  = true,
+                        bool force_clip_f32 = false)
+        : GGMLRunner(backend, offload_params_to_cpu), model(version, with_final_ln, force_clip_f32) {
+        model.init(params_ctx, tensor_types, prefix);
     }
 
     std::string get_desc() {
         return "clip";
     }
 
-    void set_clip_skip(int clip_skip) {
-        model.set_clip_skip(clip_skip);
-    }
-
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
         model.get_param_tensors(tensors, prefix);
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* embeddings,
                                 size_t max_token_idx = 0,
-                                bool return_pooled   = false) {
+                                bool return_pooled   = false,
+                                int clip_skip        = -1) {
         size_t N       = input_ids->ne[1];
         size_t n_token = input_ids->ne[0];
         if (input_ids->ne[0] > model.n_token) {
@@ -893,14 +916,15 @@ struct CLIPTextModelRunner : public GGMLRunner {
             input_ids = ggml_reshape_2d(ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
         }
 
-        return model.forward(ctx, input_ids, embeddings, max_token_idx, return_pooled);
+        return model.forward(ctx, backend, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
                                     int num_custom_embeddings    = 0,
                                     void* custom_embeddings_data = NULL,
                                     size_t max_token_idx         = 0,
-                                    bool return_pooled           = false) {
+                                    bool return_pooled           = false,
+                                    int clip_skip                = -1) {
         struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
 
         input_ids = to_backend(input_ids);
@@ -908,18 +932,18 @@ struct CLIPTextModelRunner : public GGMLRunner {
         struct ggml_tensor* embeddings = NULL;
 
         if (num_custom_embeddings > 0 && custom_embeddings_data != NULL) {
-            auto custom_embeddings = ggml_new_tensor_2d(compute_ctx,
-                                                        wtype,
-                                                        model.hidden_size,
-                                                        num_custom_embeddings);
+            auto token_embed_weight = model.get_token_embed_weight();
+            auto custom_embeddings  = ggml_new_tensor_2d(compute_ctx,
+                                                         token_embed_weight->type,
+                                                         model.hidden_size,
+                                                         num_custom_embeddings);
             set_backend_tensor_data(custom_embeddings, custom_embeddings_data);
 
-            auto token_embed_weight = model.get_token_embed_weight();
             // concatenate custom embeddings
             embeddings = ggml_concat(compute_ctx, token_embed_weight, custom_embeddings, 1);
         }
 
-        struct ggml_tensor* hidden_states = forward(compute_ctx, input_ids, embeddings, max_token_idx, return_pooled);
+        struct ggml_tensor* hidden_states = forward(compute_ctx, runtime_backend, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
 
         ggml_build_forward_expand(gf, hidden_states);
 
@@ -932,10 +956,11 @@ struct CLIPTextModelRunner : public GGMLRunner {
                  void* custom_embeddings_data,
                  size_t max_token_idx,
                  bool return_pooled,
+                 int clip_skip,
                  ggml_tensor** output,
                  ggml_context* output_ctx = NULL) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled);
+            return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
         };
         GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
     }

common.hpp

@@ -56,8 +56,8 @@ public:
         // x: [N, channels, h, w]
         auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-        x = ggml_upscale(ctx, x, 2);  // [N, channels, h*2, w*2]
-        x = conv->forward(ctx, x);    // [N, out_channels, h*2, w*2]
+        x = ggml_upscale(ctx, x, 2, GGML_SCALE_MODE_NEAREST);  // [N, channels, h*2, w*2]
+        x = conv->forward(ctx, x);                             // [N, out_channels, h*2, w*2]
         return x;
     }
 };
@@ -182,9 +182,11 @@ protected:
     int64_t dim_in;
     int64_t dim_out;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["proj.weight"] = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
-        params["proj.bias"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, dim_out * 2);
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, std::string prefix = "") {
+        enum ggml_type wtype      = get_type(prefix + "proj.weight", tensor_types, GGML_TYPE_F32);
+        enum ggml_type bias_wtype = GGML_TYPE_F32;
+        params["proj.weight"]     = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
+        params["proj.bias"]       = ggml_new_tensor_1d(ctx, bias_wtype, dim_out * 2);
     }
 
 public:
@@ -268,7 +270,10 @@ public:
         // to_out_1 is nn.Dropout(), skip for inference
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
+                                struct ggml_tensor* x,
+                                struct ggml_tensor* context) {
         // x: [N, n_token, query_dim]
         // context: [N, n_context, context_dim]
         // return: [N, n_token, query_dim]
@@ -286,7 +291,7 @@ public:
         auto k = to_k->forward(ctx, context);  // [N, n_context, inner_dim]
         auto v = to_v->forward(ctx, context);  // [N, n_context, inner_dim]
 
-        x = ggml_nn_attention_ext(ctx, q, k, v, n_head, NULL, false, false, flash_attn);  // [N, n_token, inner_dim]
+        x = ggml_nn_attention_ext(ctx, backend, q, k, v, n_head, NULL, false, false, flash_attn);  // [N, n_token, inner_dim]
 
         x = to_out_0->forward(ctx, x);  // [N, n_token, query_dim]
         return x;
@@ -325,7 +330,10 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
+                                struct ggml_tensor* x,
+                                struct ggml_tensor* context) {
         // x: [N, n_token, query_dim]
         // context: [N, n_context, context_dim]
         // return: [N, n_token, query_dim]
@@ -350,11 +358,11 @@ public:
 
         auto r = x;
         x      = norm1->forward(ctx, x);
-        x      = attn1->forward(ctx, x, x);  // self-attention
+        x      = attn1->forward(ctx, backend, x, x);  // self-attention
         x      = ggml_add(ctx, x, r);
         r      = x;
         x      = norm2->forward(ctx, x);
-        x      = attn2->forward(ctx, x, context);  // cross-attention
+        x      = attn2->forward(ctx, backend, x, context);  // cross-attention
         x      = ggml_add(ctx, x, r);
         r      = x;
         x      = norm3->forward(ctx, x);
@@ -399,7 +407,10 @@ public:
         blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(inner_dim, in_channels, {1, 1}));
     }
 
-    virtual struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
+    virtual struct ggml_tensor* forward(struct ggml_context* ctx,
+                                        ggml_backend_t backend,
+                                        struct ggml_tensor* x,
+                                        struct ggml_tensor* context) {
         // x: [N, in_channels, h, w]
         // context: [N, max_position(aka n_token), hidden_size(aka context_dim)]
         auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
@@ -422,7 +433,7 @@ public:
             std::string name       = "transformer_blocks." + std::to_string(i);
             auto transformer_block = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[name]);
 
-            x = transformer_block->forward(ctx, x, context);
+            x = transformer_block->forward(ctx, backend, x, context);
         }
 
         x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
@@ -438,8 +449,10 @@ public:
 
 class AlphaBlender : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["mix_factor"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, std::string prefix = "") {
+        // Get the type of the "mix_factor" tensor from the input tensors map with the specified prefix
+        enum ggml_type wtype = GGML_TYPE_F32;
+        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
     }
 
     float get_alpha() {

conditioner.hpp

@@ -21,12 +21,12 @@ struct Conditioner {
                                               int clip_skip,
                                               int width,
                                               int height,
-                                              int adm_in_channels        = -1,
-                                              bool force_zero_embeddings = false)                                             = 0;
-    virtual void alloc_params_buffer()                                                                                        = 0;
-    virtual void free_params_buffer()                                                                                         = 0;
-    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors)                                       = 0;
-    virtual size_t get_params_buffer_size()                                                                                   = 0;
+                                              int adm_in_channels  = -1,
+                                              bool zero_out_masked = false)                                             = 0;
+    virtual void alloc_params_buffer()                                                                                  = 0;
+    virtual void free_params_buffer()                                                                                   = 0;
+    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors)                                 = 0;
+    virtual size_t get_params_buffer_size()                                                                             = 0;
     virtual std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
                                                                                           int n_threads,
                                                                                           const std::string& text,
@@ -34,10 +34,10 @@ struct Conditioner {
                                                                                           int width,
                                                                                           int height,
                                                                                           int num_input_imgs,
-                                                                                          int adm_in_channels        = -1,
-                                                                                          bool force_zero_embeddings = false) = 0;
+                                                                                          int adm_in_channels  = -1,
+                                                                                          bool zero_out_masked = false) = 0;
     virtual std::string remove_trigger_from_prompt(ggml_context* work_ctx,
-                                                   const std::string& prompt)                                                 = 0;
+                                                   const std::string& prompt)                                           = 0;
 };
 
 // ldm.modules.encoders.modules.FrozenCLIPEmbedder
@@ -46,70 +46,58 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
     SDVersion version    = VERSION_SD1;
     PMVersion pm_version = PM_VERSION_1;
     CLIPTokenizer tokenizer;
-    ggml_type wtype;
     std::shared_ptr<CLIPTextModelRunner> text_model;
     std::shared_ptr<CLIPTextModelRunner> text_model2;
 
     std::string trigger_word = "img";  // should be user settable
     std::string embd_dir;
-    int32_t num_custom_embeddings = 0;
+    int32_t num_custom_embeddings   = 0;
+    int32_t num_custom_embeddings_2 = 0;
     std::vector<uint8_t> token_embed_custom;
     std::vector<std::string> readed_embeddings;
 
     FrozenCLIPEmbedderWithCustomWords(ggml_backend_t backend,
-                                      ggml_type wtype,
+                                      bool offload_params_to_cpu,
+                                      const String2GGMLType& tensor_types,
                                       const std::string& embd_dir,
                                       SDVersion version = VERSION_SD1,
-                                      PMVersion pv      = PM_VERSION_1,
-                                      int clip_skip     = -1)
-        : version(version), pm_version(pv), tokenizer(version == VERSION_SD2 ? 0 : 49407), embd_dir(embd_dir), wtype(wtype) {
-        if (clip_skip <= 0) {
-            clip_skip = 1;
-            if (version == VERSION_SD2 || version == VERSION_SDXL) {
-                clip_skip = 2;
-            }
-        }
-        if (version == VERSION_SD1) {
-            text_model = std::make_shared<CLIPTextModelRunner>(backend, wtype, OPENAI_CLIP_VIT_L_14, clip_skip);
-        } else if (version == VERSION_SD2) {
-            text_model = std::make_shared<CLIPTextModelRunner>(backend, wtype, OPEN_CLIP_VIT_H_14, clip_skip);
-        } else if (version == VERSION_SDXL) {
-            text_model  = std::make_shared<CLIPTextModelRunner>(backend, wtype, OPENAI_CLIP_VIT_L_14, clip_skip, false);
-            text_model2 = std::make_shared<CLIPTextModelRunner>(backend, wtype, OPEN_CLIP_VIT_BIGG_14, clip_skip, false);
-        }
-    }
-
-    void set_clip_skip(int clip_skip) {
-        text_model->set_clip_skip(clip_skip);
-        if (version == VERSION_SDXL) {
-            text_model2->set_clip_skip(clip_skip);
+                                      PMVersion pv      = PM_VERSION_1)
+        : version(version), pm_version(pv), tokenizer(sd_version_is_sd2(version) ? 0 : 49407), embd_dir(embd_dir) {
+        bool force_clip_f32 = embd_dir.size() > 0;
+        if (sd_version_is_sd1(version)) {
+            text_model = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, force_clip_f32);
+        } else if (sd_version_is_sd2(version)) {
+            text_model = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, true, force_clip_f32);
+        } else if (sd_version_is_sdxl(version)) {
+            text_model  = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, force_clip_f32);
+            text_model2 = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, force_clip_f32);
         }
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
         text_model->get_param_tensors(tensors, "cond_stage_model.transformer.text_model");
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             text_model2->get_param_tensors(tensors, "cond_stage_model.1.transformer.text_model");
         }
     }
 
     void alloc_params_buffer() {
         text_model->alloc_params_buffer();
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             text_model2->alloc_params_buffer();
         }
     }
 
     void free_params_buffer() {
         text_model->free_params_buffer();
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             text_model2->free_params_buffer();
         }
     }
 
     size_t get_params_buffer_size() {
         size_t buffer_size = text_model->get_params_buffer_size();
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             buffer_size += text_model2->get_params_buffer_size();
         }
         return buffer_size;
@@ -132,28 +120,55 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         params.no_alloc               = false;
         struct ggml_context* embd_ctx = ggml_init(params);
         struct ggml_tensor* embd      = NULL;
-        int64_t hidden_size           = text_model->model.hidden_size;
+        struct ggml_tensor* embd2     = NULL;
         auto on_load                  = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) {
-            if (tensor_storage.ne[0] != hidden_size) {
-                LOG_DEBUG("embedding wrong hidden size, got %i, expected %i", tensor_storage.ne[0], hidden_size);
-                return false;
+            if (tensor_storage.ne[0] != text_model->model.hidden_size) {
+                if (text_model2) {
+                    if (tensor_storage.ne[0] == text_model2->model.hidden_size) {
+                        embd2       = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model2->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
+                        *dst_tensor = embd2;
+                    } else {
+                        LOG_DEBUG("embedding wrong hidden size, got %i, expected %i or %i", tensor_storage.ne[0], text_model->model.hidden_size, text_model2->model.hidden_size);
+                        return false;
+                    }
+                } else {
+                    LOG_DEBUG("embedding wrong hidden size, got %i, expected %i", tensor_storage.ne[0], text_model->model.hidden_size);
+                    return false;
+                }
+            } else {
+                embd        = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
+                *dst_tensor = embd;
             }
-            embd        = ggml_new_tensor_2d(embd_ctx, wtype, hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
-            *dst_tensor = embd;
             return true;
         };
-        model_loader.load_tensors(on_load, NULL);
+        model_loader.load_tensors(on_load);
         readed_embeddings.push_back(embd_name);
-        token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd));
-        memcpy((void*)(token_embed_custom.data() + num_custom_embeddings * hidden_size * ggml_type_size(wtype)),
-               embd->data,
-               ggml_nbytes(embd));
-        for (int i = 0; i < embd->ne[1]; i++) {
-            bpe_tokens.push_back(text_model->model.vocab_size + num_custom_embeddings);
-            // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
-            num_custom_embeddings++;
+        if (embd) {
+            int64_t hidden_size = text_model->model.hidden_size;
+            token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd));
+            memcpy((void*)(token_embed_custom.data() + num_custom_embeddings * hidden_size * ggml_type_size(embd->type)),
+                   embd->data,
+                   ggml_nbytes(embd));
+            for (int i = 0; i < embd->ne[1]; i++) {
+                bpe_tokens.push_back(text_model->model.vocab_size + num_custom_embeddings);
+                // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
+                num_custom_embeddings++;
+            }
+            LOG_DEBUG("embedding '%s' applied, custom embeddings: %i", embd_name.c_str(), num_custom_embeddings);
+        }
+        if (embd2) {
+            int64_t hidden_size = text_model2->model.hidden_size;
+            token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd2));
+            memcpy((void*)(token_embed_custom.data() + num_custom_embeddings_2 * hidden_size * ggml_type_size(embd2->type)),
+                   embd2->data,
+                   ggml_nbytes(embd2));
+            for (int i = 0; i < embd2->ne[1]; i++) {
+                bpe_tokens.push_back(text_model2->model.vocab_size + num_custom_embeddings_2);
+                // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
+                num_custom_embeddings_2++;
+            }
+            LOG_DEBUG("embedding '%s' applied, custom embeddings: %i (text model 2)", embd_name.c_str(), num_custom_embeddings_2);
         }
-        LOG_DEBUG("embedding '%s' applied, custom embeddings: %i", embd_name.c_str(), num_custom_embeddings);
         return true;
     }
 
@@ -381,9 +396,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                              int clip_skip,
                                              int width,
                                              int height,
-                                             int adm_in_channels        = -1,
-                                             bool force_zero_embeddings = false) {
-        set_clip_skip(clip_skip);
+                                             int adm_in_channels  = -1,
+                                             bool zero_out_masked = false) {
         int64_t t0                               = ggml_time_ms();
         struct ggml_tensor* hidden_states        = NULL;  // [N, n_token, hidden_size]
         struct ggml_tensor* chunk_hidden_states  = NULL;  // [n_token, hidden_size] or [n_token, hidden_size + hidden_size2]
@@ -392,6 +406,10 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         struct ggml_tensor* pooled               = NULL;
         std::vector<float> hidden_states_vec;
 
+        if (clip_skip <= 0) {
+            clip_skip = (sd_version_is_sd2(version) || sd_version_is_sdxl(version)) ? 2 : 1;
+        }
+
         size_t chunk_len   = 77;
         size_t chunk_count = tokens.size() / chunk_len;
         for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
@@ -403,7 +421,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
             auto input_ids                 = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
             struct ggml_tensor* input_ids2 = NULL;
             size_t max_token_idx           = 0;
-            if (version == VERSION_SDXL) {
+            if (sd_version_is_sdxl(version)) {
                 auto it = std::find(chunk_tokens.begin(), chunk_tokens.end(), tokenizer.EOS_TOKEN_ID);
                 if (it != chunk_tokens.end()) {
                     std::fill(std::next(it), chunk_tokens.end(), 0);
@@ -426,15 +444,17 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                     token_embed_custom.data(),
                                     max_token_idx,
                                     false,
+                                    clip_skip,
                                     &chunk_hidden_states1,
                                     work_ctx);
-                if (version == VERSION_SDXL) {
+                if (sd_version_is_sdxl(version)) {
                     text_model2->compute(n_threads,
                                          input_ids2,
-                                         0,
-                                         NULL,
+                                         num_custom_embeddings,
+                                         token_embed_custom.data(),
                                          max_token_idx,
                                          false,
+                                         clip_skip,
                                          &chunk_hidden_states2, work_ctx);
                     // concat
                     chunk_hidden_states = ggml_tensor_concat(work_ctx, chunk_hidden_states1, chunk_hidden_states2, 0);
@@ -442,10 +462,11 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                     if (chunk_idx == 0) {
                         text_model2->compute(n_threads,
                                              input_ids2,
-                                             0,
-                                             NULL,
+                                             num_custom_embeddings,
+                                             token_embed_custom.data(),
                                              max_token_idx,
                                              true,
+                                             clip_skip,
                                              &pooled,
                                              work_ctx);
                     }
@@ -471,7 +492,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                 float new_mean = ggml_tensor_mean(result);
                 ggml_tensor_scale(result, (original_mean / new_mean));
             }
-            if (force_zero_embeddings) {
+            if (zero_out_masked) {
                 float* vec = (float*)result->data;
                 for (int i = 0; i < ggml_nelements(result); i++) {
                     vec[i] = 0;
@@ -487,7 +508,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                         ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
 
         ggml_tensor* vec = NULL;
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             int out_dim = 256;
             vec         = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, adm_in_channels);
             // [0:1280]
@@ -534,8 +555,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                        int width,
                                        int height,
                                        int num_input_imgs,
-                                       int adm_in_channels        = -1,
-                                       bool force_zero_embeddings = false) {
+                                       int adm_in_channels  = -1,
+                                       bool zero_out_masked = false) {
         auto image_tokens = convert_token_to_id(trigger_word);
         // if(image_tokens.size() == 1){
         //     printf(" image token id is: %d \n", image_tokens[0]);
@@ -556,7 +577,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         // for(int i = 0; i < clsm.size(); ++i)
         //    printf("%d ", clsm[i]?1:0);
         // printf("\n");
-        auto cond = get_learned_condition_common(work_ctx, n_threads, tokens, weights, clip_skip, width, height, adm_in_channels, force_zero_embeddings);
+        auto cond = get_learned_condition_common(work_ctx, n_threads, tokens, weights, clip_skip, width, height, adm_in_channels, zero_out_masked);
         return std::make_tuple(cond, clsm);
     }
 
@@ -578,21 +599,23 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                       int clip_skip,
                                       int width,
                                       int height,
-                                      int adm_in_channels        = -1,
-                                      bool force_zero_embeddings = false) {
+                                      int adm_in_channels  = -1,
+                                      bool zero_out_masked = false) {
         auto tokens_and_weights     = tokenize(text, true);
         std::vector<int>& tokens    = tokens_and_weights.first;
         std::vector<float>& weights = tokens_and_weights.second;
-        return get_learned_condition_common(work_ctx, n_threads, tokens, weights, clip_skip, width, height, adm_in_channels, force_zero_embeddings);
+        return get_learned_condition_common(work_ctx, n_threads, tokens, weights, clip_skip, width, height, adm_in_channels, zero_out_masked);
     }
 };
 
 struct FrozenCLIPVisionEmbedder : public GGMLRunner {
     CLIPVisionModelProjection vision_model;
 
-    FrozenCLIPVisionEmbedder(ggml_backend_t backend, ggml_type wtype)
-        : vision_model(OPEN_CLIP_VIT_H_14, true), GGMLRunner(backend, wtype) {
-        vision_model.init(params_ctx, wtype);
+    FrozenCLIPVisionEmbedder(ggml_backend_t backend,
+                             bool offload_params_to_cpu,
+                             const String2GGMLType& tensor_types = {})
+        : vision_model(OPEN_CLIP_VIT_H_14), GGMLRunner(backend, offload_params_to_cpu) {
+        vision_model.init(params_ctx, tensor_types, "cond_stage_model.transformer");
     }
 
     std::string get_desc() {
@@ -603,12 +626,12 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
         vision_model.get_param_tensors(tensors, "cond_stage_model.transformer");
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* pixel_values) {
+    struct ggml_cgraph* build_graph(struct ggml_tensor* pixel_values, bool return_pooled, int clip_skip) {
         struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
 
         pixel_values = to_backend(pixel_values);
 
-        struct ggml_tensor* hidden_states = vision_model.forward(compute_ctx, pixel_values);
+        struct ggml_tensor* hidden_states = vision_model.forward(compute_ctx, runtime_backend, pixel_values, return_pooled, clip_skip);
 
         ggml_build_forward_expand(gf, hidden_states);
 
@@ -617,17 +640,18 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
 
     void compute(const int n_threads,
                  ggml_tensor* pixel_values,
+                 bool return_pooled,
+                 int clip_skip,
                  ggml_tensor** output,
                  ggml_context* output_ctx) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(pixel_values);
+            return build_graph(pixel_values, return_pooled, clip_skip);
         };
         GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
     }
 };
 
 struct SD3CLIPEmbedder : public Conditioner {
-    ggml_type wtype;
     CLIPTokenizer clip_l_tokenizer;
     CLIPTokenizer clip_g_tokenizer;
     T5UniGramTokenizer t5_tokenizer;
@@ -636,20 +660,12 @@ struct SD3CLIPEmbedder : public Conditioner {
     std::shared_ptr<T5Runner> t5;
 
     SD3CLIPEmbedder(ggml_backend_t backend,
-                    ggml_type wtype,
-                    int clip_skip = -1)
-        : wtype(wtype), clip_g_tokenizer(0) {
-        if (clip_skip <= 0) {
-            clip_skip = 2;
-        }
-        clip_l = std::make_shared<CLIPTextModelRunner>(backend, wtype, OPENAI_CLIP_VIT_L_14, clip_skip, false);
-        clip_g = std::make_shared<CLIPTextModelRunner>(backend, wtype, OPEN_CLIP_VIT_BIGG_14, clip_skip, false);
-        t5     = std::make_shared<T5Runner>(backend, wtype);
-    }
-
-    void set_clip_skip(int clip_skip) {
-        clip_l->set_clip_skip(clip_skip);
-        clip_g->set_clip_skip(clip_skip);
+                    bool offload_params_to_cpu,
+                    const String2GGMLType& tensor_types = {})
+        : clip_g_tokenizer(0) {
+        clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false);
+        clip_g = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false);
+        t5     = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer");
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
@@ -721,7 +737,7 @@ struct SD3CLIPEmbedder : public Conditioner {
 
         clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, max_length, padding);
         clip_g_tokenizer.pad_tokens(clip_g_tokens, clip_g_weights, max_length, padding);
-        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, max_length, padding);
+        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, NULL, max_length, padding);
 
         // for (int i = 0; i < clip_l_tokens.size(); i++) {
         //     std::cout << clip_l_tokens[i] << ":" << clip_l_weights[i] << ", ";
@@ -745,8 +761,7 @@ struct SD3CLIPEmbedder : public Conditioner {
                                              int n_threads,
                                              std::vector<std::pair<std::vector<int>, std::vector<float>>> token_and_weights,
                                              int clip_skip,
-                                             bool force_zero_embeddings = false) {
-        set_clip_skip(clip_skip);
+                                             bool zero_out_masked = false) {
         auto& clip_l_tokens  = token_and_weights[0].first;
         auto& clip_l_weights = token_and_weights[0].second;
         auto& clip_g_tokens  = token_and_weights[1].first;
@@ -754,6 +769,10 @@ struct SD3CLIPEmbedder : public Conditioner {
         auto& t5_tokens      = token_and_weights[2].first;
         auto& t5_weights     = token_and_weights[2].second;
 
+        if (clip_skip <= 0) {
+            clip_skip = 2;
+        }
+
         int64_t t0                                 = ggml_time_ms();
         struct ggml_tensor* hidden_states          = NULL;  // [N, n_token*2, 4096]
         struct ggml_tensor* chunk_hidden_states    = NULL;  // [n_token*2, 4096]
@@ -784,6 +803,7 @@ struct SD3CLIPEmbedder : public Conditioner {
                                 NULL,
                                 max_token_idx,
                                 false,
+                                clip_skip,
                                 &chunk_hidden_states_l,
                                 work_ctx);
                 {
@@ -811,6 +831,7 @@ struct SD3CLIPEmbedder : public Conditioner {
                                     NULL,
                                     max_token_idx,
                                     true,
+                                    clip_skip,
                                     &pooled_l,
                                     work_ctx);
                 }
@@ -832,6 +853,7 @@ struct SD3CLIPEmbedder : public Conditioner {
                                 NULL,
                                 max_token_idx,
                                 false,
+                                clip_skip,
                                 &chunk_hidden_states_g,
                                 work_ctx);
 
@@ -860,6 +882,7 @@ struct SD3CLIPEmbedder : public Conditioner {
                                     NULL,
                                     max_token_idx,
                                     true,
+                                    clip_skip,
                                     &pooled_g,
                                     work_ctx);
                 }
@@ -876,6 +899,7 @@ struct SD3CLIPEmbedder : public Conditioner {
 
                 t5->compute(n_threads,
                             input_ids,
+                            NULL,
                             &chunk_hidden_states_t5,
                             work_ctx);
                 {
@@ -923,7 +947,7 @@ struct SD3CLIPEmbedder : public Conditioner {
 
             int64_t t1 = ggml_time_ms();
             LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
-            if (force_zero_embeddings) {
+            if (zero_out_masked) {
                 float* vec = (float*)chunk_hidden_states->data;
                 for (int i = 0; i < ggml_nelements(chunk_hidden_states); i++) {
                     vec[i] = 0;
@@ -949,10 +973,10 @@ struct SD3CLIPEmbedder : public Conditioner {
                                       int clip_skip,
                                       int width,
                                       int height,
-                                      int adm_in_channels        = -1,
-                                      bool force_zero_embeddings = false) {
+                                      int adm_in_channels  = -1,
+                                      bool zero_out_masked = false) {
         auto tokens_and_weights = tokenize(text, 77, true);
-        return get_learned_condition_common(work_ctx, n_threads, tokens_and_weights, clip_skip, force_zero_embeddings);
+        return get_learned_condition_common(work_ctx, n_threads, tokens_and_weights, clip_skip, zero_out_masked);
     }
 
     std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
@@ -962,8 +986,8 @@ struct SD3CLIPEmbedder : public Conditioner {
                                                                                   int width,
                                                                                   int height,
                                                                                   int num_input_imgs,
-                                                                                  int adm_in_channels        = -1,
-                                                                                  bool force_zero_embeddings = false) {
+                                                                                  int adm_in_channels  = -1,
+                                                                                  bool zero_out_masked = false) {
         GGML_ASSERT(0 && "Not implemented yet!");
     }
 
@@ -974,25 +998,17 @@ struct SD3CLIPEmbedder : public Conditioner {
 };
 
 struct FluxCLIPEmbedder : public Conditioner {
-    ggml_type wtype;
     CLIPTokenizer clip_l_tokenizer;
     T5UniGramTokenizer t5_tokenizer;
     std::shared_ptr<CLIPTextModelRunner> clip_l;
     std::shared_ptr<T5Runner> t5;
+    size_t chunk_len = 256;
 
     FluxCLIPEmbedder(ggml_backend_t backend,
-                     ggml_type wtype,
-                     int clip_skip = -1)
-        : wtype(wtype) {
-        if (clip_skip <= 0) {
-            clip_skip = 2;
-        }
-        clip_l = std::make_shared<CLIPTextModelRunner>(backend, wtype, OPENAI_CLIP_VIT_L_14, clip_skip, true);
-        t5     = std::make_shared<T5Runner>(backend, wtype);
-    }
-
-    void set_clip_skip(int clip_skip) {
-        clip_l->set_clip_skip(clip_skip);
+                     bool offload_params_to_cpu,
+                     const String2GGMLType& tensor_types = {}) {
+        clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true);
+        t5     = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer");
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
@@ -1053,7 +1069,7 @@ struct FluxCLIPEmbedder : public Conditioner {
         }
 
         clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, 77, padding);
-        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, max_length, padding);
+        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, NULL, max_length, padding);
 
         // for (int i = 0; i < clip_l_tokens.size(); i++) {
         //     std::cout << clip_l_tokens[i] << ":" << clip_l_weights[i] << ", ";
@@ -1072,20 +1088,22 @@ struct FluxCLIPEmbedder : public Conditioner {
                                              int n_threads,
                                              std::vector<std::pair<std::vector<int>, std::vector<float>>> token_and_weights,
                                              int clip_skip,
-                                             bool force_zero_embeddings = false) {
-        set_clip_skip(clip_skip);
+                                             bool zero_out_masked = false) {
         auto& clip_l_tokens  = token_and_weights[0].first;
         auto& clip_l_weights = token_and_weights[0].second;
         auto& t5_tokens      = token_and_weights[1].first;
         auto& t5_weights     = token_and_weights[1].second;
 
+        if (clip_skip <= 0) {
+            clip_skip = 2;
+        }
+
         int64_t t0                              = ggml_time_ms();
         struct ggml_tensor* hidden_states       = NULL;  // [N, n_token, 4096]
         struct ggml_tensor* chunk_hidden_states = NULL;  // [n_token, 4096]
         struct ggml_tensor* pooled              = NULL;  // [768,]
         std::vector<float> hidden_states_vec;
 
-        size_t chunk_len   = 256;
         size_t chunk_count = t5_tokens.size() / chunk_len;
         for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
             // clip_l
@@ -1108,6 +1126,7 @@ struct FluxCLIPEmbedder : public Conditioner {
                                 NULL,
                                 max_token_idx,
                                 true,
+                                clip_skip,
                                 &pooled,
                                 work_ctx);
             }
@@ -1123,6 +1142,7 @@ struct FluxCLIPEmbedder : public Conditioner {
 
                 t5->compute(n_threads,
                             input_ids,
+                            NULL,
                             &chunk_hidden_states,
                             work_ctx);
                 {
@@ -1144,7 +1164,7 @@ struct FluxCLIPEmbedder : public Conditioner {
 
             int64_t t1 = ggml_time_ms();
             LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
-            if (force_zero_embeddings) {
+            if (zero_out_masked) {
                 float* vec = (float*)chunk_hidden_states->data;
                 for (int i = 0; i < ggml_nelements(chunk_hidden_states); i++) {
                     vec[i] = 0;
@@ -1170,10 +1190,10 @@ struct FluxCLIPEmbedder : public Conditioner {
                                       int clip_skip,
                                       int width,
                                       int height,
-                                      int adm_in_channels        = -1,
-                                      bool force_zero_embeddings = false) {
-        auto tokens_and_weights = tokenize(text, 256, true);
-        return get_learned_condition_common(work_ctx, n_threads, tokens_and_weights, clip_skip, force_zero_embeddings);
+                                      int adm_in_channels  = -1,
+                                      bool zero_out_masked = false) {
+        auto tokens_and_weights = tokenize(text, chunk_len, true);
+        return get_learned_condition_common(work_ctx, n_threads, tokens_and_weights, clip_skip, zero_out_masked);
     }
 
     std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
@@ -1183,8 +1203,8 @@ struct FluxCLIPEmbedder : public Conditioner {
                                                                                   int width,
                                                                                   int height,
                                                                                   int num_input_imgs,
-                                                                                  int adm_in_channels        = -1,
-                                                                                  bool force_zero_embeddings = false) {
+                                                                                  int adm_in_channels  = -1,
+                                                                                  bool zero_out_masked = false) {
         GGML_ASSERT(0 && "Not implemented yet!");
     }
 
@@ -1194,4 +1214,207 @@ struct FluxCLIPEmbedder : public Conditioner {
     }
 };
 
-#endif
+struct T5CLIPEmbedder : public Conditioner {
+    T5UniGramTokenizer t5_tokenizer;
+    std::shared_ptr<T5Runner> t5;
+    size_t chunk_len = 512;
+    bool use_mask    = false;
+    int mask_pad     = 1;
+    bool is_umt5     = false;
+
+    T5CLIPEmbedder(ggml_backend_t backend,
+                   bool offload_params_to_cpu,
+                   const String2GGMLType& tensor_types = {},
+                   bool use_mask                       = false,
+                   int mask_pad                        = 1,
+                   bool is_umt5                        = false)
+        : use_mask(use_mask), mask_pad(mask_pad), t5_tokenizer(is_umt5) {
+        t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer", is_umt5);
+    }
+
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+        t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+    }
+
+    void alloc_params_buffer() {
+        t5->alloc_params_buffer();
+    }
+
+    void free_params_buffer() {
+        t5->free_params_buffer();
+    }
+
+    size_t get_params_buffer_size() {
+        size_t buffer_size = 0;
+
+        buffer_size += t5->get_params_buffer_size();
+
+        return buffer_size;
+    }
+
+    std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> tokenize(std::string text,
+                                                                                  size_t max_length = 0,
+                                                                                  bool padding      = false) {
+        auto parsed_attention = parse_prompt_attention(text);
+
+        {
+            std::stringstream ss;
+            ss << "[";
+            for (const auto& item : parsed_attention) {
+                ss << "['" << item.first << "', " << item.second << "], ";
+            }
+            ss << "]";
+            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
+        }
+
+        auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
+            return false;
+        };
+
+        std::vector<int> t5_tokens;
+        std::vector<float> t5_weights;
+        std::vector<float> t5_mask;
+        for (const auto& item : parsed_attention) {
+            const std::string& curr_text = item.first;
+            float curr_weight            = item.second;
+
+            std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
+            t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+            t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+        }
+
+        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, &t5_mask, max_length, padding);
+
+        return {t5_tokens, t5_weights, t5_mask};
+    }
+
+    void modify_mask_to_attend_padding(struct ggml_tensor* mask, int max_seq_length, int num_extra_padding = 8) {
+        float* mask_data = (float*)mask->data;
+        int num_pad      = 0;
+        for (int64_t i = 0; i < max_seq_length; i++) {
+            if (num_pad >= num_extra_padding) {
+                break;
+            }
+            if (std::isinf(mask_data[i])) {
+                mask_data[i] = 0;
+                ++num_pad;
+            }
+        }
+        // LOG_DEBUG("PAD: %d", num_pad);
+    }
+
+    SDCondition get_learned_condition_common(ggml_context* work_ctx,
+                                             int n_threads,
+                                             std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> token_and_weights,
+                                             int clip_skip,
+                                             bool zero_out_masked = false) {
+        auto& t5_tokens        = std::get<0>(token_and_weights);
+        auto& t5_weights       = std::get<1>(token_and_weights);
+        auto& t5_attn_mask_vec = std::get<2>(token_and_weights);
+
+        int64_t t0                              = ggml_time_ms();
+        struct ggml_tensor* hidden_states       = NULL;  // [N, n_token, 4096]
+        struct ggml_tensor* chunk_hidden_states = NULL;  // [n_token, 4096]
+        struct ggml_tensor* pooled              = NULL;
+        struct ggml_tensor* t5_attn_mask        = vector_to_ggml_tensor(work_ctx, t5_attn_mask_vec);  // [n_token]
+
+        std::vector<float> hidden_states_vec;
+
+        size_t chunk_count = t5_tokens.size() / chunk_len;
+
+        for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
+            // t5
+            std::vector<int> chunk_tokens(t5_tokens.begin() + chunk_idx * chunk_len,
+                                          t5_tokens.begin() + (chunk_idx + 1) * chunk_len);
+            std::vector<float> chunk_weights(t5_weights.begin() + chunk_idx * chunk_len,
+                                             t5_weights.begin() + (chunk_idx + 1) * chunk_len);
+            std::vector<float> chunk_mask(t5_attn_mask_vec.begin() + chunk_idx * chunk_len,
+                                          t5_attn_mask_vec.begin() + (chunk_idx + 1) * chunk_len);
+
+            auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
+            auto t5_attn_mask_chunk = use_mask ? vector_to_ggml_tensor(work_ctx, chunk_mask) : NULL;
+
+            t5->compute(n_threads,
+                        input_ids,
+                        t5_attn_mask_chunk,
+                        &chunk_hidden_states,
+                        work_ctx);
+            {
+                auto tensor         = chunk_hidden_states;
+                float original_mean = ggml_tensor_mean(tensor);
+                for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
+                    for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
+                        for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
+                            float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
+                            value *= chunk_weights[i1];
+                            ggml_tensor_set_f32(tensor, value, i0, i1, i2);
+                        }
+                    }
+                }
+                float new_mean = ggml_tensor_mean(tensor);
+                ggml_tensor_scale(tensor, (original_mean / new_mean));
+            }
+
+            int64_t t1 = ggml_time_ms();
+            LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
+            if (zero_out_masked) {
+                auto tensor = chunk_hidden_states;
+                for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
+                    for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
+                        for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
+                            if (chunk_mask[i1] < 0.f) {
+                                ggml_tensor_set_f32(tensor, 0.f, i0, i1, i2);
+                            }
+                        }
+                    }
+                }
+            }
+
+            hidden_states_vec.insert(hidden_states_vec.end(),
+                                     (float*)chunk_hidden_states->data,
+                                     ((float*)chunk_hidden_states->data) + ggml_nelements(chunk_hidden_states));
+        }
+
+        GGML_ASSERT(hidden_states_vec.size() > 0);
+        hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
+        hidden_states = ggml_reshape_2d(work_ctx,
+                                        hidden_states,
+                                        chunk_hidden_states->ne[0],
+                                        ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
+
+        modify_mask_to_attend_padding(t5_attn_mask, ggml_nelements(t5_attn_mask), mask_pad);
+
+        return SDCondition(hidden_states, t5_attn_mask, NULL);
+    }
+
+    SDCondition get_learned_condition(ggml_context* work_ctx,
+                                      int n_threads,
+                                      const std::string& text,
+                                      int clip_skip,
+                                      int width,
+                                      int height,
+                                      int adm_in_channels  = -1,
+                                      bool zero_out_masked = false) {
+        auto tokens_and_weights = tokenize(text, chunk_len, true);
+        return get_learned_condition_common(work_ctx, n_threads, tokens_and_weights, clip_skip, zero_out_masked);
+    }
+
+    std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
+                                                                                  int n_threads,
+                                                                                  const std::string& text,
+                                                                                  int clip_skip,
+                                                                                  int width,
+                                                                                  int height,
+                                                                                  int num_input_imgs,
+                                                                                  int adm_in_channels  = -1,
+                                                                                  bool zero_out_masked = false) {
+        GGML_ASSERT(0 && "Not implemented yet!");
+    }
+
+    std::string remove_trigger_from_prompt(ggml_context* work_ctx,
+                                           const std::string& prompt) {
+        GGML_ASSERT(0 && "Not implemented yet!");
+    }
+};
+
+#endif

control.hpp

@@ -34,11 +34,11 @@ public:
 
     ControlNetBlock(SDVersion version = VERSION_SD1)
         : version(version) {
-        if (version == VERSION_SD2) {
+        if (sd_version_is_sd2(version)) {
             context_dim       = 1024;
             num_head_channels = 64;
             num_heads         = -1;
-        } else if (version == VERSION_SDXL) {
+        } else if (sd_version_is_sdxl(version)) {
             context_dim           = 2048;
             attention_resolutions = {4, 2};
             channel_mult          = {1, 2, 4};
@@ -58,7 +58,7 @@ public:
         // time_embed_1 is nn.SiLU()
         blocks["time_embed.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
 
-        if (version == VERSION_SDXL || version == VERSION_SVD) {
+        if (sd_version_is_sdxl(version) || version == VERSION_SVD) {
             blocks["label_emb.0.0"] = std::shared_ptr<GGMLBlock>(new Linear(adm_in_channels, time_embed_dim));
             // label_emb_1 is nn.SiLU()
             blocks["label_emb.0.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
@@ -174,10 +174,11 @@ public:
 
     struct ggml_tensor* attention_layer_forward(std::string name,
                                                 struct ggml_context* ctx,
+                                                ggml_backend_t backend,
                                                 struct ggml_tensor* x,
                                                 struct ggml_tensor* context) {
         auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
-        return block->forward(ctx, x, context);
+        return block->forward(ctx, backend, x, context);
     }
 
     struct ggml_tensor* input_hint_block_forward(struct ggml_context* ctx,
@@ -199,6 +200,7 @@ public:
     }
 
     std::vector<struct ggml_tensor*> forward(struct ggml_context* ctx,
+                                             ggml_backend_t backend,
                                              struct ggml_tensor* x,
                                              struct ggml_tensor* hint,
                                              struct ggml_tensor* guided_hint,
@@ -272,7 +274,7 @@ public:
                 h                = resblock_forward(name, ctx, h, emb);  // [N, mult*model_channels, h, w]
                 if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
                     std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
-                    h                = attention_layer_forward(name, ctx, h, context);  // [N, mult*model_channels, h, w]
+                    h                = attention_layer_forward(name, ctx, backend, h, context);  // [N, mult*model_channels, h, w]
                 }
 
                 auto zero_conv = std::dynamic_pointer_cast<Conv2d>(blocks["zero_convs." + std::to_string(input_block_idx) + ".0"]);
@@ -296,9 +298,9 @@ public:
         // [N, 4*model_channels, h/8, w/8]
 
         // middle_block
-        h = resblock_forward("middle_block.0", ctx, h, emb);             // [N, 4*model_channels, h/8, w/8]
-        h = attention_layer_forward("middle_block.1", ctx, h, context);  // [N, 4*model_channels, h/8, w/8]
-        h = resblock_forward("middle_block.2", ctx, h, emb);             // [N, 4*model_channels, h/8, w/8]
+        h = resblock_forward("middle_block.0", ctx, h, emb);                      // [N, 4*model_channels, h/8, w/8]
+        h = attention_layer_forward("middle_block.1", ctx, backend, h, context);  // [N, 4*model_channels, h/8, w/8]
+        h = resblock_forward("middle_block.2", ctx, h, emb);                      // [N, 4*model_channels, h/8, w/8]
 
         // out
         outs.push_back(middle_block_out->forward(ctx, h));
@@ -317,10 +319,22 @@ struct ControlNet : public GGMLRunner {
     bool guided_hint_cached         = false;
 
     ControlNet(ggml_backend_t backend,
-               ggml_type wtype,
-               SDVersion version = VERSION_SD1)
-        : GGMLRunner(backend, wtype), control_net(version) {
-        control_net.init(params_ctx, wtype);
+               bool offload_params_to_cpu,
+               const String2GGMLType& tensor_types = {},
+               SDVersion version                   = VERSION_SD1)
+        : GGMLRunner(backend, offload_params_to_cpu), control_net(version) {
+        control_net.init(params_ctx, tensor_types, "");
+    }
+
+    void enable_conv2d_direct() {
+        std::vector<GGMLBlock*> blocks;
+        control_net.get_all_blocks(blocks);
+        for (auto block : blocks) {
+            if (block->get_desc() == "Conv2d") {
+                auto conv_block = (Conv2d*)block;
+                conv_block->enable_direct();
+            }
+        }
     }
 
     ~ControlNet() {
@@ -346,7 +360,7 @@ struct ControlNet : public GGMLRunner {
             control_buffer_size += ggml_nbytes(controls[i]);
         }
 
-        control_buffer = ggml_backend_alloc_ctx_tensors(control_ctx, backend);
+        control_buffer = ggml_backend_alloc_ctx_tensors(control_ctx, runtime_backend);
 
         LOG_DEBUG("control buffer size %.2fMB", control_buffer_size * 1.f / 1024.f / 1024.f);
     }
@@ -391,6 +405,7 @@ struct ControlNet : public GGMLRunner {
         timesteps = to_backend(timesteps);
 
         auto outs = control_net.forward(compute_ctx,
+                                        runtime_backend,
                                         x,
                                         hint,
                                         guided_hint_cached ? guided_hint : NULL,
@@ -443,7 +458,7 @@ struct ControlNet : public GGMLRunner {
             return false;
         }
 
-        bool success = model_loader.load_tensors(tensors, backend, ignore_tensors);
+        bool success = model_loader.load_tensors(tensors, ignore_tensors);
 
         if (!success) {
             LOG_ERROR("load control net tensors from model loader failed");

denoiser.hpp

@@ -168,24 +168,21 @@ struct AYSSchedule : SigmaSchedule {
         std::vector<float> inputs;
         std::vector<float> results(n + 1);
 
-        switch (version) {
-            case VERSION_SD2: /* fallthrough */
-                LOG_WARN("AYS not designed for SD2.X models");
-            case VERSION_SD1:
-                LOG_INFO("AYS using SD1.5 noise levels");
-                inputs = noise_levels[0];
-                break;
-            case VERSION_SDXL:
-                LOG_INFO("AYS using SDXL noise levels");
-                inputs = noise_levels[1];
-                break;
-            case VERSION_SVD:
-                LOG_INFO("AYS using SVD noise levels");
-                inputs = noise_levels[2];
-                break;
-            default:
-                LOG_ERROR("Version not compatable with AYS scheduler");
-                return results;
+        if (sd_version_is_sd2((SDVersion)version)) {
+            LOG_WARN("AYS not designed for SD2.X models");
+        } /* fallthrough */
+        else if (sd_version_is_sd1((SDVersion)version)) {
+            LOG_INFO("AYS using SD1.5 noise levels");
+            inputs = noise_levels[0];
+        } else if (sd_version_is_sdxl((SDVersion)version)) {
+            LOG_INFO("AYS using SDXL noise levels");
+            inputs = noise_levels[1];
+        } else if (version == VERSION_SVD) {
+            LOG_INFO("AYS using SVD noise levels");
+            inputs = noise_levels[2];
+        } else {
+            LOG_ERROR("Version not compatible with AYS scheduler");
+            return results;
         }
 
         /* Stretches those pre-calculated reference levels out to the desired
@@ -254,8 +251,37 @@ struct KarrasSchedule : SigmaSchedule {
     }
 };
 
+// Close to Beta Schedule, but increadably simple in code.
+struct SmoothStepSchedule : SigmaSchedule {
+    static constexpr float smoothstep(float x) {
+        return x * x * (3.0f - 2.0f * x);
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t t_to_sigma) override {
+        std::vector<float> result;
+        result.reserve(n + 1);
+
+        const int t_max = TIMESTEPS - 1;
+        if (n == 0) {
+            return result;
+        } else if (n == 1) {
+            result.push_back(t_to_sigma((float)t_max));
+            result.push_back(0.f);
+            return result;
+        }
+
+        for (uint32_t i = 0; i < n; i++) {
+            float u = 1.f - float(i) / float(n);
+            result.push_back(t_to_sigma(std::round(smoothstep(u) * t_max)));
+        }
+
+        result.push_back(0.f);
+        return result;
+    }
+};
+
 struct Denoiser {
-    std::shared_ptr<SigmaSchedule> schedule                                                  = std::make_shared<DiscreteSchedule>();
+    std::shared_ptr<SigmaSchedule> scheduler                                                 = std::make_shared<DiscreteSchedule>();
     virtual float sigma_min()                                                                = 0;
     virtual float sigma_max()                                                                = 0;
     virtual float sigma_to_t(float sigma)                                                    = 0;
@@ -266,7 +292,7 @@ struct Denoiser {
 
     virtual std::vector<float> get_sigmas(uint32_t n) {
         auto bound_t_to_sigma = std::bind(&Denoiser::t_to_sigma, this, std::placeholders::_1);
-        return schedule->get_sigmas(n, sigma_min(), sigma_max(), bound_t_to_sigma);
+        return scheduler->get_sigmas(n, sigma_min(), sigma_max(), bound_t_to_sigma);
     }
 };
 
@@ -346,6 +372,32 @@ struct CompVisVDenoiser : public CompVisDenoiser {
     }
 };
 
+struct EDMVDenoiser : public CompVisVDenoiser {
+    float min_sigma = 0.002;
+    float max_sigma = 120.0;
+
+    EDMVDenoiser(float min_sigma = 0.002, float max_sigma = 120.0)
+        : min_sigma(min_sigma), max_sigma(max_sigma) {
+        scheduler = std::make_shared<ExponentialSchedule>();
+    }
+
+    float t_to_sigma(float t) {
+        return std::exp(t * 4 / (float)TIMESTEPS);
+    }
+
+    float sigma_to_t(float s) {
+        return 0.25 * std::log(s);
+    }
+
+    float sigma_min() {
+        return min_sigma;
+    }
+
+    float sigma_max() {
+        return max_sigma;
+    }
+};
+
 float time_snr_shift(float alpha, float t) {
     if (alpha == 1.0f) {
         return t;
@@ -359,7 +411,8 @@ struct DiscreteFlowDenoiser : public Denoiser {
 
     float sigma_data = 1.0f;
 
-    DiscreteFlowDenoiser() {
+    DiscreteFlowDenoiser(float shift = 3.0f)
+        : shift(shift) {
         set_parameters();
     }
 
@@ -474,7 +527,8 @@ static void sample_k_diffusion(sample_method_t method,
                                ggml_context* work_ctx,
                                ggml_tensor* x,
                                std::vector<float> sigmas,
-                               std::shared_ptr<RNG> rng) {
+                               std::shared_ptr<RNG> rng,
+                               float eta) {
     size_t steps = sigmas.size() - 1;
     // sample_euler_ancestral
     switch (method) {
@@ -1005,6 +1059,370 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
+        case DDIM_TRAILING:  // Denoising Diffusion Implicit Models
+                             // with the "trailing" timestep spacing
+        {
+            // See J. Song et al., "Denoising Diffusion Implicit
+            // Models", arXiv:2010.02502 [cs.LG]
+            //
+            // DDIM itself needs alphas_cumprod (DDPM, J. Ho et al.,
+            // arXiv:2006.11239 [cs.LG] with k-diffusion's start and
+            // end beta) (which unfortunately k-diffusion's data
+            // structure hides from the denoiser), and the sigmas are
+            // also needed to invert the behavior of CompVisDenoiser
+            // (k-diffusion's LMSDiscreteScheduler)
+            float beta_start = 0.00085f;
+            float beta_end   = 0.0120f;
+            std::vector<double> alphas_cumprod;
+            std::vector<double> compvis_sigmas;
+
+            alphas_cumprod.reserve(TIMESTEPS);
+            compvis_sigmas.reserve(TIMESTEPS);
+            for (int i = 0; i < TIMESTEPS; i++) {
+                alphas_cumprod[i] =
+                    (i == 0 ? 1.0f : alphas_cumprod[i - 1]) *
+                    (1.0f -
+                     std::pow(sqrtf(beta_start) +
+                                  (sqrtf(beta_end) - sqrtf(beta_start)) *
+                                      ((float)i / (TIMESTEPS - 1)),
+                              2));
+                compvis_sigmas[i] =
+                    std::sqrt((1 - alphas_cumprod[i]) /
+                              alphas_cumprod[i]);
+            }
+
+            struct ggml_tensor* pred_original_sample =
+                ggml_dup_tensor(work_ctx, x);
+            struct ggml_tensor* variance_noise =
+                ggml_dup_tensor(work_ctx, x);
+
+            for (int i = 0; i < steps; i++) {
+                // The "trailing" DDIM timestep, see S. Lin et al.,
+                // "Common Diffusion Noise Schedules and Sample Steps
+                // are Flawed", arXiv:2305.08891 [cs], p. 4, Table
+                // 2. Most variables below follow Diffusers naming
+                //
+                // Diffuser naming vs. Song et al. (2010), p. 5, (12)
+                // and p. 16, (16) (<variable name> -> <name in
+                // paper>):
+                //
+                // - pred_noise_t -> epsilon_theta^(t)(x_t)
+                // - pred_original_sample -> f_theta^(t)(x_t) or x_0
+                // - std_dev_t -> sigma_t (not the LMS sigma)
+                // - eta -> eta (set to 0 at the moment)
+                // - pred_sample_direction -> "direction pointing to
+                //   x_t"
+                // - pred_prev_sample -> "x_t-1"
+                int timestep =
+                    roundf(TIMESTEPS -
+                           i * ((float)TIMESTEPS / steps)) -
+                    1;
+                // 1. get previous step value (=t-1)
+                int prev_timestep = timestep - TIMESTEPS / steps;
+                // The sigma here is chosen to cause the
+                // CompVisDenoiser to produce t = timestep
+                float sigma = compvis_sigmas[timestep];
+                if (i == 0) {
+                    // The function add_noise intializes x to
+                    // Diffusers' latents * sigma (as in Diffusers'
+                    // pipeline) or sample * sigma (Diffusers'
+                    // scheduler), where this sigma = init_noise_sigma
+                    // in Diffusers. For DDPM and DDIM however,
+                    // init_noise_sigma = 1. But the k-diffusion
+                    // model() also evaluates F_theta(c_in(sigma) x;
+                    // ...) instead of the bare U-net F_theta, with
+                    // c_in = 1 / sqrt(sigma^2 + 1), as defined in
+                    // T. Karras et al., "Elucidating the Design Space
+                    // of Diffusion-Based Generative Models",
+                    // arXiv:2206.00364 [cs.CV], p. 3, Table 1. Hence
+                    // the first call has to be prescaled as x <- x /
+                    // (c_in * sigma) with the k-diffusion pipeline
+                    // and CompVisDenoiser.
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1) /
+                                    sigma;
+                    }
+                } else {
+                    // For the subsequent steps after the first one,
+                    // at this point x = latents or x = sample, and
+                    // needs to be prescaled with x <- sample / c_in
+                    // to compensate for model() applying the scale
+                    // c_in before the U-net F_theta
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1);
+                    }
+                }
+                // Note (also noise_pred in Diffuser's pipeline)
+                // model_output = model() is the D(x, sigma) as
+                // defined in Karras et al. (2022), p. 3, Table 1 and
+                // p. 8 (7), compare also p. 38 (226) therein.
+                struct ggml_tensor* model_output =
+                    model(x, sigma, i + 1);
+                // Here model_output is still the k-diffusion denoiser
+                // output, not the U-net output F_theta(c_in(sigma) x;
+                // ...) in Karras et al. (2022), whereas Diffusers'
+                // model_output is F_theta(...). Recover the actual
+                // model_output, which is also referred to as the
+                // "Karras ODE derivative" d or d_cur in several
+                // samplers above.
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_model_output[j] =
+                            (vec_x[j] - vec_model_output[j]) *
+                            (1 / sigma);
+                    }
+                }
+                // 2. compute alphas, betas
+                float alpha_prod_t = alphas_cumprod[timestep];
+                // Note final_alpha_cumprod = alphas_cumprod[0] due to
+                // trailing timestep spacing
+                float alpha_prod_t_prev = prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0];
+                float beta_prod_t       = 1 - alpha_prod_t;
+                // 3. compute predicted original sample from predicted
+                // noise also called "predicted x_0" of formula (12)
+                // from https://arxiv.org/pdf/2010.02502.pdf
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    // Note the substitution of latents or sample = x
+                    // * c_in = x / sqrt(sigma^2 + 1)
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_pred_original_sample[j] =
+                            (vec_x[j] / std::sqrt(sigma * sigma + 1) -
+                             std::sqrt(beta_prod_t) *
+                                 vec_model_output[j]) *
+                            (1 / std::sqrt(alpha_prod_t));
+                    }
+                }
+                // Assuming the "epsilon" prediction type, where below
+                // pred_epsilon = model_output is inserted, and is not
+                // defined/copied explicitly.
+                //
+                // 5. compute variance: "sigma_t(eta)" -> see formula
+                // (16)
+                //
+                // sigma_t = sqrt((1 - alpha_t-1)/(1 - alpha_t)) *
+                // sqrt(1 - alpha_t/alpha_t-1)
+                float beta_prod_t_prev = 1 - alpha_prod_t_prev;
+                float variance         = (beta_prod_t_prev / beta_prod_t) *
+                                 (1 - alpha_prod_t / alpha_prod_t_prev);
+                float std_dev_t = eta * std::sqrt(variance);
+                // 6. compute "direction pointing to x_t" of formula
+                // (12) from https://arxiv.org/pdf/2010.02502.pdf
+                // 7. compute x_t without "random noise" of formula
+                // (12) from https://arxiv.org/pdf/2010.02502.pdf
+                {
+                    float* vec_model_output = (float*)model_output->data;
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        // Two step inner loop without an explicit
+                        // tensor
+                        float pred_sample_direction =
+                            std::sqrt(1 - alpha_prod_t_prev -
+                                      std::pow(std_dev_t, 2)) *
+                            vec_model_output[j];
+                        vec_x[j] = std::sqrt(alpha_prod_t_prev) *
+                                       vec_pred_original_sample[j] +
+                                   pred_sample_direction;
+                    }
+                }
+                if (eta > 0) {
+                    ggml_tensor_set_f32_randn(variance_noise, rng);
+                    float* vec_variance_noise =
+                        (float*)variance_noise->data;
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] += std_dev_t * vec_variance_noise[j];
+                    }
+                }
+                // See the note above: x = latents or sample here, and
+                // is not scaled by the c_in. For the final output
+                // this is correct, but for subsequent iterations, x
+                // needs to be prescaled again, since k-diffusion's
+                // model() differes from the bare U-net F_theta by the
+                // factor c_in.
+            }
+        } break;
+        case TCD:  // Strategic Stochastic Sampling (Algorithm 4) in
+                   // Trajectory Consistency Distillation
+        {
+            // See J. Zheng et al., "Trajectory Consistency
+            // Distillation: Improved Latent Consistency Distillation
+            // by Semi-Linear Consistency Function with Trajectory
+            // Mapping", arXiv:2402.19159 [cs.CV]
+            float beta_start = 0.00085f;
+            float beta_end   = 0.0120f;
+            std::vector<double> alphas_cumprod;
+            std::vector<double> compvis_sigmas;
+
+            alphas_cumprod.reserve(TIMESTEPS);
+            compvis_sigmas.reserve(TIMESTEPS);
+            for (int i = 0; i < TIMESTEPS; i++) {
+                alphas_cumprod[i] =
+                    (i == 0 ? 1.0f : alphas_cumprod[i - 1]) *
+                    (1.0f -
+                     std::pow(sqrtf(beta_start) +
+                                  (sqrtf(beta_end) - sqrtf(beta_start)) *
+                                      ((float)i / (TIMESTEPS - 1)),
+                              2));
+                compvis_sigmas[i] =
+                    std::sqrt((1 - alphas_cumprod[i]) /
+                              alphas_cumprod[i]);
+            }
+            int original_steps = 50;
+
+            struct ggml_tensor* pred_original_sample =
+                ggml_dup_tensor(work_ctx, x);
+            struct ggml_tensor* noise =
+                ggml_dup_tensor(work_ctx, x);
+
+            for (int i = 0; i < steps; i++) {
+                // Analytic form for TCD timesteps
+                int timestep = TIMESTEPS - 1 -
+                               (TIMESTEPS / original_steps) *
+                                   (int)floor(i * ((float)original_steps / steps));
+                // 1. get previous step value
+                int prev_timestep = i >= steps - 1 ? 0 : TIMESTEPS - 1 - (TIMESTEPS / original_steps) * (int)floor((i + 1) * ((float)original_steps / steps));
+                // Here timestep_s is tau_n' in Algorithm 4. The _s
+                // notation appears to be that from C. Lu,
+                // "DPM-Solver: A Fast ODE Solver for Diffusion
+                // Probabilistic Model Sampling in Around 10 Steps",
+                // arXiv:2206.00927 [cs.LG], but this notation is not
+                // continued in Algorithm 4, where _n' is used.
+                int timestep_s =
+                    (int)floor((1 - eta) * prev_timestep);
+                // Begin k-diffusion specific workaround for
+                // evaluating F_theta(x; ...) from D(x, sigma), same
+                // as in DDIM (and see there for detailed comments)
+                float sigma = compvis_sigmas[timestep];
+                if (i == 0) {
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1) /
+                                    sigma;
+                    }
+                } else {
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] *= std::sqrt(sigma * sigma + 1);
+                    }
+                }
+                struct ggml_tensor* model_output =
+                    model(x, sigma, i + 1);
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_model_output[j] =
+                            (vec_x[j] - vec_model_output[j]) *
+                            (1 / sigma);
+                    }
+                }
+                // 2. compute alphas, betas
+                //
+                // When comparing TCD with DDPM/DDIM note that Zheng
+                // et al. (2024) follows the DPM-Solver notation for
+                // alpha. One can find the following comment in the
+                // original DPM-Solver code
+                // (https://github.com/LuChengTHU/dpm-solver/):
+                // "**Important**: Please pay special attention for
+                // the args for `alphas_cumprod`: The `alphas_cumprod`
+                // is the \hat{alpha_n} arrays in the notations of
+                // DDPM. [...] Therefore, the notation \hat{alpha_n}
+                // is different from the notation alpha_t in
+                // DPM-Solver. In fact, we have alpha_{t_n} =
+                // \sqrt{\hat{alpha_n}}, [...]"
+                float alpha_prod_t = alphas_cumprod[timestep];
+                float beta_prod_t  = 1 - alpha_prod_t;
+                // Note final_alpha_cumprod = alphas_cumprod[0] since
+                // TCD is always "trailing"
+                float alpha_prod_t_prev = prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0];
+                // The subscript _s are the only portion in this
+                // section (2) unique to TCD
+                float alpha_prod_s = alphas_cumprod[timestep_s];
+                float beta_prod_s  = 1 - alpha_prod_s;
+                // 3. Compute the predicted noised sample x_s based on
+                // the model parameterization
+                //
+                // This section is also exactly the same as DDIM
+                {
+                    float* vec_x = (float*)x->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_pred_original_sample[j] =
+                            (vec_x[j] / std::sqrt(sigma * sigma + 1) -
+                             std::sqrt(beta_prod_t) *
+                                 vec_model_output[j]) *
+                            (1 / std::sqrt(alpha_prod_t));
+                    }
+                }
+                // This consistency function step can be difficult to
+                // decipher from Algorithm 4, as it is simply stated
+                // using a consistency function. This step is the
+                // modified DDIM, i.e. p. 8 (32) in Zheng et
+                // al. (2024), with eta set to 0 (see the paragraph
+                // immediately thereafter that states this somewhat
+                // obliquely).
+                {
+                    float* vec_pred_original_sample =
+                        (float*)pred_original_sample->data;
+                    float* vec_model_output =
+                        (float*)model_output->data;
+                    float* vec_x = (float*)x->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        // Substituting x = pred_noised_sample and
+                        // pred_epsilon = model_output
+                        vec_x[j] =
+                            std::sqrt(alpha_prod_s) *
+                                vec_pred_original_sample[j] +
+                            std::sqrt(beta_prod_s) *
+                                vec_model_output[j];
+                    }
+                }
+                // 4. Sample and inject noise z ~ N(0, I) for
+                // MultiStep Inference Noise is not used on the final
+                // timestep of the timestep schedule. This also means
+                // that noise is not used for one-step sampling. Eta
+                // (referred to as "gamma" in the paper) was
+                // introduced to control the stochasticity in every
+                // step. When eta = 0, it represents deterministic
+                // sampling, whereas eta = 1 indicates full stochastic
+                // sampling.
+                if (eta > 0 && i != steps - 1) {
+                    // In this case, x is still pred_noised_sample,
+                    // continue in-place
+                    ggml_tensor_set_f32_randn(noise, rng);
+                    float* vec_x     = (float*)x->data;
+                    float* vec_noise = (float*)noise->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        // Corresponding to (35) in Zheng et
+                        // al. (2024), substituting x =
+                        // pred_noised_sample
+                        vec_x[j] =
+                            std::sqrt(alpha_prod_t_prev /
+                                      alpha_prod_s) *
+                                vec_x[j] +
+                            std::sqrt(1 - alpha_prod_t_prev /
+                                              alpha_prod_s) *
+                                vec_noise[j];
+                    }
+                }
+            }
+        } break;
 
         default:
             LOG_ERROR("Attempting to sample with nonexisting sample method %i", method);

diffusion_model.hpp

@@ -4,8 +4,10 @@
 #include "flux.hpp"
 #include "mmdit.hpp"
 #include "unet.hpp"
+#include "wan.hpp"
 
 struct DiffusionModel {
+    virtual std::string get_desc()                                                      = 0;
     virtual void compute(int n_threads,
                          struct ggml_tensor* x,
                          struct ggml_tensor* timesteps,
@@ -13,6 +15,8 @@ struct DiffusionModel {
                          struct ggml_tensor* c_concat,
                          struct ggml_tensor* y,
                          struct ggml_tensor* guidance,
+                         std::vector<ggml_tensor*> ref_latents     = {},
+                         bool increase_ref_index                   = false,
                          int num_video_frames                      = -1,
                          std::vector<struct ggml_tensor*> controls = {},
                          float control_strength                    = 0.f,
@@ -31,10 +35,15 @@ struct UNetModel : public DiffusionModel {
     UNetModelRunner unet;
 
     UNetModel(ggml_backend_t backend,
-              ggml_type wtype,
-              SDVersion version = VERSION_SD1,
-              bool flash_attn   = false)
-        : unet(backend, wtype, version, flash_attn) {
+              bool offload_params_to_cpu,
+              const String2GGMLType& tensor_types = {},
+              SDVersion version                   = VERSION_SD1,
+              bool flash_attn                     = false)
+        : unet(backend, offload_params_to_cpu, tensor_types, "model.diffusion_model", version, flash_attn) {
+    }
+
+    std::string get_desc() {
+        return unet.get_desc();
     }
 
     void alloc_params_buffer() {
@@ -68,6 +77,8 @@ struct UNetModel : public DiffusionModel {
                  struct ggml_tensor* c_concat,
                  struct ggml_tensor* y,
                  struct ggml_tensor* guidance,
+                 std::vector<ggml_tensor*> ref_latents     = {},
+                 bool increase_ref_index                   = false,
                  int num_video_frames                      = -1,
                  std::vector<struct ggml_tensor*> controls = {},
                  float control_strength                    = 0.f,
@@ -83,9 +94,14 @@ struct MMDiTModel : public DiffusionModel {
     MMDiTRunner mmdit;
 
     MMDiTModel(ggml_backend_t backend,
-               ggml_type wtype,
-               SDVersion version = VERSION_SD3_2B)
-        : mmdit(backend, wtype, version) {
+               bool offload_params_to_cpu,
+               bool flash_attn                     = false,
+               const String2GGMLType& tensor_types = {})
+        : mmdit(backend, offload_params_to_cpu, flash_attn, tensor_types, "model.diffusion_model") {
+    }
+
+    std::string get_desc() {
+        return mmdit.get_desc();
     }
 
     void alloc_params_buffer() {
@@ -119,6 +135,8 @@ struct MMDiTModel : public DiffusionModel {
                  struct ggml_tensor* c_concat,
                  struct ggml_tensor* y,
                  struct ggml_tensor* guidance,
+                 std::vector<ggml_tensor*> ref_latents     = {},
+                 bool increase_ref_index                   = false,
                  int num_video_frames                      = -1,
                  std::vector<struct ggml_tensor*> controls = {},
                  float control_strength                    = 0.f,
@@ -133,10 +151,16 @@ struct FluxModel : public DiffusionModel {
     Flux::FluxRunner flux;
 
     FluxModel(ggml_backend_t backend,
-              ggml_type wtype,
-              SDVersion version = VERSION_FLUX_DEV,
-              bool flash_attn   = false)
-        : flux(backend, wtype, version, flash_attn) {
+              bool offload_params_to_cpu,
+              const String2GGMLType& tensor_types = {},
+              SDVersion version                   = VERSION_FLUX,
+              bool flash_attn                     = false,
+              bool use_mask                       = false)
+        : flux(backend, offload_params_to_cpu, tensor_types, "model.diffusion_model", version, flash_attn, use_mask) {
+    }
+
+    std::string get_desc() {
+        return flux.get_desc();
     }
 
     void alloc_params_buffer() {
@@ -170,13 +194,75 @@ struct FluxModel : public DiffusionModel {
                  struct ggml_tensor* c_concat,
                  struct ggml_tensor* y,
                  struct ggml_tensor* guidance,
+                 std::vector<ggml_tensor*> ref_latents     = {},
+                 bool increase_ref_index                   = false,
                  int num_video_frames                      = -1,
                  std::vector<struct ggml_tensor*> controls = {},
                  float control_strength                    = 0.f,
                  struct ggml_tensor** output               = NULL,
                  struct ggml_context* output_ctx           = NULL,
                  std::vector<int> skip_layers              = std::vector<int>()) {
-        return flux.compute(n_threads, x, timesteps, context, y, guidance, output, output_ctx, skip_layers);
+        return flux.compute(n_threads, x, timesteps, context, c_concat, y, guidance, ref_latents, increase_ref_index, output, output_ctx, skip_layers);
+    }
+};
+
+struct WanModel : public DiffusionModel {
+    std::string prefix;
+    WAN::WanRunner wan;
+
+    WanModel(ggml_backend_t backend,
+             bool offload_params_to_cpu,
+             const String2GGMLType& tensor_types = {},
+             const std::string prefix            = "model.diffusion_model",
+             SDVersion version                   = VERSION_WAN2,
+             bool flash_attn                     = false)
+        : prefix(prefix), wan(backend, offload_params_to_cpu, tensor_types, prefix, version, flash_attn) {
+    }
+
+    std::string get_desc() {
+        return wan.get_desc();
+    }
+
+    void alloc_params_buffer() {
+        wan.alloc_params_buffer();
+    }
+
+    void free_params_buffer() {
+        wan.free_params_buffer();
+    }
+
+    void free_compute_buffer() {
+        wan.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+        wan.get_param_tensors(tensors, prefix);
+    }
+
+    size_t get_params_buffer_size() {
+        return wan.get_params_buffer_size();
+    }
+
+    int64_t get_adm_in_channels() {
+        return 768;
+    }
+
+    void compute(int n_threads,
+                 struct ggml_tensor* x,
+                 struct ggml_tensor* timesteps,
+                 struct ggml_tensor* context,
+                 struct ggml_tensor* c_concat,
+                 struct ggml_tensor* y,
+                 struct ggml_tensor* guidance,
+                 std::vector<ggml_tensor*> ref_latents     = {},
+                 bool increase_ref_index                   = false,
+                 int num_video_frames                      = -1,
+                 std::vector<struct ggml_tensor*> controls = {},
+                 float control_strength                    = 0.f,
+                 struct ggml_tensor** output               = NULL,
+                 struct ggml_context* output_ctx           = NULL,
+                 std::vector<int> skip_layers              = std::vector<int>()) {
+        return wan.compute(n_threads, x, timesteps, context, y, c_concat, NULL, output, output_ctx);
     }
 };
 

docs/chroma.md

@@ -0,0 +1,33 @@
+# How to Use
+
+You can run Chroma using stable-diffusion.cpp with a GPU that has 6GB or even 4GB of VRAM, without needing to offload to RAM.
+
+## Download weights
+
+- Download Chroma
+    - If you don't want to do the conversion yourself, download the preconverted gguf model from [silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF)
+    - Otherwise, download chroma's safetensors from [lodestones/Chroma](https://huggingface.co/lodestones/Chroma)
+- Download vae from https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors
+- Download t5xxl from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors
+
+## Convert Chroma weights
+
+You can download the preconverted gguf weights from [silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF), this way you don't have to do the conversion yourself.
+
+```
+.\bin\Release\sd.exe -M convert -m ..\..\ComfyUI\models\unet\chroma-unlocked-v40.safetensors -o ..\models\chroma-unlocked-v40-q8_0.gguf -v --type q8_0
+```
+
+## Run
+
+### Example
+For example:
+
+```
+ .\bin\Release\sd.exe --diffusion-model  ..\models\chroma-unlocked-v40-q8_0.gguf --vae ..\models\ae.sft --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma.cpp'" --cfg-scale 4.0 --sampling-method euler -v --chroma-disable-dit-mask
+```
+
+![](../assets/flux/chroma_v40.png)
+
+
+

docs/kontext.md

@@ -0,0 +1,39 @@
+# How to Use
+
+You can run Kontext using stable-diffusion.cpp with a GPU that has 6GB or even 4GB of VRAM, without needing to offload to RAM.
+
+## Download weights
+
+- Download Kontext
+    - If you don't want to do the conversion yourself, download the preconverted gguf model from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF)
+    - Otherwise, download FLUX.1-Kontext-dev from https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/blob/main/flux1-kontext-dev.safetensors
+- Download vae from https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors
+- Download clip_l from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors
+- Download t5xxl from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors
+
+## Convert Kontext weights
+
+You can download the preconverted gguf weights from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF), this way you don't have to do the conversion yourself.
+
+```
+.\bin\Release\sd.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-kontext-dev.safetensors -o ..\models\flux1-kontext-dev-q8_0.gguf -v --type q8_0
+```
+
+## Run
+
+- `--cfg-scale` is recommended to be set to 1. 
+
+### Example
+For example:
+
+```
+ .\bin\Release\sd.exe -r .\flux1-dev-q8_0.png --diffusion-model  ..\models\flux1-kontext-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors -p "change 'flux.cpp' to 'kontext.cpp'" --cfg-scale 1.0 --sampling-method euler -v
+```
+
+
+| ref_image | prompt  | output  |
+| ---- | ----  |----  |
+| ![](../assets/flux/flux1-dev-q8_0.png) | change 'flux.cpp' to 'kontext.cpp' |![](../assets/flux/kontext1_dev_output.png) |
+
+
+

docs/lora.md

@@ -10,4 +10,30 @@ Here's a simple example:
 ./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models
 ```
 
-`../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model
+`../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model
+
+# Support matrix
+
+> ℹ️ CUDA `get_rows` support is defined here:  
+> [ggml-org/ggml/src/ggml-cuda/getrows.cu#L156](https://github.com/ggml-org/ggml/blob/7dee1d6a1e7611f238d09be96738388da97c88ed/src/ggml-cuda/getrows.cu#L156)  
+> Currently only the basic types + Q4/Q5/Q8 are implemented. K-quants are **not** supported.
+
+NOTE: The other backends may have different support.
+
+| Quant / Type | CUDA |
+|--------------|------|
+| F32          | ✔️   |
+| F16          | ✔️   |
+| BF16         | ✔️   |
+| I32          | ✔️   |
+| Q4_0         | ✔️   |
+| Q4_1         | ✔️   |
+| Q5_0         | ✔️   |
+| Q5_1         | ✔️   |
+| Q8_0         | ✔️   |
+| Q2_K         | ❌   |
+| Q3_K         | ❌   |
+| Q4_K         | ❌   |
+| Q5_K         | ❌   |
+| Q6_K         | ❌   |
+| Q8_K         | ❌   |

docs/wan.md

@@ -0,0 +1,139 @@
+# How to Use
+
+## Download weights
+
+- Download Wan
+    - Wan2.1
+        - Wan2.1 T2V 1.3B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+        - Wan2.1 T2V 14B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-T2V-14B-gguf/tree/main
+        - Wan2.1 I2V 14B 480P
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-I2V-14B-480P-gguf/tree/main
+        - Wan2.1 I2V 14B 720P
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-I2V-14B-720P-gguf/tree/main
+        - Wan2.1 FLF2V 14B 720P
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-FLF2V-14B-720P-gguf/tree/main
+    - Wan2.2
+        - Wan2.2 TI2V 5B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF/tree/main
+        - Wan2.2 T2V A14B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/QuantStack/Wan2.2-T2V-A14B-GGUF/tree/main
+        - Wan2.2 I2V A14B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/QuantStack/Wan2.2-I2V-A14B-GGUF/tree/main
+- Download vae
+    - wan_2.1_vae (for all the wan model except Wan2.2 TI2V 5B)
+        - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors
+    - wan_2.2_vae (for Wan2.2 TI2V 5B only)
+        - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/vae/wan2.2_vae.safetensors
+- Download umt5_xxl
+    - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp16.safetensors
+    - gguf: https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main
+
+- Download clip_vison_h (for Wan2.1 I2V/FLF2V only)
+    - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/clip_vision/clip_vision_h.safetensors
+
+
+## Examples
+
+### Wan2.1 T2V 1.3B
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.1_1.3B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.1 T2V 14B
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.1_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+
+
+### Wan2.1 I2V 14B
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.1_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 T2V A14B
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.2_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 I2V A14B
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.2_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 T2V A14B T2I
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<img width="832" height="480" alt="Wan2 2_14B_t2i" src="../assets/wan/Wan2.2_14B_t2i.png" />
+
+### Wan2.2 T2V 14B with Lora
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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 ..\..\ComfyUI\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>
+
+
+
+### Wan2.2 TI2V 5B
+
+#### T2V
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.2_5B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+#### I2V
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.2_5B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.1 FLF2V 14B
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\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
+```
+
+
+<video src=../assets/wan/Wan2.1_14B_flf2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 FLF2V 14B
+
+```
+.\bin\Release\sd.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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
+```
+
+<video src=../assets/wan/Wan2.2_14B_flf2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>

esrgan.hpp

@@ -130,8 +130,8 @@ public:
         body_feat = conv_body->forward(ctx, body_feat);
         feat      = ggml_add(ctx, feat, body_feat);
         // upsample
-        feat     = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2)));
-        feat     = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2)));
+        feat     = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+        feat     = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
         auto out = conv_last->forward(ctx, lrelu(ctx, conv_hr->forward(ctx, feat)));
         return out;
     }
@@ -143,9 +143,21 @@ struct ESRGAN : public GGMLRunner {
     int tile_size = 128;  // avoid cuda OOM for 4gb VRAM
 
     ESRGAN(ggml_backend_t backend,
-           ggml_type wtype)
-        : GGMLRunner(backend, wtype) {
-        rrdb_net.init(params_ctx, wtype);
+           bool offload_params_to_cpu,
+           const String2GGMLType& tensor_types = {})
+        : GGMLRunner(backend, offload_params_to_cpu) {
+        rrdb_net.init(params_ctx, tensor_types, "");
+    }
+
+    void enable_conv2d_direct() {
+        std::vector<GGMLBlock*> blocks;
+        rrdb_net.get_all_blocks(blocks);
+        for (auto block : blocks) {
+            if (block->get_desc() == "Conv2d") {
+                auto conv_block = (Conv2d*)block;
+                conv_block->enable_direct();
+            }
+        }
     }
 
     std::string get_desc() {
@@ -165,7 +177,7 @@ struct ESRGAN : public GGMLRunner {
             return false;
         }
 
-        bool success = model_loader.load_tensors(esrgan_tensors, backend);
+        bool success = model_loader.load_tensors(esrgan_tensors);
 
         if (!success) {
             LOG_ERROR("load esrgan tensors from model loader failed");

examples/cli/CMakeLists.txt

@@ -3,4 +3,4 @@ set(TARGET sd)
 add_executable(${TARGET} main.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE stable-diffusion ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PUBLIC cxx_std_11)
+target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)

examples/cli/avi_writer.h

@@ -0,0 +1,217 @@
+#ifndef __AVI_WRITER_H__
+#define __AVI_WRITER_H__
+
+#include <stdint.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include "stable-diffusion.h"
+
+#ifndef INCLUDE_STB_IMAGE_WRITE_H
+#include "stb_image_write.h"
+#endif
+
+typedef struct {
+    uint32_t offset;
+    uint32_t size;
+} avi_index_entry;
+
+// Write 32-bit little-endian integer
+void write_u32_le(FILE* f, uint32_t val) {
+    fwrite(&val, 4, 1, f);
+}
+
+// Write 16-bit little-endian integer
+void write_u16_le(FILE* f, uint16_t val) {
+    fwrite(&val, 2, 1, f);
+}
+
+/**
+ * Create an MJPG AVI file from an array of sd_image_t images.
+ * Images are encoded to JPEG using stb_image_write.
+ *
+ * @param filename Output AVI file name.
+ * @param images Array of input images.
+ * @param num_images Number of images in the array.
+ * @param fps Frames per second for the video.
+ * @param quality JPEG quality (0-100).
+ * @return 0 on success, -1 on failure.
+ */
+int create_mjpg_avi_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality = 90) {
+    if (num_images == 0) {
+        fprintf(stderr, "Error: Image array is empty.\n");
+        return -1;
+    }
+
+    FILE* f = fopen(filename, "wb");
+    if (!f) {
+        perror("Error opening file for writing");
+        return -1;
+    }
+
+    uint32_t width    = images[0].width;
+    uint32_t height   = images[0].height;
+    uint32_t channels = images[0].channel;
+    if (channels != 3 && channels != 4) {
+        fprintf(stderr, "Error: Unsupported channel count: %u\n", channels);
+        fclose(f);
+        return -1;
+    }
+
+    // --- RIFF AVI Header ---
+    fwrite("RIFF", 4, 1, f);
+    long riff_size_pos = ftell(f);
+    write_u32_le(f, 0);  // Placeholder for file size
+    fwrite("AVI ", 4, 1, f);
+
+    // 'hdrl' LIST (header list)
+    fwrite("LIST", 4, 1, f);
+    write_u32_le(f, 4 + 8 + 56 + 8 + 4 + 8 + 56 + 8 + 40);
+    fwrite("hdrl", 4, 1, f);
+
+    // 'avih' chunk (AVI main header)
+    fwrite("avih", 4, 1, f);
+    write_u32_le(f, 56);
+    write_u32_le(f, 1000000 / fps);       // Microseconds per frame
+    write_u32_le(f, 0);                   // Max bytes per second
+    write_u32_le(f, 0);                   // Padding granularity
+    write_u32_le(f, 0x110);               // Flags (HASINDEX | ISINTERLEAVED)
+    write_u32_le(f, num_images);          // Total frames
+    write_u32_le(f, 0);                   // Initial frames
+    write_u32_le(f, 1);                   // Number of streams
+    write_u32_le(f, width * height * 3);  // Suggested buffer size
+    write_u32_le(f, width);
+    write_u32_le(f, height);
+    write_u32_le(f, 0);  // Reserved
+    write_u32_le(f, 0);  // Reserved
+    write_u32_le(f, 0);  // Reserved
+    write_u32_le(f, 0);  // Reserved
+
+    // 'strl' LIST (stream list)
+    fwrite("LIST", 4, 1, f);
+    write_u32_le(f, 4 + 8 + 56 + 8 + 40);
+    fwrite("strl", 4, 1, f);
+
+    // 'strh' chunk (stream header)
+    fwrite("strh", 4, 1, f);
+    write_u32_le(f, 56);
+    fwrite("vids", 4, 1, f);              // Stream type: video
+    fwrite("MJPG", 4, 1, f);              // Codec: Motion JPEG
+    write_u32_le(f, 0);                   // Flags
+    write_u16_le(f, 0);                   // Priority
+    write_u16_le(f, 0);                   // Language
+    write_u32_le(f, 0);                   // Initial frames
+    write_u32_le(f, 1);                   // Scale
+    write_u32_le(f, fps);                 // Rate
+    write_u32_le(f, 0);                   // Start
+    write_u32_le(f, num_images);          // Length
+    write_u32_le(f, width * height * 3);  // Suggested buffer size
+    write_u32_le(f, (uint32_t)-1);        // Quality
+    write_u32_le(f, 0);                   // Sample size
+    write_u16_le(f, 0);                   // rcFrame.left
+    write_u16_le(f, 0);                   // rcFrame.top
+    write_u16_le(f, 0);                   // rcFrame.right
+    write_u16_le(f, 0);                   // rcFrame.bottom
+
+    // 'strf' chunk (stream format: BITMAPINFOHEADER)
+    fwrite("strf", 4, 1, f);
+    write_u32_le(f, 40);
+    write_u32_le(f, 40);  // biSize
+    write_u32_le(f, width);
+    write_u32_le(f, height);
+    write_u16_le(f, 1);                   // biPlanes
+    write_u16_le(f, 24);                  // biBitCount
+    fwrite("MJPG", 4, 1, f);              // biCompression (FOURCC)
+    write_u32_le(f, width * height * 3);  // biSizeImage
+    write_u32_le(f, 0);                   // XPelsPerMeter
+    write_u32_le(f, 0);                   // YPelsPerMeter
+    write_u32_le(f, 0);                   // Colors used
+    write_u32_le(f, 0);                   // Colors important
+
+    // 'movi' LIST (video frames)
+    long movi_list_pos = ftell(f);
+    fwrite("LIST", 4, 1, f);
+    long movi_size_pos = ftell(f);
+    write_u32_le(f, 0);  // Placeholder for movi size
+    fwrite("movi", 4, 1, f);
+
+    avi_index_entry* index = (avi_index_entry*)malloc(sizeof(avi_index_entry) * num_images);
+    if (!index) {
+        fclose(f);
+        return -1;
+    }
+
+    // Encode and write each frame as JPEG
+    struct {
+        uint8_t* buf;
+        size_t size;
+    } jpeg_data;
+
+    for (int i = 0; i < num_images; i++) {
+        jpeg_data.buf  = NULL;
+        jpeg_data.size = 0;
+
+        // Callback function to collect JPEG data into memory
+        auto write_to_buf = [](void* context, void* data, int size) {
+            auto jd = (decltype(jpeg_data)*)context;
+            jd->buf = (uint8_t*)realloc(jd->buf, jd->size + size);
+            memcpy(jd->buf + jd->size, data, size);
+            jd->size += size;
+        };
+
+        // Encode to JPEG in memory
+        stbi_write_jpg_to_func(
+            write_to_buf,
+            &jpeg_data,
+            images[i].width,
+            images[i].height,
+            channels,
+            images[i].data,
+            quality);
+
+        // Write '00dc' chunk (video frame)
+        fwrite("00dc", 4, 1, f);
+        write_u32_le(f, jpeg_data.size);
+        index[i].offset = ftell(f) - 8;
+        index[i].size   = jpeg_data.size;
+        fwrite(jpeg_data.buf, 1, jpeg_data.size, f);
+
+        // Align to even byte size
+        if (jpeg_data.size % 2)
+            fputc(0, f);
+
+        free(jpeg_data.buf);
+    }
+
+    // Finalize 'movi' size
+    long cur_pos   = ftell(f);
+    long movi_size = cur_pos - movi_size_pos - 4;
+    fseek(f, movi_size_pos, SEEK_SET);
+    write_u32_le(f, movi_size);
+    fseek(f, cur_pos, SEEK_SET);
+
+    // Write 'idx1' index
+    fwrite("idx1", 4, 1, f);
+    write_u32_le(f, num_images * 16);
+    for (int i = 0; i < num_images; i++) {
+        fwrite("00dc", 4, 1, f);
+        write_u32_le(f, 0x10);
+        write_u32_le(f, index[i].offset);
+        write_u32_le(f, index[i].size);
+    }
+
+    // Finalize RIFF size
+    cur_pos        = ftell(f);
+    long file_size = cur_pos - riff_size_pos - 4;
+    fseek(f, riff_size_pos, SEEK_SET);
+    write_u32_le(f, file_size);
+    fseek(f, cur_pos, SEEK_SET);
+
+    fclose(f);
+    free(index);
+
+    return 0;
+}
+
+#endif  // __AVI_WRITER_H__

flux.hpp

@@ -5,6 +5,7 @@
 
 #include "ggml_extend.hpp"
 #include "model.h"
+#include "rope.hpp"
 
 #define FLUX_GRAPH_SIZE 10240
 
@@ -35,8 +36,9 @@ namespace Flux {
         int64_t hidden_size;
         float eps;
 
-        void init_params(struct ggml_context* ctx, ggml_type wtype) {
-            params["scale"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
+        void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+            ggml_type wtype = GGML_TYPE_F32;
+            params["scale"] = ggml_new_tensor_1d(ctx, wtype, hidden_size);
         }
 
     public:
@@ -112,10 +114,12 @@ namespace Flux {
     }
 
     __STATIC_INLINE__ struct ggml_tensor* attention(struct ggml_context* ctx,
+                                                    ggml_backend_t backend,
                                                     struct ggml_tensor* q,
                                                     struct ggml_tensor* k,
                                                     struct ggml_tensor* v,
                                                     struct ggml_tensor* pe,
+                                                    struct ggml_tensor* mask,
                                                     bool flash_attn) {
         // q,k,v: [N, L, n_head, d_head]
         // pe: [L, d_head/2, 2, 2]
@@ -123,7 +127,7 @@ namespace Flux {
         q = apply_rope(ctx, q, pe);  // [N*n_head, L, d_head]
         k = apply_rope(ctx, k, pe);  // [N*n_head, L, d_head]
 
-        auto x = ggml_nn_attention_ext(ctx, q, k, v, v->ne[1], NULL, false, true, flash_attn);  // [N, L, n_head*d_head]
+        auto x = ggml_nn_attention_ext(ctx, backend, q, k, v, v->ne[1], mask, false, true, flash_attn);  // [N, L, n_head*d_head]
         return x;
     }
 
@@ -166,13 +170,17 @@ namespace Flux {
             return x;
         }
 
-        struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* pe) {
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    ggml_backend_t backend,
+                                    struct ggml_tensor* x,
+                                    struct ggml_tensor* pe,
+                                    struct ggml_tensor* mask) {
             // x: [N, n_token, dim]
             // pe: [n_token, d_head/2, 2, 2]
             // return [N, n_token, dim]
-            auto qkv = pre_attention(ctx, x);                                   // q,k,v: [N, n_token, n_head, d_head]
-            x        = attention(ctx, qkv[0], qkv[1], qkv[2], pe, flash_attn);  // [N, n_token, dim]
-            x        = post_attention(ctx, x);                                  // [N, n_token, dim]
+            auto qkv = pre_attention(ctx, x);                                                  // q,k,v: [N, n_token, n_head, d_head]
+            x        = attention(ctx, backend, qkv[0], qkv[1], qkv[2], pe, mask, flash_attn);  // [N, n_token, dim]
+            x        = post_attention(ctx, x);                                                 // [N, n_token, dim]
             return x;
         }
     };
@@ -184,6 +192,13 @@ namespace Flux {
 
         ModulationOut(ggml_tensor* shift = NULL, ggml_tensor* scale = NULL, ggml_tensor* gate = NULL)
             : shift(shift), scale(scale), gate(gate) {}
+
+        ModulationOut(struct ggml_context* ctx, ggml_tensor* vec, int64_t offset) {
+            int64_t stride = vec->nb[1] * vec->ne[1];
+            shift          = ggml_view_2d(ctx, vec, vec->ne[0], vec->ne[1], vec->nb[1], stride * (offset + 0));  // [N, dim]
+            scale          = ggml_view_2d(ctx, vec, vec->ne[0], vec->ne[1], vec->nb[1], stride * (offset + 1));  // [N, dim]
+            gate           = ggml_view_2d(ctx, vec, vec->ne[0], vec->ne[1], vec->nb[1], stride * (offset + 2));  // [N, dim]
+        }
     };
 
     struct Modulation : public GGMLBlock {
@@ -209,19 +224,12 @@ namespace Flux {
             auto m = ggml_reshape_3d(ctx, out, vec->ne[0], multiplier, vec->ne[1]);  // [N, multiplier, dim]
             m      = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3));               // [multiplier, N, dim]
 
-            int64_t offset = m->nb[1] * m->ne[1];
-            auto shift_0   = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, dim]
-            auto scale_0   = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, dim]
-            auto gate_0    = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, dim]
-
+            ModulationOut m_0 = ModulationOut(ctx, m, 0);
             if (is_double) {
-                auto shift_1 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, dim]
-                auto scale_1 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, dim]
-                auto gate_1  = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, dim]
-                return {ModulationOut(shift_0, scale_0, gate_0), ModulationOut(shift_1, scale_1, gate_1)};
+                return {m_0, ModulationOut(ctx, m, 3)};
             }
 
-            return {ModulationOut(shift_0, scale_0, gate_0), ModulationOut()};
+            return {m_0, ModulationOut()};
         }
     };
 
@@ -241,25 +249,33 @@ namespace Flux {
 
     struct DoubleStreamBlock : public GGMLBlock {
         bool flash_attn;
+        bool prune_mod;
+        int idx = 0;
 
     public:
         DoubleStreamBlock(int64_t hidden_size,
                           int64_t num_heads,
                           float mlp_ratio,
+                          int idx         = 0,
                           bool qkv_bias   = false,
-                          bool flash_attn = false)
-            : flash_attn(flash_attn) {
+                          bool flash_attn = false,
+                          bool prune_mod  = false)
+            : idx(idx), flash_attn(flash_attn), prune_mod(prune_mod) {
             int64_t mlp_hidden_dim = hidden_size * mlp_ratio;
-            blocks["img_mod"]      = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, true));
-            blocks["img_norm1"]    = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
-            blocks["img_attn"]     = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qkv_bias, flash_attn));
+            if (!prune_mod) {
+                blocks["img_mod"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, true));
+            }
+            blocks["img_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
+            blocks["img_attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qkv_bias, flash_attn));
 
             blocks["img_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
             blocks["img_mlp.0"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, mlp_hidden_dim));
             // img_mlp.1 is nn.GELU(approximate="tanh")
             blocks["img_mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(mlp_hidden_dim, hidden_size));
 
-            blocks["txt_mod"]   = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, true));
+            if (!prune_mod) {
+                blocks["txt_mod"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, true));
+            }
             blocks["txt_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
             blocks["txt_attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qkv_bias, flash_attn));
 
@@ -269,17 +285,35 @@ namespace Flux {
             blocks["txt_mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(mlp_hidden_dim, hidden_size));
         }
 
+        std::vector<ModulationOut> get_distil_img_mod(struct ggml_context* ctx, struct ggml_tensor* vec) {
+            // TODO: not hardcoded?
+            const int single_blocks_count = 38;
+            const int double_blocks_count = 19;
+
+            int64_t offset = 6 * idx + 3 * single_blocks_count;
+            return {ModulationOut(ctx, vec, offset), ModulationOut(ctx, vec, offset + 3)};
+        }
+
+        std::vector<ModulationOut> get_distil_txt_mod(struct ggml_context* ctx, struct ggml_tensor* vec) {
+            // TODO: not hardcoded?
+            const int single_blocks_count = 38;
+            const int double_blocks_count = 19;
+
+            int64_t offset = 6 * idx + 6 * double_blocks_count + 3 * single_blocks_count;
+            return {ModulationOut(ctx, vec, offset), ModulationOut(ctx, vec, offset + 3)};
+        }
+
         std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
+                                                                    ggml_backend_t backend,
                                                                     struct ggml_tensor* img,
                                                                     struct ggml_tensor* txt,
                                                                     struct ggml_tensor* vec,
-                                                                    struct ggml_tensor* pe) {
+                                                                    struct ggml_tensor* pe,
+                                                                    struct ggml_tensor* mask = NULL) {
             // img: [N, n_img_token, hidden_size]
             // txt: [N, n_txt_token, hidden_size]
             // pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
             // return: ([N, n_img_token, hidden_size], [N, n_txt_token, hidden_size])
-
-            auto img_mod   = std::dynamic_pointer_cast<Modulation>(blocks["img_mod"]);
             auto img_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm1"]);
             auto img_attn  = std::dynamic_pointer_cast<SelfAttention>(blocks["img_attn"]);
 
@@ -287,7 +321,6 @@ namespace Flux {
             auto img_mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.0"]);
             auto img_mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.2"]);
 
-            auto txt_mod   = std::dynamic_pointer_cast<Modulation>(blocks["txt_mod"]);
             auto txt_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["txt_norm1"]);
             auto txt_attn  = std::dynamic_pointer_cast<SelfAttention>(blocks["txt_attn"]);
 
@@ -295,10 +328,22 @@ namespace Flux {
             auto txt_mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["txt_mlp.0"]);
             auto txt_mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["txt_mlp.2"]);
 
-            auto img_mods          = img_mod->forward(ctx, vec);
+            std::vector<ModulationOut> img_mods;
+            if (prune_mod) {
+                img_mods = get_distil_img_mod(ctx, vec);
+            } else {
+                auto img_mod = std::dynamic_pointer_cast<Modulation>(blocks["img_mod"]);
+                img_mods     = img_mod->forward(ctx, vec);
+            }
             ModulationOut img_mod1 = img_mods[0];
             ModulationOut img_mod2 = img_mods[1];
-            auto txt_mods          = txt_mod->forward(ctx, vec);
+            std::vector<ModulationOut> txt_mods;
+            if (prune_mod) {
+                txt_mods = get_distil_txt_mod(ctx, vec);
+            } else {
+                auto txt_mod = std::dynamic_pointer_cast<Modulation>(blocks["txt_mod"]);
+                txt_mods     = txt_mod->forward(ctx, vec);
+            }
             ModulationOut txt_mod1 = txt_mods[0];
             ModulationOut txt_mod2 = txt_mods[1];
 
@@ -323,8 +368,8 @@ namespace Flux {
             auto k = ggml_concat(ctx, txt_k, img_k, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
             auto v = ggml_concat(ctx, txt_v, img_v, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
 
-            auto attn         = attention(ctx, q, k, v, pe, flash_attn);              // [N, n_txt_token + n_img_token, n_head*d_head]
-            attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));  // [n_txt_token + n_img_token, N, hidden_size]
+            auto attn         = attention(ctx, backend, q, k, v, pe, mask, flash_attn);  // [N, n_txt_token + n_img_token, n_head*d_head]
+            attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));     // [n_txt_token + n_img_token, N, hidden_size]
             auto txt_attn_out = ggml_view_3d(ctx,
                                              attn,
                                              attn->ne[0],
@@ -372,14 +417,18 @@ namespace Flux {
         int64_t hidden_size;
         int64_t mlp_hidden_dim;
         bool flash_attn;
+        bool prune_mod;
+        int idx = 0;
 
     public:
         SingleStreamBlock(int64_t hidden_size,
                           int64_t num_heads,
                           float mlp_ratio = 4.0f,
+                          int idx         = 0,
                           float qk_scale  = 0.f,
-                          bool flash_attn = false)
-            : hidden_size(hidden_size), num_heads(num_heads), flash_attn(flash_attn) {
+                          bool flash_attn = false,
+                          bool prune_mod  = false)
+            : hidden_size(hidden_size), num_heads(num_heads), idx(idx), flash_attn(flash_attn), prune_mod(prune_mod) {
             int64_t head_dim = hidden_size / num_heads;
             float scale      = qk_scale;
             if (scale <= 0.f) {
@@ -392,26 +441,38 @@ namespace Flux {
             blocks["norm"]     = std::shared_ptr<GGMLBlock>(new QKNorm(head_dim));
             blocks["pre_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-6f, false));
             // mlp_act is nn.GELU(approximate="tanh")
-            blocks["modulation"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, false));
+            if (!prune_mod) {
+                blocks["modulation"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, false));
+            }
+        }
+
+        ModulationOut get_distil_mod(struct ggml_context* ctx, struct ggml_tensor* vec) {
+            int64_t offset = 3 * idx;
+            return ModulationOut(ctx, vec, offset);
         }
 
         struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    ggml_backend_t backend,
                                     struct ggml_tensor* x,
                                     struct ggml_tensor* vec,
-                                    struct ggml_tensor* pe) {
+                                    struct ggml_tensor* pe,
+                                    struct ggml_tensor* mask = NULL) {
             // x: [N, n_token, hidden_size]
             // pe: [n_token, d_head/2, 2, 2]
             // return: [N, n_token, hidden_size]
 
-            auto linear1    = std::dynamic_pointer_cast<Linear>(blocks["linear1"]);
-            auto linear2    = std::dynamic_pointer_cast<Linear>(blocks["linear2"]);
-            auto norm       = std::dynamic_pointer_cast<QKNorm>(blocks["norm"]);
-            auto pre_norm   = std::dynamic_pointer_cast<LayerNorm>(blocks["pre_norm"]);
-            auto modulation = std::dynamic_pointer_cast<Modulation>(blocks["modulation"]);
-
-            auto mods         = modulation->forward(ctx, vec);
-            ModulationOut mod = mods[0];
+            auto linear1  = std::dynamic_pointer_cast<Linear>(blocks["linear1"]);
+            auto linear2  = std::dynamic_pointer_cast<Linear>(blocks["linear2"]);
+            auto norm     = std::dynamic_pointer_cast<QKNorm>(blocks["norm"]);
+            auto pre_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["pre_norm"]);
+            ModulationOut mod;
+            if (prune_mod) {
+                mod = get_distil_mod(ctx, vec);
+            } else {
+                auto modulation = std::dynamic_pointer_cast<Modulation>(blocks["modulation"]);
 
+                mod = modulation->forward(ctx, vec)[0];
+            }
             auto x_mod   = Flux::modulate(ctx, pre_norm->forward(ctx, x), mod.shift, mod.scale);
             auto qkv_mlp = linear1->forward(ctx, x_mod);                            // [N, n_token, hidden_size * 3 + mlp_hidden_dim]
             qkv_mlp      = ggml_cont(ctx, ggml_permute(ctx, qkv_mlp, 2, 0, 1, 3));  // [hidden_size * 3 + mlp_hidden_dim, N, n_token]
@@ -442,7 +503,7 @@ namespace Flux {
             auto v           = ggml_reshape_4d(ctx, qkv_vec[2], head_dim, num_heads, qkv_vec[2]->ne[1], qkv_vec[2]->ne[2]);  // [N, n_token, n_head, d_head]
             q                = norm->query_norm(ctx, q);
             k                = norm->key_norm(ctx, k);
-            auto attn        = attention(ctx, q, k, v, pe, flash_attn);  // [N, n_token, hidden_size]
+            auto attn        = attention(ctx, backend, q, k, v, pe, mask, flash_attn);  // [N, n_token, hidden_size]
 
             auto attn_mlp = ggml_concat(ctx, attn, ggml_gelu_inplace(ctx, mlp), 0);  // [N, n_token, hidden_size + mlp_hidden_dim]
             auto output   = linear2->forward(ctx, attn_mlp);                         // [N, n_token, hidden_size]
@@ -453,13 +514,28 @@ namespace Flux {
     };
 
     struct LastLayer : public GGMLBlock {
+        bool prune_mod;
+
     public:
         LastLayer(int64_t hidden_size,
                   int64_t patch_size,
-                  int64_t out_channels) {
-            blocks["norm_final"]         = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
-            blocks["linear"]             = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, patch_size * patch_size * out_channels));
-            blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, 2 * hidden_size));
+                  int64_t out_channels,
+                  bool prune_mod = false)
+            : prune_mod(prune_mod) {
+            blocks["norm_final"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
+            blocks["linear"]     = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, patch_size * patch_size * out_channels));
+            if (!prune_mod) {
+                blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, 2 * hidden_size));
+            }
+        }
+
+        ModulationOut get_distil_mod(struct ggml_context* ctx, struct ggml_tensor* vec) {
+            int64_t offset = vec->ne[2] - 2;
+            int64_t stride = vec->nb[1] * vec->ne[1];
+            auto shift     = ggml_view_2d(ctx, vec, vec->ne[0], vec->ne[1], vec->nb[1], stride * (offset + 0));  // [N, dim]
+            auto scale     = ggml_view_2d(ctx, vec, vec->ne[0], vec->ne[1], vec->nb[1], stride * (offset + 1));  // [N, dim]
+            // No gate
+            return ModulationOut(shift, scale, NULL);
         }
 
         struct ggml_tensor* forward(struct ggml_context* ctx,
@@ -468,17 +544,24 @@ namespace Flux {
             // x: [N, n_token, hidden_size]
             // c: [N, hidden_size]
             // return: [N, n_token, patch_size * patch_size * out_channels]
-            auto norm_final         = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_final"]);
-            auto linear             = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
-            auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
+            auto norm_final = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_final"]);
+            auto linear     = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+            struct ggml_tensor *shift, *scale;
+            if (prune_mod) {
+                auto mod = get_distil_mod(ctx, c);
+                shift    = mod.shift;
+                scale    = mod.scale;
+            } else {
+                auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
 
-            auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx, c));  // [N, 2 * hidden_size]
-            m      = ggml_reshape_3d(ctx, m, c->ne[0], 2, c->ne[1]);       // [N, 2, hidden_size]
-            m      = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3));     // [2, N, hidden_size]
+                auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx, c));  // [N, 2 * hidden_size]
+                m      = ggml_reshape_3d(ctx, m, c->ne[0], 2, c->ne[1]);       // [N, 2, hidden_size]
+                m      = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3));     // [2, N, hidden_size]
 
-            int64_t offset = m->nb[1] * m->ne[1];
-            auto shift     = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-            auto scale     = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+                int64_t offset = m->nb[1] * m->ne[1];
+                shift          = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
+                scale          = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+            }
 
             x = Flux::modulate(ctx, norm_final->forward(ctx, x), shift, scale);
             x = linear->forward(ctx, x);
@@ -487,8 +570,37 @@ namespace Flux {
         }
     };
 
+    struct ChromaApproximator : public GGMLBlock {
+        int64_t inner_size = 5120;
+        int64_t n_layers   = 5;
+        ChromaApproximator(int64_t in_channels = 64, int64_t hidden_size = 3072) {
+            blocks["in_proj"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, inner_size, true));
+            for (int i = 0; i < n_layers; i++) {
+                blocks["norms." + std::to_string(i)]  = std::shared_ptr<GGMLBlock>(new RMSNorm(inner_size));
+                blocks["layers." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(inner_size, inner_size));
+            }
+            blocks["out_proj"] = std::shared_ptr<GGMLBlock>(new Linear(inner_size, hidden_size, true));
+        }
+
+        struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+            auto in_proj  = std::dynamic_pointer_cast<Linear>(blocks["in_proj"]);
+            auto out_proj = std::dynamic_pointer_cast<Linear>(blocks["out_proj"]);
+
+            x = in_proj->forward(ctx, x);
+            for (int i = 0; i < n_layers; i++) {
+                auto norm  = std::dynamic_pointer_cast<RMSNorm>(blocks["norms." + std::to_string(i)]);
+                auto embed = std::dynamic_pointer_cast<MLPEmbedder>(blocks["layers." + std::to_string(i)]);
+                x          = ggml_add_inplace(ctx, x, embed->forward(ctx, norm->forward(ctx, x)));
+            }
+            x = out_proj->forward(ctx, x);
+
+            return x;
+        }
+    };
+
     struct FluxParams {
         int64_t in_channels         = 64;
+        int64_t out_channels        = 64;
         int64_t vec_in_dim          = 768;
         int64_t context_in_dim      = 4096;
         int64_t hidden_size         = 3072;
@@ -502,153 +614,24 @@ namespace Flux {
         bool qkv_bias               = true;
         bool guidance_embed         = true;
         bool flash_attn             = true;
+        bool is_chroma              = false;
     };
 
     struct Flux : public GGMLBlock {
-    public:
-        std::vector<float> linspace(float start, float end, int num) {
-            std::vector<float> result(num);
-            float step = (end - start) / (num - 1);
-            for (int i = 0; i < num; ++i) {
-                result[i] = start + i * step;
-            }
-            return result;
-        }
-
-        std::vector<std::vector<float>> transpose(const std::vector<std::vector<float>>& mat) {
-            int rows = mat.size();
-            int cols = mat[0].size();
-            std::vector<std::vector<float>> transposed(cols, std::vector<float>(rows));
-            for (int i = 0; i < rows; ++i) {
-                for (int j = 0; j < cols; ++j) {
-                    transposed[j][i] = mat[i][j];
-                }
-            }
-            return transposed;
-        }
-
-        std::vector<float> flatten(const std::vector<std::vector<float>>& vec) {
-            std::vector<float> flat_vec;
-            for (const auto& sub_vec : vec) {
-                flat_vec.insert(flat_vec.end(), sub_vec.begin(), sub_vec.end());
-            }
-            return flat_vec;
-        }
-
-        std::vector<std::vector<float>> rope(const std::vector<float>& pos, int dim, int theta) {
-            assert(dim % 2 == 0);
-            int half_dim = dim / 2;
-
-            std::vector<float> scale = linspace(0, (dim * 1.0f - 2) / dim, half_dim);
-
-            std::vector<float> omega(half_dim);
-            for (int i = 0; i < half_dim; ++i) {
-                omega[i] = 1.0 / std::pow(theta, scale[i]);
-            }
-
-            int pos_size = pos.size();
-            std::vector<std::vector<float>> out(pos_size, std::vector<float>(half_dim));
-            for (int i = 0; i < pos_size; ++i) {
-                for (int j = 0; j < half_dim; ++j) {
-                    out[i][j] = pos[i] * omega[j];
-                }
-            }
-
-            std::vector<std::vector<float>> result(pos_size, std::vector<float>(half_dim * 4));
-            for (int i = 0; i < pos_size; ++i) {
-                for (int j = 0; j < half_dim; ++j) {
-                    result[i][4 * j]     = std::cos(out[i][j]);
-                    result[i][4 * j + 1] = -std::sin(out[i][j]);
-                    result[i][4 * j + 2] = std::sin(out[i][j]);
-                    result[i][4 * j + 3] = std::cos(out[i][j]);
-                }
-            }
-
-            return result;
-        }
-
-        // Generate IDs for image patches and text
-        std::vector<std::vector<float>> gen_ids(int h, int w, int patch_size, int bs, int context_len) {
-            int h_len = (h + (patch_size / 2)) / patch_size;
-            int w_len = (w + (patch_size / 2)) / patch_size;
-
-            std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(3, 0.0));
-
-            std::vector<float> row_ids = linspace(0, h_len - 1, h_len);
-            std::vector<float> col_ids = linspace(0, w_len - 1, w_len);
-
-            for (int i = 0; i < h_len; ++i) {
-                for (int j = 0; j < w_len; ++j) {
-                    img_ids[i * w_len + j][1] = row_ids[i];
-                    img_ids[i * w_len + j][2] = col_ids[j];
-                }
-            }
-
-            std::vector<std::vector<float>> img_ids_repeated(bs * img_ids.size(), std::vector<float>(3));
-            for (int i = 0; i < bs; ++i) {
-                for (int j = 0; j < img_ids.size(); ++j) {
-                    img_ids_repeated[i * img_ids.size() + j] = img_ids[j];
-                }
-            }
-
-            std::vector<std::vector<float>> txt_ids(bs * context_len, std::vector<float>(3, 0.0));
-            std::vector<std::vector<float>> ids(bs * (context_len + img_ids.size()), std::vector<float>(3));
-            for (int i = 0; i < bs; ++i) {
-                for (int j = 0; j < context_len; ++j) {
-                    ids[i * (context_len + img_ids.size()) + j] = txt_ids[j];
-                }
-                for (int j = 0; j < img_ids.size(); ++j) {
-                    ids[i * (context_len + img_ids.size()) + context_len + j] = img_ids_repeated[i * img_ids.size() + j];
-                }
-            }
-
-            return ids;
-        }
-
-        // Generate positional embeddings
-        std::vector<float> gen_pe(int h, int w, int patch_size, int bs, int context_len, int theta, const std::vector<int>& axes_dim) {
-            std::vector<std::vector<float>> ids       = gen_ids(h, w, patch_size, bs, context_len);
-            std::vector<std::vector<float>> trans_ids = transpose(ids);
-            size_t pos_len                            = ids.size();
-            int num_axes                              = axes_dim.size();
-            for (int i = 0; i < pos_len; i++) {
-                // std::cout << trans_ids[0][i] << " " << trans_ids[1][i] << " " << trans_ids[2][i] << std::endl;
-            }
-
-            int emb_dim = 0;
-            for (int d : axes_dim)
-                emb_dim += d / 2;
-
-            std::vector<std::vector<float>> emb(bs * pos_len, std::vector<float>(emb_dim * 2 * 2, 0.0));
-            int offset = 0;
-            for (int i = 0; i < num_axes; ++i) {
-                std::vector<std::vector<float>> rope_emb = rope(trans_ids[i], axes_dim[i], theta);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
-                for (int b = 0; b < bs; ++b) {
-                    for (int j = 0; j < pos_len; ++j) {
-                        for (int k = 0; k < rope_emb[0].size(); ++k) {
-                            emb[b * pos_len + j][offset + k] = rope_emb[j][k];
-                        }
-                    }
-                }
-                offset += rope_emb[0].size();
-            }
-
-            return flatten(emb);
-        }
-
     public:
         FluxParams params;
         Flux() {}
         Flux(FluxParams params)
             : params(params) {
-            int64_t out_channels = params.in_channels;
-            int64_t pe_dim       = params.hidden_size / params.num_heads;
-
-            blocks["img_in"]    = std::shared_ptr<GGMLBlock>(new Linear(params.in_channels, params.hidden_size, true));
-            blocks["time_in"]   = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
-            blocks["vector_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(params.vec_in_dim, params.hidden_size));
-            if (params.guidance_embed) {
-                blocks["guidance_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
+            blocks["img_in"] = std::shared_ptr<GGMLBlock>(new Linear(params.in_channels, params.hidden_size, true));
+            if (params.is_chroma) {
+                blocks["distilled_guidance_layer"] = std::shared_ptr<GGMLBlock>(new ChromaApproximator(params.in_channels, params.hidden_size));
+            } else {
+                blocks["time_in"]   = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
+                blocks["vector_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(params.vec_in_dim, params.hidden_size));
+                if (params.guidance_embed) {
+                    blocks["guidance_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
+                }
             }
             blocks["txt_in"] = std::shared_ptr<GGMLBlock>(new Linear(params.context_in_dim, params.hidden_size, true));
 
@@ -656,19 +639,23 @@ namespace Flux {
                 blocks["double_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new DoubleStreamBlock(params.hidden_size,
                                                                                                                 params.num_heads,
                                                                                                                 params.mlp_ratio,
+                                                                                                                i,
                                                                                                                 params.qkv_bias,
-                                                                                                                params.flash_attn));
+                                                                                                                params.flash_attn,
+                                                                                                                params.is_chroma));
             }
 
             for (int i = 0; i < params.depth_single_blocks; i++) {
                 blocks["single_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new SingleStreamBlock(params.hidden_size,
                                                                                                                 params.num_heads,
                                                                                                                 params.mlp_ratio,
+                                                                                                                i,
                                                                                                                 0.f,
-                                                                                                                params.flash_attn));
+                                                                                                                params.flash_attn,
+                                                                                                                params.is_chroma));
             }
 
-            blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new LastLayer(params.hidden_size, 1, out_channels));
+            blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new LastLayer(params.hidden_size, 1, params.out_channels, params.is_chroma));
         }
 
         struct ggml_tensor* patchify(struct ggml_context* ctx,
@@ -719,31 +706,62 @@ namespace Flux {
         }
 
         struct ggml_tensor* forward_orig(struct ggml_context* ctx,
+                                         ggml_backend_t backend,
                                          struct ggml_tensor* img,
                                          struct ggml_tensor* txt,
                                          struct ggml_tensor* timesteps,
                                          struct ggml_tensor* y,
                                          struct ggml_tensor* guidance,
                                          struct ggml_tensor* pe,
-                                         std::vector<int> skip_layers = std::vector<int>()) {
+                                         struct ggml_tensor* mod_index_arange = NULL,
+                                         std::vector<int> skip_layers         = {}) {
             auto img_in      = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
-            auto time_in     = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]);
-            auto vector_in   = std::dynamic_pointer_cast<MLPEmbedder>(blocks["vector_in"]);
             auto txt_in      = std::dynamic_pointer_cast<Linear>(blocks["txt_in"]);
             auto final_layer = std::dynamic_pointer_cast<LastLayer>(blocks["final_layer"]);
 
-            img      = img_in->forward(ctx, img);
-            auto vec = time_in->forward(ctx, ggml_nn_timestep_embedding(ctx, timesteps, 256, 10000, 1000.f));
+            img = img_in->forward(ctx, img);
+            struct ggml_tensor* vec;
+            struct ggml_tensor* txt_img_mask = NULL;
+            if (params.is_chroma) {
+                int64_t mod_index_length = 344;
+                auto approx              = std::dynamic_pointer_cast<ChromaApproximator>(blocks["distilled_guidance_layer"]);
+                auto distill_timestep    = ggml_nn_timestep_embedding(ctx, timesteps, 16, 10000, 1000.f);
+                auto distill_guidance    = ggml_nn_timestep_embedding(ctx, guidance, 16, 10000, 1000.f);
 
-            if (params.guidance_embed) {
-                GGML_ASSERT(guidance != NULL);
-                auto guidance_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["guidance_in"]);
-                // bf16 and fp16 result is different
-                auto g_in = ggml_nn_timestep_embedding(ctx, guidance, 256, 10000, 1000.f);
-                vec       = ggml_add(ctx, vec, guidance_in->forward(ctx, g_in));
+                // auto mod_index_arange  = ggml_arange(ctx, 0, (float)mod_index_length, 1);
+                // ggml_arange tot working on a lot of backends, precomputing it on CPU instead
+                GGML_ASSERT(arange != NULL);
+                auto modulation_index = ggml_nn_timestep_embedding(ctx, mod_index_arange, 32, 10000, 1000.f);  // [1, 344, 32]
+
+                // Batch broadcast (will it ever be useful)
+                modulation_index = ggml_repeat(ctx, modulation_index, ggml_new_tensor_3d(ctx, GGML_TYPE_F32, modulation_index->ne[0], modulation_index->ne[1], img->ne[2]));  // [N, 344, 32]
+
+                auto timestep_guidance = ggml_concat(ctx, distill_timestep, distill_guidance, 0);  // [N, 1, 32]
+                timestep_guidance      = ggml_repeat(ctx, timestep_guidance, modulation_index);    // [N, 344, 32]
+
+                vec = ggml_concat(ctx, timestep_guidance, modulation_index, 0);  // [N, 344, 64]
+                // Permute for consistency with non-distilled modulation implementation
+                vec = ggml_cont(ctx, ggml_permute(ctx, vec, 0, 2, 1, 3));  // [344, N, 64]
+                vec = approx->forward(ctx, vec);                           // [344, N, hidden_size]
+
+                if (y != NULL) {
+                    txt_img_mask = ggml_pad(ctx, y, img->ne[1], 0, 0, 0);
+                }
+            } else {
+                auto time_in   = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]);
+                auto vector_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["vector_in"]);
+                vec            = time_in->forward(ctx, ggml_nn_timestep_embedding(ctx, timesteps, 256, 10000, 1000.f));
+                if (params.guidance_embed) {
+                    GGML_ASSERT(guidance != NULL);
+                    auto guidance_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["guidance_in"]);
+                    // bf16 and fp16 result is different
+                    auto g_in = ggml_nn_timestep_embedding(ctx, guidance, 256, 10000, 1000.f);
+                    vec       = ggml_add(ctx, vec, guidance_in->forward(ctx, g_in));
+                }
+
+                vec = ggml_add(ctx, vec, vector_in->forward(ctx, y));
             }
 
-            vec = ggml_add(ctx, vec, vector_in->forward(ctx, y));
             txt = txt_in->forward(ctx, txt);
 
             for (int i = 0; i < params.depth; i++) {
@@ -753,7 +771,7 @@ namespace Flux {
 
                 auto block = std::dynamic_pointer_cast<DoubleStreamBlock>(blocks["double_blocks." + std::to_string(i)]);
 
-                auto img_txt = block->forward(ctx, img, txt, vec, pe);
+                auto img_txt = block->forward(ctx, backend, img, txt, vec, pe, txt_img_mask);
                 img          = img_txt.first;   // [N, n_img_token, hidden_size]
                 txt          = img_txt.second;  // [N, n_txt_token, hidden_size]
             }
@@ -765,7 +783,7 @@ namespace Flux {
                 }
                 auto block = std::dynamic_pointer_cast<SingleStreamBlock>(blocks["single_blocks." + std::to_string(i)]);
 
-                txt_img = block->forward(ctx, txt_img, vec, pe);
+                txt_img = block->forward(ctx, backend, txt_img, vec, pe, txt_img_mask);
             }
 
             txt_img = ggml_cont(ctx, ggml_permute(ctx, txt_img, 0, 2, 1, 3));  // [n_txt_token + n_img_token, N, hidden_size]
@@ -780,29 +798,11 @@ namespace Flux {
             img     = ggml_cont(ctx, ggml_permute(ctx, img, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
 
             img = final_layer->forward(ctx, img, vec);  // (N, T, patch_size ** 2 * out_channels)
-
             return img;
         }
 
-        struct ggml_tensor* forward(struct ggml_context* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* timestep,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* y,
-                                    struct ggml_tensor* guidance,
-                                    struct ggml_tensor* pe,
-                                    std::vector<int> skip_layers = std::vector<int>()) {
-            // Forward pass of DiT.
-            // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-            // timestep: (N,) tensor of diffusion timesteps
-            // context: (N, L, D)
-            // y: (N, adm_in_channels) tensor of class labels
-            // guidance: (N,)
-            // pe: (L, d_head/2, 2, 2)
-            // return: (N, C, H, W)
-
-            GGML_ASSERT(x->ne[3] == 1);
-
+        struct ggml_tensor* process_img(struct ggml_context* ctx,
+                                        struct ggml_tensor* x) {
             int64_t W          = x->ne[0];
             int64_t H          = x->ne[1];
             int64_t patch_size = 2;
@@ -812,8 +812,66 @@ namespace Flux {
 
             // img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
             auto img = patchify(ctx, x, patch_size);  // [N, h*w, C * patch_size * patch_size]
+            return img;
+        }
 
-            auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, skip_layers);  // [N, h*w, C * patch_size * patch_size]
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    ggml_backend_t backend,
+                                    struct ggml_tensor* x,
+                                    struct ggml_tensor* timestep,
+                                    struct ggml_tensor* context,
+                                    struct ggml_tensor* c_concat,
+                                    struct ggml_tensor* y,
+                                    struct ggml_tensor* guidance,
+                                    struct ggml_tensor* pe,
+                                    struct ggml_tensor* mod_index_arange  = NULL,
+                                    std::vector<ggml_tensor*> ref_latents = {},
+                                    std::vector<int> skip_layers          = {}) {
+            // Forward pass of DiT.
+            // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+            // timestep: (N,) tensor of diffusion timesteps
+            // context: (N, L, D)
+            // c_concat: NULL, or for (N,C+M, H, W) for Fill
+            // y: (N, adm_in_channels) tensor of class labels
+            // guidance: (N,)
+            // pe: (L, d_head/2, 2, 2)
+            // return: (N, C, H, W)
+
+            GGML_ASSERT(x->ne[3] == 1);
+
+            int64_t W          = x->ne[0];
+            int64_t H          = x->ne[1];
+            int64_t C          = x->ne[2];
+            int64_t patch_size = 2;
+            int pad_h          = (patch_size - H % patch_size) % patch_size;
+            int pad_w          = (patch_size - W % patch_size) % patch_size;
+
+            auto img            = process_img(ctx, x);
+            uint64_t img_tokens = img->ne[1];
+
+            if (c_concat != NULL) {
+                ggml_tensor* masked = ggml_view_4d(ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], 0);
+                ggml_tensor* mask   = ggml_view_4d(ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 8 * 8, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
+
+                masked = process_img(ctx, masked);
+                mask   = process_img(ctx, mask);
+
+                img = ggml_concat(ctx, img, ggml_concat(ctx, masked, mask, 0), 0);
+            }
+
+            if (ref_latents.size() > 0) {
+                for (ggml_tensor* ref : ref_latents) {
+                    ref = process_img(ctx, ref);
+                    img = ggml_concat(ctx, img, ref, 1);
+                }
+            }
+
+            auto out = forward_orig(ctx, backend, img, context, timestep, y, guidance, pe, mod_index_arange, skip_layers);  // [N, num_tokens, C * patch_size * patch_size]
+            if (out->ne[1] > img_tokens) {
+                out = ggml_cont(ctx, ggml_permute(ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
+                out = ggml_view_3d(ctx, out, out->ne[0], out->ne[1], img_tokens, out->nb[1], out->nb[2], 0);
+                out = ggml_cont(ctx, ggml_permute(ctx, out, 0, 2, 1, 3));  // [N, h*w, C * patch_size * patch_size]
+            }
 
             // rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
             out = unpatchify(ctx, out, (H + pad_h) / patch_size, (W + pad_w) / patch_size, patch_size);  // [N, C, H + pad_h, W + pad_w]
@@ -826,22 +884,65 @@ namespace Flux {
     public:
         FluxParams flux_params;
         Flux flux;
-        std::vector<float> pe_vec;  // for cache
+        std::vector<float> pe_vec;
+        std::vector<float> mod_index_arange_vec;  // for cache
+        SDVersion version;
+        bool use_mask = false;
 
         FluxRunner(ggml_backend_t backend,
-                   ggml_type wtype,
-                   SDVersion version = VERSION_FLUX_DEV,
-                   bool flash_attn   = false)
-            : GGMLRunner(backend, wtype) {
-            flux_params.flash_attn = flash_attn;
-            if (version == VERSION_FLUX_SCHNELL) {
-                flux_params.guidance_embed = false;
+                   bool offload_params_to_cpu,
+                   const String2GGMLType& tensor_types = {},
+                   const std::string prefix            = "",
+                   SDVersion version                   = VERSION_FLUX,
+                   bool flash_attn                     = false,
+                   bool use_mask                       = false)
+            : GGMLRunner(backend, offload_params_to_cpu), use_mask(use_mask) {
+            flux_params.flash_attn          = flash_attn;
+            flux_params.guidance_embed      = false;
+            flux_params.depth               = 0;
+            flux_params.depth_single_blocks = 0;
+            if (version == VERSION_FLUX_FILL) {
+                flux_params.in_channels = 384;
             }
-            if (version == VERSION_FLUX_LITE) {
-                flux_params.depth = 8;
+            for (auto pair : tensor_types) {
+                std::string tensor_name = pair.first;
+                if (!starts_with(tensor_name, prefix))
+                    continue;
+                if (tensor_name.find("guidance_in.in_layer.weight") != std::string::npos) {
+                    // not schnell
+                    flux_params.guidance_embed = true;
+                }
+                if (tensor_name.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {
+                    // Chroma
+                    flux_params.is_chroma = true;
+                }
+                size_t db = tensor_name.find("double_blocks.");
+                if (db != std::string::npos) {
+                    tensor_name     = tensor_name.substr(db);  // remove prefix
+                    int block_depth = atoi(tensor_name.substr(14, tensor_name.find(".", 14)).c_str());
+                    if (block_depth + 1 > flux_params.depth) {
+                        flux_params.depth = block_depth + 1;
+                    }
+                }
+                size_t sb = tensor_name.find("single_blocks.");
+                if (sb != std::string::npos) {
+                    tensor_name     = tensor_name.substr(sb);  // remove prefix
+                    int block_depth = atoi(tensor_name.substr(14, tensor_name.find(".", 14)).c_str());
+                    if (block_depth + 1 > flux_params.depth_single_blocks) {
+                        flux_params.depth_single_blocks = block_depth + 1;
+                    }
+                }
             }
+
+            LOG_INFO("Flux blocks: %d double, %d single", flux_params.depth, flux_params.depth_single_blocks);
+            if (flux_params.is_chroma) {
+                LOG_INFO("Using pruned modulation (Chroma)");
+            } else if (!flux_params.guidance_embed) {
+                LOG_INFO("Flux guidance is disabled (Schnell mode)");
+            }
+
             flux = Flux(flux_params);
-            flux.init(params_ctx, wtype);
+            flux.init(params_ctx, tensor_types, prefix);
         }
 
         std::string get_desc() {
@@ -855,21 +956,53 @@ namespace Flux {
         struct ggml_cgraph* build_graph(struct ggml_tensor* x,
                                         struct ggml_tensor* timesteps,
                                         struct ggml_tensor* context,
+                                        struct ggml_tensor* c_concat,
                                         struct ggml_tensor* y,
                                         struct ggml_tensor* guidance,
-                                        std::vector<int> skip_layers = std::vector<int>()) {
+                                        std::vector<ggml_tensor*> ref_latents = {},
+                                        bool increase_ref_index               = false,
+                                        std::vector<int> skip_layers          = {}) {
             GGML_ASSERT(x->ne[3] == 1);
             struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, FLUX_GRAPH_SIZE, false);
 
-            x         = to_backend(x);
-            context   = to_backend(context);
-            y         = to_backend(y);
+            struct ggml_tensor* mod_index_arange = NULL;
+
+            x       = to_backend(x);
+            context = to_backend(context);
+            if (c_concat != NULL) {
+                c_concat = to_backend(c_concat);
+            }
+            if (flux_params.is_chroma) {
+                guidance = ggml_set_f32(guidance, 0);
+
+                if (!use_mask) {
+                    y = NULL;
+                }
+
+                // ggml_arange is not working on some backends, precompute it
+                mod_index_arange_vec = arange(0, 344);
+                mod_index_arange     = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, mod_index_arange_vec.size());
+                set_backend_tensor_data(mod_index_arange, mod_index_arange_vec.data());
+            }
+            y = to_backend(y);
+
             timesteps = to_backend(timesteps);
-            if (flux_params.guidance_embed) {
+            if (flux_params.guidance_embed || flux_params.is_chroma) {
                 guidance = to_backend(guidance);
             }
+            for (int i = 0; i < ref_latents.size(); i++) {
+                ref_latents[i] = to_backend(ref_latents[i]);
+            }
 
-            pe_vec      = flux.gen_pe(x->ne[1], x->ne[0], 2, x->ne[3], context->ne[1], flux_params.theta, flux_params.axes_dim);
+            pe_vec      = Rope::gen_flux_pe(x->ne[1],
+                                            x->ne[0],
+                                            2,
+                                            x->ne[3],
+                                            context->ne[1],
+                                            ref_latents,
+                                            increase_ref_index,
+                                            flux_params.theta,
+                                            flux_params.axes_dim);
             int pos_len = pe_vec.size() / flux_params.axes_dim_sum / 2;
             // LOG_DEBUG("pos_len %d", pos_len);
             auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, flux_params.axes_dim_sum / 2, pos_len);
@@ -879,12 +1012,16 @@ namespace Flux {
             set_backend_tensor_data(pe, pe_vec.data());
 
             struct ggml_tensor* out = flux.forward(compute_ctx,
+                                                   runtime_backend,
                                                    x,
                                                    timesteps,
                                                    context,
+                                                   c_concat,
                                                    y,
                                                    guidance,
                                                    pe,
+                                                   mod_index_arange,
+                                                   ref_latents,
                                                    skip_layers);
 
             ggml_build_forward_expand(gf, out);
@@ -896,18 +1033,21 @@ namespace Flux {
                      struct ggml_tensor* x,
                      struct ggml_tensor* timesteps,
                      struct ggml_tensor* context,
+                     struct ggml_tensor* c_concat,
                      struct ggml_tensor* y,
                      struct ggml_tensor* guidance,
-                     struct ggml_tensor** output     = NULL,
-                     struct ggml_context* output_ctx = NULL,
-                     std::vector<int> skip_layers    = std::vector<int>()) {
+                     std::vector<ggml_tensor*> ref_latents = {},
+                     bool increase_ref_index               = false,
+                     struct ggml_tensor** output           = NULL,
+                     struct ggml_context* output_ctx       = NULL,
+                     std::vector<int> skip_layers          = std::vector<int>()) {
             // x: [N, in_channels, h, w]
             // timesteps: [N, ]
             // context: [N, max_position, hidden_size]
             // y: [N, adm_in_channels] or [1, adm_in_channels]
             // guidance: [N, ]
             auto get_graph = [&]() -> struct ggml_cgraph* {
-                return build_graph(x, timesteps, context, y, guidance, skip_layers);
+                return build_graph(x, timesteps, context, c_concat, y, guidance, ref_latents, increase_ref_index, skip_layers);
             };
 
             GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
@@ -947,7 +1087,7 @@ namespace Flux {
                 struct ggml_tensor* out = NULL;
 
                 int t0 = ggml_time_ms();
-                compute(8, x, timesteps, context, y, guidance, &out, work_ctx);
+                compute(8, x, timesteps, context, NULL, y, guidance, {}, false, &out, work_ctx);
                 int t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
@@ -959,7 +1099,7 @@ namespace Flux {
             // ggml_backend_t backend    = ggml_backend_cuda_init(0);
             ggml_backend_t backend           = ggml_backend_cpu_init();
             ggml_type model_data_type        = GGML_TYPE_Q8_0;
-            std::shared_ptr<FluxRunner> flux = std::shared_ptr<FluxRunner>(new FluxRunner(backend, model_data_type));
+            std::shared_ptr<FluxRunner> flux = std::shared_ptr<FluxRunner>(new FluxRunner(backend, false));
             {
                 LOG_INFO("loading from '%s'", file_path.c_str());
 
@@ -973,7 +1113,7 @@ namespace Flux {
                     return;
                 }
 
-                bool success = model_loader.load_tensors(tensors, backend);
+                bool success = model_loader.load_tensors(tensors);
 
                 if (!success) {
                     LOG_ERROR("load tensors from model loader failed");

format-code.sh

@@ -1,2 +1,5 @@
-clang-format -style=file -i *.cpp *.h *.hpp
-clang-format -style=file -i examples/cli/*.cpp
+for f in *.cpp *.h *.hpp examples/cli/*.cpp examples/cli/*.h; do
+  [[ "$f" == vocab* ]] && continue
+  echo "formatting '$f'"
+  clang-format -style=file -i "$f"
+done

gguf_reader.hpp

@@ -0,0 +1,231 @@
+#ifndef __GGUF_READER_HPP__
+#define __GGUF_READER_HPP__
+
+#include <cstdint>
+#include <fstream>
+#include <string>
+#include <vector>
+
+#include "ggml.h"
+#include "util.h"
+
+struct GGUFTensorInfo {
+    std::string name;
+    ggml_type type;
+    std::vector<int64_t> shape;
+    size_t offset;
+};
+
+enum class GGUFMetadataType : uint32_t {
+    UINT8   = 0,
+    INT8    = 1,
+    UINT16  = 2,
+    INT16   = 3,
+    UINT32  = 4,
+    INT32   = 5,
+    FLOAT32 = 6,
+    BOOL    = 7,
+    STRING  = 8,
+    ARRAY   = 9,
+    UINT64  = 10,
+    INT64   = 11,
+    FLOAT64 = 12,
+};
+
+class GGUFReader {
+private:
+    std::vector<GGUFTensorInfo> tensors_;
+    size_t data_offset_;
+    size_t alignment_ = 32;  // default alignment is 32
+
+    template <typename T>
+    bool safe_read(std::ifstream& fin, T& value) {
+        fin.read(reinterpret_cast<char*>(&value), sizeof(T));
+        return fin.good();
+    }
+
+    bool safe_read(std::ifstream& fin, char* buffer, size_t size) {
+        fin.read(buffer, size);
+        return fin.good();
+    }
+
+    bool safe_seek(std::ifstream& fin, std::streamoff offset, std::ios::seekdir dir) {
+        fin.seekg(offset, dir);
+        return fin.good();
+    }
+
+    bool read_metadata(std::ifstream& fin) {
+        uint64_t key_len = 0;
+        if (!safe_read(fin, key_len))
+            return false;
+
+        std::string key(key_len, '\0');
+        if (!safe_read(fin, (char*)key.data(), key_len))
+            return false;
+
+        uint32_t type = 0;
+        if (!safe_read(fin, type))
+            return false;
+
+        if (key == "general.alignment") {
+            uint32_t align_val = 0;
+            if (!safe_read(fin, align_val))
+                return false;
+
+            if (align_val != 0 && (align_val & (align_val - 1)) == 0) {
+                alignment_ = align_val;
+                LOG_DEBUG("Found alignment: %zu", alignment_);
+            } else {
+                LOG_ERROR("Invalid alignment value %u, fallback to default %zu", align_val, alignment_);
+            }
+            return true;
+        }
+
+        switch (static_cast<GGUFMetadataType>(type)) {
+            case GGUFMetadataType::UINT8:
+            case GGUFMetadataType::INT8:
+            case GGUFMetadataType::BOOL:
+                return safe_seek(fin, 1, std::ios::cur);
+
+            case GGUFMetadataType::UINT16:
+            case GGUFMetadataType::INT16:
+                return safe_seek(fin, 2, std::ios::cur);
+
+            case GGUFMetadataType::UINT32:
+            case GGUFMetadataType::INT32:
+            case GGUFMetadataType::FLOAT32:
+                return safe_seek(fin, 4, std::ios::cur);
+
+            case GGUFMetadataType::UINT64:
+            case GGUFMetadataType::INT64:
+            case GGUFMetadataType::FLOAT64:
+                return safe_seek(fin, 8, std::ios::cur);
+
+            case GGUFMetadataType::STRING: {
+                uint64_t len = 0;
+                if (!safe_read(fin, len))
+                    return false;
+                return safe_seek(fin, len, std::ios::cur);
+            }
+
+            case GGUFMetadataType::ARRAY: {
+                uint32_t elem_type = 0;
+                uint64_t len       = 0;
+                if (!safe_read(fin, elem_type))
+                    return false;
+                if (!safe_read(fin, len))
+                    return false;
+
+                for (uint64_t i = 0; i < len; i++) {
+                    if (!read_metadata(fin))
+                        return false;
+                }
+                return true;
+            }
+
+            default:
+                LOG_ERROR("Unknown metadata type=%u", type);
+                return false;
+        }
+    }
+
+    GGUFTensorInfo read_tensor_info(std::ifstream& fin) {
+        GGUFTensorInfo info;
+
+        uint64_t name_len;
+        if (!safe_read(fin, name_len))
+            throw std::runtime_error("read tensor name length failed");
+
+        info.name.resize(name_len);
+        if (!safe_read(fin, (char*)info.name.data(), name_len))
+            throw std::runtime_error("read tensor name failed");
+
+        uint32_t n_dims;
+        if (!safe_read(fin, n_dims))
+            throw std::runtime_error("read tensor dims failed");
+
+        info.shape.resize(n_dims);
+        for (uint32_t i = 0; i < n_dims; i++) {
+            if (!safe_read(fin, info.shape[i]))
+                throw std::runtime_error("read tensor shape failed");
+        }
+
+        if (n_dims > GGML_MAX_DIMS) {
+            for (int i = GGML_MAX_DIMS; i < n_dims; i++) {
+                info.shape[GGML_MAX_DIMS - 1] *= info.shape[i];  // stack to last dim;
+            }
+            info.shape.resize(GGML_MAX_DIMS);
+            n_dims = GGML_MAX_DIMS;
+        }
+
+        uint32_t type;
+        if (!safe_read(fin, type))
+            throw std::runtime_error("read tensor type failed");
+        info.type = static_cast<ggml_type>(type);
+
+        if (!safe_read(fin, info.offset))
+            throw std::runtime_error("read tensor offset failed");
+
+        return info;
+    }
+
+public:
+    bool load(const std::string& file_path) {
+        std::ifstream fin(file_path, std::ios::binary);
+        if (!fin) {
+            LOG_ERROR("failed to open '%s'", file_path.c_str());
+            return false;
+        }
+
+        // --- Header ---
+        char magic[4];
+        if (!safe_read(fin, magic, 4) || strncmp(magic, "GGUF", 4) != 0) {
+            LOG_ERROR("not a valid GGUF file");
+            return false;
+        }
+
+        uint32_t version;
+        if (!safe_read(fin, version))
+            return false;
+
+        uint64_t tensor_count, metadata_kv_count;
+        if (!safe_read(fin, tensor_count))
+            return false;
+        if (!safe_read(fin, metadata_kv_count))
+            return false;
+
+        LOG_DEBUG("GGUF v%u, tensor_count=%llu, metadata_kv_count=%llu",
+                  version, (unsigned long long)tensor_count, (unsigned long long)metadata_kv_count);
+
+        // --- Read Metadata ---
+        for (uint64_t i = 0; i < metadata_kv_count; i++) {
+            if (!read_metadata(fin)) {
+                LOG_ERROR("read meta data failed");
+                return false;
+            }
+        }
+
+        // --- Tensor Infos ---
+        tensors_.clear();
+        try {
+            for (uint64_t i = 0; i < tensor_count; i++) {
+                tensors_.push_back(read_tensor_info(fin));
+            }
+        } catch (const std::runtime_error& e) {
+            LOG_ERROR("%s", e.what());
+            return false;
+        }
+
+        data_offset_ = static_cast<size_t>(fin.tellg());
+        if ((data_offset_ % alignment_) != 0) {
+            data_offset_ = ((data_offset_ + alignment_ - 1) / alignment_) * alignment_;
+        }
+        fin.close();
+        return true;
+    }
+
+    const std::vector<GGUFTensorInfo>& tensors() const { return tensors_; }
+    size_t data_offset() const { return data_offset_; }
+};
+
+#endif  // __GGUF_READER_HPP__

gits_noise.inl

@@ -329,21 +329,21 @@ const std::vector<std::vector<float>> GITS_NOISE_1_50 = {
 };
 
 const std::vector<const std::vector<std::vector<float>>*> GITS_NOISE = {
-    { &GITS_NOISE_0_80 },
-    { &GITS_NOISE_0_85 },
-    { &GITS_NOISE_0_90 },
-    { &GITS_NOISE_0_95 },
-    { &GITS_NOISE_1_00 },
-    { &GITS_NOISE_1_05 },
-    { &GITS_NOISE_1_10 },
-    { &GITS_NOISE_1_15 },
-    { &GITS_NOISE_1_20 },
-    { &GITS_NOISE_1_25 },
-    { &GITS_NOISE_1_30 },
-    { &GITS_NOISE_1_35 },
-    { &GITS_NOISE_1_40 },
-    { &GITS_NOISE_1_45 },
-    { &GITS_NOISE_1_50 }
+    &GITS_NOISE_0_80,
+    &GITS_NOISE_0_85,
+    &GITS_NOISE_0_90,
+    &GITS_NOISE_0_95,
+    &GITS_NOISE_1_00,
+    &GITS_NOISE_1_05,
+    &GITS_NOISE_1_10,
+    &GITS_NOISE_1_15,
+    &GITS_NOISE_1_20,
+    &GITS_NOISE_1_25,
+    &GITS_NOISE_1_30,
+    &GITS_NOISE_1_35,
+    &GITS_NOISE_1_40,
+    &GITS_NOISE_1_45,
+    &GITS_NOISE_1_50
 };
 
 #endif // GITS_NOISE_INL

lora.hpp

@@ -3,23 +3,109 @@
 
 #include "ggml_extend.hpp"
 
-#define LORA_GRAPH_SIZE 10240
+#define LORA_GRAPH_BASE_SIZE 10240
 
 struct LoraModel : public GGMLRunner {
+    enum lora_t {
+        REGULAR      = 0,
+        DIFFUSERS    = 1,
+        DIFFUSERS_2  = 2,
+        DIFFUSERS_3  = 3,
+        TRANSFORMERS = 4,
+        LORA_TYPE_COUNT
+    };
+
+    const std::string lora_ups[LORA_TYPE_COUNT] = {
+        ".lora_up",
+        "_lora.up",
+        ".lora_B",
+        ".lora.up",
+        ".lora_linear_layer.up",
+    };
+
+    const std::string lora_downs[LORA_TYPE_COUNT] = {
+        ".lora_down",
+        "_lora.down",
+        ".lora_A",
+        ".lora.down",
+        ".lora_linear_layer.down",
+    };
+
+    const std::string lora_pre[LORA_TYPE_COUNT] = {
+        "lora.",
+        "",
+        "",
+        "",
+        "",
+    };
+
+    const std::map<std::string, std::string> alt_names = {
+        // mmdit
+        {"final_layer.adaLN_modulation.1", "norm_out.linear"},
+        {"pos_embed", "pos_embed.proj"},
+        {"final_layer.linear", "proj_out"},
+        {"y_embedder.mlp.0", "time_text_embed.text_embedder.linear_1"},
+        {"y_embedder.mlp.2", "time_text_embed.text_embedder.linear_2"},
+        {"t_embedder.mlp.0", "time_text_embed.timestep_embedder.linear_1"},
+        {"t_embedder.mlp.2", "time_text_embed.timestep_embedder.linear_2"},
+        {"x_block.mlp.fc1", "ff.net.0.proj"},
+        {"x_block.mlp.fc2", "ff.net.2"},
+        {"context_block.mlp.fc1", "ff_context.net.0.proj"},
+        {"context_block.mlp.fc2", "ff_context.net.2"},
+        {"x_block.adaLN_modulation.1", "norm1.linear"},
+        {"context_block.adaLN_modulation.1", "norm1_context.linear"},
+        {"context_block.attn.proj", "attn.to_add_out"},
+        {"x_block.attn.proj", "attn.to_out.0"},
+        {"x_block.attn2.proj", "attn2.to_out.0"},
+        // flux
+        {"img_in", "x_embedder"},
+        // singlestream
+        {"linear2", "proj_out"},
+        {"modulation.lin", "norm.linear"},
+        // doublestream
+        {"txt_attn.proj", "attn.to_add_out"},
+        {"img_attn.proj", "attn.to_out.0"},
+        {"txt_mlp.0", "ff_context.net.0.proj"},
+        {"txt_mlp.2", "ff_context.net.2"},
+        {"img_mlp.0", "ff.net.0.proj"},
+        {"img_mlp.2", "ff.net.2"},
+        {"txt_mod.lin", "norm1_context.linear"},
+        {"img_mod.lin", "norm1.linear"},
+    };
+
+    const std::map<std::string, std::string> qkv_prefixes = {
+        // mmdit
+        {"context_block.attn.qkv", "attn.add_"},  // suffix "_proj"
+        {"x_block.attn.qkv", "attn.to_"},
+        {"x_block.attn2.qkv", "attn2.to_"},
+        // flux
+        // doublestream
+        {"txt_attn.qkv", "attn.add_"},  // suffix "_proj"
+        {"img_attn.qkv", "attn.to_"},
+    };
+    const std::map<std::string, std::string> qkvm_prefixes = {
+        // flux
+        // singlestream
+        {"linear1", ""},
+    };
+
+    const std::string* type_fingerprints = lora_ups;
+
     float multiplier = 1.0f;
     std::map<std::string, struct ggml_tensor*> lora_tensors;
+    std::map<ggml_tensor*, ggml_tensor*> original_tensor_to_final_tensor;
     std::string file_path;
     ModelLoader model_loader;
     bool load_failed                = false;
     bool applied                    = false;
     std::vector<int> zero_index_vec = {0};
     ggml_tensor* zero_index         = NULL;
+    enum lora_t type                = REGULAR;
 
     LoraModel(ggml_backend_t backend,
-              ggml_type wtype,
               const std::string& file_path = "",
-              const std::string& prefix    = "")
-        : file_path(file_path), GGMLRunner(backend, wtype) {
+              const std::string prefix     = "")
+        : file_path(file_path), GGMLRunner(backend, false) {
         if (!model_loader.init_from_file(file_path, prefix)) {
             load_failed = true;
         }
@@ -45,6 +131,13 @@ struct LoraModel : public GGMLRunner {
                 // LOG_INFO("skipping LoRA tesnor '%s'", name.c_str());
                 return true;
             }
+            // LOG_INFO("lora_tensor %s", name.c_str());
+            for (int i = 0; i < LORA_TYPE_COUNT; i++) {
+                if (name.find(type_fingerprints[i]) != std::string::npos) {
+                    type = (lora_t)i;
+                    break;
+                }
+            }
 
             if (dry_run) {
                 struct ggml_tensor* real = ggml_new_tensor(params_ctx,
@@ -60,11 +153,13 @@ struct LoraModel : public GGMLRunner {
             return true;
         };
 
-        model_loader.load_tensors(on_new_tensor_cb, backend);
+        model_loader.load_tensors(on_new_tensor_cb);
         alloc_params_buffer();
-
+        // exit(0);
         dry_run = false;
-        model_loader.load_tensors(on_new_tensor_cb, backend);
+        model_loader.load_tensors(on_new_tensor_cb);
+
+        LOG_DEBUG("lora type: \"%s\"/\"%s\"", lora_downs[type].c_str(), lora_ups[type].c_str());
 
         LOG_DEBUG("finished loaded lora");
         return true;
@@ -74,106 +169,684 @@ struct LoraModel : public GGMLRunner {
         auto out = ggml_reshape_1d(ctx, a, ggml_nelements(a));
         out      = ggml_get_rows(ctx, out, zero_index);
         out      = ggml_reshape(ctx, out, a);
+        // auto out = ggml_cast(ctx, a, GGML_TYPE_F32);
         return out;
     }
 
-    struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors) {
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, LORA_GRAPH_SIZE, false);
+    std::vector<std::string> to_lora_keys(std::string blk_name, SDVersion version) {
+        std::vector<std::string> keys;
+        // if (!sd_version_is_sd3(version) || blk_name != "model.diffusion_model.pos_embed") {
+        size_t k_pos = blk_name.find(".weight");
+        if (k_pos == std::string::npos) {
+            return keys;
+        }
+        blk_name = blk_name.substr(0, k_pos);
+        // }
+        keys.push_back(blk_name);
+        keys.push_back("lora." + blk_name);
+        if (sd_version_is_dit(version)) {
+            if (blk_name.find("model.diffusion_model") != std::string::npos) {
+                blk_name.replace(blk_name.find("model.diffusion_model"), sizeof("model.diffusion_model") - 1, "transformer");
+            }
+
+            if (blk_name.find(".single_blocks") != std::string::npos) {
+                blk_name.replace(blk_name.find(".single_blocks"), sizeof(".single_blocks") - 1, ".single_transformer_blocks");
+            }
+            if (blk_name.find(".double_blocks") != std::string::npos) {
+                blk_name.replace(blk_name.find(".double_blocks"), sizeof(".double_blocks") - 1, ".transformer_blocks");
+            }
+
+            if (blk_name.find(".joint_blocks") != std::string::npos) {
+                blk_name.replace(blk_name.find(".joint_blocks"), sizeof(".joint_blocks") - 1, ".transformer_blocks");
+            }
+
+            if (blk_name.find("text_encoders.clip_l") != std::string::npos) {
+                blk_name.replace(blk_name.find("text_encoders.clip_l"), sizeof("text_encoders.clip_l") - 1, "cond_stage_model");
+            }
+
+            for (const auto& item : alt_names) {
+                size_t match = blk_name.find(item.first);
+                if (match != std::string::npos) {
+                    blk_name = blk_name.substr(0, match) + item.second;
+                }
+            }
+            for (const auto& prefix : qkv_prefixes) {
+                size_t match = blk_name.find(prefix.first);
+                if (match != std::string::npos) {
+                    std::string split_blk = "SPLIT|" + blk_name.substr(0, match) + prefix.second;
+                    keys.push_back(split_blk);
+                }
+            }
+            for (const auto& prefix : qkvm_prefixes) {
+                size_t match = blk_name.find(prefix.first);
+                if (match != std::string::npos) {
+                    std::string split_blk = "SPLIT_L|" + blk_name.substr(0, match) + prefix.second;
+                    keys.push_back(split_blk);
+                }
+            }
+            keys.push_back(blk_name);
+        }
+
+        std::vector<std::string> ret;
+        for (std::string& key : keys) {
+            ret.push_back(key);
+            replace_all_chars(key, '.', '_');
+            // fix for some sdxl lora, like lcm-lora-xl
+            if (key == "model_diffusion_model_output_blocks_2_2_conv") {
+                ret.push_back("model_diffusion_model_output_blocks_2_1_conv");
+            }
+            ret.push_back(key);
+        }
+        return ret;
+    }
+
+    struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version) {
+        size_t lora_graph_size = LORA_GRAPH_BASE_SIZE + lora_tensors.size() * 10;
+        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);
 
         zero_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, 1);
         set_backend_tensor_data(zero_index, zero_index_vec.data());
         ggml_build_forward_expand(gf, zero_index);
 
+        original_tensor_to_final_tensor.clear();
+
         std::set<std::string> applied_lora_tensors;
         for (auto it : model_tensors) {
-            std::string k_tensor       = it.first;
-            struct ggml_tensor* weight = model_tensors[it.first];
+            std::string model_tensor_name    = it.first;
+            struct ggml_tensor* model_tensor = model_tensors[it.first];
 
-            size_t k_pos = k_tensor.find(".weight");
-            if (k_pos == std::string::npos) {
-                continue;
-            }
-            k_tensor = k_tensor.substr(0, k_pos);
-            replace_all_chars(k_tensor, '.', '_');
-            // LOG_DEBUG("k_tensor %s", k_tensor.c_str());
-            std::string lora_up_name = "lora." + k_tensor + ".lora_up.weight";
-            if (lora_tensors.find(lora_up_name) == lora_tensors.end()) {
-                if (k_tensor == "model_diffusion_model_output_blocks_2_2_conv") {
-                    // fix for some sdxl lora, like lcm-lora-xl
-                    k_tensor     = "model_diffusion_model_output_blocks_2_1_conv";
-                    lora_up_name = "lora." + k_tensor + ".lora_up.weight";
+            std::vector<std::string> keys = to_lora_keys(model_tensor_name, version);
+            bool is_bias                  = ends_with(model_tensor_name, ".bias");
+            if (keys.size() == 0) {
+                if (is_bias) {
+                    keys.push_back(model_tensor_name.substr(0, model_tensor_name.size() - 5));  // remove .bias
+                } else {
+                    continue;
                 }
             }
 
-            std::string lora_down_name = "lora." + k_tensor + ".lora_down.weight";
-            std::string alpha_name     = "lora." + k_tensor + ".alpha";
-            std::string scale_name     = "lora." + k_tensor + ".scale";
+            for (auto& key : keys) {
+                bool is_qkv_split = starts_with(key, "SPLIT|");
+                if (is_qkv_split) {
+                    key = key.substr(sizeof("SPLIT|") - 1);
+                }
+                bool is_qkvm_split = starts_with(key, "SPLIT_L|");
+                if (is_qkvm_split) {
+                    key = key.substr(sizeof("SPLIT_L|") - 1);
+                }
+                struct ggml_tensor* updown = NULL;
+                float scale_value          = 1.0f;
+                std::string full_key       = lora_pre[type] + key;
+                if (is_bias) {
+                    if (lora_tensors.find(full_key + ".diff_b") != lora_tensors.end()) {
+                        std::string diff_name = full_key + ".diff_b";
+                        ggml_tensor* diff     = lora_tensors[diff_name];
+                        updown                = to_f32(compute_ctx, diff);
+                        applied_lora_tensors.insert(diff_name);
+                    } else {
+                        continue;
+                    }
+                } else if (lora_tensors.find(full_key + ".diff") != lora_tensors.end()) {
+                    std::string diff_name = full_key + ".diff";
+                    ggml_tensor* diff     = lora_tensors[diff_name];
+                    updown                = to_f32(compute_ctx, diff);
+                    applied_lora_tensors.insert(diff_name);
+                } else if (lora_tensors.find(full_key + ".hada_w1_a") != lora_tensors.end()) {
+                    // LoHa mode
 
-            ggml_tensor* lora_up   = NULL;
-            ggml_tensor* lora_down = NULL;
+                    // TODO: split qkv convention for LoHas (is it ever used?)
+                    if (is_qkv_split || is_qkvm_split) {
+                        LOG_ERROR("Split qkv isn't supported for LoHa models.");
+                        break;
+                    }
+                    std::string alpha_name = "";
 
-            if (lora_tensors.find(lora_up_name) != lora_tensors.end()) {
-                lora_up = lora_tensors[lora_up_name];
+                    ggml_tensor* hada_1_mid  = NULL;  // tau for tucker decomposition
+                    ggml_tensor* hada_1_up   = NULL;
+                    ggml_tensor* hada_1_down = NULL;
+
+                    ggml_tensor* hada_2_mid  = NULL;  // tau for tucker decomposition
+                    ggml_tensor* hada_2_up   = NULL;
+                    ggml_tensor* hada_2_down = NULL;
+
+                    std::string hada_1_mid_name  = "";
+                    std::string hada_1_down_name = "";
+                    std::string hada_1_up_name   = "";
+
+                    std::string hada_2_mid_name  = "";
+                    std::string hada_2_down_name = "";
+                    std::string hada_2_up_name   = "";
+
+                    hada_1_down_name = full_key + ".hada_w1_b";
+                    hada_1_up_name   = full_key + ".hada_w1_a";
+                    hada_1_mid_name  = full_key + ".hada_t1";
+                    if (lora_tensors.find(hada_1_down_name) != lora_tensors.end()) {
+                        hada_1_down = to_f32(compute_ctx, lora_tensors[hada_1_down_name]);
+                    }
+                    if (lora_tensors.find(hada_1_up_name) != lora_tensors.end()) {
+                        hada_1_up = to_f32(compute_ctx, lora_tensors[hada_1_up_name]);
+                    }
+                    if (lora_tensors.find(hada_1_mid_name) != lora_tensors.end()) {
+                        hada_1_mid = to_f32(compute_ctx, lora_tensors[hada_1_mid_name]);
+                        applied_lora_tensors.insert(hada_1_mid_name);
+                        hada_1_up = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, hada_1_up));
+                    }
+
+                    hada_2_down_name = full_key + ".hada_w2_b";
+                    hada_2_up_name   = full_key + ".hada_w2_a";
+                    hada_2_mid_name  = full_key + ".hada_t2";
+                    if (lora_tensors.find(hada_2_down_name) != lora_tensors.end()) {
+                        hada_2_down = to_f32(compute_ctx, lora_tensors[hada_2_down_name]);
+                    }
+                    if (lora_tensors.find(hada_2_up_name) != lora_tensors.end()) {
+                        hada_2_up = to_f32(compute_ctx, lora_tensors[hada_2_up_name]);
+                    }
+                    if (lora_tensors.find(hada_2_mid_name) != lora_tensors.end()) {
+                        hada_2_mid = to_f32(compute_ctx, lora_tensors[hada_2_mid_name]);
+                        applied_lora_tensors.insert(hada_2_mid_name);
+                        hada_2_up = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, hada_2_up));
+                    }
+
+                    alpha_name = full_key + ".alpha";
+
+                    applied_lora_tensors.insert(hada_1_down_name);
+                    applied_lora_tensors.insert(hada_1_up_name);
+                    applied_lora_tensors.insert(hada_2_down_name);
+                    applied_lora_tensors.insert(hada_2_up_name);
+
+                    applied_lora_tensors.insert(alpha_name);
+                    if (hada_1_up == NULL || hada_1_down == NULL || hada_2_up == NULL || hada_2_down == NULL) {
+                        continue;
+                    }
+
+                    struct ggml_tensor* updown_1 = ggml_merge_lora(compute_ctx, hada_1_down, hada_1_up, hada_1_mid);
+                    struct ggml_tensor* updown_2 = ggml_merge_lora(compute_ctx, hada_2_down, hada_2_up, hada_2_mid);
+                    updown                       = ggml_mul_inplace(compute_ctx, updown_1, updown_2);
+
+                    // calc_scale
+                    // TODO: .dora_scale?
+                    int64_t rank = hada_1_down->ne[ggml_n_dims(hada_1_down) - 1];
+                    if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                        float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                        scale_value = alpha / rank;
+                    }
+                } else if (lora_tensors.find(full_key + ".lokr_w1") != lora_tensors.end() || lora_tensors.find(full_key + ".lokr_w1_a") != lora_tensors.end()) {
+                    // LoKr mode
+
+                    // TODO: split qkv convention for LoKrs (is it ever used?)
+                    if (is_qkv_split || is_qkvm_split) {
+                        LOG_ERROR("Split qkv isn't supported for LoKr models.");
+                        break;
+                    }
+
+                    std::string alpha_name = full_key + ".alpha";
+
+                    ggml_tensor* lokr_w1 = NULL;
+                    ggml_tensor* lokr_w2 = NULL;
+
+                    std::string lokr_w1_name = "";
+                    std::string lokr_w2_name = "";
+
+                    lokr_w1_name = full_key + ".lokr_w1";
+                    lokr_w2_name = full_key + ".lokr_w2";
+
+                    if (lora_tensors.find(lokr_w1_name) != lora_tensors.end()) {
+                        lokr_w1 = to_f32(compute_ctx, lora_tensors[lokr_w1_name]);
+                        applied_lora_tensors.insert(lokr_w1_name);
+                    } else {
+                        ggml_tensor* down     = NULL;
+                        ggml_tensor* up       = NULL;
+                        std::string down_name = lokr_w1_name + "_b";
+                        std::string up_name   = lokr_w1_name + "_a";
+                        if (lora_tensors.find(down_name) != lora_tensors.end()) {
+                            // w1 should not be low rank normally, sometimes w1 and w2 are swapped
+                            down = to_f32(compute_ctx, lora_tensors[down_name]);
+                            applied_lora_tensors.insert(down_name);
+
+                            int64_t rank = down->ne[ggml_n_dims(down) - 1];
+                            if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                                float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                                scale_value = alpha / rank;
+                            }
+                        }
+                        if (lora_tensors.find(up_name) != lora_tensors.end()) {
+                            up = to_f32(compute_ctx, lora_tensors[up_name]);
+                            applied_lora_tensors.insert(up_name);
+                        }
+                        lokr_w1 = ggml_merge_lora(compute_ctx, down, up);
+                    }
+                    if (lora_tensors.find(lokr_w2_name) != lora_tensors.end()) {
+                        lokr_w2 = to_f32(compute_ctx, lora_tensors[lokr_w2_name]);
+                        applied_lora_tensors.insert(lokr_w2_name);
+                    } else {
+                        ggml_tensor* down     = NULL;
+                        ggml_tensor* up       = NULL;
+                        std::string down_name = lokr_w2_name + "_b";
+                        std::string up_name   = lokr_w2_name + "_a";
+                        if (lora_tensors.find(down_name) != lora_tensors.end()) {
+                            down = to_f32(compute_ctx, lora_tensors[down_name]);
+                            applied_lora_tensors.insert(down_name);
+
+                            int64_t rank = down->ne[ggml_n_dims(down) - 1];
+                            if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                                float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                                scale_value = alpha / rank;
+                            }
+                        }
+                        if (lora_tensors.find(up_name) != lora_tensors.end()) {
+                            up = to_f32(compute_ctx, lora_tensors[up_name]);
+                            applied_lora_tensors.insert(up_name);
+                        }
+                        lokr_w2 = ggml_merge_lora(compute_ctx, down, up);
+                    }
+
+                    // Technically it might be unused, but I believe it's the expected behavior
+                    applied_lora_tensors.insert(alpha_name);
+
+                    updown = ggml_kronecker(compute_ctx, lokr_w1, lokr_w2);
+
+                } else {
+                    // LoRA mode
+                    ggml_tensor* lora_mid  = NULL;  // tau for tucker decomposition
+                    ggml_tensor* lora_up   = NULL;
+                    ggml_tensor* lora_down = NULL;
+
+                    std::string alpha_name         = "";
+                    std::string scale_name         = "";
+                    std::string split_q_scale_name = "";
+                    std::string lora_mid_name      = "";
+                    std::string lora_down_name     = "";
+                    std::string lora_up_name       = "";
+
+                    if (is_qkv_split) {
+                        std::string suffix  = "";
+                        auto split_q_d_name = full_key + "q" + suffix + lora_downs[type] + ".weight";
+
+                        if (lora_tensors.find(split_q_d_name) == lora_tensors.end()) {
+                            suffix         = "_proj";
+                            split_q_d_name = full_key + "q" + suffix + lora_downs[type] + ".weight";
+                        }
+                        if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                            // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
+                            // find qkv and mlp up parts in LoRA model
+                            auto split_k_d_name = full_key + "k" + suffix + lora_downs[type] + ".weight";
+                            auto split_v_d_name = full_key + "v" + suffix + lora_downs[type] + ".weight";
+
+                            auto split_q_u_name = full_key + "q" + suffix + lora_ups[type] + ".weight";
+                            auto split_k_u_name = full_key + "k" + suffix + lora_ups[type] + ".weight";
+                            auto split_v_u_name = full_key + "v" + suffix + lora_ups[type] + ".weight";
+
+                            auto split_q_scale_name = full_key + "q" + suffix + ".scale";
+                            auto split_k_scale_name = full_key + "k" + suffix + ".scale";
+                            auto split_v_scale_name = full_key + "v" + suffix + ".scale";
+
+                            auto split_q_alpha_name = full_key + "q" + suffix + ".alpha";
+                            auto split_k_alpha_name = full_key + "k" + suffix + ".alpha";
+                            auto split_v_alpha_name = full_key + "v" + suffix + ".alpha";
+
+                            ggml_tensor* lora_q_down = NULL;
+                            ggml_tensor* lora_q_up   = NULL;
+                            ggml_tensor* lora_k_down = NULL;
+                            ggml_tensor* lora_k_up   = NULL;
+                            ggml_tensor* lora_v_down = NULL;
+                            ggml_tensor* lora_v_up   = NULL;
+
+                            lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
+
+                            if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
+                                lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
+                                lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
+                                lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
+                                lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
+                                lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
+                            }
+
+                            float q_rank = lora_q_up->ne[0];
+                            float k_rank = lora_k_up->ne[0];
+                            float v_rank = lora_v_up->ne[0];
+
+                            float lora_q_scale = 1;
+                            float lora_k_scale = 1;
+                            float lora_v_scale = 1;
+
+                            if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
+                                lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
+                                applied_lora_tensors.insert(split_q_scale_name);
+                            }
+                            if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
+                                lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
+                                applied_lora_tensors.insert(split_k_scale_name);
+                            }
+                            if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
+                                lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
+                                applied_lora_tensors.insert(split_v_scale_name);
+                            }
+
+                            if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
+                                float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
+                                applied_lora_tensors.insert(split_q_alpha_name);
+                                lora_q_scale = lora_q_alpha / q_rank;
+                            }
+                            if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
+                                float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
+                                applied_lora_tensors.insert(split_k_alpha_name);
+                                lora_k_scale = lora_k_alpha / k_rank;
+                            }
+                            if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
+                                float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
+                                applied_lora_tensors.insert(split_v_alpha_name);
+                                lora_v_scale = lora_v_alpha / v_rank;
+                            }
+
+                            ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
+                            ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
+                            ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
+
+                            // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_q_up, true);    //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_k_up, true);    //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_v_up, true);    //[R, 3072, 1, 1]
+
+                            // these need to be stitched together this way:
+                            //                          |q_up,0   ,0   |
+                            //                          |0   ,k_up,0   |
+                            //                          |0   ,0   ,v_up|
+                            // (q_down,k_down,v_down) . (q   ,k   ,v)
+
+                            // up_concat will be [9216, R*3, 1, 1]
+                            // down_concat will be [R*3, 3072, 1, 1]
+                            ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), lora_v_down, 1);
+
+                            ggml_tensor* z = ggml_dup_tensor(compute_ctx, lora_q_up);
+                            ggml_scale(compute_ctx, z, 0);
+                            ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
+
+                            ggml_tensor* q_up = ggml_concat(compute_ctx, lora_q_up, zz, 1);
+                            ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), z, 1);
+                            ggml_tensor* v_up = ggml_concat(compute_ctx, zz, lora_v_up, 1);
+                            // print_ggml_tensor(q_up, true);  //[R, 9216, 1, 1]
+                            // print_ggml_tensor(k_up, true);  //[R, 9216, 1, 1]
+                            // print_ggml_tensor(v_up, true);  //[R, 9216, 1, 1]
+                            ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), v_up, 0);
+                            // print_ggml_tensor(lora_up_concat, true);  //[R*3, 9216, 1, 1]
+
+                            lora_down = ggml_cont(compute_ctx, lora_down_concat);
+                            lora_up   = ggml_cont(compute_ctx, lora_up_concat);
+
+                            applied_lora_tensors.insert(split_q_u_name);
+                            applied_lora_tensors.insert(split_k_u_name);
+                            applied_lora_tensors.insert(split_v_u_name);
+
+                            applied_lora_tensors.insert(split_q_d_name);
+                            applied_lora_tensors.insert(split_k_d_name);
+                            applied_lora_tensors.insert(split_v_d_name);
+                        }
+                    } else if (is_qkvm_split) {
+                        auto split_q_d_name = full_key + "attn.to_q" + lora_downs[type] + ".weight";
+                        if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                            // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
+                            // find qkv and mlp up parts in LoRA model
+                            auto split_k_d_name = full_key + "attn.to_k" + lora_downs[type] + ".weight";
+                            auto split_v_d_name = full_key + "attn.to_v" + lora_downs[type] + ".weight";
+
+                            auto split_q_u_name = full_key + "attn.to_q" + lora_ups[type] + ".weight";
+                            auto split_k_u_name = full_key + "attn.to_k" + lora_ups[type] + ".weight";
+                            auto split_v_u_name = full_key + "attn.to_v" + lora_ups[type] + ".weight";
+
+                            auto split_m_d_name = full_key + "proj_mlp" + lora_downs[type] + ".weight";
+                            auto split_m_u_name = full_key + "proj_mlp" + lora_ups[type] + ".weight";
+
+                            auto split_q_scale_name = full_key + "attn.to_q" + ".scale";
+                            auto split_k_scale_name = full_key + "attn.to_k" + ".scale";
+                            auto split_v_scale_name = full_key + "attn.to_v" + ".scale";
+                            auto split_m_scale_name = full_key + "proj_mlp" + ".scale";
+
+                            auto split_q_alpha_name = full_key + "attn.to_q" + ".alpha";
+                            auto split_k_alpha_name = full_key + "attn.to_k" + ".alpha";
+                            auto split_v_alpha_name = full_key + "attn.to_v" + ".alpha";
+                            auto split_m_alpha_name = full_key + "proj_mlp" + ".alpha";
+
+                            ggml_tensor* lora_q_down = NULL;
+                            ggml_tensor* lora_q_up   = NULL;
+                            ggml_tensor* lora_k_down = NULL;
+                            ggml_tensor* lora_k_up   = NULL;
+                            ggml_tensor* lora_v_down = NULL;
+                            ggml_tensor* lora_v_up   = NULL;
+
+                            ggml_tensor* lora_m_down = NULL;
+                            ggml_tensor* lora_m_up   = NULL;
+
+                            lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+
+                            if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                                lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
+                                lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
+                                lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
+                                lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
+                                lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
+                                lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
+                            }
+
+                            if (lora_tensors.find(split_m_d_name) != lora_tensors.end()) {
+                                lora_m_down = to_f32(compute_ctx, lora_tensors[split_m_d_name]);
+                            }
+
+                            if (lora_tensors.find(split_m_u_name) != lora_tensors.end()) {
+                                lora_m_up = to_f32(compute_ctx, lora_tensors[split_m_u_name]);
+                            }
+
+                            float q_rank = lora_q_up->ne[0];
+                            float k_rank = lora_k_up->ne[0];
+                            float v_rank = lora_v_up->ne[0];
+                            float m_rank = lora_v_up->ne[0];
+
+                            float lora_q_scale = 1;
+                            float lora_k_scale = 1;
+                            float lora_v_scale = 1;
+                            float lora_m_scale = 1;
+
+                            if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
+                                lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
+                                applied_lora_tensors.insert(split_q_scale_name);
+                            }
+                            if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
+                                lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
+                                applied_lora_tensors.insert(split_k_scale_name);
+                            }
+                            if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
+                                lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
+                                applied_lora_tensors.insert(split_v_scale_name);
+                            }
+                            if (lora_tensors.find(split_m_scale_name) != lora_tensors.end()) {
+                                lora_m_scale = ggml_backend_tensor_get_f32(lora_tensors[split_m_scale_name]);
+                                applied_lora_tensors.insert(split_m_scale_name);
+                            }
+
+                            if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
+                                float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
+                                applied_lora_tensors.insert(split_q_alpha_name);
+                                lora_q_scale = lora_q_alpha / q_rank;
+                            }
+                            if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
+                                float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
+                                applied_lora_tensors.insert(split_k_alpha_name);
+                                lora_k_scale = lora_k_alpha / k_rank;
+                            }
+                            if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
+                                float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
+                                applied_lora_tensors.insert(split_v_alpha_name);
+                                lora_v_scale = lora_v_alpha / v_rank;
+                            }
+                            if (lora_tensors.find(split_m_alpha_name) != lora_tensors.end()) {
+                                float lora_m_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_m_alpha_name]);
+                                applied_lora_tensors.insert(split_m_alpha_name);
+                                lora_m_scale = lora_m_alpha / m_rank;
+                            }
+
+                            ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
+                            ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
+                            ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
+                            ggml_scale_inplace(compute_ctx, lora_m_down, lora_m_scale);
+
+                            // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_m_down, true);  //[3072, R, 1, 1]
+                            // print_ggml_tensor(lora_q_up, true);  //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_k_up, true);  //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_v_up, true);  //[R, 3072, 1, 1]
+                            // print_ggml_tensor(lora_m_up, true);  //[R, 12288, 1, 1]
+
+                            // these need to be stitched together this way:
+                            //                                 |q_up,0   ,0   ,0   |
+                            //                                 |0   ,k_up,0   ,0   |
+                            //                                 |0   ,0   ,v_up,0   |
+                            //                                 |0   ,0   ,0   ,m_up|
+                            // (q_down,k_down,v_down,m_down) . (q   ,k   ,v   ,m)
+
+                            // up_concat will be [21504, R*4, 1, 1]
+                            // down_concat will be [R*4, 3072, 1, 1]
+
+                            ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), ggml_concat(compute_ctx, lora_v_down, lora_m_down, 1), 1);
+                            // print_ggml_tensor(lora_down_concat, true);  //[3072, R*4, 1, 1]
+
+                            // this also means that if rank is bigger than 672, it is less memory efficient to do it this way (should be fine)
+                            // print_ggml_tensor(lora_q_up, true);  //[3072, R, 1, 1]
+                            ggml_tensor* z     = ggml_dup_tensor(compute_ctx, lora_q_up);
+                            ggml_tensor* mlp_z = ggml_dup_tensor(compute_ctx, lora_m_up);
+                            ggml_scale(compute_ctx, z, 0);
+                            ggml_scale(compute_ctx, mlp_z, 0);
+                            ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
+
+                            ggml_tensor* q_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_up, zz, 1), mlp_z, 1);
+                            ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), ggml_concat(compute_ctx, z, mlp_z, 1), 1);
+                            ggml_tensor* v_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, lora_v_up, 1), mlp_z, 1);
+                            ggml_tensor* m_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, z, 1), lora_m_up, 1);
+                            // print_ggml_tensor(q_up, true);  //[R, 21504, 1, 1]
+                            // print_ggml_tensor(k_up, true);  //[R, 21504, 1, 1]
+                            // print_ggml_tensor(v_up, true);  //[R, 21504, 1, 1]
+                            // print_ggml_tensor(m_up, true);  //[R, 21504, 1, 1]
+
+                            ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), ggml_concat(compute_ctx, v_up, m_up, 0), 0);
+                            // print_ggml_tensor(lora_up_concat, true);  //[R*4, 21504, 1, 1]
+
+                            lora_down = ggml_cont(compute_ctx, lora_down_concat);
+                            lora_up   = ggml_cont(compute_ctx, lora_up_concat);
+
+                            applied_lora_tensors.insert(split_q_u_name);
+                            applied_lora_tensors.insert(split_k_u_name);
+                            applied_lora_tensors.insert(split_v_u_name);
+                            applied_lora_tensors.insert(split_m_u_name);
+
+                            applied_lora_tensors.insert(split_q_d_name);
+                            applied_lora_tensors.insert(split_k_d_name);
+                            applied_lora_tensors.insert(split_v_d_name);
+                            applied_lora_tensors.insert(split_m_d_name);
+                        }
+                    } else {
+                        lora_up_name   = full_key + lora_ups[type] + ".weight";
+                        lora_down_name = full_key + lora_downs[type] + ".weight";
+                        lora_mid_name  = full_key + ".lora_mid.weight";
+
+                        alpha_name = full_key + ".alpha";
+                        scale_name = full_key + ".scale";
+
+                        if (lora_tensors.find(lora_up_name) != lora_tensors.end()) {
+                            lora_up = to_f32(compute_ctx, lora_tensors[lora_up_name]);
+                            applied_lora_tensors.insert(lora_up_name);
+                        }
+
+                        if (lora_tensors.find(lora_down_name) != lora_tensors.end()) {
+                            lora_down = to_f32(compute_ctx, lora_tensors[lora_down_name]);
+                            applied_lora_tensors.insert(lora_down_name);
+                        }
+
+                        if (lora_tensors.find(lora_mid_name) != lora_tensors.end()) {
+                            lora_mid = to_f32(compute_ctx, lora_tensors[lora_mid_name]);
+                            applied_lora_tensors.insert(lora_mid_name);
+                        }
+                    }
+
+                    if (lora_up == NULL || lora_down == NULL) {
+                        continue;
+                    }
+                    // calc_scale
+                    // TODO: .dora_scale?
+                    int64_t rank = lora_down->ne[ggml_n_dims(lora_down) - 1];
+                    if (lora_tensors.find(scale_name) != lora_tensors.end()) {
+                        scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
+                        applied_lora_tensors.insert(scale_name);
+                    } else if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                        float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                        scale_value = alpha / rank;
+                        // LOG_DEBUG("rank %s %ld %.2f %.2f", alpha_name.c_str(), rank, alpha, scale_value);
+                        applied_lora_tensors.insert(alpha_name);
+                    }
+
+                    updown = ggml_merge_lora(compute_ctx, lora_down, lora_up, lora_mid);
+                }
+                scale_value *= multiplier;
+                ggml_tensor* original_tensor = model_tensor;
+                if (!ggml_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
+                    model_tensor = ggml_dup_tensor(compute_ctx, model_tensor);
+                    set_backend_tensor_data(model_tensor, original_tensor->data);
+                }
+                updown = ggml_reshape(compute_ctx, updown, model_tensor);
+                GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(model_tensor));
+                updown = ggml_scale_inplace(compute_ctx, updown, scale_value);
+                ggml_tensor* final_tensor;
+                if (model_tensor->type != GGML_TYPE_F32 && model_tensor->type != GGML_TYPE_F16) {
+                    final_tensor = to_f32(compute_ctx, model_tensor);
+                    final_tensor = ggml_add_inplace(compute_ctx, final_tensor, updown);
+                    final_tensor = ggml_cpy(compute_ctx, final_tensor, model_tensor);
+                } else {
+                    final_tensor = ggml_add_inplace(compute_ctx, model_tensor, updown);
+                }
+                ggml_build_forward_expand(gf, final_tensor);
+                if (!ggml_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
+                    original_tensor_to_final_tensor[original_tensor] = final_tensor;
+                }
+                break;
             }
-
-            if (lora_tensors.find(lora_down_name) != lora_tensors.end()) {
-                lora_down = lora_tensors[lora_down_name];
-            }
-
-            if (lora_up == NULL || lora_down == NULL) {
-                continue;
-            }
-
-            applied_lora_tensors.insert(lora_up_name);
-            applied_lora_tensors.insert(lora_down_name);
-            applied_lora_tensors.insert(alpha_name);
-            applied_lora_tensors.insert(scale_name);
-
-            // calc_cale
-            int64_t dim       = lora_down->ne[ggml_n_dims(lora_down) - 1];
-            float scale_value = 1.0f;
-            if (lora_tensors.find(scale_name) != lora_tensors.end()) {
-                scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
-            } else if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
-                float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
-                scale_value = alpha / dim;
-            }
-            scale_value *= multiplier;
-
-            // flat lora tensors to multiply it
-            int64_t lora_up_rows   = lora_up->ne[ggml_n_dims(lora_up) - 1];
-            lora_up                = ggml_reshape_2d(compute_ctx, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
-            int64_t lora_down_rows = lora_down->ne[ggml_n_dims(lora_down) - 1];
-            lora_down              = ggml_reshape_2d(compute_ctx, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
-
-            // ggml_mul_mat requires tensor b transposed
-            lora_down                  = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, lora_down));
-            struct ggml_tensor* updown = ggml_mul_mat(compute_ctx, lora_up, lora_down);
-            updown                     = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, updown));
-            updown                     = ggml_reshape(compute_ctx, updown, weight);
-            GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
-            updown = ggml_scale_inplace(compute_ctx, updown, scale_value);
-            ggml_tensor* final_weight;
-            if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) {
-                // final_weight = ggml_new_tensor(compute_ctx, GGML_TYPE_F32, ggml_n_dims(weight), weight->ne);
-                // final_weight = ggml_cpy(compute_ctx, weight, final_weight);
-                final_weight = to_f32(compute_ctx, weight);
-                final_weight = ggml_add_inplace(compute_ctx, final_weight, updown);
-                final_weight = ggml_cpy(compute_ctx, final_weight, weight);
-            } else {
-                final_weight = ggml_add_inplace(compute_ctx, weight, updown);
-            }
-            // final_weight = ggml_add_inplace(compute_ctx, weight, updown);  // apply directly
-            ggml_build_forward_expand(gf, final_weight);
         }
-
         size_t total_lora_tensors_count   = 0;
         size_t applied_lora_tensors_count = 0;
 
         for (auto& kv : lora_tensors) {
             total_lora_tensors_count++;
             if (applied_lora_tensors.find(kv.first) == applied_lora_tensors.end()) {
-                LOG_WARN("unused lora tensor %s", kv.first.c_str());
+                LOG_WARN("unused lora tensor |%s|", kv.first.c_str());
+                print_ggml_tensor(kv.second, true);
+                // exit(0);
             } else {
                 applied_lora_tensors_count++;
             }
@@ -182,21 +855,29 @@ struct LoraModel : public GGMLRunner {
          * this function is called once to calculate the required buffer size
          * and then again to actually generate a graph to be used */
         if (applied_lora_tensors_count != total_lora_tensors_count) {
-            LOG_WARN("Only (%lu / %lu) LoRA tensors have been applied",
+            LOG_WARN("Only (%lu / %lu) LoRA tensors will be applied",
                      applied_lora_tensors_count, total_lora_tensors_count);
         } else {
-            LOG_DEBUG("(%lu / %lu) LoRA tensors applied successfully",
+            LOG_DEBUG("(%lu / %lu) LoRA tensors will be applied",
                       applied_lora_tensors_count, total_lora_tensors_count);
         }
 
         return gf;
     }
 
-    void apply(std::map<std::string, struct ggml_tensor*> model_tensors, int n_threads) {
+    void apply(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_lora_graph(model_tensors);
+            return build_lora_graph(model_tensors, version);
         };
-        GGMLRunner::compute(get_graph, n_threads, true);
+        GGMLRunner::compute(get_graph, n_threads, false);
+        for (auto item : original_tensor_to_final_tensor) {
+            ggml_tensor* original_tensor = item.first;
+            ggml_tensor* final_tensor    = item.second;
+
+            ggml_backend_tensor_copy(final_tensor, original_tensor);
+        }
+        original_tensor_to_final_tensor.clear();
+        GGMLRunner::free_compute_buffer();
     }
 };
 

ltxv.hpp

@@ -0,0 +1,74 @@
+#ifndef __LTXV_HPP__
+#define __LTXV_HPP__
+
+#include "common.hpp"
+#include "ggml_extend.hpp"
+
+namespace LTXV {
+
+    class CausalConv3d : public GGMLBlock {
+    protected:
+        int time_kernel_size;
+
+    public:
+        CausalConv3d(int64_t in_channels,
+                     int64_t out_channels,
+                     int kernel_size        = 3,
+                     std::tuple<int> stride = {1, 1, 1},
+                     int dilation           = 1,
+                     bool bias              = true) {
+            time_kernel_size = kernel_size / 2;
+            blocks["conv"]   = std::shared_ptr<GGMLBlock>(new Conv3d(in_channels,
+                                                                     out_channels,
+                                                                     {kernel_size, kernel_size, kernel_size},
+                                                                     stride,
+                                                                     {0, kernel_size / 2, kernel_size / 2},
+                                                                     {dilation, 1, 1},
+                                                                     bias));
+        }
+
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    struct ggml_tensor* x,
+                                    bool causal = true) {
+            // x: [N*IC, ID, IH, IW]
+            // result: [N*OC, OD, OH, OW]
+            auto conv = std::dynamic_pointer_cast<Conv3d>(blocks["conv"]);
+            if (causal) {
+                auto h               = ggml_cont(ctx, ggml_permute(ctx, x, 0, 1, 3, 2));                                                  // [ID, N*IC, IH, IW]
+                auto first_frame     = ggml_view_3d(ctx, h, h->ne[0], h->ne[1], h->ne[2], h->nb[1], h->nb[2], 0);                         // [N*IC, IH, IW]
+                first_frame          = ggml_reshape_4d(ctx, first_frame, first_frame->ne[0], first_frame->ne[1], 1, first_frame->ne[2]);  // [N*IC, 1, IH, IW]
+                auto first_frame_pad = first_frame;
+                for (int i = 1; i < time_kernel_size - 1; i++) {
+                    first_frame_pad = ggml_concat(ctx, first_frame_pad, first_frame, 2);
+                }
+                x = ggml_concat(ctx, first_frame_pad, x, 2);
+            } else {
+                auto h         = ggml_cont(ctx, ggml_permute(ctx, x, 0, 1, 3, 2));  // [ID, N*IC, IH, IW]
+                int64_t offset = h->nb[2] * h->ne[2];
+
+                auto first_frame     = ggml_view_3d(ctx, h, h->ne[0], h->ne[1], h->ne[2], h->nb[1], h->nb[2], 0);                         // [N*IC, IH, IW]
+                first_frame          = ggml_reshape_4d(ctx, first_frame, first_frame->ne[0], first_frame->ne[1], 1, first_frame->ne[2]);  // [N*IC, 1, IH, IW]
+                auto first_frame_pad = first_frame;
+                for (int i = 1; i < (time_kernel_size - 1) / 2; i++) {
+                    first_frame_pad = ggml_concat(ctx, first_frame_pad, first_frame, 2);
+                }
+
+                auto last_frame     = ggml_view_3d(ctx, h, h->ne[0], h->ne[1], h->ne[2], h->nb[1], h->nb[2], offset * (h->ne[3] - 1));  // [N*IC, IH, IW]
+                last_frame          = ggml_reshape_4d(ctx, last_frame, last_frame->ne[0], last_frame->ne[1], 1, last_frame->ne[2]);     // [N*IC, 1, IH, IW]
+                auto last_frame_pad = last_frame;
+                for (int i = 1; i < (time_kernel_size - 1) / 2; i++) {
+                    last_frame_pad = ggml_concat(ctx, last_frame_pad, last_frame, 2);
+                }
+
+                x = ggml_concat(ctx, first_frame_pad, x, 2);
+                x = ggml_concat(ctx, x, last_frame_pad, 2);
+            }
+
+            x = conv->forward(ctx, x);
+            return x;
+        }
+    };
+
+};
+
+#endif

mmdit.hpp

@@ -142,42 +142,21 @@ public:
     }
 };
 
-class RMSNorm : public UnaryBlock {
-protected:
-    int64_t hidden_size;
-    float eps;
-
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["weight"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
-    }
-
-public:
-    RMSNorm(int64_t hidden_size,
-            float eps = 1e-06f)
-        : hidden_size(hidden_size),
-          eps(eps) {}
-
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
-        struct ggml_tensor* w = params["weight"];
-        x                     = ggml_rms_norm(ctx, x, eps);
-        x                     = ggml_mul(ctx, x, w);
-        return x;
-    }
-};
-
 class SelfAttention : public GGMLBlock {
 public:
     int64_t num_heads;
     bool pre_only;
     std::string qk_norm;
+    bool flash_attn;
 
 public:
     SelfAttention(int64_t dim,
                   int64_t num_heads   = 8,
                   std::string qk_norm = "",
                   bool qkv_bias       = false,
-                  bool pre_only       = false)
-        : num_heads(num_heads), pre_only(pre_only), qk_norm(qk_norm) {
+                  bool pre_only       = false,
+                  bool flash_attn     = false)
+        : num_heads(num_heads), pre_only(pre_only), qk_norm(qk_norm), flash_attn(flash_attn) {
         int64_t d_head = dim / num_heads;
         blocks["qkv"]  = std::shared_ptr<GGMLBlock>(new Linear(dim, dim * 3, qkv_bias));
         if (!pre_only) {
@@ -225,10 +204,12 @@ public:
     }
 
     // x: [N, n_token, dim]
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
+                                struct ggml_tensor* x) {
         auto qkv = pre_attention(ctx, x);
-        x        = ggml_nn_attention_ext(ctx, qkv[0], qkv[1], qkv[2], num_heads);  // [N, n_token, dim]
-        x        = post_attention(ctx, x);                                         // [N, n_token, dim]
+        x        = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, true);  // [N, n_token, dim]
+        x        = post_attention(ctx, x);                                                                            // [N, n_token, dim]
         return x;
     }
 };
@@ -253,6 +234,7 @@ public:
     int64_t num_heads;
     bool pre_only;
     bool self_attn;
+    bool flash_attn;
 
 public:
     DismantledBlock(int64_t hidden_size,
@@ -261,16 +243,17 @@ public:
                     std::string qk_norm = "",
                     bool qkv_bias       = false,
                     bool pre_only       = false,
-                    bool self_attn      = false)
+                    bool self_attn      = false,
+                    bool flash_attn     = false)
         : num_heads(num_heads), pre_only(pre_only), self_attn(self_attn) {
         // rmsnorm is always Flase
         // scale_mod_only is always Flase
         // swiglu is always Flase
         blocks["norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
-        blocks["attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only));
+        blocks["attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only, flash_attn));
 
         if (self_attn) {
-            blocks["attn2"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, false));
+            blocks["attn2"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, false, flash_attn));
         }
 
         if (!pre_only) {
@@ -438,7 +421,10 @@ public:
         return x;
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* c) {
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
+                                struct ggml_tensor* x,
+                                struct ggml_tensor* c) {
         // x: [N, n_token, hidden_size]
         // c: [N, hidden_size]
         // return: [N, n_token, hidden_size]
@@ -453,8 +439,8 @@ public:
             auto qkv2          = std::get<1>(qkv_intermediates);
             auto intermediates = std::get<2>(qkv_intermediates);
 
-            auto attn_out  = ggml_nn_attention_ext(ctx, qkv[0], qkv[1], qkv[2], num_heads);     // [N, n_token, dim]
-            auto attn2_out = ggml_nn_attention_ext(ctx, qkv2[0], qkv2[1], qkv2[2], num_heads);  // [N, n_token, dim]
+            auto attn_out  = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, flash_attn);     // [N, n_token, dim]
+            auto attn2_out = ggml_nn_attention_ext(ctx, backend, qkv2[0], qkv2[1], qkv2[2], num_heads, NULL, false, false, flash_attn);  // [N, n_token, dim]
             x              = post_attention_x(ctx,
                                               attn_out,
                                               attn2_out,
@@ -470,7 +456,7 @@ public:
             auto qkv               = qkv_intermediates.first;
             auto intermediates     = qkv_intermediates.second;
 
-            auto attn_out = ggml_nn_attention_ext(ctx, qkv[0], qkv[1], qkv[2], num_heads);  // [N, n_token, dim]
+            auto attn_out = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, flash_attn);  // [N, n_token, dim]
             x             = post_attention(ctx,
                                            attn_out,
                                            intermediates[0],
@@ -485,6 +471,8 @@ public:
 
 __STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*>
 block_mixing(struct ggml_context* ctx,
+             ggml_backend_t backend,
+             bool flash_attn,
              struct ggml_tensor* context,
              struct ggml_tensor* x,
              struct ggml_tensor* c,
@@ -514,8 +502,8 @@ block_mixing(struct ggml_context* ctx,
         qkv.push_back(ggml_concat(ctx, context_qkv[i], x_qkv[i], 1));
     }
 
-    auto attn         = ggml_nn_attention_ext(ctx, qkv[0], qkv[1], qkv[2], x_block->num_heads);  // [N, n_context + n_token, hidden_size]
-    attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));                     // [n_context + n_token, N, hidden_size]
+    auto attn         = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, NULL, false, false, flash_attn);  // [N, n_context + n_token, hidden_size]
+    attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));                                                              // [n_context + n_token, N, hidden_size]
     auto context_attn = ggml_view_3d(ctx,
                                      attn,
                                      attn->ne[0],
@@ -548,7 +536,7 @@ block_mixing(struct ggml_context* ctx,
     }
 
     if (x_block->self_attn) {
-        auto attn2 = ggml_nn_attention_ext(ctx, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads);  // [N, n_token, hidden_size]
+        auto attn2 = ggml_nn_attention_ext(ctx, backend, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads);  // [N, n_token, hidden_size]
 
         x = x_block->post_attention_x(ctx,
                                       x_attn,
@@ -573,6 +561,8 @@ block_mixing(struct ggml_context* ctx,
 }
 
 struct JointBlock : public GGMLBlock {
+    bool flash_attn;
+
 public:
     JointBlock(int64_t hidden_size,
                int64_t num_heads,
@@ -580,19 +570,22 @@ public:
                std::string qk_norm = "",
                bool qkv_bias       = false,
                bool pre_only       = false,
-               bool self_attn_x    = false) {
-        blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only));
-        blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x));
+               bool self_attn_x    = false,
+               bool flash_attn     = false)
+        : flash_attn(flash_attn) {
+        blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only, false, flash_attn));
+        blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x, flash_attn));
     }
 
     std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
+                                                                ggml_backend_t backend,
                                                                 struct ggml_tensor* context,
                                                                 struct ggml_tensor* x,
                                                                 struct ggml_tensor* c) {
         auto context_block = std::dynamic_pointer_cast<DismantledBlock>(blocks["context_block"]);
         auto x_block       = std::dynamic_pointer_cast<DismantledBlock>(blocks["x_block"]);
 
-        return block_mixing(ctx, context, x, c, context_block, x_block);
+        return block_mixing(ctx, backend, flash_attn, context, x, c, context_block, x_block);
     }
 };
 
@@ -636,7 +629,6 @@ public:
 struct MMDiT : public GGMLBlock {
     // Diffusion model with a Transformer backbone.
 protected:
-    SDVersion version                = VERSION_SD3_2B;
     int64_t input_size               = -1;
     int64_t patch_size               = 2;
     int64_t in_channels              = 16;
@@ -651,14 +643,16 @@ protected:
     int64_t context_embedder_out_dim = 1536;
     int64_t hidden_size;
     std::string qk_norm;
+    bool flash_attn = false;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["pos_embed"] = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hidden_size, num_patchs, 1);
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, std::string prefix = "") {
+        enum ggml_type wtype = GGML_TYPE_F32;
+        params["pos_embed"]  = ggml_new_tensor_3d(ctx, wtype, hidden_size, num_patchs, 1);
     }
 
 public:
-    MMDiT(SDVersion version = VERSION_SD3_2B)
-        : version(version) {
+    MMDiT(bool flash_attn = false, const String2GGMLType& tensor_types = {})
+        : flash_attn(flash_attn) {
         // input_size is always None
         // learn_sigma is always False
         // register_length is alwalys 0
@@ -670,48 +664,44 @@ public:
         // pos_embed_scaling_factor is not used
         // pos_embed_offset is not used
         // context_embedder_config is always {'target': 'torch.nn.Linear', 'params': {'in_features': 4096, 'out_features': 1536}}
-        if (version == VERSION_SD3_2B) {
-            input_size               = -1;
-            patch_size               = 2;
-            in_channels              = 16;
-            depth                    = 24;
-            mlp_ratio                = 4.0f;
-            adm_in_channels          = 2048;
-            out_channels             = 16;
-            pos_embed_max_size       = 192;
-            num_patchs               = 36864;  // 192 * 192
-            context_size             = 4096;
-            context_embedder_out_dim = 1536;
-        } else if (version == VERSION_SD3_5_8B) {
-            input_size               = -1;
-            patch_size               = 2;
-            in_channels              = 16;
-            depth                    = 38;
-            mlp_ratio                = 4.0f;
-            adm_in_channels          = 2048;
-            out_channels             = 16;
-            pos_embed_max_size       = 192;
-            num_patchs               = 36864;  // 192 * 192
-            context_size             = 4096;
-            context_embedder_out_dim = 2432;
-            qk_norm                  = "rms";
-        } else if (version == VERSION_SD3_5_2B) {
-            input_size               = -1;
-            patch_size               = 2;
-            in_channels              = 16;
-            depth                    = 24;
-            d_self                   = 12;
-            mlp_ratio                = 4.0f;
-            adm_in_channels          = 2048;
-            out_channels             = 16;
-            pos_embed_max_size       = 384;
-            num_patchs               = 147456;
-            context_size             = 4096;
-            context_embedder_out_dim = 1536;
-            qk_norm                  = "rms";
+
+        // read tensors from tensor_types
+        for (auto pair : tensor_types) {
+            std::string tensor_name = pair.first;
+            if (tensor_name.find("model.diffusion_model.") == std::string::npos)
+                continue;
+            size_t jb = tensor_name.find("joint_blocks.");
+            if (jb != std::string::npos) {
+                tensor_name     = tensor_name.substr(jb);  // remove prefix
+                int block_depth = atoi(tensor_name.substr(13, tensor_name.find(".", 13)).c_str());
+                if (block_depth + 1 > depth) {
+                    depth = block_depth + 1;
+                }
+                if (tensor_name.find("attn.ln") != std::string::npos) {
+                    if (tensor_name.find(".bias") != std::string::npos) {
+                        qk_norm = "ln";
+                    } else {
+                        qk_norm = "rms";
+                    }
+                }
+                if (tensor_name.find("attn2") != std::string::npos) {
+                    if (block_depth > d_self) {
+                        d_self = block_depth;
+                    }
+                }
+            }
         }
+
+        if (d_self >= 0) {
+            pos_embed_max_size *= 2;
+            num_patchs *= 4;
+        }
+
+        LOG_INFO("MMDiT layers: %d (including %d MMDiT-x layers)", depth, d_self + 1);
+
         int64_t default_out_channels = in_channels;
         hidden_size                  = 64 * depth;
+        context_embedder_out_dim     = 64 * depth;
         int64_t num_heads            = depth;
 
         blocks["x_embedder"] = std::shared_ptr<GGMLBlock>(new PatchEmbed(input_size, patch_size, in_channels, hidden_size, true));
@@ -730,7 +720,8 @@ public:
                                                                                                     qk_norm,
                                                                                                     true,
                                                                                                     i == depth - 1,
-                                                                                                    i <= d_self));
+                                                                                                    i <= d_self,
+                                                                                                    flash_attn));
         }
 
         blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels));
@@ -799,6 +790,7 @@ public:
     }
 
     struct ggml_tensor* forward_core_with_concat(struct ggml_context* ctx,
+                                                 ggml_backend_t backend,
                                                  struct ggml_tensor* x,
                                                  struct ggml_tensor* c_mod,
                                                  struct ggml_tensor* context,
@@ -817,7 +809,7 @@ public:
 
             auto block = std::dynamic_pointer_cast<JointBlock>(blocks["joint_blocks." + std::to_string(i)]);
 
-            auto context_x = block->forward(ctx, context, x, c_mod);
+            auto context_x = block->forward(ctx, backend, context, x, c_mod);
             context        = context_x.first;
             x              = context_x.second;
         }
@@ -828,6 +820,7 @@ public:
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* t,
                                 struct ggml_tensor* y        = NULL,
@@ -863,22 +856,23 @@ public:
             context = context_embedder->forward(ctx, context);  // [N, L, D] aka [N, L, 1536]
         }
 
-        x = forward_core_with_concat(ctx, x, c, context, skip_layers);  // (N, H*W, patch_size ** 2 * out_channels)
+        x = forward_core_with_concat(ctx, backend, x, c, context, skip_layers);  // (N, H*W, patch_size ** 2 * out_channels)
 
         x = unpatchify(ctx, x, h, w);  // [N, C, H, W]
 
         return x;
     }
 };
-
 struct MMDiTRunner : public GGMLRunner {
     MMDiT mmdit;
 
     MMDiTRunner(ggml_backend_t backend,
-                ggml_type wtype,
-                SDVersion version = VERSION_SD3_2B)
-        : GGMLRunner(backend, wtype), mmdit(version) {
-        mmdit.init(params_ctx, wtype);
+                bool offload_params_to_cpu,
+                bool flash_attn,
+                const String2GGMLType& tensor_types = {},
+                const std::string prefix            = "")
+        : GGMLRunner(backend, offload_params_to_cpu), mmdit(flash_attn, tensor_types) {
+        mmdit.init(params_ctx, tensor_types, prefix);
     }
 
     std::string get_desc() {
@@ -902,6 +896,7 @@ struct MMDiTRunner : public GGMLRunner {
         timesteps = to_backend(timesteps);
 
         struct ggml_tensor* out = mmdit.forward(compute_ctx,
+                                                runtime_backend,
                                                 x,
                                                 timesteps,
                                                 y,
@@ -975,7 +970,7 @@ struct MMDiTRunner : public GGMLRunner {
         // ggml_backend_t backend    = ggml_backend_cuda_init(0);
         ggml_backend_t backend             = ggml_backend_cpu_init();
         ggml_type model_data_type          = GGML_TYPE_F16;
-        std::shared_ptr<MMDiTRunner> mmdit = std::shared_ptr<MMDiTRunner>(new MMDiTRunner(backend, model_data_type));
+        std::shared_ptr<MMDiTRunner> mmdit = std::shared_ptr<MMDiTRunner>(new MMDiTRunner(backend, false, false));
         {
             LOG_INFO("loading from '%s'", file_path.c_str());
 
@@ -989,7 +984,7 @@ struct MMDiTRunner : public GGMLRunner {
                 return;
             }
 
-            bool success = model_loader.load_tensors(tensors, backend);
+            bool success = model_loader.load_tensors(tensors);
 
             if (!success) {
                 LOG_ERROR("load tensors from model loader failed");

model.h

@@ -12,6 +12,7 @@
 
 #include "ggml-backend.h"
 #include "ggml.h"
+#include "gguf.h"
 #include "json.hpp"
 #include "zip.h"
 
@@ -19,39 +20,87 @@
 
 enum SDVersion {
     VERSION_SD1,
+    VERSION_SD1_INPAINT,
+    VERSION_SD1_PIX2PIX,
     VERSION_SD2,
+    VERSION_SD2_INPAINT,
     VERSION_SDXL,
+    VERSION_SDXL_INPAINT,
+    VERSION_SDXL_PIX2PIX,
     VERSION_SVD,
-    VERSION_SD3_2B,
-    VERSION_FLUX_DEV,
-    VERSION_FLUX_SCHNELL,
-    VERSION_SD3_5_8B,
-    VERSION_SD3_5_2B,
-    VERSION_FLUX_LITE,
+    VERSION_SD3,
+    VERSION_FLUX,
+    VERSION_FLUX_FILL,
+    VERSION_WAN2,
+    VERSION_WAN2_2_I2V,
+    VERSION_WAN2_2_TI2V,
     VERSION_COUNT,
 };
 
-static inline bool sd_version_is_flux(SDVersion version) {
-    if (version == VERSION_FLUX_DEV || version == VERSION_FLUX_SCHNELL || version == VERSION_FLUX_LITE) {
+static inline bool sd_version_is_sd1(SDVersion version) {
+    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_sd2(SDVersion version) {
+    if (version == VERSION_SD2 || version == VERSION_SD2_INPAINT) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_sdxl(SDVersion version) {
+    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX) {
         return true;
     }
     return false;
 }
 
 static inline bool sd_version_is_sd3(SDVersion version) {
-    if (version == VERSION_SD3_2B || version == VERSION_SD3_5_8B || version == VERSION_SD3_5_2B) {
+    if (version == VERSION_SD3) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_flux(SDVersion version) {
+    if (version == VERSION_FLUX || version == VERSION_FLUX_FILL) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_wan(SDVersion version) {
+    if (version == VERSION_WAN2 || version == VERSION_WAN2_2_I2V || version == VERSION_WAN2_2_TI2V) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_inpaint(SDVersion version) {
+    if (version == VERSION_SD1_INPAINT || version == VERSION_SD2_INPAINT || version == VERSION_SDXL_INPAINT || version == VERSION_FLUX_FILL) {
         return true;
     }
     return false;
 }
 
 static inline bool sd_version_is_dit(SDVersion version) {
-    if (sd_version_is_flux(version) || sd_version_is_sd3(version)) {
+    if (sd_version_is_flux(version) || sd_version_is_sd3(version) || sd_version_is_wan(version)) {
         return true;
     }
     return false;
 }
 
+static inline bool sd_version_is_unet_edit(SDVersion version) {
+    return version == VERSION_SD1_PIX2PIX || version == VERSION_SDXL_PIX2PIX;
+}
+
+static bool sd_version_is_inpaint_or_unet_edit(SDVersion version) {
+    return sd_version_is_unet_edit(version) || sd_version_is_inpaint(version);
+}
+
 enum PMVersion {
     PM_VERSION_1,
     PM_VERSION_2,
@@ -63,6 +112,8 @@ struct TensorStorage {
     bool is_bf16            = false;
     bool is_f8_e4m3         = false;
     bool is_f8_e5m2         = false;
+    bool is_f64             = false;
+    bool is_i64             = false;
     int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
     int n_dims              = 0;
 
@@ -72,7 +123,7 @@ struct TensorStorage {
 
     TensorStorage() = default;
 
-    TensorStorage(const std::string& name, ggml_type type, int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
+    TensorStorage(const std::string& name, ggml_type type, const int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
         : name(name), type(type), n_dims(n_dims), file_index(file_index), offset(offset) {
         for (int i = 0; i < n_dims; i++) {
             this->ne[i] = ne[i];
@@ -94,6 +145,8 @@ struct TensorStorage {
     int64_t nbytes_to_read() const {
         if (is_bf16 || is_f8_e4m3 || is_f8_e5m2) {
             return nbytes() / 2;
+        } else if (is_f64 || is_i64) {
+            return nbytes() * 2;
         } else {
             return nbytes();
         }
@@ -144,6 +197,10 @@ struct TensorStorage {
             type_name = "f8_e4m3";
         } else if (is_f8_e5m2) {
             type_name = "f8_e5m2";
+        } else if (is_f64) {
+            type_name = "f64";
+        } else if (is_i64) {
+            type_name = "i64";
         }
         ss << name << " | " << type_name << " | ";
         ss << n_dims << " [";
@@ -160,6 +217,8 @@ struct TensorStorage {
 
 typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
 
+typedef std::map<std::string, enum ggml_type> String2GGMLType;
+
 class ModelLoader {
 protected:
     std::vector<std::string> file_paths_;
@@ -170,7 +229,7 @@ protected:
                         zip_t* zip,
                         std::string dir,
                         size_t file_index,
-                        const std::string& prefix);
+                        const std::string prefix);
 
     bool init_from_gguf_file(const std::string& file_path, const std::string& prefix = "");
     bool init_from_safetensors_file(const std::string& file_path, const std::string& prefix = "");
@@ -178,24 +237,28 @@ protected:
     bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");
 
 public:
+    String2GGMLType tensor_storages_types;
+
     bool init_from_file(const std::string& file_path, const std::string& prefix = "");
+    bool model_is_unet();
     SDVersion get_sd_version();
     ggml_type get_sd_wtype();
     ggml_type get_conditioner_wtype();
     ggml_type get_diffusion_model_wtype();
     ggml_type get_vae_wtype();
-    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend_t backend);
+    void set_wtype_override(ggml_type wtype, std::string prefix = "");
+    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb);
     bool load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
-                      ggml_backend_t backend,
                       std::set<std::string> ignore_tensors = {});
 
-    bool save_to_gguf_file(const std::string& file_path, ggml_type type);
+    bool save_to_gguf_file(const std::string& file_path, ggml_type type, const std::string& tensor_type_rules);
     bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
     int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
     ~ModelLoader() = default;
 
     static std::string load_merges();
     static std::string load_t5_tokenizer_json();
+    static std::string load_umt5_tokenizer_json();
 };
 
 #endif  // __MODEL_H__

pmid.hpp

@@ -508,6 +508,7 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* id_pixel_values,
                                 struct ggml_tensor* prompt_embeds,
                                 struct ggml_tensor* class_tokens_mask,
@@ -520,9 +521,9 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
         auto visual_projection_2 = std::dynamic_pointer_cast<Linear>(blocks["visual_projection_2"]);
         auto fuse_module         = std::dynamic_pointer_cast<FuseModule>(blocks["fuse_module"]);
 
-        struct ggml_tensor* shared_id_embeds = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
-        struct ggml_tensor* id_embeds        = visual_projection->forward(ctx, shared_id_embeds);    // [N, proj_dim(768)]
-        struct ggml_tensor* id_embeds_2      = visual_projection_2->forward(ctx, shared_id_embeds);  // [N, 1280]
+        struct ggml_tensor* shared_id_embeds = vision_model->forward(ctx, backend, id_pixel_values);  // [N, hidden_size]
+        struct ggml_tensor* id_embeds        = visual_projection->forward(ctx, shared_id_embeds);     // [N, proj_dim(768)]
+        struct ggml_tensor* id_embeds_2      = visual_projection_2->forward(ctx, shared_id_embeds);   // [N, 1280]
 
         id_embeds   = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 2, 0, 1, 3));
         id_embeds_2 = ggml_cont(ctx, ggml_permute(ctx, id_embeds_2, 2, 0, 1, 3));
@@ -579,6 +580,7 @@ struct PhotoMakerIDEncoder_CLIPInsightfaceExtendtokenBlock : public CLIPVisionMo
     */
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* id_pixel_values,
                                 struct ggml_tensor* prompt_embeds,
                                 struct ggml_tensor* class_tokens_mask,
@@ -592,7 +594,7 @@ struct PhotoMakerIDEncoder_CLIPInsightfaceExtendtokenBlock : public CLIPVisionMo
         auto qformer_perceiver = std::dynamic_pointer_cast<QFormerPerceiver>(blocks["qformer_perceiver"]);
 
         // struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
-        struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values, false);  // [N, hidden_size]
+        struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, backend, id_pixel_values, false);  // [N, hidden_size]
         id_embeds                             = qformer_perceiver->forward(ctx, id_embeds, last_hidden_state);
 
         struct ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
@@ -623,15 +625,21 @@ public:
     std::vector<float> zeros_right;
 
 public:
-    PhotoMakerIDEncoder(ggml_backend_t backend, ggml_type wtype, SDVersion version = VERSION_SDXL, PMVersion pm_v = PM_VERSION_1, float sty = 20.f)
-        : GGMLRunner(backend, wtype),
+    PhotoMakerIDEncoder(ggml_backend_t backend,
+                        bool offload_params_to_cpu,
+                        const String2GGMLType& tensor_types,
+                        const std::string prefix,
+                        SDVersion version = VERSION_SDXL,
+                        PMVersion pm_v    = PM_VERSION_1,
+                        float sty         = 20.f)
+        : GGMLRunner(backend, offload_params_to_cpu),
           version(version),
           pm_version(pm_v),
           style_strength(sty) {
         if (pm_version == PM_VERSION_1) {
-            id_encoder.init(params_ctx, wtype);
+            id_encoder.init(params_ctx, tensor_types, prefix);
         } else if (pm_version == PM_VERSION_2) {
-            id_encoder2.init(params_ctx, wtype);
+            id_encoder2.init(params_ctx, tensor_types, prefix);
         }
     }
 
@@ -736,6 +744,7 @@ public:
         struct ggml_tensor* updated_prompt_embeds = NULL;
         if (pm_version == PM_VERSION_1)
             updated_prompt_embeds = id_encoder.forward(ctx0,
+                                                       runtime_backend,
                                                        id_pixel_values_d,
                                                        prompt_embeds_d,
                                                        class_tokens_mask_d,
@@ -743,6 +752,7 @@ public:
                                                        left, right);
         else if (pm_version == PM_VERSION_2)
             updated_prompt_embeds = id_encoder2.forward(ctx0,
+                                                        runtime_backend,
                                                         id_pixel_values_d,
                                                         prompt_embeds_d,
                                                         class_tokens_mask_d,
@@ -780,11 +790,11 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
     bool applied     = false;
 
     PhotoMakerIDEmbed(ggml_backend_t backend,
-                      ggml_type wtype,
+                      bool offload_params_to_cpu,
                       ModelLoader* ml,
                       const std::string& file_path = "",
                       const std::string& prefix    = "")
-        : file_path(file_path), GGMLRunner(backend, wtype), model_loader(ml) {
+        : file_path(file_path), GGMLRunner(backend, offload_params_to_cpu), model_loader(ml) {
         if (!model_loader->init_from_file(file_path, prefix)) {
             load_failed = true;
         }
@@ -824,11 +834,11 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
             return true;
         };
 
-        model_loader->load_tensors(on_new_tensor_cb, backend);
+        model_loader->load_tensors(on_new_tensor_cb);
         alloc_params_buffer();
 
         dry_run = false;
-        model_loader->load_tensors(on_new_tensor_cb, backend);
+        model_loader->load_tensors(on_new_tensor_cb);
 
         LOG_DEBUG("finished loading PhotoMaker ID Embeds ");
         return true;

rope.hpp

@@ -0,0 +1,261 @@
+#ifndef __ROPE_HPP__
+#define __ROPE_HPP__
+
+#include <vector>
+#include "ggml_extend.hpp"
+
+struct Rope {
+    template <class T>
+    static std::vector<T> linspace(T start, T end, int num) {
+        std::vector<T> result(num);
+        if (num == 1) {
+            result[0] = start;
+            return result;
+        }
+        T step = (end - start) / (num - 1);
+        for (int i = 0; i < num; ++i) {
+            result[i] = start + i * step;
+        }
+        return result;
+    }
+
+    static std::vector<std::vector<float>> transpose(const std::vector<std::vector<float>>& mat) {
+        int rows = mat.size();
+        int cols = mat[0].size();
+        std::vector<std::vector<float>> transposed(cols, std::vector<float>(rows));
+        for (int i = 0; i < rows; ++i) {
+            for (int j = 0; j < cols; ++j) {
+                transposed[j][i] = mat[i][j];
+            }
+        }
+        return transposed;
+    }
+
+    static std::vector<float> flatten(const std::vector<std::vector<float>>& vec) {
+        std::vector<float> flat_vec;
+        for (const auto& sub_vec : vec) {
+            flat_vec.insert(flat_vec.end(), sub_vec.begin(), sub_vec.end());
+        }
+        return flat_vec;
+    }
+
+    static std::vector<std::vector<float>> rope(const std::vector<float>& pos, int dim, int theta) {
+        assert(dim % 2 == 0);
+        int half_dim = dim / 2;
+
+        std::vector<float> scale = linspace(0.f, (dim * 1.f - 2) / dim, half_dim);
+
+        std::vector<float> omega(half_dim);
+        for (int i = 0; i < half_dim; ++i) {
+            omega[i] = 1.0 / std::pow(theta, scale[i]);
+        }
+
+        int pos_size = pos.size();
+        std::vector<std::vector<float>> out(pos_size, std::vector<float>(half_dim));
+        for (int i = 0; i < pos_size; ++i) {
+            for (int j = 0; j < half_dim; ++j) {
+                out[i][j] = pos[i] * omega[j];
+            }
+        }
+
+        std::vector<std::vector<float>> result(pos_size, std::vector<float>(half_dim * 4));
+        for (int i = 0; i < pos_size; ++i) {
+            for (int j = 0; j < half_dim; ++j) {
+                result[i][4 * j]     = std::cos(out[i][j]);
+                result[i][4 * j + 1] = -std::sin(out[i][j]);
+                result[i][4 * j + 2] = std::sin(out[i][j]);
+                result[i][4 * j + 3] = std::cos(out[i][j]);
+            }
+        }
+
+        return result;
+    }
+
+    // Generate IDs for image patches and text
+    static std::vector<std::vector<float>> gen_txt_ids(int bs, int context_len) {
+        return std::vector<std::vector<float>>(bs * context_len, std::vector<float>(3, 0.0));
+    }
+
+    static std::vector<std::vector<float>> gen_img_ids(int h, int w, int patch_size, int bs, int index = 0, int h_offset = 0, int w_offset = 0) {
+        int h_len = (h + (patch_size / 2)) / patch_size;
+        int w_len = (w + (patch_size / 2)) / patch_size;
+
+        std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(3, 0.0));
+
+        std::vector<float> row_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
+        std::vector<float> col_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+
+        for (int i = 0; i < h_len; ++i) {
+            for (int j = 0; j < w_len; ++j) {
+                img_ids[i * w_len + j][0] = index;
+                img_ids[i * w_len + j][1] = row_ids[i];
+                img_ids[i * w_len + j][2] = col_ids[j];
+            }
+        }
+
+        std::vector<std::vector<float>> img_ids_repeated(bs * img_ids.size(), std::vector<float>(3));
+        for (int i = 0; i < bs; ++i) {
+            for (int j = 0; j < img_ids.size(); ++j) {
+                img_ids_repeated[i * img_ids.size() + j] = img_ids[j];
+            }
+        }
+        return img_ids_repeated;
+    }
+
+    static std::vector<std::vector<float>> concat_ids(const std::vector<std::vector<float>>& a,
+                                                      const std::vector<std::vector<float>>& b,
+                                                      int bs) {
+        size_t a_len = a.size() / bs;
+        size_t b_len = b.size() / bs;
+        std::vector<std::vector<float>> ids(a.size() + b.size(), std::vector<float>(3));
+        for (int i = 0; i < bs; ++i) {
+            for (int j = 0; j < a_len; ++j) {
+                ids[i * (a_len + b_len) + j] = a[i * a_len + j];
+            }
+            for (int j = 0; j < b_len; ++j) {
+                ids[i * (a_len + b_len) + a_len + j] = b[i * b_len + j];
+            }
+        }
+        return ids;
+    }
+
+    static std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
+                                       int bs,
+                                       int theta,
+                                       const std::vector<int>& axes_dim) {
+        std::vector<std::vector<float>> trans_ids = transpose(ids);
+        size_t pos_len                            = ids.size() / bs;
+        int num_axes                              = axes_dim.size();
+        // for (int i = 0; i < pos_len; i++) {
+        //     std::cout << trans_ids[0][i] << " " << trans_ids[1][i] << " " << trans_ids[2][i] << std::endl;
+        // }
+
+        int emb_dim = 0;
+        for (int d : axes_dim)
+            emb_dim += d / 2;
+
+        std::vector<std::vector<float>> emb(bs * pos_len, std::vector<float>(emb_dim * 2 * 2, 0.0));
+        int offset = 0;
+        for (int i = 0; i < num_axes; ++i) {
+            std::vector<std::vector<float>> rope_emb = rope(trans_ids[i], axes_dim[i], theta);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
+            for (int b = 0; b < bs; ++b) {
+                for (int j = 0; j < pos_len; ++j) {
+                    for (int k = 0; k < rope_emb[0].size(); ++k) {
+                        emb[b * pos_len + j][offset + k] = rope_emb[j][k];
+                    }
+                }
+            }
+            offset += rope_emb[0].size();
+        }
+
+        return flatten(emb);
+    }
+
+    static std::vector<std::vector<float>> gen_flux_ids(int h,
+                                                        int w,
+                                                        int patch_size,
+                                                        int bs,
+                                                        int context_len,
+                                                        std::vector<ggml_tensor*> ref_latents,
+                                                        bool increase_ref_index) {
+        auto txt_ids = gen_txt_ids(bs, context_len);
+        auto img_ids = gen_img_ids(h, w, patch_size, bs);
+
+        auto ids               = concat_ids(txt_ids, img_ids, bs);
+        uint64_t curr_h_offset = 0;
+        uint64_t curr_w_offset = 0;
+        int index              = 1;
+        for (ggml_tensor* ref : ref_latents) {
+            uint64_t h_offset = 0;
+            uint64_t w_offset = 0;
+            if (!increase_ref_index) {
+                if (ref->ne[1] + curr_h_offset > ref->ne[0] + curr_w_offset) {
+                    w_offset = curr_w_offset;
+                } else {
+                    h_offset = curr_h_offset;
+                }
+            }
+
+            auto ref_ids = gen_img_ids(ref->ne[1], ref->ne[0], patch_size, bs, index, h_offset, w_offset);
+            ids          = concat_ids(ids, ref_ids, bs);
+
+            if (increase_ref_index) {
+                index++;
+            }
+
+            curr_h_offset = std::max(curr_h_offset, ref->ne[1] + h_offset);
+            curr_w_offset = std::max(curr_w_offset, ref->ne[0] + w_offset);
+        }
+        return ids;
+    }
+
+    // Generate flux positional embeddings
+    static std::vector<float> gen_flux_pe(int h,
+                                          int w,
+                                          int patch_size,
+                                          int bs,
+                                          int context_len,
+                                          std::vector<ggml_tensor*> ref_latents,
+                                          bool increase_ref_index,
+                                          int theta,
+                                          const std::vector<int>& axes_dim) {
+        std::vector<std::vector<float>> ids = gen_flux_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
+        return embed_nd(ids, bs, theta, axes_dim);
+    }
+
+    static std::vector<std::vector<float>> gen_vid_ids(int t,
+                                                       int h,
+                                                       int w,
+                                                       int pt,
+                                                       int ph,
+                                                       int pw,
+                                                       int bs,
+                                                       int t_offset = 0,
+                                                       int h_offset = 0,
+                                                       int w_offset = 0) {
+        int t_len = (t + (pt / 2)) / pt;
+        int h_len = (h + (ph / 2)) / ph;
+        int w_len = (w + (pw / 2)) / pw;
+
+        std::vector<std::vector<float>> vid_ids(t_len * h_len * w_len, std::vector<float>(3, 0.0));
+
+        std::vector<float> t_ids = linspace<float>(t_offset, t_len - 1 + t_offset, t_len);
+        std::vector<float> h_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
+        std::vector<float> w_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+
+        for (int i = 0; i < t_len; ++i) {
+            for (int j = 0; j < h_len; ++j) {
+                for (int k = 0; k < w_len; ++k) {
+                    int idx         = i * h_len * w_len + j * w_len + k;
+                    vid_ids[idx][0] = t_ids[i];
+                    vid_ids[idx][1] = h_ids[j];
+                    vid_ids[idx][2] = w_ids[k];
+                }
+            }
+        }
+
+        std::vector<std::vector<float>> vid_ids_repeated(bs * vid_ids.size(), std::vector<float>(3));
+        for (int i = 0; i < bs; ++i) {
+            for (int j = 0; j < vid_ids.size(); ++j) {
+                vid_ids_repeated[i * vid_ids.size() + j] = vid_ids[j];
+            }
+        }
+        return vid_ids_repeated;
+    }
+
+    // Generate wan positional embeddings
+    static std::vector<float> gen_wan_pe(int t,
+                                         int h,
+                                         int w,
+                                         int pt,
+                                         int ph,
+                                         int pw,
+                                         int bs,
+                                         int theta,
+                                         const std::vector<int>& axes_dim) {
+        std::vector<std::vector<float>> ids = gen_vid_ids(t, h, w, pt, ph, pw, bs);
+        return embed_nd(ids, bs, theta, axes_dim);
+    }
+};  // struct Rope
+
+#endif  // __ROPE_HPP__

stable-diffusion.h

@@ -30,7 +30,8 @@ extern "C" {
 
 enum rng_type_t {
     STD_DEFAULT_RNG,
-    CUDA_RNG
+    CUDA_RNG,
+    RNG_TYPE_COUNT
 };
 
 enum sample_method_t {
@@ -44,17 +45,20 @@ enum sample_method_t {
     IPNDM,
     IPNDM_V,
     LCM,
-    N_SAMPLE_METHODS
+    DDIM_TRAILING,
+    TCD,
+    SAMPLE_METHOD_COUNT
 };
 
-enum schedule_t {
+enum scheduler_t {
     DEFAULT,
     DISCRETE,
     KARRAS,
     EXPONENTIAL,
     AYS,
     GITS,
-    N_SCHEDULES
+    SMOOTHSTEP,
+    SCHEDULE_COUNT
 };
 
 // same as enum ggml_type
@@ -65,39 +69,43 @@ enum sd_type_t {
     SD_TYPE_Q4_1 = 3,
     // SD_TYPE_Q4_2 = 4, support has been removed
     // SD_TYPE_Q4_3 = 5, support has been removed
-    SD_TYPE_Q5_0     = 6,
-    SD_TYPE_Q5_1     = 7,
-    SD_TYPE_Q8_0     = 8,
-    SD_TYPE_Q8_1     = 9,
-    SD_TYPE_Q2_K     = 10,
-    SD_TYPE_Q3_K     = 11,
-    SD_TYPE_Q4_K     = 12,
-    SD_TYPE_Q5_K     = 13,
-    SD_TYPE_Q6_K     = 14,
-    SD_TYPE_Q8_K     = 15,
-    SD_TYPE_IQ2_XXS  = 16,
-    SD_TYPE_IQ2_XS   = 17,
-    SD_TYPE_IQ3_XXS  = 18,
-    SD_TYPE_IQ1_S    = 19,
-    SD_TYPE_IQ4_NL   = 20,
-    SD_TYPE_IQ3_S    = 21,
-    SD_TYPE_IQ2_S    = 22,
-    SD_TYPE_IQ4_XS   = 23,
-    SD_TYPE_I8       = 24,
-    SD_TYPE_I16      = 25,
-    SD_TYPE_I32      = 26,
-    SD_TYPE_I64      = 27,
-    SD_TYPE_F64      = 28,
-    SD_TYPE_IQ1_M    = 29,
-    SD_TYPE_BF16     = 30,
-    SD_TYPE_Q4_0_4_4 = 31,
-    SD_TYPE_Q4_0_4_8 = 32,
-    SD_TYPE_Q4_0_8_8 = 33,
-    SD_TYPE_COUNT,
+    SD_TYPE_Q5_0    = 6,
+    SD_TYPE_Q5_1    = 7,
+    SD_TYPE_Q8_0    = 8,
+    SD_TYPE_Q8_1    = 9,
+    SD_TYPE_Q2_K    = 10,
+    SD_TYPE_Q3_K    = 11,
+    SD_TYPE_Q4_K    = 12,
+    SD_TYPE_Q5_K    = 13,
+    SD_TYPE_Q6_K    = 14,
+    SD_TYPE_Q8_K    = 15,
+    SD_TYPE_IQ2_XXS = 16,
+    SD_TYPE_IQ2_XS  = 17,
+    SD_TYPE_IQ3_XXS = 18,
+    SD_TYPE_IQ1_S   = 19,
+    SD_TYPE_IQ4_NL  = 20,
+    SD_TYPE_IQ3_S   = 21,
+    SD_TYPE_IQ2_S   = 22,
+    SD_TYPE_IQ4_XS  = 23,
+    SD_TYPE_I8      = 24,
+    SD_TYPE_I16     = 25,
+    SD_TYPE_I32     = 26,
+    SD_TYPE_I64     = 27,
+    SD_TYPE_F64     = 28,
+    SD_TYPE_IQ1_M   = 29,
+    SD_TYPE_BF16    = 30,
+    // SD_TYPE_Q4_0_4_4 = 31, support has been removed from gguf files
+    // SD_TYPE_Q4_0_4_8 = 32,
+    // SD_TYPE_Q4_0_8_8 = 33,
+    SD_TYPE_TQ1_0 = 34,
+    SD_TYPE_TQ2_0 = 35,
+    // SD_TYPE_IQ4_NL_4_4 = 36,
+    // SD_TYPE_IQ4_NL_4_8 = 37,
+    // SD_TYPE_IQ4_NL_8_8 = 38,
+    SD_TYPE_MXFP4 = 39,  // MXFP4 (1 block)
+    SD_TYPE_COUNT = 40,
 };
 
-SD_API const char* sd_type_name(enum sd_type_t type);
-
 enum sd_log_level_t {
     SD_LOG_DEBUG,
     SD_LOG_INFO,
@@ -105,6 +113,109 @@ enum sd_log_level_t {
     SD_LOG_ERROR
 };
 
+typedef struct {
+    const char* model_path;
+    const char* clip_l_path;
+    const char* clip_g_path;
+    const char* clip_vision_path;
+    const char* t5xxl_path;
+    const char* diffusion_model_path;
+    const char* high_noise_diffusion_model_path;
+    const char* vae_path;
+    const char* taesd_path;
+    const char* control_net_path;
+    const char* lora_model_dir;
+    const char* embedding_dir;
+    const char* stacked_id_embed_dir;
+    bool vae_decode_only;
+    bool vae_tiling;
+    bool free_params_immediately;
+    int n_threads;
+    enum sd_type_t wtype;
+    enum rng_type_t rng_type;
+    bool offload_params_to_cpu;
+    bool keep_clip_on_cpu;
+    bool keep_control_net_on_cpu;
+    bool keep_vae_on_cpu;
+    bool diffusion_flash_attn;
+    bool diffusion_conv_direct;
+    bool vae_conv_direct;
+    bool chroma_use_dit_mask;
+    bool chroma_use_t5_mask;
+    int chroma_t5_mask_pad;
+    float flow_shift;
+} sd_ctx_params_t;
+
+typedef struct {
+    uint32_t width;
+    uint32_t height;
+    uint32_t channel;
+    uint8_t* data;
+} sd_image_t;
+
+typedef struct {
+    int* layers;
+    size_t layer_count;
+    float layer_start;
+    float layer_end;
+    float scale;
+} sd_slg_params_t;
+
+typedef struct {
+    float txt_cfg;
+    float img_cfg;
+    float distilled_guidance;
+    sd_slg_params_t slg;
+} sd_guidance_params_t;
+
+typedef struct {
+    sd_guidance_params_t guidance;
+    enum scheduler_t scheduler;
+    enum sample_method_t sample_method;
+    int sample_steps;
+    float eta;
+} sd_sample_params_t;
+
+typedef struct {
+    const char* prompt;
+    const char* negative_prompt;
+    int clip_skip;
+    sd_image_t init_image;
+    sd_image_t* ref_images;
+    int ref_images_count;
+    bool increase_ref_index;
+    sd_image_t mask_image;
+    int width;
+    int height;
+    sd_sample_params_t sample_params;
+    float strength;
+    int64_t seed;
+    int batch_count;
+    sd_image_t control_image;
+    float control_strength;
+    float style_strength;
+    bool normalize_input;
+    const char* input_id_images_path;
+} sd_img_gen_params_t;
+
+typedef struct {
+    const char* prompt;
+    const char* negative_prompt;
+    int clip_skip;
+    sd_image_t init_image;
+    sd_image_t end_image;
+    int width;
+    int height;
+    sd_sample_params_t sample_params;
+    sd_sample_params_t high_noise_sample_params;
+    float moe_boundary;
+    float strength;
+    int64_t seed;
+    int video_frames;
+} sd_vid_gen_params_t;
+
+typedef struct sd_ctx_t sd_ctx_t;
+
 typedef void (*sd_log_cb_t)(enum sd_log_level_t level, const char* text, void* data);
 typedef void (*sd_progress_cb_t)(int step, int steps, float time, void* data);
 
@@ -113,111 +224,48 @@ SD_API void sd_set_progress_callback(sd_progress_cb_t cb, void* data);
 SD_API int32_t get_num_physical_cores();
 SD_API const char* sd_get_system_info();
 
-typedef struct {
-    uint32_t width;
-    uint32_t height;
-    uint32_t channel;
-    uint8_t* data;
-} sd_image_t;
+SD_API const char* sd_type_name(enum sd_type_t type);
+SD_API enum sd_type_t str_to_sd_type(const char* str);
+SD_API const char* sd_rng_type_name(enum rng_type_t rng_type);
+SD_API enum rng_type_t str_to_rng_type(const char* str);
+SD_API const char* sd_sample_method_name(enum sample_method_t sample_method);
+SD_API enum sample_method_t str_to_sample_method(const char* str);
+SD_API const char* sd_schedule_name(enum scheduler_t scheduler);
+SD_API enum scheduler_t str_to_schedule(const char* str);
 
-typedef struct sd_ctx_t sd_ctx_t;
-
-SD_API sd_ctx_t* new_sd_ctx(const char* model_path,
-                            const char* clip_l_path,
-                            const char* clip_g_path,
-                            const char* t5xxl_path,
-                            const char* diffusion_model_path,
-                            const char* vae_path,
-                            const char* taesd_path,
-                            const char* control_net_path_c_str,
-                            const char* lora_model_dir,
-                            const char* embed_dir_c_str,
-                            const char* stacked_id_embed_dir_c_str,
-                            bool vae_decode_only,
-                            bool vae_tiling,
-                            bool free_params_immediately,
-                            int n_threads,
-                            enum sd_type_t wtype,
-                            enum rng_type_t rng_type,
-                            enum schedule_t s,
-                            bool keep_clip_on_cpu,
-                            bool keep_control_net_cpu,
-                            bool keep_vae_on_cpu,
-                            bool diffusion_flash_attn);
+SD_API void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params);
+SD_API char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params);
 
+SD_API sd_ctx_t* new_sd_ctx(const sd_ctx_params_t* sd_ctx_params);
 SD_API void free_sd_ctx(sd_ctx_t* sd_ctx);
 
-SD_API sd_image_t* txt2img(sd_ctx_t* sd_ctx,
-                           const char* prompt,
-                           const char* negative_prompt,
-                           int clip_skip,
-                           float cfg_scale,
-                           float guidance,
-                           int width,
-                           int height,
-                           enum sample_method_t sample_method,
-                           int sample_steps,
-                           int64_t seed,
-                           int batch_count,
-                           const sd_image_t* control_cond,
-                           float control_strength,
-                           float style_strength,
-                           bool normalize_input,
-                           const char* input_id_images_path,
-                           std::vector<int> skip_layers = {},
-                           float slg_scale              = 2.5,
-                           float skip_layer_start       = 0.01,
-                           float skip_layer_end         = 0.2);
+SD_API void sd_sample_params_init(sd_sample_params_t* sample_params);
+SD_API char* sd_sample_params_to_str(const sd_sample_params_t* sample_params);
 
-SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
-                           sd_image_t init_image,
-                           const char* prompt,
-                           const char* negative_prompt,
-                           int clip_skip,
-                           float cfg_scale,
-                           float guidance,
-                           int width,
-                           int height,
-                           enum sample_method_t sample_method,
-                           int sample_steps,
-                           float strength,
-                           int64_t seed,
-                           int batch_count,
-                           const sd_image_t* control_cond,
-                           float control_strength,
-                           float style_strength,
-                           bool normalize_input,
-                           const char* input_id_images_path,
-                           std::vector<int> skip_layers = {},
-                           float slg_scale              = 2.5,
-                           float skip_layer_start       = 0.01,
-                           float skip_layer_end         = 0.2);
+SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
+SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);
+SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params);
 
-SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
-                           sd_image_t init_image,
-                           int width,
-                           int height,
-                           int video_frames,
-                           int motion_bucket_id,
-                           int fps,
-                           float augmentation_level,
-                           float min_cfg,
-                           float cfg_scale,
-                           enum sample_method_t sample_method,
-                           int sample_steps,
-                           float strength,
-                           int64_t seed);
+SD_API void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params);
+SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* sd_vid_gen_params, int* num_frames_out);
 
 typedef struct upscaler_ctx_t upscaler_ctx_t;
 
 SD_API upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path,
-                                        int n_threads,
-                                        enum sd_type_t wtype);
+                                        bool offload_params_to_cpu,
+                                        bool direct,
+                                        int n_threads);
 SD_API void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx);
 
-SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_t upscale_factor);
+SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx,
+                          sd_image_t input_image,
+                          uint32_t upscale_factor);
 
-SD_API bool convert(const char* input_path, const char* vae_path, const char* output_path, enum sd_type_t output_type);
+SD_API bool convert(const char* input_path,
+                    const char* vae_path,
+                    const char* output_path,
+                    enum sd_type_t output_type,
+                    const char* tensor_type_rules);
 
 SD_API uint8_t* preprocess_canny(uint8_t* img,
                                  int width,

t5.hpp

@@ -124,7 +124,10 @@ protected:
                 return;
             }
             std::string piece = item[0];
-            float score       = item[1];
+            if (piece.empty()) {
+                piece = "<empty_token>";
+            }
+            float score = item[1];
             piece_score_pairs.emplace_back(piece, score);
         }
     }
@@ -147,6 +150,7 @@ protected:
         std::vector<const char*> key(pieces->size());
         std::vector<int> value(pieces->size());
         for (size_t i = 0; i < pieces->size(); ++i) {
+            // LOG_DEBUG("%s %d", (*pieces)[i].first.c_str(), (*pieces)[i].second);
             key[i]   = (*pieces)[i].first.data();  // sorted piece.
             value[i] = (*pieces)[i].second;        // vocab_id
         }
@@ -335,9 +339,9 @@ protected:
     }
 
 public:
-    explicit T5UniGramTokenizer(const std::string& json_str = "") {
-        if (json_str.size() != 0) {
-            InitializePieces(json_str);
+    explicit T5UniGramTokenizer(bool is_umt5 = false) {
+        if (is_umt5) {
+            InitializePieces(ModelLoader::load_umt5_tokenizer_json());
         } else {
             InitializePieces(ModelLoader::load_t5_tokenizer_json());
         }
@@ -385,6 +389,7 @@ public:
 
     void pad_tokens(std::vector<int>& tokens,
                     std::vector<float>& weights,
+                    std::vector<float>* attention_mask,
                     size_t max_length = 0,
                     bool padding      = false) {
         if (max_length > 0 && padding) {
@@ -397,11 +402,15 @@ public:
             LOG_DEBUG("token length: %llu", length);
             std::vector<int> new_tokens;
             std::vector<float> new_weights;
+            std::vector<float> new_attention_mask;
             int token_idx = 0;
             for (int i = 0; i < length; i++) {
                 if (token_idx >= orig_token_num) {
                     break;
                 }
+                if (attention_mask != nullptr) {
+                    new_attention_mask.push_back(0.0);
+                }
                 if (i % max_length == max_length - 1) {
                     new_tokens.push_back(eos_id_);
                     new_weights.push_back(1.0);
@@ -414,13 +423,24 @@ public:
 
             new_tokens.push_back(eos_id_);
             new_weights.push_back(1.0);
+            if (attention_mask != nullptr) {
+                new_attention_mask.push_back(0.0);
+            }
+
             tokens  = new_tokens;
             weights = new_weights;
+            if (attention_mask != nullptr) {
+                *attention_mask = new_attention_mask;
+            }
 
             if (padding) {
                 int pad_token_id = pad_id_;
                 tokens.insert(tokens.end(), length - tokens.size(), pad_token_id);
                 weights.insert(weights.end(), length - weights.size(), 1.0);
+                if (attention_mask != nullptr) {
+                    // maybe keep some padding tokens unmasked?
+                    attention_mask->insert(attention_mask->end(), length - attention_mask->size(), -HUGE_VALF);
+                }
             }
         }
     }
@@ -441,8 +461,9 @@ protected:
     int64_t hidden_size;
     float eps;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["weight"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+        enum ggml_type wtype = GGML_TYPE_F32;
+        params["weight"]     = ggml_new_tensor_1d(ctx, wtype, hidden_size);
     }
 
 public:
@@ -557,6 +578,7 @@ public:
 
     // x: [N, n_token, model_dim]
     std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
+                                                                ggml_backend_t backend,
                                                                 struct ggml_tensor* x,
                                                                 struct ggml_tensor* past_bias                = NULL,
                                                                 struct ggml_tensor* mask                     = NULL,
@@ -578,6 +600,7 @@ public:
         }
         if (past_bias != NULL) {
             if (mask != NULL) {
+                mask = ggml_repeat(ctx, mask, past_bias);
                 mask = ggml_add(ctx, mask, past_bias);
             } else {
                 mask = past_bias;
@@ -586,7 +609,7 @@ public:
 
         k = ggml_scale_inplace(ctx, k, sqrt(d_head));
 
-        x = ggml_nn_attention_ext(ctx, q, k, v, num_heads, mask);  // [N, n_token, d_head * n_head]
+        x = ggml_nn_attention_ext(ctx, backend, q, k, v, num_heads, mask);  // [N, n_token, d_head * n_head]
 
         x = out_proj->forward(ctx, x);  // [N, n_token, model_dim]
         return {x, past_bias};
@@ -605,6 +628,7 @@ public:
     }
 
     std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
+                                                                ggml_backend_t backend,
                                                                 struct ggml_tensor* x,
                                                                 struct ggml_tensor* past_bias                = NULL,
                                                                 struct ggml_tensor* mask                     = NULL,
@@ -614,7 +638,7 @@ public:
         auto layer_norm    = std::dynamic_pointer_cast<T5LayerNorm>(blocks["layer_norm"]);
 
         auto normed_hidden_state = layer_norm->forward(ctx, x);
-        auto ret                 = SelfAttention->forward(ctx, normed_hidden_state, past_bias, mask, relative_position_bucket);
+        auto ret                 = SelfAttention->forward(ctx, backend, normed_hidden_state, past_bias, mask, relative_position_bucket);
         auto output              = ret.first;
         past_bias                = ret.second;
 
@@ -631,6 +655,7 @@ public:
     }
 
     std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
+                                                                ggml_backend_t backend,
                                                                 struct ggml_tensor* x,
                                                                 struct ggml_tensor* past_bias                = NULL,
                                                                 struct ggml_tensor* mask                     = NULL,
@@ -639,7 +664,7 @@ public:
         auto layer_0 = std::dynamic_pointer_cast<T5LayerSelfAttention>(blocks["layer.0"]);
         auto layer_1 = std::dynamic_pointer_cast<T5LayerFF>(blocks["layer.1"]);
 
-        auto ret  = layer_0->forward(ctx, x, past_bias, mask, relative_position_bucket);
+        auto ret  = layer_0->forward(ctx, backend, x, past_bias, mask, relative_position_bucket);
         x         = ret.first;
         past_bias = ret.second;
         x         = layer_1->forward(ctx, x);
@@ -655,16 +680,18 @@ public:
             int64_t model_dim,
             int64_t inner_dim,
             int64_t ff_dim,
-            int64_t num_heads)
+            int64_t num_heads,
+            bool relative_attention = true)
         : num_layers(num_layers) {
         for (int i = 0; i < num_layers; i++) {
-            blocks["block." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new T5Block(model_dim, inner_dim, ff_dim, num_heads, i == 0));
+            blocks["block." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new T5Block(model_dim, inner_dim, ff_dim, num_heads, (!relative_attention || i == 0)));
         }
 
         blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new T5LayerNorm(model_dim));
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* past_bias                = NULL,
                                 struct ggml_tensor* attention_mask           = NULL,
@@ -673,7 +700,7 @@ public:
         for (int i = 0; i < num_layers; i++) {
             auto block = std::dynamic_pointer_cast<T5Block>(blocks["block." + std::to_string(i)]);
 
-            auto ret  = block->forward(ctx, x, past_bias, attention_mask, relative_position_bucket);
+            auto ret  = block->forward(ctx, backend, x, past_bias, attention_mask, relative_position_bucket);
             x         = ret.first;
             past_bias = ret.second;
         }
@@ -685,18 +712,34 @@ public:
     }
 };
 
+struct T5Params {
+    int64_t num_layers      = 24;
+    int64_t model_dim       = 4096;
+    int64_t ff_dim          = 10240;
+    int64_t num_heads       = 64;
+    int64_t vocab_size      = 32128;
+    bool relative_attention = true;
+};
+
 struct T5 : public GGMLBlock {
+    T5Params params;
+
 public:
-    T5(int64_t num_layers,
-       int64_t model_dim,
-       int64_t ff_dim,
-       int64_t num_heads,
-       int64_t vocab_size) {
-        blocks["encoder"] = std::shared_ptr<GGMLBlock>(new T5Stack(num_layers, model_dim, model_dim, ff_dim, num_heads));
-        blocks["shared"]  = std::shared_ptr<GGMLBlock>(new Embedding(vocab_size, model_dim));
+    T5() {}
+    T5(T5Params params)
+        : params(params) {
+        blocks["encoder"] = std::shared_ptr<GGMLBlock>(new T5Stack(params.num_layers,
+                                                                   params.model_dim,
+                                                                   params.model_dim,
+                                                                   params.ff_dim,
+                                                                   params.num_heads,
+                                                                   params.relative_attention));
+        blocks["shared"]  = std::shared_ptr<GGMLBlock>(new Embedding(params.vocab_size,
+                                                                     params.model_dim));
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* past_bias                = NULL,
                                 struct ggml_tensor* attention_mask           = NULL,
@@ -707,24 +750,28 @@ public:
         auto encoder = std::dynamic_pointer_cast<T5Stack>(blocks["encoder"]);
 
         auto x = shared->forward(ctx, input_ids);
-        x      = encoder->forward(ctx, x, past_bias, attention_mask, relative_position_bucket);
+        x      = encoder->forward(ctx, backend, x, past_bias, attention_mask, relative_position_bucket);
         return x;
     }
 };
 
 struct T5Runner : public GGMLRunner {
+    T5Params params;
     T5 model;
     std::vector<int> relative_position_bucket_vec;
 
     T5Runner(ggml_backend_t backend,
-             ggml_type wtype,
-             int64_t num_layers = 24,
-             int64_t model_dim  = 4096,
-             int64_t ff_dim     = 10240,
-             int64_t num_heads  = 64,
-             int64_t vocab_size = 32128)
-        : GGMLRunner(backend, wtype), model(num_layers, model_dim, ff_dim, num_heads, vocab_size) {
-        model.init(params_ctx, wtype);
+             bool offload_params_to_cpu,
+             const String2GGMLType& tensor_types,
+             const std::string prefix,
+             bool is_umt5 = false)
+        : GGMLRunner(backend, offload_params_to_cpu) {
+        if (is_umt5) {
+            params.vocab_size         = 256384;
+            params.relative_attention = false;
+        }
+        model = T5(params);
+        model.init(params_ctx, tensor_types, prefix);
     }
 
     std::string get_desc() {
@@ -736,19 +783,23 @@ struct T5Runner : public GGMLRunner {
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* input_ids,
-                                struct ggml_tensor* relative_position_bucket) {
+                                struct ggml_tensor* relative_position_bucket,
+                                struct ggml_tensor* attention_mask = NULL) {
         size_t N       = input_ids->ne[1];
         size_t n_token = input_ids->ne[0];
 
-        auto hidden_states = model.forward(ctx, input_ids, NULL, NULL, relative_position_bucket);  // [N, n_token, model_dim]
+        auto hidden_states = model.forward(ctx, backend, input_ids, NULL, attention_mask, relative_position_bucket);  // [N, n_token, model_dim]
         return hidden_states;
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids) {
+    struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
+                                    struct ggml_tensor* attention_mask = NULL) {
         struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
 
-        input_ids = to_backend(input_ids);
+        input_ids      = to_backend(input_ids);
+        attention_mask = to_backend(attention_mask);
 
         relative_position_bucket_vec = compute_relative_position_bucket(input_ids->ne[0], input_ids->ne[0]);
 
@@ -765,7 +816,7 @@ struct T5Runner : public GGMLRunner {
                                                            input_ids->ne[0]);
         set_backend_tensor_data(relative_position_bucket, relative_position_bucket_vec.data());
 
-        struct ggml_tensor* hidden_states = forward(compute_ctx, input_ids, relative_position_bucket);
+        struct ggml_tensor* hidden_states = forward(compute_ctx, runtime_backend, input_ids, relative_position_bucket, attention_mask);
 
         ggml_build_forward_expand(gf, hidden_states);
 
@@ -774,10 +825,11 @@ struct T5Runner : public GGMLRunner {
 
     void compute(const int n_threads,
                  struct ggml_tensor* input_ids,
+                 struct ggml_tensor* attention_mask,
                  ggml_tensor** output,
                  ggml_context* output_ctx = NULL) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(input_ids);
+            return build_graph(input_ids, attention_mask);
         };
         GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
     }
@@ -855,13 +907,11 @@ struct T5Embedder {
     T5Runner model;
 
     T5Embedder(ggml_backend_t backend,
-               ggml_type wtype,
-               int64_t num_layers = 24,
-               int64_t model_dim  = 4096,
-               int64_t ff_dim     = 10240,
-               int64_t num_heads  = 64,
-               int64_t vocab_size = 32128)
-        : model(backend, wtype, num_layers, model_dim, ff_dim, num_heads, vocab_size) {
+               bool offload_params_to_cpu,
+               const String2GGMLType& tensor_types = {},
+               const std::string prefix            = "",
+               bool is_umt5                        = false)
+        : model(backend, offload_params_to_cpu, tensor_types, prefix, is_umt5), tokenizer(is_umt5) {
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
@@ -872,9 +922,9 @@ struct T5Embedder {
         model.alloc_params_buffer();
     }
 
-    std::pair<std::vector<int>, std::vector<float>> tokenize(std::string text,
-                                                             size_t max_length = 0,
-                                                             bool padding      = false) {
+    std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> tokenize(std::string text,
+                                                                                  size_t max_length = 0,
+                                                                                  bool padding      = false) {
         auto parsed_attention = parse_prompt_attention(text);
 
         {
@@ -901,14 +951,16 @@ struct T5Embedder {
         tokens.push_back(EOS_TOKEN_ID);
         weights.push_back(1.0);
 
-        tokenizer.pad_tokens(tokens, weights, max_length, padding);
+        std::vector<float> attention_mask;
+
+        tokenizer.pad_tokens(tokens, weights, &attention_mask, max_length, padding);
 
         // for (int i = 0; i < tokens.size(); i++) {
         //     std::cout << tokens[i] << ":" << weights[i] << ", ";
         // }
         // std::cout << std::endl;
 
-        return {tokens, weights};
+        return {tokens, weights, attention_mask};
     }
 
     void test() {
@@ -921,25 +973,22 @@ struct T5Embedder {
         GGML_ASSERT(work_ctx != NULL);
 
         {
-            // cpu f16: pass
-            // cpu f32: pass
-            // cuda f16: nan
-            // cuda f32: pass
-            // cuda q8_0: nan
-            // TODO: fix cuda nan
             std::string text("a lovely cat");
-            auto tokens_and_weights     = tokenize(text, 77, true);
-            std::vector<int>& tokens    = tokens_and_weights.first;
-            std::vector<float>& weights = tokens_and_weights.second;
+            // std::string text("一只可爱的猫"); // umt5 chinease test
+            auto tokens_and_weights     = tokenize(text, 512, true);
+            std::vector<int>& tokens    = std::get<0>(tokens_and_weights);
+            std::vector<float>& weights = std::get<1>(tokens_and_weights);
+            std::vector<float>& masks   = std::get<2>(tokens_and_weights);
             for (auto token : tokens) {
                 printf("%d ", token);
             }
             printf("\n");
             auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
+            auto attention_mask     = vector_to_ggml_tensor(work_ctx, masks);
             struct ggml_tensor* out = NULL;
 
             int t0 = ggml_time_ms();
-            model.compute(8, input_ids, &out, work_ctx);
+            model.compute(8, input_ids, attention_mask, &out, work_ctx);
             int t1 = ggml_time_ms();
 
             print_ggml_tensor(out);
@@ -948,32 +997,43 @@ struct T5Embedder {
     }
 
     static void load_from_file_and_test(const std::string& file_path) {
-        // ggml_backend_t backend    = ggml_backend_cuda_init(0);
-        ggml_backend_t backend         = ggml_backend_cpu_init();
-        ggml_type model_data_type      = GGML_TYPE_F32;
-        std::shared_ptr<T5Embedder> t5 = std::shared_ptr<T5Embedder>(new T5Embedder(backend, model_data_type));
-        {
-            LOG_INFO("loading from '%s'", file_path.c_str());
+        // cpu f16: pass
+        // cpu f32: pass
+        // cuda f16: pass
+        // cuda f32: pass
+        // cuda q8_0: pass
+        // ggml_backend_t backend = ggml_backend_cuda_init(0);
+        ggml_backend_t backend    = ggml_backend_cpu_init();
+        ggml_type model_data_type = GGML_TYPE_F16;
 
-            t5->alloc_params_buffer();
-            std::map<std::string, ggml_tensor*> tensors;
-            t5->get_param_tensors(tensors, "");
-
-            ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path)) {
-                LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
-                return;
-            }
-
-            bool success = model_loader.load_tensors(tensors, backend);
-
-            if (!success) {
-                LOG_ERROR("load tensors from model loader failed");
-                return;
-            }
-
-            LOG_INFO("t5 model loaded");
+        ModelLoader model_loader;
+        if (!model_loader.init_from_file(file_path)) {
+            LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
+            return;
         }
+
+        auto tensor_types = model_loader.tensor_storages_types;
+        for (auto& item : tensor_types) {
+            // LOG_DEBUG("%s %u", item.first.c_str(), item.second);
+            if (ends_with(item.first, "weight")) {
+                item.second = model_data_type;
+            }
+        }
+
+        std::shared_ptr<T5Embedder> t5 = std::shared_ptr<T5Embedder>(new T5Embedder(backend, false, tensor_types, "", true));
+
+        t5->alloc_params_buffer();
+        std::map<std::string, ggml_tensor*> tensors;
+        t5->get_param_tensors(tensors, "");
+
+        bool success = model_loader.load_tensors(tensors);
+
+        if (!success) {
+            LOG_ERROR("load tensors from model loader failed");
+            return;
+        }
+
+        LOG_INFO("t5 model loaded");
         t5->test();
     }
 };

tae.hpp

@@ -62,7 +62,8 @@ class TinyEncoder : public UnaryBlock {
     int num_blocks  = 3;
 
 public:
-    TinyEncoder() {
+    TinyEncoder(int z_channels = 4)
+        : z_channels(z_channels) {
         int index                       = 0;
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, channels, {3, 3}, {1, 1}, {1, 1}));
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
@@ -106,7 +107,10 @@ class TinyDecoder : public UnaryBlock {
     int num_blocks   = 3;
 
 public:
-    TinyDecoder(int index = 0) {
+    TinyDecoder(int z_channels = 4)
+        : z_channels(z_channels) {
+        int index = 0;
+
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, channels, {3, 3}, {1, 1}, {1, 1}));
         index++;  // nn.ReLU()
 
@@ -145,7 +149,7 @@ public:
                 if (i == 1) {
                     h = ggml_relu_inplace(ctx, h);
                 } else {
-                    h = ggml_upscale(ctx, h, 2);
+                    h = ggml_upscale(ctx, h, 2, GGML_SCALE_MODE_NEAREST);
                 }
                 continue;
             }
@@ -163,12 +167,16 @@ protected:
     bool decode_only;
 
 public:
-    TAESD(bool decode_only = true)
+    TAESD(bool decode_only = true, SDVersion version = VERSION_SD1)
         : decode_only(decode_only) {
-        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder());
+        int z_channels = 4;
+        if (sd_version_is_dit(version)) {
+            z_channels = 16;
+        }
+        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder(z_channels));
 
         if (!decode_only) {
-            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder());
+            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder(z_channels));
         }
     }
 
@@ -188,12 +196,26 @@ struct TinyAutoEncoder : public GGMLRunner {
     bool decode_only = false;
 
     TinyAutoEncoder(ggml_backend_t backend,
-                    ggml_type wtype,
-                    bool decoder_only = true)
+                    bool offload_params_to_cpu,
+                    const String2GGMLType& tensor_types,
+                    const std::string prefix,
+                    bool decoder_only = true,
+                    SDVersion version = VERSION_SD1)
         : decode_only(decoder_only),
-          taesd(decode_only),
-          GGMLRunner(backend, wtype) {
-        taesd.init(params_ctx, wtype);
+          taesd(decoder_only, version),
+          GGMLRunner(backend, offload_params_to_cpu) {
+        taesd.init(params_ctx, tensor_types, prefix);
+    }
+
+    void enable_conv2d_direct() {
+        std::vector<GGMLBlock*> blocks;
+        taesd.get_all_blocks(blocks);
+        for (auto block : blocks) {
+            if (block->get_desc() == "Conv2d") {
+                auto conv_block = (Conv2d*)block;
+                conv_block->enable_direct();
+            }
+        }
     }
 
     std::string get_desc() {
@@ -216,7 +238,7 @@ struct TinyAutoEncoder : public GGMLRunner {
             return false;
         }
 
-        bool success = model_loader.load_tensors(taesd_tensors, backend, ignore_tensors);
+        bool success = model_loader.load_tensors(taesd_tensors, ignore_tensors);
 
         if (!success) {
             LOG_ERROR("load tae tensors from model loader failed");

thirdparty/darts.h

@@ -4,6 +4,7 @@
 #include <cstdio>
 #include <exception>
 #include <new>
+#include <iostream>
 
 #define DARTS_VERSION "0.32"
 
@@ -1140,9 +1141,11 @@ inline void DawgBuilder::insert(const char *key, std::size_t length,
   if (value < 0) {
     DARTS_THROW("failed to insert key: negative value");
   } else if (length == 0) {
+    std::cout << value << std::endl;
     DARTS_THROW("failed to insert key: zero-length key");
   }
 
+
   id_type id = 0;
   std::size_t key_pos = 0;
 

thirdparty/stb_image_write.h

@@ -177,7 +177,7 @@ STBIWDEF int stbi_write_png(char const *filename, int w, int h, int comp, const
 STBIWDEF int stbi_write_bmp(char const *filename, int w, int h, int comp, const void  *data);
 STBIWDEF int stbi_write_tga(char const *filename, int w, int h, int comp, const void  *data);
 STBIWDEF int stbi_write_hdr(char const *filename, int w, int h, int comp, const float *data);
-STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void  *data, int quality);
+STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void  *data, int quality, const char* parameters = NULL);
 
 #ifdef STBIW_WINDOWS_UTF8
 STBIWDEF int stbiw_convert_wchar_to_utf8(char *buffer, size_t bufferlen, const wchar_t* input);
@@ -1412,7 +1412,7 @@ static int stbiw__jpg_processDU(stbi__write_context *s, int *bitBuf, int *bitCnt
    return DU[0];
 }
 
-static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality) {
+static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, int comp, const void* data, int quality, const char* parameters) {
    // Constants that don't pollute global namespace
    static const unsigned char std_dc_luminance_nrcodes[] = {0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0};
    static const unsigned char std_dc_luminance_values[] = {0,1,2,3,4,5,6,7,8,9,10,11};
@@ -1521,6 +1521,20 @@ static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, in
       s->func(s->context, (void*)YTable, sizeof(YTable));
       stbiw__putc(s, 1);
       s->func(s->context, UVTable, sizeof(UVTable));
+
+      // comment block with parameters of generation
+      if(parameters != NULL) {
+         stbiw__putc(s, 0xFF /* comnent */ );
+         stbiw__putc(s, 0xFE /* marker  */ );
+         size_t param_length = std::min(2 + strlen("parameters") + 1 + strlen(parameters) + 1, (size_t) 0xFFFF);
+         stbiw__putc(s, param_length >> 8); // no need to mask, length < 65536
+         stbiw__putc(s, param_length & 0xFF);
+         s->func(s->context, (void*)"parameters", strlen("parameters") + 1); // std::string is zero-terminated
+         s->func(s->context, (void*)parameters, std::min(param_length, (size_t) 65534) - 2 - strlen("parameters") - 1);
+         if(param_length > 65534) stbiw__putc(s, 0); // always zero-terminate for safety
+         if(param_length & 1) stbiw__putc(s, 0xFF); // pad to even length
+      }
+
       s->func(s->context, (void*)head1, sizeof(head1));
       s->func(s->context, (void*)(std_dc_luminance_nrcodes+1), sizeof(std_dc_luminance_nrcodes)-1);
       s->func(s->context, (void*)std_dc_luminance_values, sizeof(std_dc_luminance_values));
@@ -1625,16 +1639,16 @@ STBIWDEF int stbi_write_jpg_to_func(stbi_write_func *func, void *context, int x,
 {
    stbi__write_context s = { 0 };
    stbi__start_write_callbacks(&s, func, context);
-   return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality);
+   return stbi_write_jpg_core(&s, x, y, comp, (void *) data, quality, NULL);
 }
 
 
 #ifndef STBI_WRITE_NO_STDIO
-STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality)
+STBIWDEF int stbi_write_jpg(char const *filename, int x, int y, int comp, const void *data, int quality, const char* parameters)
 {
    stbi__write_context s = { 0 };
    if (stbi__start_write_file(&s,filename)) {
-      int r = stbi_write_jpg_core(&s, x, y, comp, data, quality);
+      int r = stbi_write_jpg_core(&s, x, y, comp, data, quality, parameters);
       stbi__end_write_file(&s);
       return r;
    } else

unet.hpp

@@ -61,6 +61,7 @@ public:
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* context,
                                 int timesteps) {
@@ -127,7 +128,7 @@ public:
             auto block     = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[transformer_name]);
             auto mix_block = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[time_stack_name]);
 
-            x = block->forward(ctx, x, spatial_context);  // [N, h * w, inner_dim]
+            x = block->forward(ctx, backend, x, spatial_context);  // [N, h * w, inner_dim]
 
             // in_channels == inner_dim
             auto x_mix = x;
@@ -143,7 +144,7 @@ public:
             x_mix = ggml_cont(ctx, ggml_permute(ctx, x_mix, 0, 2, 1, 3));  // b t s c -> b s t c
             x_mix = ggml_reshape_3d(ctx, x_mix, C, T, S * B);              // b s t c -> (b s) t c
 
-            x_mix = mix_block->forward(ctx, x_mix, time_context);  // [B * h * w, T, inner_dim]
+            x_mix = mix_block->forward(ctx, backend, x_mix, time_context);  // [B * h * w, T, inner_dim]
 
             x_mix = ggml_reshape_4d(ctx, x_mix, C, T, S, B);               // (b s) t c -> b s t c
             x_mix = ggml_cont(ctx, ggml_permute(ctx, x_mix, 0, 2, 1, 3));  // b s t c -> b t s c
@@ -183,13 +184,13 @@ public:
     int model_channels  = 320;
     int adm_in_channels = 2816;  // only for VERSION_SDXL/SVD
 
-    UnetModelBlock(SDVersion version = VERSION_SD1, bool flash_attn = false)
+    UnetModelBlock(SDVersion version = VERSION_SD1, const String2GGMLType& tensor_types = {}, bool flash_attn = false)
         : version(version) {
-        if (version == VERSION_SD2) {
+        if (sd_version_is_sd2(version)) {
             context_dim       = 1024;
             num_head_channels = 64;
             num_heads         = -1;
-        } else if (version == VERSION_SDXL) {
+        } else if (sd_version_is_sdxl(version)) {
             context_dim           = 2048;
             attention_resolutions = {4, 2};
             channel_mult          = {1, 2, 4};
@@ -204,6 +205,12 @@ public:
             num_head_channels = 64;
             num_heads         = -1;
         }
+        if (sd_version_is_inpaint(version)) {
+            in_channels = 9;
+        } else if (sd_version_is_unet_edit(version)) {
+            in_channels = 8;
+        }
+
         // dims is always 2
         // use_temporal_attention is always True for SVD
 
@@ -211,7 +218,7 @@ public:
         // time_embed_1 is nn.SiLU()
         blocks["time_embed.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
 
-        if (version == VERSION_SDXL || version == VERSION_SVD) {
+        if (sd_version_is_sdxl(version) || version == VERSION_SVD) {
             blocks["label_emb.0.0"] = std::shared_ptr<GGMLBlock>(new Linear(adm_in_channels, time_embed_dim));
             // label_emb_1 is nn.SiLU()
             blocks["label_emb.0.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
@@ -357,21 +364,23 @@ public:
 
     struct ggml_tensor* attention_layer_forward(std::string name,
                                                 struct ggml_context* ctx,
+                                                ggml_backend_t backend,
                                                 struct ggml_tensor* x,
                                                 struct ggml_tensor* context,
                                                 int timesteps) {
         if (version == VERSION_SVD) {
             auto block = std::dynamic_pointer_cast<SpatialVideoTransformer>(blocks[name]);
 
-            return block->forward(ctx, x, context, timesteps);
+            return block->forward(ctx, backend, x, context, timesteps);
         } else {
             auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
 
-            return block->forward(ctx, x, context);
+            return block->forward(ctx, backend, x, context);
         }
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx,
+                                ggml_backend_t backend,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* timesteps,
                                 struct ggml_tensor* context,
@@ -450,7 +459,7 @@ public:
                 h                = resblock_forward(name, ctx, h, emb, num_video_frames);  // [N, mult*model_channels, h, w]
                 if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
                     std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
-                    h                = attention_layer_forward(name, ctx, h, context, num_video_frames);  // [N, mult*model_channels, h, w]
+                    h                = attention_layer_forward(name, ctx, backend, h, context, num_video_frames);  // [N, mult*model_channels, h, w]
                 }
                 hs.push_back(h);
             }
@@ -468,9 +477,9 @@ public:
         // [N, 4*model_channels, h/8, w/8]
 
         // middle_block
-        h = resblock_forward("middle_block.0", ctx, h, emb, num_video_frames);             // [N, 4*model_channels, h/8, w/8]
-        h = attention_layer_forward("middle_block.1", ctx, h, context, num_video_frames);  // [N, 4*model_channels, h/8, w/8]
-        h = resblock_forward("middle_block.2", ctx, h, emb, num_video_frames);             // [N, 4*model_channels, h/8, w/8]
+        h = resblock_forward("middle_block.0", ctx, h, emb, num_video_frames);                      // [N, 4*model_channels, h/8, w/8]
+        h = attention_layer_forward("middle_block.1", ctx, backend, h, context, num_video_frames);  // [N, 4*model_channels, h/8, w/8]
+        h = resblock_forward("middle_block.2", ctx, h, emb, num_video_frames);                      // [N, 4*model_channels, h/8, w/8]
 
         if (controls.size() > 0) {
             auto cs = ggml_scale_inplace(ctx, controls[controls.size() - 1], control_strength);
@@ -501,7 +510,7 @@ public:
                 if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
                     std::string name = "output_blocks." + std::to_string(output_block_idx) + ".1";
 
-                    h = attention_layer_forward(name, ctx, h, context, num_video_frames);
+                    h = attention_layer_forward(name, ctx, backend, h, context, num_video_frames);
 
                     up_sample_idx++;
                 }
@@ -532,11 +541,25 @@ struct UNetModelRunner : public GGMLRunner {
     UnetModelBlock unet;
 
     UNetModelRunner(ggml_backend_t backend,
-                    ggml_type wtype,
+                    bool offload_params_to_cpu,
+                    const String2GGMLType& tensor_types,
+                    const std::string prefix,
                     SDVersion version = VERSION_SD1,
                     bool flash_attn   = false)
-        : GGMLRunner(backend, wtype), unet(version, flash_attn) {
-        unet.init(params_ctx, wtype);
+        : GGMLRunner(backend, offload_params_to_cpu), unet(version, tensor_types, flash_attn) {
+        unet.init(params_ctx, tensor_types, prefix);
+    }
+
+    void enable_conv2d_direct() {
+        std::vector<GGMLBlock*> blocks;
+        unet.get_all_blocks(blocks);
+        for (auto block : blocks) {
+            if (block->get_desc() == "Conv2d") {
+                LOG_DEBUG("block %s", block->get_desc().c_str());
+                auto conv_block = (Conv2d*)block;
+                conv_block->enable_direct();
+            }
+        }
     }
 
     std::string get_desc() {
@@ -565,12 +588,14 @@ struct UNetModelRunner : public GGMLRunner {
         context   = to_backend(context);
         y         = to_backend(y);
         timesteps = to_backend(timesteps);
+        c_concat  = to_backend(c_concat);
 
         for (int i = 0; i < controls.size(); i++) {
             controls[i] = to_backend(controls[i]);
         }
 
         struct ggml_tensor* out = unet.forward(compute_ctx,
+                                               runtime_backend,
                                                x,
                                                timesteps,
                                                context,

upscaler.cpp

@@ -9,36 +9,51 @@ struct UpscalerGGML {
     std::shared_ptr<ESRGAN> esrgan_upscaler;
     std::string esrgan_path;
     int n_threads;
+    bool direct = false;
 
-    UpscalerGGML(int n_threads)
-        : n_threads(n_threads) {
+    UpscalerGGML(int n_threads,
+                 bool direct = false)
+        : n_threads(n_threads),
+          direct(direct) {
     }
 
-    bool load_from_file(const std::string& esrgan_path) {
-#ifdef SD_USE_CUBLAS
+    bool load_from_file(const std::string& esrgan_path,
+                        bool offload_params_to_cpu) {
+        ggml_log_set(ggml_log_callback_default, nullptr);
+#ifdef SD_USE_CUDA
         LOG_DEBUG("Using CUDA backend");
         backend = ggml_backend_cuda_init(0);
 #endif
 #ifdef SD_USE_METAL
         LOG_DEBUG("Using Metal backend");
-        ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
         backend = ggml_backend_metal_init();
 #endif
 #ifdef SD_USE_VULKAN
         LOG_DEBUG("Using Vulkan backend");
         backend = ggml_backend_vk_init(0);
 #endif
+#ifdef SD_USE_OPENCL
+        LOG_DEBUG("Using OpenCL backend");
+        backend = ggml_backend_opencl_init();
+#endif
 #ifdef SD_USE_SYCL
         LOG_DEBUG("Using SYCL backend");
         backend = ggml_backend_sycl_init(0);
 #endif
-
+        ModelLoader model_loader;
+        if (!model_loader.init_from_file(esrgan_path)) {
+            LOG_ERROR("init model loader from file failed: '%s'", esrgan_path.c_str());
+        }
+        model_loader.set_wtype_override(model_data_type);
         if (!backend) {
             LOG_DEBUG("Using CPU backend");
             backend = ggml_backend_cpu_init();
         }
         LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
-        esrgan_upscaler = std::make_shared<ESRGAN>(backend, model_data_type);
+        esrgan_upscaler = std::make_shared<ESRGAN>(backend, offload_params_to_cpu, model_loader.tensor_storages_types);
+        if (direct) {
+            esrgan_upscaler->enable_conv2d_direct();
+        }
         if (!esrgan_upscaler->load_from_file(esrgan_path)) {
             return false;
         }
@@ -96,20 +111,21 @@ struct upscaler_ctx_t {
 };
 
 upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
-                                 int n_threads,
-                                 enum sd_type_t wtype) {
+                                 bool offload_params_to_cpu,
+                                 bool direct,
+                                 int n_threads) {
     upscaler_ctx_t* upscaler_ctx = (upscaler_ctx_t*)malloc(sizeof(upscaler_ctx_t));
     if (upscaler_ctx == NULL) {
         return NULL;
     }
     std::string esrgan_path(esrgan_path_c_str);
 
-    upscaler_ctx->upscaler = new UpscalerGGML(n_threads);
+    upscaler_ctx->upscaler = new UpscalerGGML(n_threads, direct);
     if (upscaler_ctx->upscaler == NULL) {
         return NULL;
     }
 
-    if (!upscaler_ctx->upscaler->load_from_file(esrgan_path)) {
+    if (!upscaler_ctx->upscaler->load_from_file(esrgan_path, offload_params_to_cpu)) {
         delete upscaler_ctx->upscaler;
         upscaler_ctx->upscaler = NULL;
         free(upscaler_ctx);

util.cpp

@@ -22,6 +22,7 @@
 #include <unistd.h>
 #endif
 
+#include "ggml-cpu.h"
 #include "ggml.h"
 #include "stable-diffusion.h"
 
@@ -71,6 +72,17 @@ std::string format(const char* fmt, ...) {
     return std::string(buf.data(), size);
 }
 
+int round_up_to(int value, int base) {
+    if (base <= 0) {
+        return value;
+    }
+    if (value % base == 0) {
+        return value;
+    } else {
+        return ((value / base) + 1) * base;
+    }
+}
+
 #ifdef _WIN32  // code for windows
 #include <windows.h>
 
@@ -111,19 +123,32 @@ std::vector<std::string> get_files_from_dir(const std::string& dir) {
     sprintf(directoryPath, "%s\\%s\\*", currentDirectory, dir.c_str());
 
     // Find the first file in the directory
-    hFind = FindFirstFile(directoryPath, &findFileData);
-
+    hFind               = FindFirstFile(directoryPath, &findFileData);
+    bool isAbsolutePath = false;
     // Check if the directory was found
     if (hFind == INVALID_HANDLE_VALUE) {
-        printf("Unable to find directory.\n");
-        return files;
+        printf("Unable to find directory. Try with original path \n");
+
+        char directoryPathAbsolute[MAX_PATH];
+        sprintf(directoryPathAbsolute, "%s*", dir.c_str());
+
+        hFind          = FindFirstFile(directoryPathAbsolute, &findFileData);
+        isAbsolutePath = true;
+        if (hFind == INVALID_HANDLE_VALUE) {
+            printf("Absolute path was also wrong.\n");
+            return files;
+        }
     }
 
     // Loop through all files in the directory
     do {
         // Check if the found file is a regular file (not a directory)
         if (!(findFileData.dwFileAttributes & FILE_ATTRIBUTE_DIRECTORY)) {
-            files.push_back(std::string(currentDirectory) + "\\" + dir + "\\" + std::string(findFileData.cFileName));
+            if (isAbsolutePath) {
+                files.push_back(dir + "\\" + std::string(findFileData.cFileName));
+            } else {
+                files.push_back(std::string(currentDirectory) + "\\" + dir + "\\" + std::string(findFileData.cFileName));
+            }
         }
     } while (FindNextFile(hFind, &findFileData) != 0);
 
@@ -276,7 +301,7 @@ std::string path_join(const std::string& p1, const std::string& p2) {
     return p1 + "/" + p2;
 }
 
-std::vector<std::string> splitString(const std::string& str, char delimiter) {
+std::vector<std::string> split_string(const std::string& str, char delimiter) {
     std::vector<std::string> result;
     size_t start = 0;
     size_t end   = str.find(delimiter);
@@ -347,7 +372,7 @@ void pretty_progress(int step, int steps, float time) {
         }
     }
     progress += "|";
-    printf(time > 1.0f ? "\r%s %i/%i - %.2fs/it" : "\r%s %i/%i - %.2fit/s",
+    printf(time > 1.0f ? "\r%s %i/%i - %.2fs/it" : "\r%s %i/%i - %.2fit/s\033[K",
            progress.c_str(), step, steps,
            time > 1.0f || time == 0 ? time : (1.0f / time));
     fflush(stdout);  // for linux
@@ -389,7 +414,10 @@ void log_printf(sd_log_level_t level, const char* file, int line, const char* fo
     if (written >= 0 && written < LOG_BUFFER_SIZE) {
         vsnprintf(log_buffer + written, LOG_BUFFER_SIZE - written, format, args);
     }
-    strncat(log_buffer, "\n", LOG_BUFFER_SIZE - strlen(log_buffer));
+    size_t len = strlen(log_buffer);
+    if (log_buffer[len - 1] != '\n') {
+        strncat(log_buffer, "\n", LOG_BUFFER_SIZE - len);
+    }
 
     if (sd_log_cb) {
         sd_log_cb(level, log_buffer, sd_log_cb_data);
@@ -410,7 +438,6 @@ const char* sd_get_system_info() {
     static char buffer[1024];
     std::stringstream ss;
     ss << "System Info: \n";
-    ss << "    BLAS = " << ggml_cpu_has_blas() << std::endl;
     ss << "    SSE3 = " << ggml_cpu_has_sse3() << std::endl;
     ss << "    AVX = " << ggml_cpu_has_avx() << std::endl;
     ss << "    AVX2 = " << ggml_cpu_has_avx2() << std::endl;
@@ -428,10 +455,6 @@ const char* sd_get_system_info() {
     return buffer;
 }
 
-const char* sd_type_name(enum sd_type_t type) {
-    return ggml_type_name((ggml_type)type);
-}
-
 sd_image_f32_t sd_image_t_to_sd_image_f32_t(sd_image_t image) {
     sd_image_f32_t converted_image;
     converted_image.width   = image.width;

util.h

@@ -7,6 +7,9 @@
 
 #include "stable-diffusion.h"
 
+#define SAFE_STR(s) ((s) ? (s) : "")
+#define BOOL_STR(b) ((b) ? "true" : "false")
+
 bool ends_with(const std::string& str, const std::string& ending);
 bool starts_with(const std::string& str, const std::string& start);
 bool contains(const std::string& str, const std::string& substr);
@@ -15,6 +18,8 @@ std::string format(const char* fmt, ...);
 
 void replace_all_chars(std::string& str, char target, char replacement);
 
+int round_up_to(int value, int base);
+
 bool file_exists(const std::string& filename);
 bool is_directory(const std::string& path);
 std::string get_full_path(const std::string& dir, const std::string& filename);
@@ -45,7 +50,7 @@ sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int
 sd_image_f32_t clip_preprocess(sd_image_f32_t image, int size);
 
 std::string path_join(const std::string& p1, const std::string& p2);
-std::vector<std::string> splitString(const std::string& str, char delimiter);
+std::vector<std::string> split_string(const std::string& str, char delimiter);
 void pretty_progress(int step, int steps, float time);
 
 void log_printf(sd_log_level_t level, const char* file, int line, const char* format, ...);

vae.hpp

@@ -99,12 +99,10 @@ public:
         k      = ggml_cont(ctx, ggml_permute(ctx, k, 1, 2, 0, 3));  // [N, h, w, in_channels]
         k      = ggml_reshape_3d(ctx, k, c, h * w, n);              // [N, h * w, in_channels]
 
-        auto v = v_proj->forward(ctx, h_);                          // [N, in_channels, h, w]
-        v      = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3));  // [N, h, w, in_channels]
-        v      = ggml_reshape_3d(ctx, v, c, h * w, n);              // [N, h * w, in_channels]
+        auto v = v_proj->forward(ctx, h_);              // [N, in_channels, h, w]
+        v      = ggml_reshape_3d(ctx, v, h * w, c, n);  // [N, in_channels, h * w]
 
-        // h_ = ggml_nn_attention(ctx, q, k, v, false);  // [N, h * w, in_channels]
-        h_ = ggml_nn_attention_ext(ctx, q, k, v, 1, nullptr, false, true, false);
+        h_ = ggml_nn_attention(ctx, q, k, v, false);  // [N, h * w, in_channels]
 
         h_ = ggml_cont(ctx, ggml_permute(ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
         h_ = ggml_reshape_4d(ctx, h_, w, h, c, n);               // [N, in_channels, h, w]
@@ -165,8 +163,9 @@ public:
 
 class VideoResnetBlock : public ResnetBlock {
 protected:
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["mix_factor"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+        enum ggml_type wtype = get_type(prefix + "mix_factor", tensor_types, GGML_TYPE_F32);
+        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
     }
 
     float get_alpha() {
@@ -521,17 +520,42 @@ public:
     }
 };
 
-struct AutoEncoderKL : public GGMLRunner {
+struct VAE : public GGMLRunner {
+    VAE(ggml_backend_t backend, bool offload_params_to_cpu)
+        : GGMLRunner(backend, offload_params_to_cpu) {}
+    virtual void compute(const int n_threads,
+                         struct ggml_tensor* z,
+                         bool decode_graph,
+                         struct ggml_tensor** output,
+                         struct ggml_context* output_ctx)                                                         = 0;
+    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
+    virtual void enable_conv2d_direct(){};
+};
+
+struct AutoEncoderKL : public VAE {
     bool decode_only = true;
     AutoencodingEngine ae;
 
     AutoEncoderKL(ggml_backend_t backend,
-                  ggml_type wtype,
+                  bool offload_params_to_cpu,
+                  const String2GGMLType& tensor_types,
+                  const std::string prefix,
                   bool decode_only       = false,
                   bool use_video_decoder = false,
                   SDVersion version      = VERSION_SD1)
-        : decode_only(decode_only), ae(decode_only, use_video_decoder, version), GGMLRunner(backend, wtype) {
-        ae.init(params_ctx, wtype);
+        : decode_only(decode_only), ae(decode_only, use_video_decoder, version), VAE(backend, offload_params_to_cpu) {
+        ae.init(params_ctx, tensor_types, prefix);
+    }
+
+    void enable_conv2d_direct() {
+        std::vector<GGMLBlock*> blocks;
+        ae.get_all_blocks(blocks);
+        for (auto block : blocks) {
+            if (block->get_desc() == "Conv2d") {
+                auto conv_block = (Conv2d*)block;
+                conv_block->enable_direct();
+            }
+        }
     }
 
     std::string get_desc() {
@@ -564,7 +588,7 @@ struct AutoEncoderKL : public GGMLRunner {
         };
         // ggml_set_f32(z, 0.5f);
         // print_ggml_tensor(z);
-        GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+        GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
     }
 
     void test() {