Merge branch 'master' into wan2.2_5B_flf2v

.github/workflows/build.yml

@@ -21,11 +21,13 @@ on:
         "**/*.c",
         "**/*.cpp",
         "**/*.cu",
+        "examples/server/frontend/**",
       ]
   pull_request:
     types: [opened, synchronize, reopened]
     paths:
       [
+        ".github/workflows/**",
         "**/CMakeLists.txt",
         "**/Makefile",
         "**/*.h",
@@ -33,6 +35,7 @@ on:
         "**/*.c",
         "**/*.cpp",
         "**/*.cu",
+        "examples/server/frontend/**",
       ]
 
 env:
@@ -53,6 +56,16 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
       - name: Dependencies
         id: depends
         run: |
@@ -70,7 +83,7 @@ jobs:
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Fetch system info
         id: system-info
@@ -106,6 +119,16 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
       - name: Dependencies
         id: depends
         run: |
@@ -123,7 +146,7 @@ jobs:
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Fetch system info
         id: system-info
@@ -162,7 +185,7 @@ jobs:
 
     strategy:
       matrix:
-        variant: [musa, sycl, vulkan]
+        variant: [musa, sycl, vulkan, cuda]
 
     env:
       REGISTRY: ghcr.io
@@ -174,10 +197,20 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
@@ -223,6 +256,16 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
       - name: Dependencies
         id: depends
         run: |
@@ -240,7 +283,7 @@ jobs:
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Fetch system info
         id: system-info
@@ -294,6 +337,16 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
       - name: Install cuda-toolkit
         id: cuda-toolkit
         if: ${{ matrix.build == 'cuda12' }}
@@ -340,7 +393,7 @@ jobs:
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Pack artifacts
         id: pack_artifacts
@@ -399,6 +452,16 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
       - name: Cache ROCm Installation
         id: cache-rocm
         uses: actions/cache@v4
@@ -463,7 +526,7 @@ jobs:
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Pack artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -502,6 +565,16 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 9
+
       - name: Free disk space
         run: |
           # Remove preinstalled SDKs and caches not needed for this job
@@ -581,7 +654,7 @@ jobs:
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Prepare artifacts
         id: prepare_artifacts
@@ -660,7 +733,7 @@ jobs:
 
       - name: Get commit hash
         id: commit
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2
 
       - name: Create release
         id: create_release

.gitmodules

@@ -1,3 +1,6 @@
 [submodule "ggml"]
     path = ggml
 	url = https://github.com/ggml-org/ggml.git
+[submodule "examples/server/frontend"]
+	path = examples/server/frontend
+	url = https://github.com/leejet/stable-ui.git

CMakeLists.txt

@@ -36,7 +36,6 @@ 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_BUILD_SHARED_GGML_LIB      "sd: build ggml as a separate shared lib" OFF)
 option(SD_USE_SYSTEM_GGML            "sd: use system-installed GGML library" OFF)
@@ -70,18 +69,12 @@ if (SD_HIPBLAS)
     message("-- Use HIPBLAS as backend stable-diffusion")
     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)

Dockerfile.cuda

@@ -0,0 +1,25 @@
+ARG CUDA_VERSION=12.6.3
+ARG UBUNTU_VERSION=24.04
+
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu${UBUNTU_VERSION} AS build
+
+RUN apt-get update && apt-get install -y --no-install-recommends build-essential git ccache cmake
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+ARG CUDACXX=/usr/local/cuda/bin/nvcc
+RUN cmake . -B ./build -DSD_CUDA=ON
+RUN cmake --build ./build --config Release -j$(nproc)
+
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-runtime-ubuntu${UBUNTU_VERSION} AS runtime
+
+RUN apt-get update && \
+    apt-get install --yes --no-install-recommends libgomp1 && \
+    apt-get clean
+
+COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server
+
+ENTRYPOINT [ "/sd-cli" ]

docs/anima.md

@@ -5,6 +5,7 @@
 - Download Anima
     - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/diffusion_models
     - gguf: https://huggingface.co/Bedovyy/Anima-GGUF/tree/main
+    - gguf Anima2: https://huggingface.co/JusteLeo/Anima2-GGUF/tree/main
 - Download vae
     - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/vae
 - Download Qwen3-0.6B-Base

docs/caching.md

@@ -11,6 +11,7 @@ Caching methods accelerate diffusion inference by reusing intermediate computati
 | `dbcache` | DiT models | Block-level L1 residual threshold |
 | `taylorseer` | DiT models | Taylor series approximation |
 | `cache-dit` | DiT models | Combined DBCache + TaylorSeer |
+| `spectrum` | UNET and DiT models | Chebyshev + Taylor output forecasting |
 
 ### UCache (UNET Models)
 
@@ -79,7 +80,7 @@ Uses Taylor series approximation to predict block outputs:
 Combines DBCache and TaylorSeer:
 
 ```bash
---cache-mode cache-dit --cache-preset fast
+--cache-mode cache-dit
 ```
 
 #### Parameters
@@ -91,14 +92,6 @@ Combines DBCache and TaylorSeer:
 | `threshold` | L1 residual difference threshold | 0.08 |
 | `warmup` | Steps before caching starts | 8 |
 
-#### Presets
-
-Available presets: `slow`, `medium`, `fast`, `ultra` (or `s`, `m`, `f`, `u`).
-
-```bash
---cache-mode cache-dit --cache-preset fast
-```
-
 #### SCM Options
 
 Steps Computation Mask controls which steps can be cached:
@@ -118,6 +111,28 @@ Mask values: `1` = compute, `0` = can cache.
 --scm-policy dynamic
 ```
 
+### Spectrum (UNET and DiT Models)
+
+Spectrum uses Chebyshev polynomial fitting blended with Taylor extrapolation to predict denoised outputs, skipping entire forward passes. Based on the paper [Spectrum: Adaptive Spectral Feature Forecasting for Efficient Diffusion Sampling](https://github.com/tingyu215/Spectrum).
+
+```bash
+sd-cli -m model.safetensors -p "a cat" --cache-mode spectrum
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `w` | Chebyshev vs Taylor blend weight (0=Taylor, 1=Chebyshev) | 0.40 |
+| `m` | Chebyshev polynomial degree | 3 |
+| `lam` | Ridge regression regularization | 1.0 |
+| `window` | Initial window size (compute every N steps) | 2 |
+| `flex` | Window growth per computed step after warmup | 0.50 |
+| `warmup` | Steps to always compute before caching starts | 4 |
+| `stop` | Stop caching at this fraction of total steps | 0.9 |
+
+```
+
 ### Performance Tips
 
 - Start with default thresholds and adjust based on output quality

examples/cli/README.md

@@ -138,11 +138,12 @@ Generation Options:
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level),
+                                           'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
   --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
-                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
-                                           "threshold=0.25" or "threshold=1.5,reset=0"
-  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=;
+                                           spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=. Examples:
+                                           "threshold=0.25" or "threshold=1.5,reset=0" or "w=0.4,window=2"
   --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
   --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
 ```

examples/cli/main.cpp

@@ -601,7 +601,7 @@ int main(int argc, const char* argv[]) {
 
     if (gen_params.end_image_path.size() > 0) {
         vae_decode_only = false;
-        if (!load_image_and_update_size(gen_params.init_image_path, end_image)) {
+        if (!load_image_and_update_size(gen_params.end_image_path, end_image)) {
             return 1;
         }
     }

examples/common/common.hpp

@@ -1047,7 +1047,6 @@ struct SDGenerationParams {
 
     std::string cache_mode;
     std::string cache_option;
-    std::string cache_preset;
     std::string scm_mask;
     bool scm_policy_dynamic = true;
     sd_cache_params_t cache_params{};
@@ -1422,8 +1421,8 @@ struct SDGenerationParams {
             }
             cache_mode = argv_to_utf8(index, argv);
             if (cache_mode != "easycache" && cache_mode != "ucache" &&
-                cache_mode != "dbcache" && cache_mode != "taylorseer" && cache_mode != "cache-dit") {
-                fprintf(stderr, "error: invalid cache mode '%s', must be 'easycache', 'ucache', 'dbcache', 'taylorseer', or 'cache-dit'\n", cache_mode.c_str());
+                cache_mode != "dbcache" && cache_mode != "taylorseer" && cache_mode != "cache-dit" && cache_mode != "spectrum") {
+                fprintf(stderr, "error: invalid cache mode '%s', must be 'easycache', 'ucache', 'dbcache', 'taylorseer', 'cache-dit', or 'spectrum'\n", cache_mode.c_str());
                 return -1;
             }
             return 1;
@@ -1461,21 +1460,6 @@ struct SDGenerationParams {
             return 1;
         };
 
-        auto on_cache_preset_arg = [&](int argc, const char** argv, int index) {
-            if (++index >= argc) {
-                return -1;
-            }
-            cache_preset = argv_to_utf8(index, argv);
-            if (cache_preset != "slow" && cache_preset != "s" && cache_preset != "S" &&
-                cache_preset != "medium" && cache_preset != "m" && cache_preset != "M" &&
-                cache_preset != "fast" && cache_preset != "f" && cache_preset != "F" &&
-                cache_preset != "ultra" && cache_preset != "u" && cache_preset != "U") {
-                fprintf(stderr, "error: invalid cache preset '%s', must be 'slow'/'s', 'medium'/'m', 'fast'/'f', or 'ultra'/'u'\n", cache_preset.c_str());
-                return -1;
-            }
-            return 1;
-        };
-
         options.manual_options = {
             {"-s",
              "--seed",
@@ -1513,16 +1497,12 @@ struct SDGenerationParams {
              on_ref_image_arg},
             {"",
              "--cache-mode",
-             "caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)",
+             "caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)",
              on_cache_mode_arg},
             {"",
              "--cache-option",
-             "named cache params (key=value format, comma-separated). easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples: \"threshold=0.25\" or \"threshold=1.5,reset=0\"",
+             "named cache params (key=value format, comma-separated). easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=; spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=. Examples: \"threshold=0.25\" or \"threshold=1.5,reset=0\"",
              on_cache_option_arg},
-            {"",
-             "--cache-preset",
-             "cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'",
-             on_cache_preset_arg},
             {"",
              "--scm-mask",
              "SCM steps mask for cache-dit: comma-separated 0/1 (e.g., \"1,1,1,0,0,1,0,0,1,0\") - 1=compute, 0=can cache",
@@ -1575,7 +1555,6 @@ struct SDGenerationParams {
         load_if_exists("negative_prompt", negative_prompt);
         load_if_exists("cache_mode", cache_mode);
         load_if_exists("cache_option", cache_option);
-        load_if_exists("cache_preset", cache_preset);
         load_if_exists("scm_mask", scm_mask);
 
         load_if_exists("clip_skip", clip_skip);
@@ -1779,7 +1758,23 @@ struct SDGenerationParams {
                     } else if (key == "Bn" || key == "bn") {
                         cache_params.Bn_compute_blocks = std::stoi(val);
                     } else if (key == "warmup") {
+                        if (cache_mode == "spectrum") {
+                            cache_params.spectrum_warmup_steps = std::stoi(val);
+                        } else {
                             cache_params.max_warmup_steps = std::stoi(val);
+                        }
+                    } else if (key == "w") {
+                        cache_params.spectrum_w = std::stof(val);
+                    } else if (key == "m") {
+                        cache_params.spectrum_m = std::stoi(val);
+                    } else if (key == "lam") {
+                        cache_params.spectrum_lam = std::stof(val);
+                    } else if (key == "window") {
+                        cache_params.spectrum_window_size = std::stoi(val);
+                    } else if (key == "flex") {
+                        cache_params.spectrum_flex_window = std::stof(val);
+                    } else if (key == "stop") {
+                        cache_params.spectrum_stop_percent = std::stof(val);
                     } else {
                         LOG_ERROR("error: unknown cache parameter '%s'", key.c_str());
                         return false;
@@ -1795,38 +1790,16 @@ struct SDGenerationParams {
         if (!cache_mode.empty()) {
             if (cache_mode == "easycache") {
                 cache_params.mode = SD_CACHE_EASYCACHE;
-                cache_params.reuse_threshold        = 0.2f;
-                cache_params.start_percent          = 0.15f;
-                cache_params.end_percent            = 0.95f;
-                cache_params.error_decay_rate       = 1.0f;
-                cache_params.use_relative_threshold = true;
-                cache_params.reset_error_on_compute = true;
             } else if (cache_mode == "ucache") {
                 cache_params.mode = SD_CACHE_UCACHE;
-                cache_params.reuse_threshold        = 1.0f;
-                cache_params.start_percent          = 0.15f;
-                cache_params.end_percent            = 0.95f;
-                cache_params.error_decay_rate       = 1.0f;
-                cache_params.use_relative_threshold = true;
-                cache_params.reset_error_on_compute = true;
             } else if (cache_mode == "dbcache") {
                 cache_params.mode = SD_CACHE_DBCACHE;
-                cache_params.Fn_compute_blocks       = 8;
-                cache_params.Bn_compute_blocks       = 0;
-                cache_params.residual_diff_threshold = 0.08f;
-                cache_params.max_warmup_steps        = 8;
             } else if (cache_mode == "taylorseer") {
                 cache_params.mode = SD_CACHE_TAYLORSEER;
-                cache_params.Fn_compute_blocks       = 8;
-                cache_params.Bn_compute_blocks       = 0;
-                cache_params.residual_diff_threshold = 0.08f;
-                cache_params.max_warmup_steps        = 8;
             } else if (cache_mode == "cache-dit") {
                 cache_params.mode = SD_CACHE_CACHE_DIT;
-                cache_params.Fn_compute_blocks       = 8;
-                cache_params.Bn_compute_blocks       = 0;
-                cache_params.residual_diff_threshold = 0.08f;
-                cache_params.max_warmup_steps        = 8;
+            } else if (cache_mode == "spectrum") {
+                cache_params.mode = SD_CACHE_SPECTRUM;
             }
 
             if (!cache_option.empty()) {

examples/server/CMakeLists.txt

@@ -1,6 +1,73 @@
 set(TARGET sd-server)
 
+option(SD_SERVER_BUILD_FRONTEND "Build server frontend with pnpm" ON)
+
+set(FRONTEND_DIR "${CMAKE_CURRENT_SOURCE_DIR}/frontend")
+set(GENERATED_HTML_HEADER "${FRONTEND_DIR}/dist/gen_index_html.h")
+
+set(HAVE_FRONTEND_BUILD OFF)
+
+if(SD_SERVER_BUILD_FRONTEND AND EXISTS "${FRONTEND_DIR}")
+    if(WIN32)
+        find_program(PNPM_EXECUTABLE NAMES pnpm.cmd pnpm)
+    else()
+        find_program(PNPM_EXECUTABLE NAMES pnpm)
+    endif()
+
+    if(PNPM_EXECUTABLE)
+        message(STATUS "Frontend dir found: ${FRONTEND_DIR}")
+        message(STATUS "pnpm found: ${PNPM_EXECUTABLE}")
+
+        set(HAVE_FRONTEND_BUILD ON)
+
+        add_custom_target(${TARGET}_frontend_install
+            COMMAND "${PNPM_EXECUTABLE}" -C "${FRONTEND_DIR}" install
+            WORKING_DIRECTORY "${FRONTEND_DIR}"
+            COMMENT "Installing frontend dependencies"
+            VERBATIM
+        )
+
+        add_custom_target(${TARGET}_frontend_build
+            COMMAND "${PNPM_EXECUTABLE}" -C "${FRONTEND_DIR}" run build
+            WORKING_DIRECTORY "${FRONTEND_DIR}"
+            COMMENT "Building frontend"
+            VERBATIM
+        )
+
+        add_custom_target(${TARGET}_frontend_header
+            COMMAND "${PNPM_EXECUTABLE}" -C "${FRONTEND_DIR}" run build:header
+            WORKING_DIRECTORY "${FRONTEND_DIR}"
+            COMMENT "Generating gen_index_html.h"
+            VERBATIM
+        )
+
+        add_dependencies(${TARGET}_frontend_build ${TARGET}_frontend_install)
+        add_dependencies(${TARGET}_frontend_header ${TARGET}_frontend_build)
+
+        add_custom_target(${TARGET}_frontend
+            DEPENDS ${TARGET}_frontend_header
+        )
+
+        set_source_files_properties("${GENERATED_HTML_HEADER}" PROPERTIES GENERATED TRUE)
+    else()
+        message(WARNING "pnpm not found, frontend build disabled")
+    endif()
+else()
+    message(STATUS "Frontend disabled or directory not found: ${FRONTEND_DIR}")
+endif()
+
 add_executable(${TARGET} main.cpp)
+
+if(HAVE_FRONTEND_BUILD)
+    add_dependencies(${TARGET} ${TARGET}_frontend)
+    target_sources(${TARGET} PRIVATE "${GENERATED_HTML_HEADER}")
+    target_include_directories(${TARGET} PRIVATE "${FRONTEND_DIR}/dist")
+    target_compile_definitions(${TARGET} PRIVATE HAVE_INDEX_HTML)
+    message(STATUS "HAVE_INDEX_HTML enabled")
+else()
+    message(STATUS "HAVE_INDEX_HTML disabled")
+endif()
+
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE stable-diffusion ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)

examples/server/README.md

@@ -1,3 +1,92 @@
+# Frontend
+
+## Build with Frontend
+
+The server can optionally build the web frontend and embed it into the binary as `gen_index_html.h`.
+
+### Requirements
+
+Install the following tools:
+
+* **Node.js** ≥ 22.18
+  https://nodejs.org/
+
+* **pnpm** ≥ 10
+  Install via npm:
+
+```bash
+npm install -g pnpm
+```
+
+Verify installation:
+
+```bash
+node -v
+pnpm -v
+```
+
+### Install frontend dependencies
+
+Go to the frontend directory and install dependencies:
+
+```bash
+cd examples/server/frontend
+pnpm install
+```
+
+### Build the server with CMake
+
+Enable the frontend build option when configuring CMake:
+
+```bash
+cmake -B build -DSD_SERVER_BUILD_FRONTEND=ON
+cmake --build build --config Release
+```
+
+If `pnpm` is available, the build system will automatically run:
+
+```
+pnpm run build
+pnpm run build:header
+```
+
+and embed the generated frontend into the server binary.
+
+## Frontend Repository
+
+The web frontend is maintained in a **separate repository**, https://github.com/leejet/stable-ui.
+
+If you want to modify the UI or frontend logic, please submit pull requests to the **frontend repository**.
+
+This repository (`stable-diffusion.cpp`) only vendors the frontend periodically. Changes from the frontend repo are synchronized:
+
+* approximately **every 1–2 weeks**, or
+* when there are **major frontend updates**
+
+Because of this, frontend changes will **not appear here immediately** after being merged upstream.
+
+## Using an external frontend
+
+By default, the server uses the **embedded frontend** generated during the build (`gen_index_html.h`).
+
+You can also serve a custom frontend file instead of the embedded one by using:
+
+```bash
+--serve-html-path <path-to-index.html>
+```
+
+For example:
+
+```bash
+sd-server --serve-html-path ./index.html
+```
+
+In this case, the server will load and serve the specified `index.html` file instead of the embedded frontend. This is useful when:
+
+* developing or testing frontend changes
+* using a custom UI
+* avoiding rebuilding the binary after frontend modifications
+
 # Run
 
 ```
@@ -129,11 +218,10 @@ Default Generation Options:
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
   --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
                                            threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
                                            "threshold=0.25" or "threshold=1.5,reset=0"
-  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
   --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
   --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
 ```

examples/server/frontend

@@ -0,0 +1 @@
+Subproject commit 1a34176cd6d39ad3a226b2b69047e71f6797f6bc

examples/server/main.cpp

@@ -13,6 +13,10 @@
 
 #include "common/common.hpp"
 
+#ifdef HAVE_INDEX_HTML
+#include "frontend/dist/gen_index_html.h"
+#endif
+
 namespace fs = std::filesystem;
 
 // ----------------------- helpers -----------------------
@@ -380,7 +384,13 @@ int main(int argc, const char** argv) {
         return httplib::Server::HandlerResponse::Unhandled;
     });
 
-    // root
+    // index html
+    std::string index_html;
+#ifdef HAVE_INDEX_HTML
+    index_html.assign(reinterpret_cast<const char*>(index_html_bytes), index_html_size);
+#else
+    index_html = "Stable Diffusion Server is running";
+#endif
     svr.Get("/", [&](const httplib::Request&, httplib::Response& res) {
         if (!svr_params.serve_html_path.empty()) {
             std::ifstream file(svr_params.serve_html_path);
@@ -392,7 +402,7 @@ int main(int argc, const char** argv) {
                 res.set_content("Error: Unable to read HTML file", "text/plain");
             }
         } else {
-            res.set_content("Stable Diffusion Server is running", "text/plain");
+            res.set_content(index_html, "text/html");
         }
     });
 

include/stable-diffusion.h

@@ -251,6 +251,7 @@ enum sd_cache_mode_t {
     SD_CACHE_DBCACHE,
     SD_CACHE_TAYLORSEER,
     SD_CACHE_CACHE_DIT,
+    SD_CACHE_SPECTRUM,
 };
 
 typedef struct {
@@ -271,6 +272,13 @@ typedef struct {
     int taylorseer_skip_interval;
     const char* scm_mask;
     bool scm_policy_dynamic;
+    float spectrum_w;
+    int spectrum_m;
+    float spectrum_lam;
+    int spectrum_window_size;
+    float spectrum_flex_window;
+    int spectrum_warmup_steps;
+    float spectrum_stop_percent;
 } sd_cache_params_t;
 
 typedef struct {

src/anima.hpp

@@ -13,9 +13,9 @@
 namespace Anima {
     constexpr int ANIMA_GRAPH_SIZE = 65536;
 
-    __STATIC_INLINE__ struct ggml_tensor* apply_gate(struct ggml_context* ctx,
-                                                     struct ggml_tensor* x,
-                                                     struct ggml_tensor* gate) {
+    __STATIC_INLINE__ ggml_tensor* apply_gate(ggml_context* ctx,
+                                              ggml_tensor* x,
+                                              ggml_tensor* gate) {
         gate = ggml_reshape_3d(ctx, gate, gate->ne[0], 1, gate->ne[1]);  // [N, 1, C]
         return ggml_mul(ctx, x, gate);
     }
@@ -26,7 +26,7 @@ namespace Anima {
             blocks["proj.1"] = std::make_shared<Linear>(in_dim, out_dim, false);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj.1"]);
             return proj->forward(ctx, x);
         }
@@ -39,7 +39,7 @@ namespace Anima {
             blocks["1.linear_2"] = std::make_shared<Linear>(in_dim, out_dim, false);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1.linear_1"]);
             auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["1.linear_2"]);
 
@@ -62,10 +62,10 @@ namespace Anima {
             blocks["2"]    = std::make_shared<Linear>(hidden_features, 3 * in_features, false);
         }
 
-        std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                                    struct ggml_tensor* hidden_states,
-                                                                    struct ggml_tensor* embedded_timestep,
-                                                                    struct ggml_tensor* temb = nullptr) {
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                      ggml_tensor* hidden_states,
+                                                      ggml_tensor* embedded_timestep,
+                                                      ggml_tensor* temb = nullptr) {
             auto norm     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
             auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
             auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
@@ -102,10 +102,10 @@ namespace Anima {
             blocks["2"]    = std::make_shared<Linear>(hidden_features, 2 * in_features, false);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* hidden_states,
-                                    struct ggml_tensor* embedded_timestep,
-                                    struct ggml_tensor* temb = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* embedded_timestep,
+                             ggml_tensor* temb = nullptr) {
             auto norm     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
             auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
             auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
@@ -152,11 +152,11 @@ namespace Anima {
             blocks[this->out_proj_name] = std::make_shared<Linear>(inner_dim, query_dim, false);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* hidden_states,
-                                    struct ggml_tensor* encoder_hidden_states = nullptr,
-                                    struct ggml_tensor* pe_q                  = nullptr,
-                                    struct ggml_tensor* pe_k                  = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* encoder_hidden_states = nullptr,
+                             ggml_tensor* pe_q                  = nullptr,
+                             ggml_tensor* pe_k                  = nullptr) {
             if (encoder_hidden_states == nullptr) {
                 encoder_hidden_states = hidden_states;
             }
@@ -183,7 +183,7 @@ namespace Anima {
             q4 = q_norm->forward(ctx, q4);
             k4 = k_norm->forward(ctx, k4);
 
-            struct ggml_tensor* attn_out = nullptr;
+            ggml_tensor* attn_out = nullptr;
             if (pe_q != nullptr || pe_k != nullptr) {
                 if (pe_q == nullptr) {
                     pe_q = pe_k;
@@ -227,7 +227,7 @@ namespace Anima {
             blocks["layer2"] = std::make_shared<Linear>(hidden_dim, dim, false);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto layer1 = std::dynamic_pointer_cast<Linear>(blocks["layer1"]);
             auto layer2 = std::dynamic_pointer_cast<Linear>(blocks["layer2"]);
 
@@ -245,7 +245,7 @@ namespace Anima {
             blocks["2"] = std::make_shared<Linear>(hidden_dim, dim, true);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto layer0 = std::dynamic_pointer_cast<Linear>(blocks["0"]);
             auto layer2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
 
@@ -267,11 +267,11 @@ namespace Anima {
             blocks["mlp"]             = std::make_shared<AdapterMLP>(model_dim, model_dim * 4);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* target_pe,
-                                    struct ggml_tensor* context_pe) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* context,
+                             ggml_tensor* target_pe,
+                             ggml_tensor* context_pe) {
             auto norm_self_attn  = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_self_attn"]);
             auto self_attn       = std::dynamic_pointer_cast<AnimaAttention>(blocks["self_attn"]);
             auto norm_cross_attn = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_cross_attn"]);
@@ -317,11 +317,11 @@ namespace Anima {
             blocks["norm"]     = std::make_shared<RMSNorm>(target_dim, 1e-6f);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* source_hidden_states,
-                                    struct ggml_tensor* target_input_ids,
-                                    struct ggml_tensor* target_pe,
-                                    struct ggml_tensor* source_pe) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* source_hidden_states,
+                             ggml_tensor* target_input_ids,
+                             ggml_tensor* target_pe,
+                             ggml_tensor* source_pe) {
             GGML_ASSERT(target_input_ids != nullptr);
             if (ggml_n_dims(target_input_ids) == 1) {
                 target_input_ids = ggml_reshape_2d(ctx->ggml_ctx, target_input_ids, target_input_ids->ne[0], 1);
@@ -360,12 +360,12 @@ namespace Anima {
             blocks["mlp"]                         = std::make_shared<AnimaMLP>(hidden_size, hidden_size * mlp_ratio);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* hidden_states,
-                                    struct ggml_tensor* encoder_hidden_states,
-                                    struct ggml_tensor* embedded_timestep,
-                                    struct ggml_tensor* temb,
-                                    struct ggml_tensor* image_pe) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* encoder_hidden_states,
+                             ggml_tensor* embedded_timestep,
+                             ggml_tensor* temb,
+                             ggml_tensor* image_pe) {
             auto norm1 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_self_attn"]);
             auto attn1 = std::dynamic_pointer_cast<AnimaAttention>(blocks["self_attn"]);
             auto norm2 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_cross_attn"]);
@@ -402,10 +402,10 @@ namespace Anima {
             blocks["linear"]           = std::make_shared<Linear>(hidden_size, patch_size * patch_size * out_channels, false);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* hidden_states,
-                                    struct ggml_tensor* embedded_timestep,
-                                    struct ggml_tensor* temb) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* embedded_timestep,
+                             ggml_tensor* temb) {
             auto adaln  = std::dynamic_pointer_cast<AdaLayerNorm>(blocks["adaln_modulation"]);
             auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
 
@@ -445,15 +445,15 @@ namespace Anima {
             blocks["llm_adapter"] = std::make_shared<LLMAdapter>(1024, 1024, 1024, 6, 16);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* timestep,
-                                    struct ggml_tensor* encoder_hidden_states,
-                                    struct ggml_tensor* image_pe,
-                                    struct ggml_tensor* t5_ids       = nullptr,
-                                    struct ggml_tensor* t5_weights   = nullptr,
-                                    struct ggml_tensor* adapter_q_pe = nullptr,
-                                    struct ggml_tensor* adapter_k_pe = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* encoder_hidden_states,
+                             ggml_tensor* image_pe,
+                             ggml_tensor* t5_ids       = nullptr,
+                             ggml_tensor* t5_weights   = nullptr,
+                             ggml_tensor* adapter_q_pe = nullptr,
+                             ggml_tensor* adapter_k_pe = nullptr) {
             GGML_ASSERT(x->ne[3] == 1);
 
             auto x_embedder       = std::dynamic_pointer_cast<XEmbedder>(blocks["x_embedder"]);
@@ -553,7 +553,7 @@ namespace Anima {
             return "anima";
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
             net.get_param_tensors(tensors, prefix + ".net");
         }
 
@@ -602,19 +602,18 @@ namespace Anima {
             return Rope::embed_nd(ids, bs, axis_thetas, axes_dim);
         }
 
-        struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                        struct ggml_tensor* timesteps,
-                                        struct ggml_tensor* context,
-                                        struct ggml_tensor* t5_ids     = nullptr,
-                                        struct ggml_tensor* t5_weights = nullptr) {
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor    = {},
+                                 const sd::Tensor<int32_t>& t5_ids_tensor   = {},
+                                 const sd::Tensor<float>& t5_weights_tensor = {}) {
+            ggml_tensor* x          = make_input(x_tensor);
+            ggml_tensor* timesteps  = make_input(timesteps_tensor);
+            ggml_tensor* context    = make_optional_input(context_tensor);
+            ggml_tensor* t5_ids     = make_optional_input(t5_ids_tensor);
+            ggml_tensor* t5_weights = make_optional_input(t5_weights_tensor);
             GGML_ASSERT(x->ne[3] == 1);
-            struct ggml_cgraph* gf = new_graph_custom(ANIMA_GRAPH_SIZE);
-
-            x          = to_backend(x);
-            timesteps  = to_backend(timesteps);
-            context    = to_backend(context);
-            t5_ids     = to_backend(t5_ids);
-            t5_weights = to_backend(t5_weights);
+            ggml_cgraph* gf = new_graph_custom(ANIMA_GRAPH_SIZE);
 
             int64_t pad_h = (net.patch_size - x->ne[1] % net.patch_size) % net.patch_size;
             int64_t pad_w = (net.patch_size - x->ne[0] % net.patch_size) % net.patch_size;
@@ -667,18 +666,16 @@ namespace Anima {
             return gf;
         }
 
-        bool compute(int n_threads,
-                     struct ggml_tensor* x,
-                     struct ggml_tensor* timesteps,
-                     struct ggml_tensor* context,
-                     struct ggml_tensor* t5_ids      = nullptr,
-                     struct ggml_tensor* t5_weights  = nullptr,
-                     struct ggml_tensor** output     = nullptr,
-                     struct ggml_context* output_ctx = nullptr) {
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context    = {},
+                                  const sd::Tensor<int32_t>& t5_ids   = {},
+                                  const sd::Tensor<float>& t5_weights = {}) {
+            auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, t5_ids, t5_weights);
             };
-            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
         }
     };
 }  // namespace Anima

src/auto_encoder_kl.hpp

@@ -0,0 +1,852 @@
+#ifndef __AUTO_ENCODER_KL_HPP__
+#define __AUTO_ENCODER_KL_HPP__
+
+#include "vae.hpp"
+
+/*================================================== AutoEncoderKL ===================================================*/
+
+#define VAE_GRAPH_SIZE 20480
+
+class ResnetBlock : public UnaryBlock {
+protected:
+    int64_t in_channels;
+    int64_t out_channels;
+
+public:
+    ResnetBlock(int64_t in_channels,
+                int64_t out_channels)
+        : in_channels(in_channels),
+          out_channels(out_channels) {
+        // temb_channels is always 0
+        blocks["norm1"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
+        blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
+
+        blocks["norm2"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(out_channels));
+        blocks["conv2"] = std::shared_ptr<GGMLBlock>(new Conv2d(out_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
+
+        if (out_channels != in_channels) {
+            blocks["nin_shortcut"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, {1, 1}));
+        }
+    }
+
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+        // x: [N, in_channels, h, w]
+        // t_emb is always None
+        auto norm1 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm1"]);
+        auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv1"]);
+        auto norm2 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm2"]);
+        auto conv2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv2"]);
+
+        auto h = x;
+        h      = norm1->forward(ctx, h);
+        h      = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
+        h      = conv1->forward(ctx, h);
+        // return h;
+
+        h = norm2->forward(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
+        // dropout, skip for inference
+        h = conv2->forward(ctx, h);
+
+        // skip connection
+        if (out_channels != in_channels) {
+            auto nin_shortcut = std::dynamic_pointer_cast<Conv2d>(blocks["nin_shortcut"]);
+
+            x = nin_shortcut->forward(ctx, x);  // [N, out_channels, h, w]
+        }
+
+        h = ggml_add(ctx->ggml_ctx, h, x);
+        return h;  // [N, out_channels, h, w]
+    }
+};
+
+class AttnBlock : public UnaryBlock {
+protected:
+    int64_t in_channels;
+    bool use_linear;
+
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
+        if (iter != tensor_storage_map.end()) {
+            if (iter->second.n_dims == 4 && use_linear) {
+                use_linear         = false;
+                blocks["q"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["k"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["v"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["proj_out"] = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+            } else if (iter->second.n_dims == 2 && !use_linear) {
+                use_linear         = true;
+                blocks["q"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["k"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["v"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["proj_out"] = std::make_shared<Linear>(in_channels, in_channels);
+            }
+        }
+    }
+
+public:
+    AttnBlock(int64_t in_channels, bool use_linear)
+        : in_channels(in_channels), use_linear(use_linear) {
+        blocks["norm"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
+        if (use_linear) {
+            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+        } else {
+            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+        }
+    }
+
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+        // x: [N, in_channels, h, w]
+        auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
+        auto q_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["q"]);
+        auto k_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["k"]);
+        auto v_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["v"]);
+        auto proj_out = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_out"]);
+
+        auto h_ = norm->forward(ctx, x);
+
+        const int64_t n = h_->ne[3];
+        const int64_t c = h_->ne[2];
+        const int64_t h = h_->ne[1];
+        const int64_t w = h_->ne[0];
+
+        ggml_tensor* q;
+        ggml_tensor* k;
+        ggml_tensor* v;
+        if (use_linear) {
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            h_ = ggml_reshape_3d(ctx->ggml_ctx, h_, c, h * w, n);                        // [N, h * w, in_channels]
+
+            q = q_proj->forward(ctx, h_);  // [N, h * w, in_channels]
+            k = k_proj->forward(ctx, h_);  // [N, h * w, in_channels]
+            v = v_proj->forward(ctx, h_);  // [N, h * w, in_channels]
+        } else {
+            q = q_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            q = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, q, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            q = ggml_reshape_3d(ctx->ggml_ctx, q, c, h * w, n);                        // [N, h * w, in_channels]
+
+            k = k_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            k = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, k, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            k = ggml_reshape_3d(ctx->ggml_ctx, k, c, h * w, n);                        // [N, h * w, in_channels]
+
+            v = v_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            v = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, v, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            v = ggml_reshape_3d(ctx->ggml_ctx, v, c, h * w, n);                        // [N, h * w, in_channels]
+        }
+
+        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, ctx->flash_attn_enabled);
+
+        if (use_linear) {
+            h_ = proj_out->forward(ctx, h_);  // [N, h * w, in_channels]
+
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
+            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
+        } else {
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
+            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
+
+            h_ = proj_out->forward(ctx, h_);  // [N, in_channels, h, w]
+        }
+
+        h_ = ggml_add(ctx->ggml_ctx, h_, x);
+        return h_;
+    }
+};
+
+class AE3DConv : public Conv2d {
+public:
+    AE3DConv(int64_t in_channels,
+             int64_t out_channels,
+             std::pair<int, int> kernel_size,
+             int video_kernel_size        = 3,
+             std::pair<int, int> stride   = {1, 1},
+             std::pair<int, int> padding  = {0, 0},
+             std::pair<int, int> dilation = {1, 1},
+             bool bias                    = true)
+        : Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias) {
+        int kernel_padding      = video_kernel_size / 2;
+        blocks["time_mix_conv"] = std::shared_ptr<GGMLBlock>(new Conv3d(out_channels,
+                                                                        out_channels,
+                                                                        {video_kernel_size, 1, 1},
+                                                                        {1, 1, 1},
+                                                                        {kernel_padding, 0, 0}));
+    }
+
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x) override {
+        // timesteps always None
+        // skip_video always False
+        // x: [N, IC, IH, IW]
+        // result: [N, OC, OH, OW]
+        auto time_mix_conv = std::dynamic_pointer_cast<Conv3d>(blocks["time_mix_conv"]);
+
+        x = Conv2d::forward(ctx, x);
+        // timesteps = x.shape[0]
+        // x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+        // x = conv3d(x)
+        // return rearrange(x, "b c t h w -> (b t) c h w")
+        int64_t T = x->ne[3];
+        int64_t B = x->ne[3] / T;
+        int64_t C = x->ne[2];
+        int64_t H = x->ne[1];
+        int64_t W = x->ne[0];
+
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        x = time_mix_conv->forward(ctx, x);                                        // [B, OC, T, OH * OW]
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
+        return x;                                                                  // [B*T, OC, OH, OW]
+    }
+};
+
+class VideoResnetBlock : public ResnetBlock {
+protected:
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        enum ggml_type wtype = get_type(prefix + "mix_factor", tensor_storage_map, GGML_TYPE_F32);
+        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
+    }
+
+    float get_alpha() {
+        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
+        return sigmoid(alpha);
+    }
+
+public:
+    VideoResnetBlock(int64_t in_channels,
+                     int64_t out_channels,
+                     int video_kernel_size = 3)
+        : ResnetBlock(in_channels, out_channels) {
+        // merge_strategy is always learned
+        blocks["time_stack"] = std::shared_ptr<GGMLBlock>(new ResBlock(out_channels, 0, out_channels, {video_kernel_size, 1}, 3, false, true));
+    }
+
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+        // x: [N, in_channels, h, w] aka [b*t, in_channels, h, w]
+        // return: [N, out_channels, h, w] aka [b*t, out_channels, h, w]
+        // t_emb is always None
+        // skip_video is always False
+        // timesteps is always None
+        auto time_stack = std::dynamic_pointer_cast<ResBlock>(blocks["time_stack"]);
+
+        x = ResnetBlock::forward(ctx, x);  // [N, out_channels, h, w]
+        // return x;
+
+        int64_t T = x->ne[3];
+        int64_t B = x->ne[3] / T;
+        int64_t C = x->ne[2];
+        int64_t H = x->ne[1];
+        int64_t W = x->ne[0];
+
+        x          = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x          = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        auto x_mix = x;
+
+        x = time_stack->forward(ctx, x);  // b t c (h w)
+
+        float alpha = get_alpha();
+        x           = ggml_add(ctx->ggml_ctx,
+                               ggml_ext_scale(ctx->ggml_ctx, x, alpha),
+                               ggml_ext_scale(ctx->ggml_ctx, x_mix, 1.0f - alpha));
+
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
+
+        return x;
+    }
+};
+
+// ldm.modules.diffusionmodules.model.Encoder
+class Encoder : public GGMLBlock {
+protected:
+    int ch                   = 128;
+    std::vector<int> ch_mult = {1, 2, 4, 4};
+    int num_res_blocks       = 2;
+    int in_channels          = 3;
+    int z_channels           = 4;
+    bool double_z            = true;
+
+public:
+    Encoder(int ch,
+            std::vector<int> ch_mult,
+            int num_res_blocks,
+            int in_channels,
+            int z_channels,
+            bool double_z              = true,
+            bool use_linear_projection = false)
+        : ch(ch),
+          ch_mult(ch_mult),
+          num_res_blocks(num_res_blocks),
+          in_channels(in_channels),
+          z_channels(z_channels),
+          double_z(double_z) {
+        blocks["conv_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, ch, {3, 3}, {1, 1}, {1, 1}));
+
+        size_t num_resolutions = ch_mult.size();
+
+        int block_in = 1;
+        for (int i = 0; i < num_resolutions; i++) {
+            if (i == 0) {
+                block_in = ch;
+            } else {
+                block_in = ch * ch_mult[i - 1];
+            }
+            int block_out = ch * ch_mult[i];
+            for (int j = 0; j < num_res_blocks; j++) {
+                std::string name = "down." + std::to_string(i) + ".block." + std::to_string(j);
+                blocks[name]     = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_out));
+                block_in         = block_out;
+            }
+            if (i != num_resolutions - 1) {
+                std::string name = "down." + std::to_string(i) + ".downsample";
+                blocks[name]     = std::shared_ptr<GGMLBlock>(new DownSampleBlock(block_in, block_in, true));
+            }
+        }
+
+        blocks["mid.block_1"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
+        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
+        blocks["mid.block_2"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
+
+        blocks["norm_out"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(block_in));
+        blocks["conv_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(block_in, double_z ? z_channels * 2 : z_channels, {3, 3}, {1, 1}, {1, 1}));
+    }
+
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+        // x: [N, in_channels, h, w]
+
+        auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
+        auto mid_block_1 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_1"]);
+        auto mid_attn_1  = std::dynamic_pointer_cast<AttnBlock>(blocks["mid.attn_1"]);
+        auto mid_block_2 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_2"]);
+        auto norm_out    = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm_out"]);
+        auto conv_out    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_out"]);
+
+        auto h = conv_in->forward(ctx, x);  // [N, ch, h, w]
+
+        // downsampling
+        size_t num_resolutions = ch_mult.size();
+        for (int i = 0; i < num_resolutions; i++) {
+            for (int j = 0; j < num_res_blocks; j++) {
+                std::string name = "down." + std::to_string(i) + ".block." + std::to_string(j);
+                auto down_block  = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);
+
+                h = down_block->forward(ctx, h);
+            }
+            if (i != num_resolutions - 1) {
+                std::string name = "down." + std::to_string(i) + ".downsample";
+                auto down_sample = std::dynamic_pointer_cast<DownSampleBlock>(blocks[name]);
+
+                h = down_sample->forward(ctx, h);
+            }
+        }
+
+        // middle
+        h = mid_block_1->forward(ctx, h);
+        h = mid_attn_1->forward(ctx, h);
+        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
+
+        // end
+        h = norm_out->forward(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
+        h = conv_out->forward(ctx, h);            // [N, z_channels*2, h, w]
+        return h;
+    }
+};
+
+// ldm.modules.diffusionmodules.model.Decoder
+class Decoder : public GGMLBlock {
+protected:
+    int ch                   = 128;
+    int out_ch               = 3;
+    std::vector<int> ch_mult = {1, 2, 4, 4};
+    int num_res_blocks       = 2;
+    int z_channels           = 4;
+    bool video_decoder       = false;
+    int video_kernel_size    = 3;
+
+    virtual std::shared_ptr<GGMLBlock> get_conv_out(int64_t in_channels,
+                                                    int64_t out_channels,
+                                                    std::pair<int, int> kernel_size,
+                                                    std::pair<int, int> stride  = {1, 1},
+                                                    std::pair<int, int> padding = {0, 0}) {
+        if (video_decoder) {
+            return std::shared_ptr<GGMLBlock>(new AE3DConv(in_channels, out_channels, kernel_size, video_kernel_size, stride, padding));
+        } else {
+            return std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, kernel_size, stride, padding));
+        }
+    }
+
+    virtual std::shared_ptr<GGMLBlock> get_resnet_block(int64_t in_channels,
+                                                        int64_t out_channels) {
+        if (video_decoder) {
+            return std::shared_ptr<GGMLBlock>(new VideoResnetBlock(in_channels, out_channels, video_kernel_size));
+        } else {
+            return std::shared_ptr<GGMLBlock>(new ResnetBlock(in_channels, out_channels));
+        }
+    }
+
+public:
+    Decoder(int ch,
+            int out_ch,
+            std::vector<int> ch_mult,
+            int num_res_blocks,
+            int z_channels,
+            bool use_linear_projection = false,
+            bool video_decoder         = false,
+            int video_kernel_size      = 3)
+        : ch(ch),
+          out_ch(out_ch),
+          ch_mult(ch_mult),
+          num_res_blocks(num_res_blocks),
+          z_channels(z_channels),
+          video_decoder(video_decoder),
+          video_kernel_size(video_kernel_size) {
+        int num_resolutions = static_cast<int>(ch_mult.size());
+        int block_in        = ch * ch_mult[num_resolutions - 1];
+
+        blocks["conv_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, block_in, {3, 3}, {1, 1}, {1, 1}));
+
+        blocks["mid.block_1"] = get_resnet_block(block_in, block_in);
+        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
+        blocks["mid.block_2"] = get_resnet_block(block_in, block_in);
+
+        for (int i = num_resolutions - 1; i >= 0; i--) {
+            int mult      = ch_mult[i];
+            int block_out = ch * mult;
+            for (int j = 0; j < num_res_blocks + 1; j++) {
+                std::string name = "up." + std::to_string(i) + ".block." + std::to_string(j);
+                blocks[name]     = get_resnet_block(block_in, block_out);
+
+                block_in = block_out;
+            }
+            if (i != 0) {
+                std::string name = "up." + std::to_string(i) + ".upsample";
+                blocks[name]     = std::shared_ptr<GGMLBlock>(new UpSampleBlock(block_in, block_in));
+            }
+        }
+
+        blocks["norm_out"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(block_in));
+        blocks["conv_out"] = get_conv_out(block_in, out_ch, {3, 3}, {1, 1}, {1, 1});
+    }
+
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) {
+        // z: [N, z_channels, h, w]
+        // alpha is always 0
+        // merge_strategy is always learned
+        // time_mode is always conv-only, so we need to replace conv_out_op/resnet_op to AE3DConv/VideoResBlock
+        // AttnVideoBlock will not be used
+        auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
+        auto mid_block_1 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_1"]);
+        auto mid_attn_1  = std::dynamic_pointer_cast<AttnBlock>(blocks["mid.attn_1"]);
+        auto mid_block_2 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_2"]);
+        auto norm_out    = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm_out"]);
+        auto conv_out    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_out"]);
+
+        // conv_in
+        auto h = conv_in->forward(ctx, z);  // [N, block_in, h, w]
+
+        // middle
+        h = mid_block_1->forward(ctx, h);
+        // return h;
+
+        h = mid_attn_1->forward(ctx, h);
+        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
+
+        // upsampling
+        int num_resolutions = static_cast<int>(ch_mult.size());
+        for (int i = num_resolutions - 1; i >= 0; i--) {
+            for (int j = 0; j < num_res_blocks + 1; j++) {
+                std::string name = "up." + std::to_string(i) + ".block." + std::to_string(j);
+                auto up_block    = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);
+
+                h = up_block->forward(ctx, h);
+            }
+            if (i != 0) {
+                std::string name = "up." + std::to_string(i) + ".upsample";
+                auto up_sample   = std::dynamic_pointer_cast<UpSampleBlock>(blocks[name]);
+
+                h = up_sample->forward(ctx, h);
+            }
+        }
+
+        h = norm_out->forward(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
+        h = conv_out->forward(ctx, h);            // [N, out_ch, h*8, w*8]
+        return h;
+    }
+};
+
+// ldm.models.autoencoder.AutoencoderKL
+class AutoEncoderKLModel : public GGMLBlock {
+protected:
+    SDVersion version;
+    bool decode_only       = true;
+    bool use_video_decoder = false;
+    bool use_quant         = true;
+    int embed_dim          = 4;
+    struct {
+        int z_channels           = 4;
+        int resolution           = 256;
+        int in_channels          = 3;
+        int out_ch               = 3;
+        int ch                   = 128;
+        std::vector<int> ch_mult = {1, 2, 4, 4};
+        int num_res_blocks       = 2;
+        bool double_z            = true;
+    } dd_config;
+
+public:
+    AutoEncoderKLModel(SDVersion version          = VERSION_SD1,
+                       bool decode_only           = true,
+                       bool use_linear_projection = false,
+                       bool use_video_decoder     = false)
+        : version(version), decode_only(decode_only), use_video_decoder(use_video_decoder) {
+        if (sd_version_is_dit(version)) {
+            if (sd_version_is_flux2(version)) {
+                dd_config.z_channels = 32;
+                embed_dim            = 32;
+            } else {
+                use_quant            = false;
+                dd_config.z_channels = 16;
+            }
+        }
+        if (use_video_decoder) {
+            use_quant = false;
+        }
+        blocks["decoder"] = std::shared_ptr<GGMLBlock>(new Decoder(dd_config.ch,
+                                                                   dd_config.out_ch,
+                                                                   dd_config.ch_mult,
+                                                                   dd_config.num_res_blocks,
+                                                                   dd_config.z_channels,
+                                                                   use_linear_projection,
+                                                                   use_video_decoder));
+        if (use_quant) {
+            blocks["post_quant_conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(dd_config.z_channels,
+                                                                              embed_dim,
+                                                                              {1, 1}));
+        }
+        if (!decode_only) {
+            blocks["encoder"] = std::shared_ptr<GGMLBlock>(new Encoder(dd_config.ch,
+                                                                       dd_config.ch_mult,
+                                                                       dd_config.num_res_blocks,
+                                                                       dd_config.in_channels,
+                                                                       dd_config.z_channels,
+                                                                       dd_config.double_z,
+                                                                       use_linear_projection));
+            if (use_quant) {
+                int factor = dd_config.double_z ? 2 : 1;
+
+                blocks["quant_conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(embed_dim * factor,
+                                                                             dd_config.z_channels * factor,
+                                                                             {1, 1}));
+            }
+        }
+    }
+
+    ggml_tensor* decode(GGMLRunnerContext* ctx, ggml_tensor* z) {
+        // z: [N, z_channels, h, w]
+        if (sd_version_is_flux2(version)) {
+            // [N, C*p*p, h, w] -> [N, C, h*p, w*p]
+            int64_t p = 2;
+
+            int64_t N = z->ne[3];
+            int64_t C = z->ne[2] / p / p;
+            int64_t h = z->ne[1];
+            int64_t w = z->ne[0];
+            int64_t H = h * p;
+            int64_t W = w * p;
+
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, w * h, p * p, C, N);                           // [N, C, p*p, h*w]
+            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, h*w, p*p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, p, w, h * C * N);                           // [N*C*h, w, p, p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, p, w, p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, W, H, C, N);                                   // [N, C, h*p, w*p]
+        }
+
+        if (use_quant) {
+            auto post_quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["post_quant_conv"]);
+            z                    = post_quant_conv->forward(ctx, z);  // [N, z_channels, h, w]
+        }
+        auto decoder = std::dynamic_pointer_cast<Decoder>(blocks["decoder"]);
+
+        ggml_set_name(z, "bench-start");
+        auto h = decoder->forward(ctx, z);
+        ggml_set_name(h, "bench-end");
+        return h;
+    }
+
+    ggml_tensor* encode(GGMLRunnerContext* ctx, ggml_tensor* x) {
+        // x: [N, in_channels, h, w]
+        auto encoder = std::dynamic_pointer_cast<Encoder>(blocks["encoder"]);
+
+        auto z = encoder->forward(ctx, x);  // [N, 2*z_channels, h/8, w/8]
+        if (use_quant) {
+            auto quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["quant_conv"]);
+            z               = quant_conv->forward(ctx, z);  // [N, 2*embed_dim, h/8, w/8]
+        }
+        if (sd_version_is_flux2(version)) {
+            z = ggml_ext_chunk(ctx->ggml_ctx, z, 2, 2)[0];
+
+            // [N, C, H, W] -> [N, C*p*p, H/p, W/p]
+            int64_t p = 2;
+            int64_t N = z->ne[3];
+            int64_t C = z->ne[2];
+            int64_t H = z->ne[1];
+            int64_t W = z->ne[0];
+            int64_t h = H / p;
+            int64_t w = W / p;
+
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, w, p, h * C * N);                 // [N*C*h, p, w, p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, w, p, p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p * p, w * h, C, N);                 // [N, C, h*w, p*p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, p*p, h*w]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, w, h, p * p * C, N);                 // [N, C*p*p, h*w]
+        }
+        return z;
+    }
+
+    int get_encoder_output_channels() {
+        int factor = dd_config.double_z ? 2 : 1;
+        if (sd_version_is_flux2(version)) {
+            return dd_config.z_channels * 4;
+        }
+        return dd_config.z_channels * factor;
+    }
+};
+
+struct AutoEncoderKL : public VAE {
+    float scale_factor = 1.f;
+    float shift_factor = 0.f;
+    bool decode_only   = true;
+    AutoEncoderKLModel ae;
+
+    AutoEncoderKL(ggml_backend_t backend,
+                  bool offload_params_to_cpu,
+                  const String2TensorStorage& tensor_storage_map,
+                  const std::string prefix,
+                  bool decode_only       = false,
+                  bool use_video_decoder = false,
+                  SDVersion version      = VERSION_SD1)
+        : decode_only(decode_only), VAE(version, backend, offload_params_to_cpu) {
+        if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
+            scale_factor = 0.18215f;
+            shift_factor = 0.f;
+        } else if (sd_version_is_sdxl(version)) {
+            scale_factor = 0.13025f;
+            shift_factor = 0.f;
+        } else if (sd_version_is_sd3(version)) {
+            scale_factor = 1.5305f;
+            shift_factor = 0.0609f;
+        } else if (sd_version_is_flux(version) || sd_version_is_z_image(version)) {
+            scale_factor = 0.3611f;
+            shift_factor = 0.1159f;
+        } else if (sd_version_is_flux2(version)) {
+            scale_factor = 1.0f;
+            shift_factor = 0.f;
+        }
+        bool use_linear_projection = false;
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            if (!starts_with(name, prefix)) {
+                continue;
+            }
+            if (ends_with(name, "attn_1.proj_out.weight")) {
+                if (tensor_storage.n_dims == 2) {
+                    use_linear_projection = true;
+                }
+                break;
+            }
+        }
+        ae = AutoEncoderKLModel(version, decode_only, use_linear_projection, use_video_decoder);
+        ae.init(params_ctx, tensor_storage_map, prefix);
+    }
+
+    void set_conv2d_scale(float scale) override {
+        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->set_scale(scale);
+            }
+        }
+    }
+
+    std::string get_desc() override {
+        return "vae";
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) override {
+        ae.get_param_tensors(tensors, prefix);
+    }
+
+    ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
+        ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+        ggml_tensor* z  = make_input(z_tensor);
+
+        auto runner_ctx = get_context();
+
+        ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);
+
+        ggml_build_forward_expand(gf, out);
+
+        return gf;
+    }
+
+    sd::Tensor<float> _compute(const int n_threads,
+                               const sd::Tensor<float>& z,
+                               bool decode_graph) override {
+        GGML_ASSERT(!decode_only || decode_graph);
+        auto get_graph = [&]() -> ggml_cgraph* {
+            return build_graph(z, decode_graph);
+        };
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z.dim());
+    }
+
+    sd::Tensor<float> gaussian_latent_sample(const sd::Tensor<float>& moments, std::shared_ptr<RNG> rng) {
+        // ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample
+        auto chunks               = sd::ops::chunk(moments, 2, 2);
+        const auto& mean          = chunks[0];
+        const auto& logvar        = chunks[1];
+        sd::Tensor<float> stddev  = sd::ops::exp(0.5f * sd::ops::clamp(logvar, -30.0f, 20.0f));
+        sd::Tensor<float> noise   = sd::Tensor<float>::randn_like(mean, rng);
+        sd::Tensor<float> latents = mean + stddev * noise;
+        return latents;
+    }
+
+    sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
+        if (sd_version_is_flux2(version)) {
+            return vae_output;
+        } else if (version == VERSION_SD1_PIX2PIX) {
+            return sd::ops::chunk(vae_output, 2, 2)[0];
+        } else {
+            return gaussian_latent_sample(vae_output, rng);
+        }
+    }
+
+    std::pair<sd::Tensor<float>, sd::Tensor<float>> get_latents_mean_std(const sd::Tensor<float>& latents, int channel_dim) {
+        GGML_ASSERT(channel_dim >= 0 && static_cast<size_t>(channel_dim) < static_cast<size_t>(latents.dim()));
+        if (sd_version_is_flux2(version)) {
+            GGML_ASSERT(latents.shape()[channel_dim] == 128);
+            std::vector<int64_t> stats_shape(static_cast<size_t>(latents.dim()), 1);
+            stats_shape[static_cast<size_t>(channel_dim)] = latents.shape()[channel_dim];
+
+            auto mean_tensor = sd::Tensor<float>::from_vector({-0.0676f, -0.0715f, -0.0753f, -0.0745f, 0.0223f, 0.0180f, 0.0142f, 0.0184f,
+                                                               -0.0001f, -0.0063f, -0.0002f, -0.0031f, -0.0272f, -0.0281f, -0.0276f, -0.0290f,
+                                                               -0.0769f, -0.0672f, -0.0902f, -0.0892f, 0.0168f, 0.0152f, 0.0079f, 0.0086f,
+                                                               0.0083f, 0.0015f, 0.0003f, -0.0043f, -0.0439f, -0.0419f, -0.0438f, -0.0431f,
+                                                               -0.0102f, -0.0132f, -0.0066f, -0.0048f, -0.0311f, -0.0306f, -0.0279f, -0.0180f,
+                                                               0.0030f, 0.0015f, 0.0126f, 0.0145f, 0.0347f, 0.0338f, 0.0337f, 0.0283f,
+                                                               0.0020f, 0.0047f, 0.0047f, 0.0050f, 0.0123f, 0.0081f, 0.0081f, 0.0146f,
+                                                               0.0681f, 0.0679f, 0.0767f, 0.0732f, -0.0462f, -0.0474f, -0.0392f, -0.0511f,
+                                                               -0.0528f, -0.0477f, -0.0470f, -0.0517f, -0.0317f, -0.0316f, -0.0345f, -0.0283f,
+                                                               0.0510f, 0.0445f, 0.0578f, 0.0458f, -0.0412f, -0.0458f, -0.0487f, -0.0467f,
+                                                               -0.0088f, -0.0106f, -0.0088f, -0.0046f, -0.0376f, -0.0432f, -0.0436f, -0.0499f,
+                                                               0.0118f, 0.0166f, 0.0203f, 0.0279f, 0.0113f, 0.0129f, 0.0016f, 0.0072f,
+                                                               -0.0118f, -0.0018f, -0.0141f, -0.0054f, -0.0091f, -0.0138f, -0.0145f, -0.0187f,
+                                                               0.0323f, 0.0305f, 0.0259f, 0.0300f, 0.0540f, 0.0614f, 0.0495f, 0.0590f,
+                                                               -0.0511f, -0.0603f, -0.0478f, -0.0524f, -0.0227f, -0.0274f, -0.0154f, -0.0255f,
+                                                               -0.0572f, -0.0565f, -0.0518f, -0.0496f, 0.0116f, 0.0054f, 0.0163f, 0.0104f});
+            mean_tensor.reshape_(stats_shape);
+            auto std_tensor = sd::Tensor<float>::from_vector({1.8029f, 1.7786f, 1.7868f, 1.7837f, 1.7717f, 1.7590f, 1.7610f, 1.7479f,
+                                                              1.7336f, 1.7373f, 1.7340f, 1.7343f, 1.8626f, 1.8527f, 1.8629f, 1.8589f,
+                                                              1.7593f, 1.7526f, 1.7556f, 1.7583f, 1.7363f, 1.7400f, 1.7355f, 1.7394f,
+                                                              1.7342f, 1.7246f, 1.7392f, 1.7304f, 1.7551f, 1.7513f, 1.7559f, 1.7488f,
+                                                              1.8449f, 1.8454f, 1.8550f, 1.8535f, 1.8240f, 1.7813f, 1.7854f, 1.7945f,
+                                                              1.8047f, 1.7876f, 1.7695f, 1.7676f, 1.7782f, 1.7667f, 1.7925f, 1.7848f,
+                                                              1.7579f, 1.7407f, 1.7483f, 1.7368f, 1.7961f, 1.7998f, 1.7920f, 1.7925f,
+                                                              1.7780f, 1.7747f, 1.7727f, 1.7749f, 1.7526f, 1.7447f, 1.7657f, 1.7495f,
+                                                              1.7775f, 1.7720f, 1.7813f, 1.7813f, 1.8162f, 1.8013f, 1.8023f, 1.8033f,
+                                                              1.7527f, 1.7331f, 1.7563f, 1.7482f, 1.7610f, 1.7507f, 1.7681f, 1.7613f,
+                                                              1.7665f, 1.7545f, 1.7828f, 1.7726f, 1.7896f, 1.7999f, 1.7864f, 1.7760f,
+                                                              1.7613f, 1.7625f, 1.7560f, 1.7577f, 1.7783f, 1.7671f, 1.7810f, 1.7799f,
+                                                              1.7201f, 1.7068f, 1.7265f, 1.7091f, 1.7793f, 1.7578f, 1.7502f, 1.7455f,
+                                                              1.7587f, 1.7500f, 1.7525f, 1.7362f, 1.7616f, 1.7572f, 1.7444f, 1.7430f,
+                                                              1.7509f, 1.7610f, 1.7634f, 1.7612f, 1.7254f, 1.7135f, 1.7321f, 1.7226f,
+                                                              1.7664f, 1.7624f, 1.7718f, 1.7664f, 1.7457f, 1.7441f, 1.7569f, 1.7530f});
+            std_tensor.reshape_(stats_shape);
+            return {std::move(mean_tensor), std::move(std_tensor)};
+        } else {
+            GGML_ABORT("unknown version %d", version);
+        }
+    }
+
+    sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+        if (sd_version_is_flux2(version)) {
+            int channel_dim                = 2;
+            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents, channel_dim);
+            return (latents * std_tensor) / scale_factor + mean_tensor;
+        }
+        return (latents / scale_factor) + shift_factor;
+    }
+
+    sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
+        if (sd_version_is_flux2(version)) {
+            int channel_dim                = 2;
+            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents, channel_dim);
+            return ((latents - mean_tensor) * scale_factor) / std_tensor;
+        }
+        return (latents - shift_factor) * scale_factor;
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return ae.get_encoder_output_channels();
+    }
+
+    void test() {
+        ggml_init_params params;
+        params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
+        params.mem_buffer = nullptr;
+        params.no_alloc   = false;
+
+        ggml_context* ctx = ggml_init(params);
+        GGML_ASSERT(ctx != nullptr);
+
+        {
+            // CPU, x{1, 3, 64, 64}: Pass
+            // CUDA, x{1, 3, 64, 64}: Pass, but sill get wrong result for some image, may be due to interlnal nan
+            // CPU, x{2, 3, 64, 64}: Wrong result
+            // CUDA, x{2, 3, 64, 64}: Wrong result, and different from CPU result
+            sd::Tensor<float> x({64, 64, 3, 2});
+            x.fill_(0.5f);
+            print_sd_tensor(x);
+            sd::Tensor<float> out;
+
+            int64_t t0   = ggml_time_ms();
+            auto out_opt = _compute(8, x, false);
+            int64_t t1   = ggml_time_ms();
+
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
+            LOG_DEBUG("encode test done in %lldms", t1 - t0);
+        }
+
+        if (false) {
+            // CPU, z{1, 4, 8, 8}: Pass
+            // CUDA, z{1, 4, 8, 8}: Pass
+            // CPU, z{3, 4, 8, 8}: Wrong result
+            // CUDA, z{3, 4, 8, 8}: Wrong result, and different from CPU result
+            sd::Tensor<float> z({8, 8, 4, 1});
+            z.fill_(0.5f);
+            print_sd_tensor(z);
+            sd::Tensor<float> out;
+
+            int64_t t0   = ggml_time_ms();
+            auto out_opt = _compute(8, z, true);
+            int64_t t1   = ggml_time_ms();
+
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
+            LOG_DEBUG("decode test done in %lldms", t1 - t0);
+        }
+    };
+};
+
+#endif  // __AUTO_ENCODER_KL_HPP__

src/cache_dit.hpp

@@ -8,7 +8,9 @@
 #include <unordered_map>
 #include <vector>
 
+#include "condition_cache_utils.hpp"
 #include "ggml_extend.hpp"
+#include "tensor.hpp"
 
 struct DBCacheConfig {
     bool enabled                        = false;
@@ -603,87 +605,6 @@ inline std::vector<int> generate_scm_mask(
     return mask;
 }
 
-inline std::vector<int> get_scm_preset(const std::string& preset, int total_steps) {
-    struct Preset {
-        std::vector<int> compute_bins;
-        std::vector<int> cache_bins;
-    };
-
-    Preset slow   = {{8, 3, 3, 2, 1, 1}, {1, 2, 2, 2, 3}};
-    Preset medium = {{6, 2, 2, 2, 2, 1}, {1, 3, 3, 3, 3}};
-    Preset fast   = {{6, 1, 1, 1, 1, 1}, {1, 3, 4, 5, 4}};
-    Preset ultra  = {{4, 1, 1, 1, 1}, {2, 5, 6, 7}};
-
-    Preset* p = nullptr;
-    if (preset == "slow" || preset == "s" || preset == "S")
-        p = &slow;
-    else if (preset == "medium" || preset == "m" || preset == "M")
-        p = &medium;
-    else if (preset == "fast" || preset == "f" || preset == "F")
-        p = &fast;
-    else if (preset == "ultra" || preset == "u" || preset == "U")
-        p = &ultra;
-    else
-        return {};
-
-    if (total_steps != 28 && total_steps > 0) {
-        float scale = static_cast<float>(total_steps) / 28.0f;
-        std::vector<int> scaled_compute, scaled_cache;
-
-        for (int v : p->compute_bins) {
-            scaled_compute.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
-        }
-        for (int v : p->cache_bins) {
-            scaled_cache.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
-        }
-
-        return generate_scm_mask(scaled_compute, scaled_cache, total_steps);
-    }
-
-    return generate_scm_mask(p->compute_bins, p->cache_bins, total_steps);
-}
-
-inline float get_preset_threshold(const std::string& preset) {
-    if (preset == "slow" || preset == "s" || preset == "S")
-        return 0.20f;
-    if (preset == "medium" || preset == "m" || preset == "M")
-        return 0.25f;
-    if (preset == "fast" || preset == "f" || preset == "F")
-        return 0.30f;
-    if (preset == "ultra" || preset == "u" || preset == "U")
-        return 0.34f;
-    return 0.08f;
-}
-
-inline int get_preset_warmup(const std::string& preset) {
-    if (preset == "slow" || preset == "s" || preset == "S")
-        return 8;
-    if (preset == "medium" || preset == "m" || preset == "M")
-        return 6;
-    if (preset == "fast" || preset == "f" || preset == "F")
-        return 6;
-    if (preset == "ultra" || preset == "u" || preset == "U")
-        return 4;
-    return 8;
-}
-
-inline int get_preset_Fn(const std::string& preset) {
-    if (preset == "slow" || preset == "s" || preset == "S")
-        return 8;
-    if (preset == "medium" || preset == "m" || preset == "M")
-        return 8;
-    if (preset == "fast" || preset == "f" || preset == "F")
-        return 6;
-    if (preset == "ultra" || preset == "u" || preset == "U")
-        return 4;
-    return 8;
-}
-
-inline int get_preset_Bn(const std::string& preset) {
-    (void)preset;
-    return 0;
-}
-
 inline void parse_dbcache_options(const std::string& opts, DBCacheConfig& cfg) {
     if (opts.empty())
         return;
@@ -852,35 +773,37 @@ struct CacheDitConditionState {
         return it != cache_diffs.end() && !it->second.diff.empty();
     }
 
-    void update_cache(const void* cond, const float* input, const float* output, size_t size) {
+    void update_cache(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         CacheEntry& entry = cache_diffs[cond];
-        entry.diff.resize(size);
-        for (size_t i = 0; i < size; i++) {
-            entry.diff[i] = output[i] - input[i];
+        if (!sd::store_condition_cache_diff(&entry.diff, input, output)) {
+            entry.prev_input.clear();
+            entry.prev_output.clear();
+            entry.has_prev = false;
+            return;
         }
 
+        size_t size              = static_cast<size_t>(output.numel());
+        const float* input_data  = input.data();
+        const float* output_data = output.data();
         entry.prev_input.resize(size);
         entry.prev_output.resize(size);
         for (size_t i = 0; i < size; i++) {
-            entry.prev_input[i]  = input[i];
-            entry.prev_output[i] = output[i];
+            entry.prev_input[i]  = input_data[i];
+            entry.prev_output[i] = output_data[i];
         }
         entry.has_prev = true;
     }
 
-    void apply_cache(const void* cond, const float* input, float* output, size_t size) {
+    void apply_cache(const void* cond,
+                     const sd::Tensor<float>& input,
+                     sd::Tensor<float>* output) {
         auto it = cache_diffs.find(cond);
         if (it == cache_diffs.end() || it->second.diff.empty())
             return;
-        if (it->second.diff.size() != size)
-            return;
-
-        for (size_t i = 0; i < size; i++) {
-            output[i] = input[i] + it->second.diff[i];
-        }
+        sd::apply_condition_cache_diff(it->second.diff, input, output);
     }
 
-    bool before_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output, float sigma, int step_index) {
+    bool before_condition(const void* cond, const sd::Tensor<float>& input, sd::Tensor<float>* output, float sigma, int step_index) {
         if (!enabled() || step_index < 0)
             return false;
 
@@ -900,8 +823,7 @@ struct CacheDitConditionState {
 
         if (skip_current_step) {
             if (has_cache(cond)) {
-                apply_cache(cond, (float*)input->data, (float*)output->data,
-                            static_cast<size_t>(ggml_nelements(output)));
+                apply_cache(cond, input, output);
                 return true;
             }
             return false;
@@ -914,11 +836,11 @@ struct CacheDitConditionState {
         if (it == cache_diffs.end() || !it->second.has_prev)
             return false;
 
-        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        size_t ne = static_cast<size_t>(input.numel());
         if (it->second.prev_input.size() != ne)
             return false;
 
-        float* input_data = (float*)input->data;
+        const float* input_data = input.data();
         float diff              = CacheDitState::calculate_residual_diff(
                          it->second.prev_input.data(), input_data, ne);
 
@@ -940,7 +862,7 @@ struct CacheDitConditionState {
             cached_steps.push_back(current_step_index);
             continuous_cached_steps++;
             accumulated_residual_diff += diff;
-            apply_cache(cond, input_data, (float*)output->data, ne);
+            apply_cache(cond, input, output);
             return true;
         }
 
@@ -948,15 +870,14 @@ struct CacheDitConditionState {
         return false;
     }
 
-    void after_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output) {
+    void after_condition(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         if (!step_is_active())
             return;
 
-        size_t ne = static_cast<size_t>(ggml_nelements(output));
-        update_cache(cond, (float*)input->data, (float*)output->data, ne);
+        update_cache(cond, input, output);
 
         if (cond == anchor_condition && taylor_config.enabled) {
-            taylor_state.update_derivatives((float*)output->data, ne, current_step_index);
+            taylor_state.update_derivatives(output.data(), static_cast<size_t>(output.numel()), current_step_index);
         }
     }
 

src/clip.hpp

@@ -473,7 +473,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [N, n_token, d_model]
         auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
         auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
@@ -511,7 +511,7 @@ public:
         blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* mask = nullptr) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* mask = nullptr) {
         // 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"]);
@@ -541,9 +541,9 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* mask = nullptr,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* mask = nullptr,
                          int clip_skip     = -1) {
         // x: [N, n_token, d_model]
         int layer_idx = n_layer - 1;
@@ -573,7 +573,7 @@ protected:
     int64_t num_positions;
     bool force_clip_f32;
 
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         enum ggml_type token_wtype = GGML_TYPE_F32;
         if (!force_clip_f32) {
             token_wtype = get_type(prefix + "token_embedding.weight", tensor_storage_map, GGML_TYPE_F32);
@@ -597,13 +597,13 @@ public:
           force_clip_f32(force_clip_f32) {
     }
 
-    struct ggml_tensor* get_token_embed_weight() {
+    ggml_tensor* get_token_embed_weight() {
         return params["token_embedding.weight"];
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* custom_embed_weight) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* custom_embed_weight) {
         // input_ids: [N, n_token]
         auto token_embed_weight    = params["token_embedding.weight"];
         auto position_embed_weight = params["position_embedding.weight"];
@@ -630,7 +630,7 @@ protected:
     int num_patches;
     int64_t num_positions;
 
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         enum ggml_type patch_wtype    = GGML_TYPE_F16;
         enum ggml_type class_wtype    = GGML_TYPE_F32;
         enum ggml_type position_wtype = GGML_TYPE_F32;
@@ -653,7 +653,7 @@ public:
         num_positions = num_patches + 1;
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* pixel_values) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* pixel_values) {
         // pixel_values: [N, num_channels, image_size, image_size]
         // return: [N, num_positions, embed_dim]
         GGML_ASSERT(pixel_values->ne[0] == image_size && pixel_values->ne[1] == image_size && pixel_values->ne[2] == num_channels);
@@ -663,18 +663,18 @@ public:
         auto position_embed_weight = params["position_embedding.weight"];
 
         // concat(patch_embedding, class_embedding) + position_embedding
-        struct ggml_tensor* patch_embedding;
+        ggml_tensor* patch_embedding;
         int64_t N       = pixel_values->ne[3];
         patch_embedding = ggml_ext_conv_2d(ctx->ggml_ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size);  // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
         patch_embedding = ggml_reshape_3d(ctx->ggml_ctx, patch_embedding, num_patches, embed_dim, N);                          // [N, embed_dim, num_patches]
         patch_embedding = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, patch_embedding, 1, 0, 2, 3));                  // [N, num_patches, embed_dim]
         patch_embedding = ggml_reshape_4d(ctx->ggml_ctx, patch_embedding, 1, embed_dim, num_patches, N);                       // [N, num_patches, embed_dim, 1]
 
-        struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, embed_dim, N);
+        ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, embed_dim, N);
         class_embedding              = ggml_repeat(ctx->ggml_ctx, class_embed_weight, class_embedding);      // [N, embed_dim]
         class_embedding              = ggml_reshape_4d(ctx->ggml_ctx, class_embedding, 1, embed_dim, 1, N);  // [N, 1, embed_dim, 1]
 
-        struct ggml_tensor* x = ggml_concat(ctx->ggml_ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
+        ggml_tensor* x = ggml_concat(ctx->ggml_ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
         x              = ggml_reshape_3d(ctx->ggml_ctx, x, embed_dim, num_positions, N);   // [N, num_positions, embed_dim]
         x              = ggml_add(ctx->ggml_ctx, x, position_embed_weight);
         return x;  // [N, num_positions, embed_dim]
@@ -693,7 +693,7 @@ enum CLIPVersion {
 
 class CLIPTextModel : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         if (version == OPEN_CLIP_VIT_BIGG_14) {
             enum ggml_type wtype      = GGML_TYPE_F32;
             params["text_projection"] = ggml_new_tensor_2d(ctx, wtype, projection_dim, hidden_size);
@@ -734,15 +734,15 @@ public:
         blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
-    struct ggml_tensor* get_token_embed_weight() {
+    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(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* tkn_embeddings,
-                                struct ggml_tensor* mask = nullptr,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* tkn_embeddings,
+                         ggml_tensor* mask    = nullptr,
                          size_t max_token_idx = 0,
                          bool return_pooled   = false,
                          int clip_skip        = -1) {
@@ -804,8 +804,8 @@ public:
         blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* pixel_values,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* pixel_values,
                          bool return_pooled = true,
                          int clip_skip      = -1) {
         // pixel_values: [N, num_channels, image_size, image_size]
@@ -839,7 +839,7 @@ protected:
     int64_t out_features;
     bool transpose_weight;
 
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
         if (transpose_weight) {
             params["weight"] = ggml_new_tensor_2d(ctx, wtype, out_features, in_features);
@@ -856,8 +856,8 @@ public:
           out_features(out_features),
           transpose_weight(transpose_weight) {}
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
-        struct ggml_tensor* w = params["weight"];
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+        ggml_tensor* w = params["weight"];
         if (transpose_weight) {
             w = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, w));
         }
@@ -886,8 +886,8 @@ public:
         blocks["visual_projection"] = std::shared_ptr<GGMLBlock>(new CLIPProjection(hidden_size, projection_dim, transpose_proj_w));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* pixel_values,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* pixel_values,
                          bool return_pooled = true,
                          int clip_skip      = -1) {
         // pixel_values: [N, num_channels, image_size, image_size]
@@ -936,14 +936,14 @@ struct CLIPTextModelRunner : public GGMLRunner {
         return "clip";
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         model.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* embeddings,
-                                struct ggml_tensor* mask,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* embeddings,
+                         ggml_tensor* mask,
                          size_t max_token_idx = 0,
                          bool return_pooled   = false,
                          int clip_skip        = -1) {
@@ -957,17 +957,16 @@ struct CLIPTextModelRunner : public GGMLRunner {
         return model.forward(ctx, input_ids, embeddings, mask, max_token_idx, return_pooled, clip_skip);
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
+    ggml_cgraph* build_graph(const sd::Tensor<int32_t>& input_ids_tensor,
                              int num_custom_embeddings    = 0,
                              void* custom_embeddings_data = nullptr,
                              size_t max_token_idx         = 0,
                              bool return_pooled           = false,
                              int clip_skip                = -1) {
-        struct ggml_cgraph* gf = new_graph_custom(2048);
+        ggml_cgraph* gf        = new_graph_custom(2048);
+        ggml_tensor* input_ids = make_input(input_ids_tensor);
 
-        input_ids = to_backend(input_ids);
-
-        struct ggml_tensor* embeddings = nullptr;
+        ggml_tensor* embeddings = nullptr;
 
         if (num_custom_embeddings > 0 && custom_embeddings_data != nullptr) {
             auto token_embed_weight = model.get_token_embed_weight();
@@ -997,26 +996,28 @@ struct CLIPTextModelRunner : public GGMLRunner {
 
         auto runner_ctx = get_context();
 
-        struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, attention_mask, max_token_idx, return_pooled, clip_skip);
+        ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, attention_mask, max_token_idx, return_pooled, clip_skip);
 
         ggml_build_forward_expand(gf, hidden_states);
 
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* input_ids,
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<int32_t>& input_ids,
                               int num_custom_embeddings,
                               void* custom_embeddings_data,
                               size_t max_token_idx,
                               bool return_pooled,
-                 int clip_skip,
-                 ggml_tensor** output,
-                 ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+                              int clip_skip) {
+        auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
         };
-        return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+        auto result = GGMLRunner::compute<float>(get_graph, n_threads, true);
+        if (return_pooled) {
+            return take_or_empty(std::move(result));
+        }
+        return restore_trailing_singleton_dims(std::move(result), 3);
     }
 };
 

src/common_block.hpp

@@ -23,7 +23,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [N, channels, h, w]
         if (vae_downsample) {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
@@ -52,7 +52,7 @@ public:
         blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [N, channels, h, w]
         auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
@@ -121,7 +121,7 @@ public:
         }
     }
 
-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* emb = nullptr) {
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* emb = nullptr) {
         // For dims==3, we reduce dimension from 5d to 4d by merging h and w, in order not to change ggml
         // [N, c, t, h, w] => [N, c, t, h * w]
         // x: [N, channels, h, w] if dims == 2 else [N, channels, t, h, w]
@@ -188,7 +188,7 @@ public:
         blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out * 2));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         // x: [ne3, ne2, ne1, dim_in]
         // return: [ne3, ne2, ne1, dim_out]
         auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
@@ -214,7 +214,7 @@ public:
         blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out, bias));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         // x: [ne3, ne2, ne1, dim_in]
         // return: [ne3, ne2, ne1, dim_out]
         auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
@@ -258,7 +258,7 @@ public:
         blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out, true, false, force_prec_f32, scale));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [ne3, ne2, ne1, dim]
         // return: [ne3, ne2, ne1, dim_out]
 
@@ -297,9 +297,9 @@ public:
         // to_out_1 is nn.Dropout(), skip for inference
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* context) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* context) {
         // x: [N, n_token, query_dim]
         // context: [N, n_context, context_dim]
         // return: [N, n_token, query_dim]
@@ -355,9 +355,9 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* context) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* context) {
         // x: [N, n_token, query_dim]
         // context: [N, n_context, context_dim]
         // return: [N, n_token, query_dim]
@@ -406,7 +406,7 @@ protected:
     int64_t context_dim = 768;  // hidden_size, 1024 for VERSION_SD2
     bool use_linear     = false;
 
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
         auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
         if (iter != tensor_storage_map.end()) {
             int64_t inner_dim = n_head * d_head;
@@ -456,9 +456,9 @@ public:
         }
     }
 
-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                        struct ggml_tensor* x,
-                                        struct ggml_tensor* context) {
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                 ggml_tensor* x,
+                                 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"]);
@@ -510,7 +510,7 @@ public:
 
 class AlphaBlender : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
         // 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);
@@ -530,9 +530,9 @@ public:
         // since mix_factor.shape is [1,], we don't need rearrange using  rearrange_pattern
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x_spatial,
-                                struct ggml_tensor* x_temporal) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x_spatial,
+                         ggml_tensor* x_temporal) {
         // image_only_indicator is always tensor([0.])
         float alpha = get_alpha();
         auto x      = ggml_add(ctx->ggml_ctx,
@@ -555,9 +555,9 @@ public:
         blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* emb,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* emb,
                          int num_video_frames) {
         // x: [N, channels, h, w] aka [b*t, channels, h, w]
         // emb: [N, emb_channels] aka [b*t, emb_channels]

src/common_dit.hpp

@@ -4,7 +4,7 @@
 #include "ggml_extend.hpp"
 
 namespace DiT {
-    ggml_tensor* patchify(ggml_context* ctx,
+    inline ggml_tensor* patchify(ggml_context* ctx,
                                  ggml_tensor* x,
                                  int pw,
                                  int ph,
@@ -33,7 +33,7 @@ namespace DiT {
         return x;
     }
 
-    ggml_tensor* unpatchify(ggml_context* ctx,
+    inline ggml_tensor* unpatchify(ggml_context* ctx,
                                    ggml_tensor* x,
                                    int64_t h,
                                    int64_t w,
@@ -64,7 +64,7 @@ namespace DiT {
         return x;
     }
 
-    ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
+    inline ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                           ggml_tensor* x,
                                           int ph,
                                           int pw) {
@@ -77,7 +77,7 @@ namespace DiT {
         return x;
     }
 
-    ggml_tensor* pad_and_patchify(GGMLRunnerContext* ctx,
+    inline ggml_tensor* pad_and_patchify(GGMLRunnerContext* ctx,
                                          ggml_tensor* x,
                                          int ph,
                                          int pw,
@@ -87,7 +87,7 @@ namespace DiT {
         return x;
     }
 
-    ggml_tensor* unpatchify_and_crop(ggml_context* ctx,
+    inline ggml_tensor* unpatchify_and_crop(ggml_context* ctx,
                                             ggml_tensor* x,
                                             int64_t H,
                                             int64_t W,

src/condition_cache_utils.hpp

@@ -0,0 +1,64 @@
+#ifndef __CONDITION_CACHE_UTILS_HPP__
+#define __CONDITION_CACHE_UTILS_HPP__
+
+#include <vector>
+
+#include "tensor.hpp"
+
+namespace sd {
+
+    inline bool store_condition_cache_diff(std::vector<float>* diff,
+                                           const sd::Tensor<float>& input,
+                                           const sd::Tensor<float>& output) {
+        if (diff == nullptr || input.empty() || output.empty()) {
+            return false;
+        }
+
+        size_t input_size  = static_cast<size_t>(input.numel());
+        size_t output_size = static_cast<size_t>(output.numel());
+        if (input_size == 0 || input_size != output_size) {
+            diff->clear();
+            return false;
+        }
+
+        const float* input_data  = input.data();
+        const float* output_data = output.data();
+        if (input_data == nullptr || output_data == nullptr) {
+            diff->clear();
+            return false;
+        }
+
+        diff->resize(output_size);
+        for (size_t i = 0; i < output_size; ++i) {
+            (*diff)[i] = output_data[i] - input_data[i];
+        }
+        return true;
+    }
+
+    inline bool apply_condition_cache_diff(const std::vector<float>& diff,
+                                           const sd::Tensor<float>& input,
+                                           sd::Tensor<float>* output) {
+        if (output == nullptr || input.empty() || diff.empty()) {
+            return false;
+        }
+
+        size_t input_size = static_cast<size_t>(input.numel());
+        if (input_size == 0 || diff.size() != input_size) {
+            return false;
+        }
+
+        *output            = input;
+        float* output_data = output->data();
+        if (output_data == nullptr) {
+            return false;
+        }
+
+        for (size_t i = 0; i < input_size; ++i) {
+            output_data[i] += diff[i];
+        }
+        return true;
+    }
+
+}  // namespace sd
+
+#endif  // __CONDITION_CACHE_UTILS_HPP__

src/control.hpp

@@ -164,26 +164,26 @@ public:
         blocks["middle_block_out.0"] = std::shared_ptr<GGMLBlock>(make_zero_conv(ch));
     }
 
-    struct ggml_tensor* resblock_forward(std::string name,
+    ggml_tensor* resblock_forward(std::string name,
                                   GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* emb) {
+                                  ggml_tensor* x,
+                                  ggml_tensor* emb) {
         auto block = std::dynamic_pointer_cast<ResBlock>(blocks[name]);
         return block->forward(ctx, x, emb);
     }
 
-    struct ggml_tensor* attention_layer_forward(std::string name,
+    ggml_tensor* attention_layer_forward(std::string name,
                                          GGMLRunnerContext* ctx,
-                                                struct ggml_tensor* x,
-                                                struct ggml_tensor* context) {
+                                         ggml_tensor* x,
+                                         ggml_tensor* context) {
         auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
         return block->forward(ctx, x, context);
     }
 
-    struct ggml_tensor* input_hint_block_forward(GGMLRunnerContext* ctx,
-                                                 struct ggml_tensor* hint,
-                                                 struct ggml_tensor* emb,
-                                                 struct ggml_tensor* context) {
+    ggml_tensor* input_hint_block_forward(GGMLRunnerContext* ctx,
+                                          ggml_tensor* hint,
+                                          ggml_tensor* emb,
+                                          ggml_tensor* context) {
         int num_input_blocks = 15;
         auto h               = hint;
         for (int i = 0; i < num_input_blocks; i++) {
@@ -198,13 +198,13 @@ public:
         return h;
     }
 
-    std::vector<struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                             struct ggml_tensor* x,
-                                             struct ggml_tensor* hint,
-                                             struct ggml_tensor* guided_hint,
-                                             struct ggml_tensor* timesteps,
-                                             struct ggml_tensor* context,
-                                             struct ggml_tensor* y = nullptr) {
+    std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                      ggml_tensor* x,
+                                      ggml_tensor* hint,
+                                      ggml_tensor* guided_hint,
+                                      ggml_tensor* timesteps,
+                                      ggml_tensor* context,
+                                      ggml_tensor* y = nullptr) {
         // x: [N, in_channels, h, w] or [N, in_channels/2, h, w]
         // timesteps: [N,]
         // context: [N, max_position, hidden_size] or [1, max_position, hidden_size]. for example, [N, 77, 768]
@@ -246,7 +246,7 @@ public:
             emb = ggml_add(ctx->ggml_ctx, emb, label_emb);  // [N, time_embed_dim]
         }
 
-        std::vector<struct ggml_tensor*> outs;
+        std::vector<ggml_tensor*> outs;
 
         if (guided_hint == nullptr) {
             guided_hint = input_hint_block_forward(ctx, hint, emb, context);
@@ -310,10 +310,12 @@ struct ControlNet : public GGMLRunner {
     SDVersion version = VERSION_SD1;
     ControlNetBlock control_net;
 
-    ggml_backend_buffer_t control_buffer = nullptr;  // keep control output tensors in backend memory
+    ggml_backend_buffer_t control_buffer = nullptr;
     ggml_context* control_ctx            = nullptr;
-    std::vector<struct ggml_tensor*> controls;  // (12 input block outputs, 1 middle block output) SD 1.5
-    struct ggml_tensor* guided_hint = nullptr;  // guided_hint cache, for faster inference
+    std::vector<ggml_tensor*> control_outputs_ggml;
+    ggml_tensor* guided_hint_output_ggml = nullptr;
+    std::vector<sd::Tensor<float>> controls;
+    sd::Tensor<float> guided_hint;
     bool guided_hint_cached = false;
 
     ControlNet(ggml_backend_t backend,
@@ -328,23 +330,23 @@ struct ControlNet : public GGMLRunner {
         free_control_ctx();
     }
 
-    void alloc_control_ctx(std::vector<struct ggml_tensor*> outs) {
-        struct ggml_init_params params;
+    void alloc_control_ctx(std::vector<ggml_tensor*> outs) {
+        ggml_init_params params;
         params.mem_size   = static_cast<size_t>(outs.size() * ggml_tensor_overhead()) + 1024 * 1024;
         params.mem_buffer = nullptr;
         params.no_alloc   = true;
         control_ctx       = ggml_init(params);
 
-        controls.resize(outs.size() - 1);
+        control_outputs_ggml.resize(outs.size() - 1);
 
         size_t control_buffer_size = 0;
 
-        guided_hint = ggml_dup_tensor(control_ctx, outs[0]);
-        control_buffer_size += ggml_nbytes(guided_hint);
+        guided_hint_output_ggml = ggml_dup_tensor(control_ctx, outs[0]);
+        control_buffer_size += ggml_nbytes(guided_hint_output_ggml);
 
         for (int i = 0; i < outs.size() - 1; i++) {
-            controls[i] = ggml_dup_tensor(control_ctx, outs[i + 1]);
-            control_buffer_size += ggml_nbytes(controls[i]);
+            control_outputs_ggml[i] = ggml_dup_tensor(control_ctx, outs[i + 1]);
+            control_buffer_size += ggml_nbytes(control_outputs_ggml[i]);
         }
 
         control_buffer = ggml_backend_alloc_ctx_tensors(control_ctx, runtime_backend);
@@ -361,8 +363,10 @@ struct ControlNet : public GGMLRunner {
             ggml_free(control_ctx);
             control_ctx = nullptr;
         }
-        guided_hint        = nullptr;
+        guided_hint_output_ggml = nullptr;
         guided_hint_cached      = false;
+        guided_hint             = {};
+        control_outputs_ggml.clear();
         controls.clear();
     }
 
@@ -370,33 +374,37 @@ struct ControlNet : public GGMLRunner {
         return "control_net";
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         control_net.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                    struct ggml_tensor* hint,
-                                    struct ggml_tensor* timesteps,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* y = nullptr) {
-        struct ggml_cgraph* gf = new_graph_custom(CONTROL_NET_GRAPH_SIZE);
+    ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                             const sd::Tensor<float>& hint_tensor,
+                             const sd::Tensor<float>& timesteps_tensor,
+                             const sd::Tensor<float>& context_tensor = {},
+                             const sd::Tensor<float>& y_tensor       = {}) {
+        ggml_cgraph* gf = new_graph_custom(CONTROL_NET_GRAPH_SIZE);
 
-        x = to_backend(x);
-        if (guided_hint_cached) {
+        ggml_tensor* x         = make_input(x_tensor);
+        ggml_tensor* hint      = nullptr;
+        ggml_tensor* timesteps = make_input(timesteps_tensor);
+        ggml_tensor* context   = make_optional_input(context_tensor);
+        ggml_tensor* y         = make_optional_input(y_tensor);
+
+        ggml_tensor* guided_hint_input = nullptr;
+        if (guided_hint_cached && !guided_hint.empty()) {
+            guided_hint_input = make_input(guided_hint);
             hint              = nullptr;
         } else {
-            hint = to_backend(hint);
+            hint = make_input(hint_tensor);
         }
-        context   = to_backend(context);
-        y         = to_backend(y);
-        timesteps = to_backend(timesteps);
 
         auto runner_ctx = get_context();
 
         auto outs = control_net.forward(&runner_ctx,
                                         x,
                                         hint,
-                                        guided_hint_cached ? guided_hint : nullptr,
+                                        guided_hint_input,
                                         timesteps,
                                         context,
                                         y);
@@ -405,36 +413,46 @@ struct ControlNet : public GGMLRunner {
             alloc_control_ctx(outs);
         }
 
-        ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[0], guided_hint));
+        ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[0], guided_hint_output_ggml));
         for (int i = 0; i < outs.size() - 1; i++) {
-            ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[i + 1], controls[i]));
+            ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[i + 1], control_outputs_ggml[i]));
         }
 
         return gf;
     }
 
-    bool compute(int n_threads,
-                 struct ggml_tensor* x,
-                 struct ggml_tensor* hint,
-                 struct ggml_tensor* timesteps,
-                 struct ggml_tensor* context,
-                 struct ggml_tensor* y,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) {
+    std::optional<std::vector<sd::Tensor<float>>> compute(int n_threads,
+                                                          const sd::Tensor<float>& x,
+                                                          const sd::Tensor<float>& hint,
+                                                          const sd::Tensor<float>& timesteps,
+                                                          const sd::Tensor<float>& context = {},
+                                                          const sd::Tensor<float>& y       = {}) {
         // x: [N, in_channels, h, w]
         // timesteps: [N, ]
         // context: [N, max_position, hidden_size]([N, 77, 768]) or [1, max_position, hidden_size]
         // y: [N, adm_in_channels] or [1, adm_in_channels]
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+        auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(x, hint, timesteps, context, y);
         };
 
-        bool res = GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
-        if (res) {
-            // cache guided_hint
-            guided_hint_cached = true;
+        auto compute_result = GGMLRunner::compute<float>(get_graph, n_threads, false);
+        if (!compute_result.has_value()) {
+            return std::nullopt;
         }
-        return res;
+
+        if (guided_hint_output_ggml != nullptr) {
+            guided_hint = restore_trailing_singleton_dims(sd::make_sd_tensor_from_ggml<float>(guided_hint_output_ggml),
+                                                          4);
+        }
+        controls.clear();
+        controls.reserve(control_outputs_ggml.size());
+        for (ggml_tensor* control : control_outputs_ggml) {
+            auto control_host = restore_trailing_singleton_dims(sd::make_sd_tensor_from_ggml<float>(control), 4);
+            GGML_ASSERT(!control_host.empty());
+            controls.push_back(std::move(control_host));
+        }
+        guided_hint_cached = true;
+        return controls;
     }
 
     bool load_from_file(const std::string& file_path, int n_threads) {

src/diffusion_model.hpp

@@ -1,41 +1,49 @@
 #ifndef __DIFFUSION_MODEL_H__
 #define __DIFFUSION_MODEL_H__
 
+#include <optional>
 #include "anima.hpp"
 #include "flux.hpp"
 #include "mmdit.hpp"
 #include "qwen_image.hpp"
+#include "tensor_ggml.hpp"
 #include "unet.hpp"
 #include "wan.hpp"
 #include "z_image.hpp"
 
 struct DiffusionParams {
-    struct ggml_tensor* x                     = nullptr;
-    struct ggml_tensor* timesteps             = nullptr;
-    struct ggml_tensor* context               = nullptr;
-    struct ggml_tensor* c_concat              = nullptr;
-    struct ggml_tensor* y                     = nullptr;
-    struct ggml_tensor* guidance              = nullptr;
-    std::vector<ggml_tensor*> ref_latents     = {};
+    const sd::Tensor<float>* x                        = nullptr;
+    const sd::Tensor<float>* timesteps                = nullptr;
+    const sd::Tensor<float>* context                  = nullptr;
+    const sd::Tensor<float>* c_concat                 = nullptr;
+    const sd::Tensor<float>* y                        = nullptr;
+    const sd::Tensor<int32_t>* t5_ids                 = nullptr;
+    const sd::Tensor<float>* t5_weights               = nullptr;
+    const sd::Tensor<float>* guidance                 = nullptr;
+    const std::vector<sd::Tensor<float>>* ref_latents = nullptr;
     bool increase_ref_index                           = false;
     int num_video_frames                              = -1;
-    std::vector<struct ggml_tensor*> controls = {};
+    const std::vector<sd::Tensor<float>>* controls    = nullptr;
     float control_strength                            = 0.f;
-    struct ggml_tensor* vace_context          = nullptr;
+    const sd::Tensor<float>* vace_context             = nullptr;
     float vace_strength                               = 1.f;
-    std::vector<int> skip_layers              = {};
+    const std::vector<int>* skip_layers               = nullptr;
 };
 
+template <typename T>
+static inline const sd::Tensor<T>& tensor_or_empty(const sd::Tensor<T>* tensor) {
+    static const sd::Tensor<T> kEmpty;
+    return tensor != nullptr ? *tensor : kEmpty;
+}
+
 struct DiffusionModel {
     virtual std::string get_desc()                                               = 0;
-    virtual bool compute(int n_threads,
-                         DiffusionParams diffusion_params,
-                         struct ggml_tensor** output     = nullptr,
-                         struct ggml_context* output_ctx = nullptr)                     = 0;
+    virtual sd::Tensor<float> compute(int n_threads,
+                                      const DiffusionParams& diffusion_params)   = 0;
     virtual void alloc_params_buffer()                                           = 0;
     virtual void free_params_buffer()                                            = 0;
     virtual void free_compute_buffer()                                           = 0;
-    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) = 0;
+    virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) = 0;
     virtual size_t get_params_buffer_size()                                      = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
     virtual int64_t get_adm_in_channels()                            = 0;
@@ -69,7 +77,7 @@ struct UNetModel : public DiffusionModel {
         unet.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         unet.get_param_tensors(tensors, "model.diffusion_model");
     }
 
@@ -93,19 +101,20 @@ struct UNetModel : public DiffusionModel {
         unet.set_circular_axes(circular_x, circular_y);
     }
 
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_controls;
         return unet.compute(n_threads,
-                            diffusion_params.x,
-                            diffusion_params.timesteps,
-                            diffusion_params.context,
-                            diffusion_params.c_concat,
-                            diffusion_params.y,
+                            *diffusion_params.x,
+                            *diffusion_params.timesteps,
+                            tensor_or_empty(diffusion_params.context),
+                            tensor_or_empty(diffusion_params.c_concat),
+                            tensor_or_empty(diffusion_params.y),
                             diffusion_params.num_video_frames,
-                            diffusion_params.controls,
-                            diffusion_params.control_strength, output, output_ctx);
+                            diffusion_params.controls ? *diffusion_params.controls : empty_controls,
+                            diffusion_params.control_strength);
     }
 };
 
@@ -134,7 +143,7 @@ struct MMDiTModel : public DiffusionModel {
         mmdit.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         mmdit.get_param_tensors(tensors, "model.diffusion_model");
     }
 
@@ -158,18 +167,17 @@ struct MMDiTModel : public DiffusionModel {
         mmdit.set_circular_axes(circular_x, circular_y);
     }
 
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<int> empty_skip_layers;
         return mmdit.compute(n_threads,
-                             diffusion_params.x,
-                             diffusion_params.timesteps,
-                             diffusion_params.context,
-                             diffusion_params.y,
-                             output,
-                             output_ctx,
-                             diffusion_params.skip_layers);
+                             *diffusion_params.x,
+                             *diffusion_params.timesteps,
+                             tensor_or_empty(diffusion_params.context),
+                             tensor_or_empty(diffusion_params.y),
+                             diffusion_params.skip_layers ? *diffusion_params.skip_layers : empty_skip_layers);
     }
 };
 
@@ -200,7 +208,7 @@ struct FluxModel : public DiffusionModel {
         flux.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         flux.get_param_tensors(tensors, "model.diffusion_model");
     }
 
@@ -224,22 +232,22 @@ struct FluxModel : public DiffusionModel {
         flux.set_circular_axes(circular_x, circular_y);
     }
 
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_ref_latents;
+        static const std::vector<int> empty_skip_layers;
         return flux.compute(n_threads,
-                            diffusion_params.x,
-                            diffusion_params.timesteps,
-                            diffusion_params.context,
-                            diffusion_params.c_concat,
-                            diffusion_params.y,
-                            diffusion_params.guidance,
-                            diffusion_params.ref_latents,
+                            *diffusion_params.x,
+                            *diffusion_params.timesteps,
+                            tensor_or_empty(diffusion_params.context),
+                            tensor_or_empty(diffusion_params.c_concat),
+                            tensor_or_empty(diffusion_params.y),
+                            tensor_or_empty(diffusion_params.guidance),
+                            diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
                             diffusion_params.increase_ref_index,
-                            output,
-                            output_ctx,
-                            diffusion_params.skip_layers);
+                            diffusion_params.skip_layers ? *diffusion_params.skip_layers : empty_skip_layers);
     }
 };
 
@@ -270,7 +278,7 @@ struct AnimaModel : public DiffusionModel {
         anima.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         anima.get_param_tensors(tensors, prefix);
     }
 
@@ -294,18 +302,16 @@ struct AnimaModel : public DiffusionModel {
         anima.set_circular_axes(circular_x, circular_y);
     }
 
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
         return anima.compute(n_threads,
-                             diffusion_params.x,
-                             diffusion_params.timesteps,
-                             diffusion_params.context,
-                             diffusion_params.c_concat,
-                             diffusion_params.y,
-                             output,
-                             output_ctx);
+                             *diffusion_params.x,
+                             *diffusion_params.timesteps,
+                             tensor_or_empty(diffusion_params.context),
+                             tensor_or_empty(diffusion_params.t5_ids),
+                             tensor_or_empty(diffusion_params.t5_weights));
     }
 };
 
@@ -337,7 +343,7 @@ struct WanModel : public DiffusionModel {
         wan.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         wan.get_param_tensors(tensors, prefix);
     }
 
@@ -361,21 +367,19 @@ struct WanModel : public DiffusionModel {
         wan.set_circular_axes(circular_x, circular_y);
     }
 
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
         return wan.compute(n_threads,
-                           diffusion_params.x,
-                           diffusion_params.timesteps,
-                           diffusion_params.context,
-                           diffusion_params.y,
-                           diffusion_params.c_concat,
-                           nullptr,
-                           diffusion_params.vace_context,
-                           diffusion_params.vace_strength,
-                           output,
-                           output_ctx);
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context),
+                           tensor_or_empty(diffusion_params.y),
+                           tensor_or_empty(diffusion_params.c_concat),
+                           sd::Tensor<float>(),
+                           tensor_or_empty(diffusion_params.vace_context),
+                           diffusion_params.vace_strength);
     }
 };
 
@@ -408,7 +412,7 @@ struct QwenImageModel : public DiffusionModel {
         qwen_image.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         qwen_image.get_param_tensors(tensors, prefix);
     }
 
@@ -432,18 +436,17 @@ struct QwenImageModel : public DiffusionModel {
         qwen_image.set_circular_axes(circular_x, circular_y);
     }
 
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_ref_latents;
         return qwen_image.compute(n_threads,
-                                  diffusion_params.x,
-                                  diffusion_params.timesteps,
-                                  diffusion_params.context,
-                                  diffusion_params.ref_latents,
-                                  true,  // increase_ref_index
-                                  output,
-                                  output_ctx);
+                                  *diffusion_params.x,
+                                  *diffusion_params.timesteps,
+                                  tensor_or_empty(diffusion_params.context),
+                                  diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
+                                  true);
     }
 };
 
@@ -475,7 +478,7 @@ struct ZImageModel : public DiffusionModel {
         z_image.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         z_image.get_param_tensors(tensors, prefix);
     }
 
@@ -499,18 +502,17 @@ struct ZImageModel : public DiffusionModel {
         z_image.set_circular_axes(circular_x, circular_y);
     }
 
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_ref_latents;
         return z_image.compute(n_threads,
-                               diffusion_params.x,
-                               diffusion_params.timesteps,
-                               diffusion_params.context,
-                               diffusion_params.ref_latents,
-                               true,  // increase_ref_index
-                               output,
-                               output_ctx);
+                               *diffusion_params.x,
+                               *diffusion_params.timesteps,
+                               tensor_or_empty(diffusion_params.context),
+                               diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
+                               true);
     }
 };
 

src/easycache.hpp

@@ -1,10 +1,15 @@
+#ifndef __EASYCACHE_HPP__
+#define __EASYCACHE_HPP__
+
 #include <cmath>
 #include <limits>
 #include <unordered_map>
 #include <vector>
 
+#include "condition_cache_utils.hpp"
 #include "denoiser.hpp"
 #include "ggml_extend.hpp"
+#include "tensor.hpp"
 
 struct EasyCacheConfig {
     bool enabled          = false;
@@ -26,8 +31,8 @@ struct EasyCacheState {
     bool initial_step            = true;
     bool skip_current_step       = false;
     bool step_active             = false;
-    const SDCondition* anchor_condition = nullptr;
-    std::unordered_map<const SDCondition*, EasyCacheCacheEntry> cache_diffs;
+    const void* anchor_condition = nullptr;
+    std::unordered_map<const void*, EasyCacheCacheEntry> cache_diffs;
     std::vector<float> prev_input;
     std::vector<float> prev_output;
     float output_prev_norm                = 0.0f;
@@ -120,41 +125,30 @@ struct EasyCacheState {
         return enabled() && step_active && skip_current_step;
     }
 
-    bool has_cache(const SDCondition* cond) const {
+    bool has_cache(const void* cond) const {
         auto it = cache_diffs.find(cond);
         return it != cache_diffs.end() && !it->second.diff.empty();
     }
 
-    void update_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void update_cache(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         EasyCacheCacheEntry& entry = cache_diffs[cond];
-        size_t ne                  = static_cast<size_t>(ggml_nelements(output));
-        entry.diff.resize(ne);
-        float* out_data = (float*)output->data;
-        float* in_data  = (float*)input->data;
-        for (size_t i = 0; i < ne; ++i) {
-            entry.diff[i] = out_data[i] - in_data[i];
-        }
+        sd::store_condition_cache_diff(&entry.diff, input, output);
     }
 
-    void apply_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void apply_cache(const void* cond, const sd::Tensor<float>& input, sd::Tensor<float>* output) {
         auto it = cache_diffs.find(cond);
         if (it == cache_diffs.end() || it->second.diff.empty()) {
             return;
         }
-        copy_ggml_tensor(output, input);
-        float* out_data                = (float*)output->data;
-        const std::vector<float>& diff = it->second.diff;
-        for (size_t i = 0; i < diff.size(); ++i) {
-            out_data[i] += diff[i];
-        }
+        sd::apply_condition_cache_diff(it->second.diff, input, output);
     }
 
-    bool before_condition(const SDCondition* cond,
-                          ggml_tensor* input,
-                          ggml_tensor* output,
+    bool before_condition(const void* cond,
+                          const sd::Tensor<float>& input,
+                          sd::Tensor<float>* output,
                           float sigma,
                           int step_index) {
-        if (!enabled() || step_index < 0) {
+        if (!enabled() || step_index < 0 || output == nullptr) {
             return false;
         }
         if (step_index != current_step_index) {
@@ -181,11 +175,11 @@ struct EasyCacheState {
         if (!has_prev_input || !has_prev_output || !has_cache(cond)) {
             return false;
         }
-        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        size_t ne = static_cast<size_t>(input.numel());
         if (prev_input.size() != ne) {
             return false;
         }
-        float* input_data = (float*)input->data;
+        const float* input_data = input.data();
         last_input_change       = 0.0f;
         for (size_t i = 0; i < ne; ++i) {
             last_input_change += std::fabs(input_data[i] - prev_input[i]);
@@ -211,7 +205,7 @@ struct EasyCacheState {
         return false;
     }
 
-    void after_condition(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void after_condition(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         if (!step_is_active()) {
             return;
         }
@@ -220,15 +214,15 @@ struct EasyCacheState {
             return;
         }
 
-        size_t ne      = static_cast<size_t>(ggml_nelements(input));
-        float* in_data = (float*)input->data;
+        size_t ne            = static_cast<size_t>(input.numel());
+        const float* in_data = input.data();
         prev_input.resize(ne);
         for (size_t i = 0; i < ne; ++i) {
             prev_input[i] = in_data[i];
         }
         has_prev_input = true;
 
-        float* out_data     = (float*)output->data;
+        const float* out_data = output.data();
         float output_change   = 0.0f;
         if (has_prev_output && prev_output.size() == ne) {
             for (size_t i = 0; i < ne; ++i) {
@@ -263,3 +257,5 @@ struct EasyCacheState {
         has_last_input_change  = false;
     }
 };
+
+#endif

src/esrgan.hpp

@@ -27,11 +27,11 @@ public:
         blocks["conv5"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 4 * num_grow_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* lrelu(GGMLRunnerContext* ctx, ggml_tensor* x) {
         return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [n, num_feat, h, w]
         // return: [n, num_feat, h, w]
 
@@ -64,7 +64,7 @@ public:
         blocks["rdb3"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [n, num_feat, h, w]
         // return: [n, num_feat, h, w]
 
@@ -112,11 +112,11 @@ public:
     int get_scale() { return scale; }
     int get_num_block() { return num_block; }
 
-    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* lrelu(GGMLRunnerContext* ctx, ggml_tensor* x) {
         return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [n, num_in_ch, h, w]
         // return: [n, num_out_ch, h*scale, w*scale]
         auto conv_first = std::dynamic_pointer_cast<Conv2d>(blocks["conv_first"]);
@@ -341,27 +341,24 @@ struct ESRGAN : public GGMLRunner {
         return success;
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* x) {
+    ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor) {
         if (!rrdb_net)
             return nullptr;
         constexpr int kGraphNodes = 1 << 16;  // 65k
-        struct ggml_cgraph* gf    = new_graph_custom(kGraphNodes);
-        x                         = to_backend(x);
+        ggml_cgraph* gf           = new_graph_custom(kGraphNodes);
+        ggml_tensor* x            = make_input(x_tensor);
 
         auto runner_ctx  = get_context();
-        struct ggml_tensor* out = rrdb_net->forward(&runner_ctx, x);
+        ggml_tensor* out = rrdb_net->forward(&runner_ctx, x);
         ggml_build_forward_expand(gf, out);
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* x,
-                 ggml_tensor** output,
-                 ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(x);
-        };
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<float>& x) {
+        auto get_graph = [&]() -> ggml_cgraph* { return build_graph(x); };
+        auto result    = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        return result;
     }
 };
 

src/flux.hpp

@@ -19,7 +19,7 @@ namespace Flux {
             blocks["out_layer"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_dim, hidden_dim, bias));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
             // x: [..., in_dim]
             // return: [..., hidden_dim]
             auto in_layer  = std::dynamic_pointer_cast<Linear>(blocks["in_layer"]);
@@ -37,7 +37,7 @@ namespace Flux {
         int64_t hidden_size;
         float eps;
 
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             ggml_type wtype = GGML_TYPE_F32;
             params["scale"] = ggml_new_tensor_1d(ctx, wtype, hidden_size);
         }
@@ -48,8 +48,8 @@ namespace Flux {
             : hidden_size(hidden_size),
               eps(eps) {}
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
-            struct ggml_tensor* w = params["scale"];
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            ggml_tensor* w = params["scale"];
             x              = ggml_rms_norm(ctx->ggml_ctx, x, eps);
             x              = ggml_mul(ctx->ggml_ctx, x, w);
             return x;
@@ -63,7 +63,7 @@ namespace Flux {
             blocks["key_norm"]   = std::shared_ptr<GGMLBlock>(new RMSNorm(dim));
         }
 
-        struct ggml_tensor* query_norm(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* query_norm(GGMLRunnerContext* ctx, ggml_tensor* x) {
             // x: [..., dim]
             // return: [..., dim]
             auto norm = std::dynamic_pointer_cast<RMSNorm>(blocks["query_norm"]);
@@ -72,7 +72,7 @@ namespace Flux {
             return x;
         }
 
-        struct ggml_tensor* key_norm(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* key_norm(GGMLRunnerContext* ctx, ggml_tensor* x) {
             // x: [..., dim]
             // return: [..., dim]
             auto norm = std::dynamic_pointer_cast<RMSNorm>(blocks["key_norm"]);
@@ -98,7 +98,7 @@ namespace Flux {
             blocks["proj"]   = std::shared_ptr<GGMLBlock>(new Linear(dim, dim, proj_bias));
         }
 
-        std::vector<struct ggml_tensor*> pre_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        std::vector<ggml_tensor*> pre_attention(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
             auto norm     = std::dynamic_pointer_cast<QKNorm>(blocks["norm"]);
 
@@ -115,17 +115,17 @@ namespace Flux {
             return {q, k, v};
         }
 
-        struct ggml_tensor* post_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* post_attention(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
 
             x = proj->forward(ctx, x);  // [N, n_token, dim]
             return x;
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask) {
             // x: [N, n_token, dim]
             // pe: [n_token, d_head/2, 2, 2]
             // return [N, n_token, dim]
@@ -147,7 +147,7 @@ namespace Flux {
             blocks["2"]             = std::make_shared<Linear>(intermediate_size, hidden_size, bias);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["0"]);
             auto mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
 
@@ -170,7 +170,7 @@ namespace Flux {
             blocks["down_proj"] = std::make_shared<Linear>(intermediate_size, hidden_size, bias);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto gate_proj = std::dynamic_pointer_cast<Linear>(blocks["gate_proj"]);
             auto up_proj   = std::dynamic_pointer_cast<Linear>(blocks["up_proj"]);
             auto down_proj = std::dynamic_pointer_cast<Linear>(blocks["down_proj"]);
@@ -212,7 +212,7 @@ namespace Flux {
             blocks["lin"] = std::shared_ptr<GGMLBlock>(new Linear(dim, dim * multiplier, bias));
         }
 
-        std::vector<ModulationOut> forward(GGMLRunnerContext* ctx, struct ggml_tensor* vec) {
+        std::vector<ModulationOut> forward(GGMLRunnerContext* ctx, ggml_tensor* vec) {
             // x: [N, dim]
             // return: [ModulationOut, ModulationOut]
             auto lin = std::dynamic_pointer_cast<Linear>(blocks["lin"]);
@@ -232,10 +232,10 @@ namespace Flux {
         }
     };
 
-    __STATIC_INLINE__ struct ggml_tensor* modulate(struct ggml_context* ctx,
-                                                   struct ggml_tensor* x,
-                                                   struct ggml_tensor* shift,
-                                                   struct ggml_tensor* scale,
+    __STATIC_INLINE__ ggml_tensor* modulate(ggml_context* ctx,
+                                            ggml_tensor* x,
+                                            ggml_tensor* shift,
+                                            ggml_tensor* scale,
                                             bool skip_reshape = false) {
         // x: [N, L, C]
         // scale: [N, C]
@@ -294,7 +294,7 @@ namespace Flux {
             }
         }
 
-        std::vector<ModulationOut> get_distil_img_mod(GGMLRunnerContext* ctx, struct ggml_tensor* vec) {
+        std::vector<ModulationOut> get_distil_img_mod(GGMLRunnerContext* ctx, ggml_tensor* vec) {
             // TODO: not hardcoded?
             const int single_blocks_count = 38;
             const int double_blocks_count = 19;
@@ -303,7 +303,7 @@ namespace Flux {
             return {ModulationOut(ctx, vec, offset), ModulationOut(ctx, vec, offset + 3)};
         }
 
-        std::vector<ModulationOut> get_distil_txt_mod(GGMLRunnerContext* ctx, struct ggml_tensor* vec) {
+        std::vector<ModulationOut> get_distil_txt_mod(GGMLRunnerContext* ctx, ggml_tensor* vec) {
             // TODO: not hardcoded?
             const int single_blocks_count = 38;
             const int double_blocks_count = 19;
@@ -312,12 +312,12 @@ namespace Flux {
             return {ModulationOut(ctx, vec, offset), ModulationOut(ctx, vec, offset + 3)};
         }
 
-        std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                                    struct ggml_tensor* img,
-                                                                    struct ggml_tensor* txt,
-                                                                    struct ggml_tensor* vec,
-                                                                    struct ggml_tensor* pe,
-                                                                    struct ggml_tensor* mask            = nullptr,
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                      ggml_tensor* img,
+                                                      ggml_tensor* txt,
+                                                      ggml_tensor* vec,
+                                                      ggml_tensor* pe,
+                                                      ggml_tensor* mask                   = nullptr,
                                                       std::vector<ModulationOut> img_mods = {},
                                                       std::vector<ModulationOut> txt_mods = {}) {
             // img: [N, n_img_token, hidden_size]
@@ -457,16 +457,16 @@ namespace Flux {
             }
         }
 
-        ModulationOut get_distil_mod(GGMLRunnerContext* ctx, struct ggml_tensor* vec) {
+        ModulationOut get_distil_mod(GGMLRunnerContext* ctx, ggml_tensor* vec) {
             int64_t offset = 3 * idx;
             return ModulationOut(ctx, vec, offset);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* vec,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask        = nullptr,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* vec,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask               = nullptr,
                              std::vector<ModulationOut> mods = {}) {
             // x: [N, n_token, hidden_size]
             // pe: [n_token, d_head/2, 2, 2]
@@ -539,7 +539,7 @@ namespace Flux {
             }
         }
 
-        ModulationOut get_distil_mod(GGMLRunnerContext* ctx, struct ggml_tensor* vec) {
+        ModulationOut get_distil_mod(GGMLRunnerContext* ctx, 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->ggml_ctx, vec, vec->ne[0], vec->ne[1], vec->nb[1], stride * (offset + 0));  // [N, dim]
@@ -548,15 +548,15 @@ namespace Flux {
             return {shift, scale, nullptr};
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* c) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* c) {
             // 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"]);
-            struct ggml_tensor *shift, *scale;
+            ggml_tensor *shift, *scale;
             if (prune_mod) {
                 auto mod = get_distil_mod(ctx, c);
                 shift    = mod.shift;
@@ -589,7 +589,7 @@ namespace Flux {
             blocks["out_proj"] = std::shared_ptr<GGMLBlock>(new Linear(inner_size, hidden_size, true));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, 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"]);
 
@@ -612,9 +612,9 @@ namespace Flux {
             blocks["embedder.0"] = std::make_shared<Linear>(in_channels + max_freqs * max_freqs, hidden_size_input);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* dct) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* dct) {
             // x: (B, P^2, C)
             // dct: (1, P^2, max_freqs^2)
             // return: (B, P^2, hidden_size_input)
@@ -639,9 +639,9 @@ namespace Flux {
             blocks["norm"]            = std::make_shared<RMSNorm>(hidden_size_x);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* s) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* s) {
             // x: (batch_size, n_token, hidden_size_x)
             // s: (batch_size, hidden_size_s)
             // return: (batch_size, n_token, hidden_size_x)
@@ -689,8 +689,8 @@ namespace Flux {
             blocks["linear"] = std::make_shared<Linear>(hidden_size, out_channels);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x) {
             auto norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
             auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
 
@@ -708,8 +708,8 @@ namespace Flux {
             blocks["conv"] = std::make_shared<Conv2d>(hidden_size, out_channels, std::pair{3, 3}, std::pair{1, 1}, std::pair{1, 1});
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x) {
             // x: [N, C, H, W]
             auto norm = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
@@ -847,14 +847,14 @@ namespace Flux {
             }
         }
 
-        struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* img,
-                                         struct ggml_tensor* txt,
-                                         struct ggml_tensor* timesteps,
-                                         struct ggml_tensor* y,
-                                         struct ggml_tensor* guidance,
-                                         struct ggml_tensor* pe,
-                                         struct ggml_tensor* mod_index_arange = nullptr,
+        ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
+                                  ggml_tensor* img,
+                                  ggml_tensor* txt,
+                                  ggml_tensor* timesteps,
+                                  ggml_tensor* y,
+                                  ggml_tensor* guidance,
+                                  ggml_tensor* pe,
+                                  ggml_tensor* mod_index_arange = nullptr,
                                   std::vector<int> skip_layers  = {}) {
             auto img_in      = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
             auto txt_in      = std::dynamic_pointer_cast<Linear>(blocks["txt_in"]);
@@ -864,8 +864,8 @@ namespace Flux {
                 img = img_in->forward(ctx, img);
             }
 
-            struct ggml_tensor* vec;
-            struct ggml_tensor* txt_img_mask = nullptr;
+            ggml_tensor* vec;
+            ggml_tensor* txt_img_mask = nullptr;
             if (params.is_chroma) {
                 int64_t mod_index_length = 344;
                 auto approx              = std::dynamic_pointer_cast<ChromaApproximator>(blocks["distilled_guidance_layer"]);
@@ -967,25 +967,25 @@ namespace Flux {
             return img;
         }
 
-        struct ggml_tensor* _apply_x0_residual(GGMLRunnerContext* ctx,
-                                               struct ggml_tensor* predicted,
-                                               struct ggml_tensor* noisy,
-                                               struct ggml_tensor* timesteps) {
+        ggml_tensor* _apply_x0_residual(GGMLRunnerContext* ctx,
+                                        ggml_tensor* predicted,
+                                        ggml_tensor* noisy,
+                                        ggml_tensor* timesteps) {
             auto x = ggml_sub(ctx->ggml_ctx, noisy, predicted);
             x      = ggml_div(ctx->ggml_ctx, x, timesteps);
             return x;
         }
 
-        struct ggml_tensor* forward_chroma_radiance(GGMLRunnerContext* ctx,
-                                                    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  = nullptr,
-                                                    struct ggml_tensor* dct               = nullptr,
+        ggml_tensor* forward_chroma_radiance(GGMLRunnerContext* ctx,
+                                             ggml_tensor* x,
+                                             ggml_tensor* timestep,
+                                             ggml_tensor* context,
+                                             ggml_tensor* c_concat,
+                                             ggml_tensor* y,
+                                             ggml_tensor* guidance,
+                                             ggml_tensor* pe,
+                                             ggml_tensor* mod_index_arange         = nullptr,
+                                             ggml_tensor* dct                      = nullptr,
                                              std::vector<ggml_tensor*> ref_latents = {},
                                              std::vector<int> skip_layers          = {}) {
             GGML_ASSERT(x->ne[3] == 1);
@@ -1050,16 +1050,16 @@ namespace Flux {
             return out;
         }
 
-        struct ggml_tensor* forward_flux_chroma(GGMLRunnerContext* ctx,
-                                                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  = nullptr,
-                                                struct ggml_tensor* dct               = nullptr,
+        ggml_tensor* forward_flux_chroma(GGMLRunnerContext* ctx,
+                                         ggml_tensor* x,
+                                         ggml_tensor* timestep,
+                                         ggml_tensor* context,
+                                         ggml_tensor* c_concat,
+                                         ggml_tensor* y,
+                                         ggml_tensor* guidance,
+                                         ggml_tensor* pe,
+                                         ggml_tensor* mod_index_arange         = nullptr,
+                                         ggml_tensor* dct                      = nullptr,
                                          std::vector<ggml_tensor*> ref_latents = {},
                                          std::vector<int> skip_layers          = {}) {
             GGML_ASSERT(x->ne[3] == 1);
@@ -1119,16 +1119,16 @@ namespace Flux {
             return out;
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    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  = nullptr,
-                                    struct ggml_tensor* dct               = nullptr,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* c_concat,
+                             ggml_tensor* y,
+                             ggml_tensor* guidance,
+                             ggml_tensor* pe,
+                             ggml_tensor* mod_index_arange         = nullptr,
+                             ggml_tensor* dct                      = nullptr,
                              std::vector<ggml_tensor*> ref_latents = {},
                              std::vector<int> skip_layers          = {}) {
             // Forward pass of DiT.
@@ -1178,6 +1178,7 @@ namespace Flux {
         std::vector<float> pe_vec;
         std::vector<float> mod_index_arange_vec;
         std::vector<float> dct_vec;
+        sd::Tensor<float> guidance_tensor;
         SDVersion version;
         bool use_mask = false;
 
@@ -1299,7 +1300,7 @@ namespace Flux {
             return "flux";
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
             flux.get_param_tensors(tensors, prefix);
         }
 
@@ -1353,29 +1354,42 @@ namespace Flux {
             return dct;
         }
 
-        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<ggml_tensor*> ref_latents = {},
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor                  = {},
+                                 const sd::Tensor<float>& c_concat_tensor                 = {},
+                                 const sd::Tensor<float>& y_tensor                        = {},
+                                 const sd::Tensor<float>& guidance_tensor                 = {},
+                                 const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
                                  bool increase_ref_index                                  = false,
                                  std::vector<int> skip_layers                             = {}) {
-            GGML_ASSERT(x->ne[3] == 1);
-            struct ggml_cgraph* gf = new_graph_custom(FLUX_GRAPH_SIZE);
-
-            struct ggml_tensor* mod_index_arange = nullptr;
-            struct ggml_tensor* dct              = nullptr;  // for chroma radiance
-
-            x       = to_backend(x);
-            context = to_backend(context);
-            if (c_concat != nullptr) {
-                c_concat = to_backend(c_concat);
-            }
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
+            ggml_tensor* context   = make_optional_input(context_tensor);
+            ggml_tensor* c_concat  = make_optional_input(c_concat_tensor);
+            ggml_tensor* y         = make_optional_input(y_tensor);
+            if (flux_params.guidance_embed || flux_params.is_chroma) {
+                if (!guidance_tensor.empty()) {
+                    this->guidance_tensor = guidance_tensor;
                     if (flux_params.is_chroma) {
-                guidance = ggml_set_f32(guidance, 0);
+                        this->guidance_tensor.fill_(0.f);
+                    }
+                }
+            }
+            ggml_tensor* guidance = make_optional_input(this->guidance_tensor);
+            std::vector<ggml_tensor*> ref_latents;
+            ref_latents.reserve(ref_latents_tensor.size());
+            for (const auto& ref_latent_tensor : ref_latents_tensor) {
+                ref_latents.push_back(make_input(ref_latent_tensor));
+            }
 
+            GGML_ASSERT(x->ne[3] == 1);
+            ggml_cgraph* gf = new_graph_custom(FLUX_GRAPH_SIZE);
+
+            ggml_tensor* mod_index_arange = nullptr;
+            ggml_tensor* dct              = nullptr;  // for chroma radiance
+
+            if (flux_params.is_chroma) {
                 if (!use_mask) {
                     y = nullptr;
                 }
@@ -1385,16 +1399,6 @@ namespace Flux {
                 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 || 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]);
-            }
-
             std::set<int> txt_arange_dims;
             if (sd_version_is_flux2(version)) {
                 txt_arange_dims    = {3};
@@ -1437,7 +1441,7 @@ namespace Flux {
 
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* out = flux.forward(&runner_ctx,
+            ggml_tensor* out = flux.forward(&runner_ctx,
                                             x,
                                             timesteps,
                                             context,
@@ -1455,71 +1459,80 @@ namespace Flux {
             return gf;
         }
 
-        bool 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 = {},
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context                  = {},
+                                  const sd::Tensor<float>& c_concat                 = {},
+                                  const sd::Tensor<float>& y                        = {},
+                                  const sd::Tensor<float>& guidance                 = {},
+                                  const std::vector<sd::Tensor<float>>& ref_latents = {},
                                   bool increase_ref_index                           = false,
-                     struct ggml_tensor** output           = nullptr,
-                     struct ggml_context* output_ctx       = nullptr,
                                   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* {
+            auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, c_concat, y, guidance, ref_latents, increase_ref_index, skip_layers);
             };
 
-            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return result;
         }
 
         void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
             params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
             params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);
 
             {
                 // cpu f16:
                 // cuda f16: nan
                 // cuda q8_0: pass
-                auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 128, 1);
+                sd::Tensor<float> x({16, 16, 128, 1});
                 // ggml_set_f32(x, 0.01f);
-                // auto x = load_tensor_from_file(work_ctx, "chroma_x.bin");
+                // auto x = load_tensor_from_file(ctx, "chroma_x.bin");
                 // print_ggml_tensor(x);
 
                 std::vector<float> timesteps_vec(1, 1.f);
-                auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
+                auto timesteps = sd::Tensor<float>::from_vector(timesteps_vec);
 
                 std::vector<float> guidance_vec(1, 0.f);
-                auto guidance = vector_to_ggml_tensor(work_ctx, guidance_vec);
+                auto guidance = sd::Tensor<float>::from_vector(guidance_vec);
 
-                auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 15360, 256, 1);
+                sd::Tensor<float> context({15360, 256, 1});
                 // ggml_set_f32(context, 0.01f);
-                // auto context = load_tensor_from_file(work_ctx, "chroma_context.bin");
+                // auto context = load_tensor_from_file(ctx, "chroma_context.bin");
                 // print_ggml_tensor(context);
 
-                // auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, 1);
+                // auto y = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 768, 1);
                 // ggml_set_f32(y, 0.01f);
                 auto y = nullptr;
                 // print_ggml_tensor(y);
 
-                struct ggml_tensor* out = nullptr;
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                compute(8, x, timesteps, context, nullptr, y, guidance, {}, false, &out, work_ctx);
+                auto out_opt = compute(8,
+                                       x,
+                                       timesteps,
+                                       context,
+                                       {},
+                                       {},
+                                       guidance,
+                                       {},
+                                       false);
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("flux test done in %lldms", t1 - t0);
             }
         }

src/latent-preview.h

@@ -1,6 +1,8 @@
+#include <algorithm>
 #include <cstddef>
 #include <cstdint>
 #include "ggml.h"
+#include "tensor.hpp"
 
 const float wan_21_latent_rgb_proj[16][3] = {
     {0.015123f, -0.148418f, 0.479828f},
@@ -163,7 +165,7 @@ const float sd_latent_rgb_proj[4][3] = {
     {-0.178022f, -0.200862f, -0.678514f}};
 float sd_latent_rgb_bias[3] = {-0.017478f, -0.055834f, -0.105825f};
 
-void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
+void preview_latent_video(uint8_t* buffer, ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
     size_t buffer_head = 0;
 
     uint32_t latent_width  = static_cast<uint32_t>(latents->ne[0]);
@@ -232,3 +234,67 @@ void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const fl
         }
     }
 }
+
+static inline bool preview_latent_tensor_is_video(const sd::Tensor<float>& latents) {
+    return latents.dim() == 5;
+}
+
+void preview_latent_video(uint8_t* buffer, const sd::Tensor<float>& latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
+    uint32_t latent_width  = static_cast<uint32_t>(latents.shape()[0]);
+    uint32_t latent_height = static_cast<uint32_t>(latents.shape()[1]);
+    bool is_video          = preview_latent_tensor_is_video(latents);
+    uint32_t frames        = is_video ? static_cast<uint32_t>(latents.shape()[2]) : 1;
+    uint32_t dim           = is_video ? static_cast<uint32_t>(latents.shape()[3]) : static_cast<uint32_t>(latents.shape()[2]);
+
+    uint32_t rgb_width     = latent_width * patch_size;
+    uint32_t rgb_height    = latent_height * patch_size;
+    uint32_t unpatched_dim = dim / (patch_size * patch_size);
+
+    for (uint32_t k = 0; k < frames; k++) {
+        for (uint32_t rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
+            for (uint32_t rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
+                uint32_t latent_x = rgb_x / patch_size;
+                uint32_t latent_y = rgb_y / patch_size;
+
+                uint32_t channel_offset = 0;
+                if (patch_size > 1) {
+                    channel_offset = ((rgb_y % patch_size) * patch_size + (rgb_x % patch_size));
+                }
+
+                size_t pixel_id   = k * rgb_width * rgb_height + rgb_y * rgb_width + rgb_x;
+                auto latent_value = [&](uint32_t latent_channel) -> float {
+                    return is_video
+                               ? latents.values()[latent_x + latent_width * (latent_y + latent_height * (k + frames * latent_channel))]
+                               : latents.values()[latent_x + latent_width * (latent_y + latent_height * latent_channel)];
+                };
+
+                float r = 0.f, g = 0.f, b = 0.f;
+                if (latent_rgb_proj != nullptr) {
+                    for (uint32_t d = 0; d < unpatched_dim; d++) {
+                        uint32_t latent_channel = d * patch_size * patch_size + channel_offset;
+                        float value             = latent_value(latent_channel);
+                        r += value * latent_rgb_proj[d][0];
+                        g += value * latent_rgb_proj[d][1];
+                        b += value * latent_rgb_proj[d][2];
+                    }
+                } else {
+                    r = latent_value(0);
+                    g = latent_value(1);
+                    b = latent_value(2);
+                }
+                if (latent_rgb_bias != nullptr) {
+                    r += latent_rgb_bias[0];
+                    g += latent_rgb_bias[1];
+                    b += latent_rgb_bias[2];
+                }
+                r = std::min(1.0f, std::max(0.0f, r * .5f + .5f));
+                g = std::min(1.0f, std::max(0.0f, g * .5f + .5f));
+                b = std::min(1.0f, std::max(0.0f, b * .5f + .5f));
+
+                buffer[pixel_id * 3 + 0] = (uint8_t)(r * 255);
+                buffer[pixel_id * 3 + 1] = (uint8_t)(g * 255);
+                buffer[pixel_id * 3 + 2] = (uint8_t)(b * 255);
+            }
+        }
+    }
+}

src/llm.hpp

@@ -194,6 +194,7 @@ namespace LLM {
                         bool padding      = false) {
             if (add_bos_token) {
                 tokens.insert(tokens.begin(), BOS_TOKEN_ID);
+                weights.insert(weights.begin(), 1.f);
             }
             if (max_length > 0 && padding) {
                 size_t n = static_cast<size_t>(std::ceil(tokens.size() * 1.f / max_length));
@@ -522,7 +523,7 @@ namespace LLM {
             blocks["down_proj"] = std::shared_ptr<GGMLBlock>(new Linear(intermediate_size, hidden_size, bias));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             // x: [N, n_token, hidden_size]
             auto gate_proj = std::dynamic_pointer_cast<Linear>(blocks["gate_proj"]);
             auto up_proj   = std::dynamic_pointer_cast<Linear>(blocks["up_proj"]);
@@ -582,7 +583,7 @@ namespace LLM {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             // x: [N*grid_t*grid_h*grid_w, in_channels, temporal_patch_size*patch_size*patch_size]
             // return: [N*grid_t*grid_h*grid_w, embed_dim]
             x = ggml_reshape_4d(ctx->ggml_ctx,
@@ -631,7 +632,7 @@ namespace LLM {
             blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, dim));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto ln_q  = std::dynamic_pointer_cast<RMSNorm>(blocks["ln_q"]);
             auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
             auto mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["mlp.2"]);
@@ -668,10 +669,10 @@ namespace LLM {
             blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask = nullptr) {
             // x: [N, n_token, hidden_size]
             int64_t n_token = x->ne[1];
             int64_t N       = x->ne[2];
@@ -718,10 +719,10 @@ namespace LLM {
             blocks["norm2"] = std::shared_ptr<GGMLBlock>(new RMSNorm(hidden_size, eps));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask = nullptr) {
             // x: [N, n_token, hidden_size]
             auto attn  = std::dynamic_pointer_cast<VisionAttention>(blocks["attn"]);
             auto mlp   = std::dynamic_pointer_cast<MLP>(blocks["mlp"]);
@@ -778,12 +779,12 @@ namespace LLM {
             blocks["merger"] = std::shared_ptr<GGMLBlock>(new PatchMerger(out_hidden_size, hidden_size, spatial_merge_size));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* pixel_values,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* window_index,
-                                    struct ggml_tensor* window_inverse_index,
-                                    struct ggml_tensor* window_mask) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* pixel_values,
+                             ggml_tensor* pe,
+                             ggml_tensor* window_index,
+                             ggml_tensor* window_inverse_index,
+                             ggml_tensor* window_mask) {
             // pixel_values: [grid_t*(H/mh/ph)*(W/mw/pw)*mh*mw, C*pt*ph*pw]
             // window_index: [grid_t*(H/mh/ph)*(W/mw/pw)]
             // window_inverse_index: [grid_t*(H/mh/ph)*(W/mw/pw)]
@@ -836,10 +837,10 @@ namespace LLM {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* input_pos,
-                                    struct ggml_tensor* attention_mask = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask = nullptr) {
             // x: [N, n_token, hidden_size]
             int64_t n_token = x->ne[1];
             int64_t N       = x->ne[2];
@@ -898,10 +899,10 @@ namespace LLM {
             blocks["post_attention_layernorm"] = std::make_shared<RMSNorm>(params.hidden_size, params.rms_norm_eps);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* input_pos,
-                                    struct ggml_tensor* attention_mask = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask = nullptr) {
             // x: [N, n_token, hidden_size]
             auto self_attn                = std::dynamic_pointer_cast<Attention>(blocks["self_attn"]);
             auto mlp                      = std::dynamic_pointer_cast<MLP>(blocks["mlp"]);
@@ -936,10 +937,10 @@ namespace LLM {
             blocks["norm"] = std::shared_ptr<GGMLBlock>(new RMSNorm(params.hidden_size, params.rms_norm_eps));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* input_pos,
-                                    struct ggml_tensor* attention_mask,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* input_ids,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask,
                              std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                              std::set<int> out_layers) {
             // input_ids: [N, n_token]
@@ -1037,10 +1038,10 @@ namespace LLM {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* input_pos,
-                                    struct ggml_tensor* attention_mask,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* input_ids,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask,
                              std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                              std::set<int> out_layers) {
             // input_ids: [N, n_token]
@@ -1050,12 +1051,12 @@ namespace LLM {
             return x;
         }
 
-        struct ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
-                                           struct ggml_tensor* pixel_values,
-                                           struct ggml_tensor* pe,
-                                           struct ggml_tensor* window_index,
-                                           struct ggml_tensor* window_inverse_index,
-                                           struct ggml_tensor* window_mask) {
+        ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
+                                    ggml_tensor* pixel_values,
+                                    ggml_tensor* pe,
+                                    ggml_tensor* window_index,
+                                    ggml_tensor* window_inverse_index,
+                                    ggml_tensor* window_mask) {
             GGML_ASSERT(enable_vision);
             auto vision_model = std::dynamic_pointer_cast<VisionModel>(blocks["visual"]);
             return vision_model->forward(ctx, pixel_values, pe, window_index, window_inverse_index, window_mask);
@@ -1156,40 +1157,41 @@ namespace LLM {
             return llm_arch_to_str[static_cast<int>(params.arch)];
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
             model.get_param_tensors(tensors, prefix);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* input_pos,
-                                    struct ggml_tensor* attention_mask,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* input_ids,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask,
                              std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                              std::set<int> out_layers) {
             auto hidden_states = model.forward(ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);  // [N, n_token, hidden_size]
             return hidden_states;
         }
 
-        struct ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
-                                           struct ggml_tensor* pixel_values,
-                                           struct ggml_tensor* input_pos,
-                                           struct ggml_tensor* window_index,
-                                           struct ggml_tensor* window_inverse_index,
-                                           struct ggml_tensor* window_mask) {
+        ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
+                                    ggml_tensor* pixel_values,
+                                    ggml_tensor* input_pos,
+                                    ggml_tensor* window_index,
+                                    ggml_tensor* window_inverse_index,
+                                    ggml_tensor* window_mask) {
             auto hidden_states = model.vision_forward(ctx, pixel_values, input_pos, window_index, window_inverse_index, window_mask);
             return hidden_states;
         }
 
-        struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
-                                        struct ggml_tensor* attention_mask,
-                                        std::vector<std::pair<int, ggml_tensor*>> image_embeds,
+        ggml_cgraph* build_graph(const sd::Tensor<int32_t>& input_ids_tensor,
+                                 const sd::Tensor<float>& attention_mask_tensor,
+                                 const std::vector<std::pair<int, sd::Tensor<float>>>& image_embeds_tensor,
                                  std::set<int> out_layers) {
-            struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
-
-            input_ids = to_backend(input_ids);
-
-            for (auto& image_embed : image_embeds) {
-                image_embed.second = to_backend(image_embed.second);
+            ggml_cgraph* gf        = ggml_new_graph(compute_ctx);
+            ggml_tensor* input_ids = make_input(input_ids_tensor);
+            std::vector<std::pair<int, ggml_tensor*>> image_embeds;
+            image_embeds.reserve(image_embeds_tensor.size());
+            for (const auto& [idx, embed_tensor] : image_embeds_tensor) {
+                ggml_tensor* embed = make_input(embed_tensor);
+                image_embeds.emplace_back(idx, embed);
             }
 
             int64_t n_tokens = input_ids->ne[0];
@@ -1213,8 +1215,9 @@ namespace LLM {
                                                 input_pos_vec.size());
             set_backend_tensor_data(input_pos, input_pos_vec.data());
 
-            if (attention_mask != nullptr) {
-                attention_mask = to_backend(attention_mask);
+            ggml_tensor* attention_mask = nullptr;
+            if (!attention_mask_tensor.empty()) {
+                attention_mask = make_input(attention_mask_tensor);
             } else {
                 attention_mask_vec.resize(n_tokens * n_tokens);
                 for (int i0 = 0; i0 < n_tokens; i0++) {
@@ -1232,24 +1235,22 @@ namespace LLM {
 
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);
+            ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);
 
             ggml_build_forward_expand(gf, hidden_states);
 
             return gf;
         }
 
-        bool compute(const int n_threads,
-                     struct ggml_tensor* input_ids,
-                     struct ggml_tensor* attention_mask,
-                     std::vector<std::pair<int, ggml_tensor*>> image_embeds,
-                     std::set<int> out_layers,
-                     ggml_tensor** output,
-                     ggml_context* output_ctx = nullptr) {
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+        sd::Tensor<float> compute(const int n_threads,
+                                  const sd::Tensor<int32_t>& input_ids,
+                                  const sd::Tensor<float>& attention_mask,
+                                  const std::vector<std::pair<int, sd::Tensor<float>>>& image_embeds,
+                                  std::set<int> out_layers) {
+            auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(input_ids, attention_mask, image_embeds, out_layers);
             };
-            return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+            return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true));
         }
 
         int64_t get_num_image_tokens(int64_t t, int64_t h, int64_t w) {
@@ -1261,7 +1262,7 @@ namespace LLM {
             return grid_t * grid_h * grid_w;
         }
 
-        struct ggml_tensor* process_image(struct ggml_context* ctx, struct ggml_tensor* image) {
+        ggml_tensor* process_image(ggml_context* ctx, ggml_tensor* image) {
             // image: [C, H, W]
             // return: [grid_t*(H/mh/ph)*(W/mw/pw)*mh*mw, C*pt*ph*pw], grid_t == 1
             int64_t C  = image->ne[2];
@@ -1288,8 +1289,9 @@ namespace LLM {
             return image;
         }
 
-        struct ggml_cgraph* build_encode_image_graph(struct ggml_tensor* image) {
-            struct ggml_cgraph* gf = new_graph_custom(LLM_GRAPH_SIZE);
+        ggml_cgraph* build_encode_image_graph(const sd::Tensor<float>& image_tensor) {
+            ggml_cgraph* gf    = new_graph_custom(LLM_GRAPH_SIZE);
+            ggml_tensor* image = make_input(image_tensor);
 
             GGML_ASSERT(image->ne[1] % (params.vision.patch_size * params.vision.spatial_merge_size) == 0);
             GGML_ASSERT(image->ne[0] % (params.vision.patch_size * params.vision.spatial_merge_size) == 0);
@@ -1301,8 +1303,6 @@ namespace LLM {
             int llm_grid_w             = grid_w / params.vision.spatial_merge_size;
             int vit_merger_window_size = params.vision.window_size / params.vision.patch_size / params.vision.spatial_merge_size;
 
-            image = to_backend(image);
-
             auto pixel_values = process_image(compute_ctx, image);
 
             // window index
@@ -1400,7 +1400,7 @@ namespace LLM {
             set_backend_tensor_data(pe, pe_vec.data());
 
             auto runnter_ctx           = get_context();
-            struct ggml_tensor* hidden_states = vision_forward(&runnter_ctx,
+            ggml_tensor* hidden_states = vision_forward(&runnter_ctx,
                                                         pixel_values,
                                                         pe,
                                                         window_index,
@@ -1411,14 +1411,12 @@ namespace LLM {
             return gf;
         }
 
-        void encode_image(const int n_threads,
-                          struct ggml_tensor* image,
-                          ggml_tensor** output,
-                          ggml_context* output_ctx = nullptr) {
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+        sd::Tensor<float> encode_image(const int n_threads,
+                                       const sd::Tensor<float>& image) {
+            auto get_graph = [&]() -> ggml_cgraph* {
                 return build_encode_image_graph(image);
             };
-            GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, false));
         }
     };
 
@@ -1440,7 +1438,7 @@ namespace LLM {
             }
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
             model.get_param_tensors(tensors, prefix);
         }
 
@@ -1492,44 +1490,46 @@ namespace LLM {
         }
 
         void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
             params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
             params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);
             bool test_mistral          = false;
             bool test_qwen3            = true;
             bool test_vit              = false;
             bool test_decoder_with_vit = false;
 
             if (test_decoder_with_vit) {
-                ggml_tensor* image_embed = nullptr;
+                sd::Tensor<float> image_embed;
                 {
-                    auto image = load_tensor_from_file(work_ctx, "qwen2vl_normalized.bin");
-                    print_ggml_tensor(image, false, "image");
-                    struct ggml_tensor* out = nullptr;
+                    auto image = sd::load_tensor_from_file_as_tensor<float>("qwen2vl_normalized.bin");
+                    print_sd_tensor(image, false, "image");
+                    sd::Tensor<float> out;
 
                     int64_t t0   = ggml_time_ms();
-                    model.encode_image(8, image, &out, work_ctx);
+                    auto out_opt = model.encode_image(8, image);
                     int64_t t1   = ggml_time_ms();
 
-                    print_ggml_tensor(out, false, "image_embed");
+                    GGML_ASSERT(!out_opt.empty());
+                    out = std::move(out_opt);
+                    print_sd_tensor(out, false, "image_embed");
                     image_embed = out;
                     LOG_DEBUG("llm encode_image test done in %lldms", t1 - t0);
                 }
 
                 std::string placeholder  = "<|image_pad|>";
                 std::string img_prompt   = "Picture 1: <|vision_start|>";  // [24669, 220, 16, 25, 220, 151652]
-                int64_t num_image_tokens = image_embed->ne[1];
+                int64_t num_image_tokens = image_embed.shape()[1];
                 img_prompt.reserve(num_image_tokens * placeholder.size());
                 for (int i = 0; i < num_image_tokens; i++) {
                     img_prompt += placeholder;
                 }
                 img_prompt += "<|vision_end|>";
 
-                std::vector<std::pair<int, ggml_tensor*>> image_embeds;
+                std::vector<std::pair<int, sd::Tensor<float>>> image_embeds;
                 image_embeds.emplace_back(64, image_embed);
 
                 std::pair<int, int> prompt_attn_range;
@@ -1547,29 +1547,33 @@ namespace LLM {
                     printf("%d ", token);
                 }
                 printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                model.compute(8, input_ids, nullptr, image_embeds, {}, &out, work_ctx);
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), image_embeds, {});
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("llm test done in %lldms", t1 - t0);
             } else if (test_vit) {
-                // auto image = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 280, 280, 3);
+                // auto image = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 280, 280, 3);
                 // ggml_set_f32(image, 0.f);
-                auto image = load_tensor_from_file(work_ctx, "qwen2vl_normalized.bin");
-                print_ggml_tensor(image, false, "image");
-                struct ggml_tensor* out = nullptr;
+                auto image = sd::load_tensor_from_file_as_tensor<float>("qwen2vl_normalized.bin");
+                print_sd_tensor(image, false, "image");
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                model.encode_image(8, image, &out, work_ctx);
+                auto out_opt = model.encode_image(8, image);
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out, false, "out");
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out, false, "out");
 
-                // auto ref_out = load_tensor_from_file(work_ctx, "qwen2vl.bin");
+                // auto ref_out = load_tensor_from_file(ctx, "qwen2vl.bin");
                 // ggml_ext_tensor_diff(ref_out, out, 0.01f);
 
                 LOG_DEBUG("llm test done in %lldms", t1 - t0);
@@ -1587,14 +1591,16 @@ namespace LLM {
                     printf("%d ", token);
                 }
                 printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                model.compute(8, input_ids, nullptr, {}, {10, 20, 30}, &out, work_ctx);
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), {}, {10, 20, 30});
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("llm test done in %lldms", t1 - t0);
             } else if (test_qwen3) {
                 std::pair<int, int> prompt_attn_range;
@@ -1610,14 +1616,16 @@ namespace LLM {
                     printf("%d ", token);
                 }
                 printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                model.compute(8, input_ids, nullptr, {}, {35}, &out, work_ctx);
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), {}, {35});
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("llm test done in %lldms", t1 - t0);
             } else {
                 std::pair<int, int> prompt_attn_range;
@@ -1633,14 +1641,16 @@ namespace LLM {
                     printf("%d ", token);
                 }
                 printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                model.compute(8, input_ids, nullptr, {}, {}, &out, work_ctx);
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), {}, {});
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("llm test done in %lldms", t1 - t0);
             }
         }

src/lora.hpp

@@ -9,7 +9,7 @@
 struct LoraModel : public GGMLRunner {
     std::string lora_id;
     float multiplier = 1.0f;
-    std::unordered_map<std::string, struct ggml_tensor*> lora_tensors;
+    std::unordered_map<std::string, ggml_tensor*> lora_tensors;
     std::map<ggml_tensor*, ggml_tensor*> original_tensor_to_final_tensor;
     std::set<std::string> applied_lora_tensors;
     std::string file_path;
@@ -78,7 +78,7 @@ struct LoraModel : public GGMLRunner {
         for (const auto& pair : tensors_to_create) {
             const auto& name   = pair.first;
             const auto& ts     = pair.second;
-            struct ggml_tensor* real = ggml_new_tensor(params_ctx,
+            ggml_tensor* real  = ggml_new_tensor(params_ctx,
                                                  ts.type,
                                                  ts.n_dims,
                                                  ts.ne);
@@ -337,8 +337,8 @@ struct LoraModel : public GGMLRunner {
             }
             scale_value *= multiplier;
 
-            struct ggml_tensor* updown_1 = ggml_ext_merge_lora(ctx, hada_1_down, hada_1_up, hada_1_mid);
-            struct ggml_tensor* updown_2 = ggml_ext_merge_lora(ctx, hada_2_down, hada_2_up, hada_2_mid);
+            ggml_tensor* updown_1 = ggml_ext_merge_lora(ctx, hada_1_down, hada_1_up, hada_1_mid);
+            ggml_tensor* updown_2 = ggml_ext_merge_lora(ctx, hada_2_down, hada_2_up, hada_2_mid);
             auto curr_updown      = ggml_mul_inplace(ctx, updown_1, updown_2);
             curr_updown           = ggml_ext_scale(ctx, curr_updown, scale_value, true);
             if (updown == nullptr) {
@@ -747,9 +747,9 @@ struct LoraModel : public GGMLRunner {
         return out_diff;
     }
 
-    struct ggml_cgraph* build_lora_graph(const std::map<std::string, ggml_tensor*>& model_tensors, SDVersion version) {
+    ggml_cgraph* build_lora_graph(const std::map<std::string, 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);
+        ggml_cgraph* gf        = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);
 
         preprocess_lora_tensors(model_tensors);
 
@@ -788,11 +788,11 @@ struct LoraModel : public GGMLRunner {
         return gf;
     }
 
-    void apply(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+    void apply(std::map<std::string, ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
+        auto get_graph = [&]() -> ggml_cgraph* {
             return build_lora_graph(model_tensors, version);
         };
-        GGMLRunner::compute(get_graph, n_threads, false);
+        GGMLRunner::compute<float>(get_graph, n_threads, false, true);
         stat();
         for (auto item : original_tensor_to_final_tensor) {
             ggml_tensor* original_tensor = item.first;

src/ltxv.hpp

@@ -26,8 +26,8 @@ namespace LTXV {
                                                                      bias));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              bool causal = true) {
             // x: [N*IC, ID, IH, IW]
             // result: [N*OC, OD, OH, OW]

src/mmdit.hpp

@@ -27,7 +27,7 @@ public:
         blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_features, out_features, bias));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [N, n_token, in_features]
         auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
         auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
@@ -72,7 +72,7 @@ public:
                                                                bias));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [N, C, H, W]
         // return: [N, H*W, embed_dim]
         auto proj = std::dynamic_pointer_cast<Conv2d>(blocks["proj"]);
@@ -111,7 +111,7 @@ public:
         blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, out_channels, true, true));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* t) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* t) {
         // t: [N, ]
         // return: [N, hidden_size]
         auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
@@ -135,7 +135,7 @@ public:
         blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size, true, true));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [N, input_dim]
         // return: [N, hidden_size]
         auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
@@ -175,7 +175,7 @@ public:
         }
     }
 
-    std::vector<struct ggml_tensor*> pre_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    std::vector<ggml_tensor*> pre_attention(GGMLRunnerContext* ctx, ggml_tensor* x) {
         auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
 
         auto qkv         = qkv_proj->forward(ctx, x);
@@ -198,7 +198,7 @@ public:
         return {q, k, v};
     }
 
-    struct ggml_tensor* post_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* post_attention(GGMLRunnerContext* ctx, ggml_tensor* x) {
         GGML_ASSERT(!pre_only);
 
         auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
@@ -208,8 +208,8 @@ public:
     }
 
     // x: [N, n_token, dim]
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x) {
         auto qkv = pre_attention(ctx, x);
         x        = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
         x        = post_attention(ctx, x);                                                                                                           // [N, n_token, dim]
@@ -217,10 +217,10 @@ public:
     }
 };
 
-__STATIC_INLINE__ struct ggml_tensor* modulate(struct ggml_context* ctx,
-                                               struct ggml_tensor* x,
-                                               struct ggml_tensor* shift,
-                                               struct ggml_tensor* scale) {
+__STATIC_INLINE__ ggml_tensor* modulate(ggml_context* ctx,
+                                        ggml_tensor* x,
+                                        ggml_tensor* shift,
+                                        ggml_tensor* scale) {
     // x: [N, L, C]
     // scale: [N, C]
     // shift: [N, C]
@@ -274,8 +274,8 @@ public:
     }
 
     std::tuple<std::vector<ggml_tensor*>, std::vector<ggml_tensor*>, std::vector<ggml_tensor*>> pre_attention_x(GGMLRunnerContext* ctx,
-                                                                                                                struct ggml_tensor* x,
-                                                                                                                struct ggml_tensor* c) {
+                                                                                                                ggml_tensor* x,
+                                                                                                                ggml_tensor* c) {
         GGML_ASSERT(self_attn);
         // x: [N, n_token, hidden_size]
         // c: [N, hidden_size]
@@ -309,9 +309,9 @@ public:
         return {qkv, qkv2, {x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2}};
     }
 
-    std::pair<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention(GGMLRunnerContext* ctx,
-                                                                                                struct ggml_tensor* x,
-                                                                                                struct ggml_tensor* c) {
+    std::pair<std::vector<ggml_tensor*>, std::vector<ggml_tensor*>> pre_attention(GGMLRunnerContext* ctx,
+                                                                                  ggml_tensor* x,
+                                                                                  ggml_tensor* c) {
         // x: [N, n_token, hidden_size]
         // c: [N, hidden_size]
         auto norm1              = std::dynamic_pointer_cast<LayerNorm>(blocks["norm1"]);
@@ -346,15 +346,15 @@ public:
         }
     }
 
-    struct ggml_tensor* post_attention_x(GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* attn_out,
-                                         struct ggml_tensor* attn2_out,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* gate_msa,
-                                         struct ggml_tensor* shift_mlp,
-                                         struct ggml_tensor* scale_mlp,
-                                         struct ggml_tensor* gate_mlp,
-                                         struct ggml_tensor* gate_msa2) {
+    ggml_tensor* post_attention_x(GGMLRunnerContext* ctx,
+                                  ggml_tensor* attn_out,
+                                  ggml_tensor* attn2_out,
+                                  ggml_tensor* x,
+                                  ggml_tensor* gate_msa,
+                                  ggml_tensor* shift_mlp,
+                                  ggml_tensor* scale_mlp,
+                                  ggml_tensor* gate_mlp,
+                                  ggml_tensor* gate_msa2) {
         // attn_out: [N, n_token, hidden_size]
         // x: [N, n_token, hidden_size]
         // gate_msa: [N, hidden_size]
@@ -384,13 +384,13 @@ public:
         return x;
     }
 
-    struct ggml_tensor* post_attention(GGMLRunnerContext* ctx,
-                                       struct ggml_tensor* attn_out,
-                                       struct ggml_tensor* x,
-                                       struct ggml_tensor* gate_msa,
-                                       struct ggml_tensor* shift_mlp,
-                                       struct ggml_tensor* scale_mlp,
-                                       struct ggml_tensor* gate_mlp) {
+    ggml_tensor* post_attention(GGMLRunnerContext* ctx,
+                                ggml_tensor* attn_out,
+                                ggml_tensor* x,
+                                ggml_tensor* gate_msa,
+                                ggml_tensor* shift_mlp,
+                                ggml_tensor* scale_mlp,
+                                ggml_tensor* gate_mlp) {
         // attn_out: [N, n_token, hidden_size]
         // x: [N, n_token, hidden_size]
         // gate_msa: [N, hidden_size]
@@ -416,9 +416,9 @@ public:
         return x;
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* c) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* c) {
         // x: [N, n_token, hidden_size]
         // c: [N, hidden_size]
         // return: [N, n_token, hidden_size]
@@ -463,11 +463,11 @@ public:
     }
 };
 
-__STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*>
+__STATIC_INLINE__ std::pair<ggml_tensor*, ggml_tensor*>
 block_mixing(GGMLRunnerContext* ctx,
-             struct ggml_tensor* context,
-             struct ggml_tensor* x,
-             struct ggml_tensor* c,
+             ggml_tensor* context,
+             ggml_tensor* x,
+             ggml_tensor* c,
              std::shared_ptr<DismantledBlock> context_block,
              std::shared_ptr<DismantledBlock> x_block) {
     // context: [N, n_context, hidden_size]
@@ -489,7 +489,7 @@ block_mixing(GGMLRunnerContext* ctx,
         x_qkv                    = x_qkv_intermediates.first;
         x_intermediates          = x_qkv_intermediates.second;
     }
-    std::vector<struct ggml_tensor*> qkv;
+    std::vector<ggml_tensor*> qkv;
     for (int i = 0; i < 3; i++) {
         qkv.push_back(ggml_concat(ctx->ggml_ctx, context_qkv[i], x_qkv[i], 1));
     }
@@ -563,10 +563,10 @@ public:
         blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x));
     }
 
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                                struct ggml_tensor* context,
-                                                                struct ggml_tensor* x,
-                                                                struct ggml_tensor* c) {
+    std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                  ggml_tensor* context,
+                                                  ggml_tensor* x,
+                                                  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"]);
 
@@ -586,9 +586,9 @@ public:
         blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, 2 * hidden_size));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* c) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* c) {
         // x: [N, n_token, hidden_size]
         // c: [N, hidden_size]
         // return: [N, n_token, patch_size * patch_size * out_channels]
@@ -626,7 +626,7 @@ protected:
     int64_t hidden_size;
     std::string qk_norm;
 
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
         enum ggml_type wtype = GGML_TYPE_F32;
         params["pos_embed"]  = ggml_new_tensor_3d(ctx, wtype, hidden_size, num_patchs, 1);
     }
@@ -705,8 +705,8 @@ public:
         blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels));
     }
 
-    struct ggml_tensor*
-    cropped_pos_embed(struct ggml_context* ctx,
+    ggml_tensor*
+    cropped_pos_embed(ggml_context* ctx,
                       int64_t h,
                       int64_t w) {
         auto pos_embed = params["pos_embed"];
@@ -745,10 +745,10 @@ public:
         return spatial_pos_embed;
     }
 
-    struct ggml_tensor* forward_core_with_concat(GGMLRunnerContext* ctx,
-                                                 struct ggml_tensor* x,
-                                                 struct ggml_tensor* c_mod,
-                                                 struct ggml_tensor* context,
+    ggml_tensor* forward_core_with_concat(GGMLRunnerContext* ctx,
+                                          ggml_tensor* x,
+                                          ggml_tensor* c_mod,
+                                          ggml_tensor* context,
                                           std::vector<int> skip_layers = std::vector<int>()) {
         // x: [N, H*W, hidden_size]
         // context: [N, n_context, d_context]
@@ -774,11 +774,11 @@ public:
         return x;
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* t,
-                                struct ggml_tensor* y        = nullptr,
-                                struct ggml_tensor* context  = nullptr,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* t,
+                         ggml_tensor* y               = nullptr,
+                         ggml_tensor* context         = nullptr,
                          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)
@@ -832,24 +832,24 @@ struct MMDiTRunner : public GGMLRunner {
         return "mmdit";
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         mmdit.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                    struct ggml_tensor* timesteps,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* y,
+    ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                             const sd::Tensor<float>& timesteps_tensor,
+                             const sd::Tensor<float>& context_tensor = {},
+                             const sd::Tensor<float>& y_tensor       = {},
                              std::vector<int> skip_layers            = std::vector<int>()) {
-        struct ggml_cgraph* gf = new_graph_custom(MMDIT_GRAPH_SIZE);
+        ggml_cgraph* gf = new_graph_custom(MMDIT_GRAPH_SIZE);
 
-        x         = to_backend(x);
-        context   = to_backend(context);
-        y         = to_backend(y);
-        timesteps = to_backend(timesteps);
+        ggml_tensor* x         = make_input(x_tensor);
+        ggml_tensor* timesteps = make_input(timesteps_tensor);
+        ggml_tensor* context   = make_optional_input(context_tensor);
+        ggml_tensor* y         = make_optional_input(y_tensor);
 
         auto runner_ctx  = get_context();
-        struct ggml_tensor* out = mmdit.forward(&runner_ctx,
+        ggml_tensor* out = mmdit.forward(&runner_ctx,
                                          x,
                                          timesteps,
                                          y,
@@ -861,60 +861,64 @@ struct MMDiTRunner : public GGMLRunner {
         return gf;
     }
 
-    bool compute(int n_threads,
-                 struct ggml_tensor* x,
-                 struct ggml_tensor* timesteps,
-                 struct ggml_tensor* context,
-                 struct ggml_tensor* y,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr,
+    sd::Tensor<float> compute(int n_threads,
+                              const sd::Tensor<float>& x,
+                              const sd::Tensor<float>& timesteps,
+                              const sd::Tensor<float>& context = {},
+                              const sd::Tensor<float>& y       = {},
                               std::vector<int> skip_layers     = std::vector<int>()) {
         // x: [N, in_channels, h, w]
         // timesteps: [N, ]
         // context: [N, max_position, hidden_size]([N, 154, 4096]) or [1, max_position, hidden_size]
         // y: [N, adm_in_channels] or [1, adm_in_channels]
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+        auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(x, timesteps, context, y, skip_layers);
         };
 
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
     }
 
     void test() {
-        struct ggml_init_params params;
+        ggml_init_params params;
         params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
         params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
-        struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != nullptr);
+        ggml_context* ctx = ggml_init(params);
+        GGML_ASSERT(ctx != nullptr);
 
         {
             // cpu f16: pass
             // cpu f32: pass
             // cuda f16: pass
             // cuda f32: pass
-            auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 128, 128, 16, 1);
+            sd::Tensor<float> x({128, 128, 16, 1});
             std::vector<float> timesteps_vec(1, 999.f);
-            auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
-            ggml_set_f32(x, 0.01f);
+            auto timesteps = sd::Tensor<float>::from_vector(timesteps_vec);
+            x.fill_(0.01f);
             // print_ggml_tensor(x);
 
-            auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 4096, 154, 1);
-            ggml_set_f32(context, 0.01f);
+            sd::Tensor<float> context({4096, 154, 1});
+            context.fill_(0.01f);
             // print_ggml_tensor(context);
 
-            auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 2048, 1);
-            ggml_set_f32(y, 0.01f);
+            sd::Tensor<float> y({2048, 1});
+            y.fill_(0.01f);
             // print_ggml_tensor(y);
 
-            struct ggml_tensor* out = nullptr;
+            sd::Tensor<float> out;
 
             int64_t t0   = ggml_time_ms();
-            compute(8, x, timesteps, context, y, &out, work_ctx);
+            auto out_opt = compute(8,
+                                   x,
+                                   timesteps,
+                                   context,
+                                   y);
             int64_t t1   = ggml_time_ms();
 
-            print_ggml_tensor(out);
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
             LOG_DEBUG("mmdit test done in %lldms", t1 - t0);
         }
     }

src/model.cpp

@@ -162,43 +162,7 @@ uint16_t f8_e4m3_to_f16(uint8_t f8) {
 }
 
 uint16_t f8_e5m2_to_f16(uint8_t fp8) {
-    uint8_t sign     = (fp8 >> 7) & 0x1;
-    uint8_t exponent = (fp8 >> 2) & 0x1F;
-    uint8_t mantissa = fp8 & 0x3;
-
-    uint16_t fp16_sign = sign << 15;
-    uint16_t fp16_exponent;
-    uint16_t fp16_mantissa;
-
-    if (exponent == 0 && mantissa == 0) {  // zero
-        return fp16_sign;
-    }
-
-    if (exponent == 0x1F) {  // NAN and INF
-        fp16_exponent = 0x1F;
-        fp16_mantissa = mantissa ? (mantissa << 8) : 0;
-        return fp16_sign | (fp16_exponent << 10) | fp16_mantissa;
-    }
-
-    if (exponent == 0) {  // subnormal numbers
-        fp16_mantissa = (mantissa << 8);
-        return fp16_sign | fp16_mantissa;
-    }
-
-    // normal numbers
-    int16_t true_exponent = (int16_t)exponent - 15 + 15;
-    if (true_exponent <= 0) {
-        fp16_exponent = 0;
-        fp16_mantissa = (mantissa << 8);
-    } else if (true_exponent >= 0x1F) {
-        fp16_exponent = 0x1F;
-        fp16_mantissa = 0;
-    } else {
-        fp16_exponent = (uint16_t)true_exponent;
-        fp16_mantissa = mantissa << 8;
-    }
-
-    return fp16_sign | (fp16_exponent << 10) | fp16_mantissa;
+    return static_cast<uint16_t>(fp8) << 8;
 }
 
 void f8_e4m3_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
@@ -287,7 +251,7 @@ void ModelLoader::add_tensor_storage(const TensorStorage& tensor_storage) {
 }
 
 bool is_zip_file(const std::string& file_path) {
-    struct zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
+    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
     if (zip == nullptr) {
         return false;
     }
@@ -454,7 +418,7 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
     size_t data_offset = gguf_get_data_offset(ctx_gguf_);
     for (int i = 0; i < n_tensors; i++) {
         std::string name   = gguf_get_tensor_name(ctx_gguf_, i);
-        struct ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
+        ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
         size_t offset      = data_offset + gguf_get_tensor_offset(ctx_gguf_, i);
 
         // LOG_DEBUG("%s", name.c_str());
@@ -812,7 +776,7 @@ struct PickleTensorReader {
         }
     }
 
-    void read_string(const std::string& str, struct zip_t* zip, std::string dir) {
+    void read_string(const std::string& str, zip_t* zip, std::string dir) {
         if (str == "storage") {
             read_global_type = true;
         } else if (str != "state_dict") {
@@ -995,7 +959,7 @@ bool ModelLoader::init_from_ckpt_file(const std::string& file_path, const std::s
     file_paths_.push_back(file_path);
     size_t file_index = file_paths_.size() - 1;
 
-    struct zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
+    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
     if (zip == nullptr) {
         LOG_ERROR("failed to open '%s'", file_path.c_str());
         return false;
@@ -1104,10 +1068,12 @@ SDVersion ModelLoader::get_sd_version() {
             tensor_storage.name.find("unet.mid_block.resnets.1.") != std::string::npos) {
             has_middle_block_1 = true;
         }
-        if (tensor_storage.name.find("model.diffusion_model.output_blocks.3.1.transformer_blocks.1") != std::string::npos) {
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.3.1.transformer_blocks.1") != std::string::npos ||
+            tensor_storage.name.find("unet.up_blocks.1.attentions.0.transformer_blocks.1") != std::string::npos) {
             has_output_block_311 = true;
         }
-        if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos) {
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos ||
+            tensor_storage.name.find("unet.up_blocks.2.attentions.1") != std::string::npos) {
             has_output_block_71 = true;
         }
         if (tensor_storage.name == "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight" ||
@@ -1411,7 +1377,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
         for (int i = 0; i < n_threads; ++i) {
             workers.emplace_back([&, file_path, is_zip]() {
                 std::ifstream file;
-                struct zip_t* zip = nullptr;
+                zip_t* zip = nullptr;
                 if (is_zip) {
                     zip = zip_open(file_path.c_str(), 0, 'r');
                     if (zip == nullptr) {
@@ -1599,7 +1565,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
     return success;
 }
 
-bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
+bool ModelLoader::load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                                std::set<std::string> ignore_tensors,
                                int n_threads,
                                bool enable_mmap) {
@@ -1613,7 +1579,7 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
             tensor_names_in_file.insert(name);
         }
 
-        struct ggml_tensor* real;
+        ggml_tensor* real;
         if (tensors.find(name) != tensors.end()) {
             real = tensors[name];
         } else {

src/model.h

@@ -323,7 +323,7 @@ public:
     String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
     void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
     bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
-    bool load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
+    bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                       std::set<std::string> ignore_tensors = {},
                       int n_threads                        = 0,
                       bool use_mmap                        = false);

src/name_conversion.cpp

@@ -1120,7 +1120,11 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         for (const auto& prefix : first_stage_model_prefix_vec) {
             if (starts_with(name, prefix)) {
                 name = convert_first_stage_model_name(name.substr(prefix.size()), prefix);
+                if (version == VERSION_SDXS) {
+                    name = "tae." + name;
+                } else {
                     name = prefix + name;
+                }
                 break;
             }
         }

src/pmid.hpp

@@ -21,14 +21,14 @@ public:
         blocks["layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(in_dim));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
         // x: [N, channels, h, w]
 
         auto fc1        = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
         auto fc2        = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
         auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layernorm"]);
 
-        struct ggml_tensor* r = x;
+        ggml_tensor* r = x;
         // x = ggml_ext_layer_norm(ctx, x, ln_w, ln_b);
         x = layer_norm->forward(ctx, x);
         // x = ggml_add(ctx, ggml_mul_mat(ctx, fc1_w, x),  fc1_b);
@@ -54,8 +54,8 @@ public:
         blocks["1"]   = std::shared_ptr<GGMLBlock>(new Mlp(dim, inner_dim, dim, false));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x) {
         auto norm = std::dynamic_pointer_cast<LayerNorm>(blocks["0"]);
         auto ff   = std::dynamic_pointer_cast<Mlp>(blocks["1"]);
 
@@ -81,8 +81,8 @@ public:
         blocks["to_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim, false));
     }
 
-    struct ggml_tensor* reshape_tensor(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
+    ggml_tensor* reshape_tensor(ggml_context* ctx,
+                                ggml_tensor* x,
                                 int heads) {
         int64_t ne[4];
         for (int i = 0; i < 4; ++i)
@@ -92,17 +92,17 @@ public:
         return x;
     }
 
-    std::vector<struct ggml_tensor*> chunk_half(struct ggml_context* ctx,
-                                                struct ggml_tensor* x) {
+    std::vector<ggml_tensor*> chunk_half(ggml_context* ctx,
+                                         ggml_tensor* x) {
         auto tlo = ggml_view_4d(ctx, x, x->ne[0] / 2, x->ne[1], x->ne[2], x->ne[3], x->nb[1], x->nb[2], x->nb[3], 0);
         auto tli = ggml_view_4d(ctx, x, x->ne[0] / 2, x->ne[1], x->ne[2], x->ne[3], x->nb[1], x->nb[2], x->nb[3], x->nb[0] * x->ne[0] / 2);
         return {ggml_cont(ctx, tlo),
                 ggml_cont(ctx, tli)};
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* latents) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* latents) {
         // x (torch.Tensor): image features
         //     shape (b, n1, D)
         // latent (torch.Tensor): latent features
@@ -176,9 +176,9 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* latents,
-                                struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* latents,
+                         ggml_tensor* x) {
         // x: [N, channels, h, w]
         auto proj_in  = std::dynamic_pointer_cast<Linear>(blocks["proj_in"]);
         auto proj_out = std::dynamic_pointer_cast<Linear>(blocks["proj_out"]);
@@ -225,9 +225,9 @@ public:
             4));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* last_hidden_state) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* last_hidden_state) {
         // x: [N, channels, h, w]
         auto token_proj          = std::dynamic_pointer_cast<Mlp>(blocks["token_proj"]);
         auto token_norm          = std::dynamic_pointer_cast<LayerNorm>(blocks["token_norm"]);
@@ -237,7 +237,7 @@ public:
         int64_t nel      = ggml_nelements(x);
         x                = ggml_reshape_3d(ctx->ggml_ctx, x, cross_attention_dim, num_tokens, nel / (cross_attention_dim * num_tokens));
         x                = token_norm->forward(ctx, x);
-        struct ggml_tensor* out = perceiver_resampler->forward(ctx, x, last_hidden_state);
+        ggml_tensor* out = perceiver_resampler->forward(ctx, x, last_hidden_state);
         if (use_residul)
             out = ggml_add(ctx->ggml_ctx, x, out);
         return out;
@@ -256,9 +256,9 @@ public:
         blocks["layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(embed_dim));
     }
 
-    struct ggml_tensor* fuse_fn(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* id_embeds) {
+    ggml_tensor* fuse_fn(GGMLRunnerContext* ctx,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* id_embeds) {
         auto mlp1       = std::dynamic_pointer_cast<FuseBlock>(blocks["mlp1"]);
         auto mlp2       = std::dynamic_pointer_cast<FuseBlock>(blocks["mlp2"]);
         auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm"]);
@@ -273,24 +273,24 @@ public:
         return stacked_id_embeds;
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* id_embeds,
-                                struct ggml_tensor* class_tokens_mask,
-                                struct ggml_tensor* class_tokens_mask_pos,
-                                struct ggml_tensor* left,
-                                struct ggml_tensor* right) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* id_embeds,
+                         ggml_tensor* class_tokens_mask,
+                         ggml_tensor* class_tokens_mask_pos,
+                         ggml_tensor* left,
+                         ggml_tensor* right) {
         // x: [N, channels, h, w]
 
-        struct ggml_tensor* valid_id_embeds = id_embeds;
+        ggml_tensor* valid_id_embeds = id_embeds;
         // # slice out the image token embeddings
         ggml_set_name(class_tokens_mask_pos, "class_tokens_mask_pos");
         ggml_set_name(prompt_embeds, "prompt_embeds");
-        struct ggml_tensor* image_token_embeds = ggml_get_rows(ctx->ggml_ctx, prompt_embeds, class_tokens_mask_pos);
+        ggml_tensor* image_token_embeds = ggml_get_rows(ctx->ggml_ctx, prompt_embeds, class_tokens_mask_pos);
         ggml_set_name(image_token_embeds, "image_token_embeds");
         valid_id_embeds                = ggml_reshape_2d(ctx->ggml_ctx, valid_id_embeds, valid_id_embeds->ne[0],
                                                          ggml_nelements(valid_id_embeds) / valid_id_embeds->ne[0]);
-        struct ggml_tensor* stacked_id_embeds = fuse_fn(ctx, image_token_embeds, valid_id_embeds);
+        ggml_tensor* stacked_id_embeds = fuse_fn(ctx, image_token_embeds, valid_id_embeds);
 
         if (left && right) {
             stacked_id_embeds = ggml_concat(ctx->ggml_ctx, left, stacked_id_embeds, 1);
@@ -304,7 +304,7 @@ public:
         class_tokens_mask                  = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, class_tokens_mask));
         class_tokens_mask                  = ggml_repeat(ctx->ggml_ctx, class_tokens_mask, prompt_embeds);
         prompt_embeds                      = ggml_mul(ctx->ggml_ctx, prompt_embeds, class_tokens_mask);
-        struct ggml_tensor* updated_prompt_embeds = ggml_add(ctx->ggml_ctx, prompt_embeds, stacked_id_embeds);
+        ggml_tensor* updated_prompt_embeds = ggml_add(ctx->ggml_ctx, prompt_embeds, stacked_id_embeds);
         ggml_set_name(updated_prompt_embeds, "updated_prompt_embeds");
         return updated_prompt_embeds;
     }
@@ -317,22 +317,22 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
         blocks["fuse_module"]         = std::shared_ptr<GGMLBlock>(new FuseModule(2048));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* id_pixel_values,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* class_tokens_mask,
-                                struct ggml_tensor* class_tokens_mask_pos,
-                                struct ggml_tensor* left,
-                                struct ggml_tensor* right) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* id_pixel_values,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* class_tokens_mask,
+                         ggml_tensor* class_tokens_mask_pos,
+                         ggml_tensor* left,
+                         ggml_tensor* right) {
         // x: [N, channels, h, w]
         auto vision_model        = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
         auto visual_projection   = std::dynamic_pointer_cast<CLIPProjection>(blocks["visual_projection"]);
         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]
+        ggml_tensor* shared_id_embeds = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
+        ggml_tensor* id_embeds        = visual_projection->forward(ctx, shared_id_embeds);    // [N, proj_dim(768)]
+        ggml_tensor* id_embeds_2      = visual_projection_2->forward(ctx, shared_id_embeds);  // [N, 1280]
 
         id_embeds   = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds, 2, 0, 1, 3));
         id_embeds_2 = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds_2, 2, 0, 1, 3));
@@ -340,7 +340,7 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
         id_embeds = ggml_concat(ctx->ggml_ctx, id_embeds, id_embeds_2, 2);  // [batch_size, seq_length, 1, 2048] check whether concat at dim 2 is right
         id_embeds = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds, 1, 2, 0, 3));
 
-        struct ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
+        ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
                                                                   prompt_embeds,
                                                                   id_embeds,
                                                                   class_tokens_mask,
@@ -365,24 +365,24 @@ struct PhotoMakerIDEncoder_CLIPInsightfaceExtendtokenBlock : public CLIPVisionMo
                                                                                         num_tokens));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* id_pixel_values,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* class_tokens_mask,
-                                struct ggml_tensor* class_tokens_mask_pos,
-                                struct ggml_tensor* id_embeds,
-                                struct ggml_tensor* left,
-                                struct ggml_tensor* right) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* id_pixel_values,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* class_tokens_mask,
+                         ggml_tensor* class_tokens_mask_pos,
+                         ggml_tensor* id_embeds,
+                         ggml_tensor* left,
+                         ggml_tensor* right) {
         // x: [N, channels, h, w]
         auto vision_model      = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
         auto fuse_module       = std::dynamic_pointer_cast<FuseModule>(blocks["fuse_module"]);
         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]
+        // ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
+        ggml_tensor* last_hidden_state = vision_model->forward(ctx, 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,
+        ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
                                                                   prompt_embeds,
                                                                   id_embeds,
                                                                   class_tokens_mask,
@@ -436,18 +436,17 @@ public:
         return pm_version;
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         if (pm_version == PM_VERSION_1)
             id_encoder.get_param_tensors(tensors, prefix);
         else if (pm_version == PM_VERSION_2)
             id_encoder2.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_cgraph* build_graph(  // struct ggml_allocr* allocr,
-        struct ggml_tensor* id_pixel_values,
-        struct ggml_tensor* prompt_embeds,
+    ggml_cgraph* build_graph(const sd::Tensor<float>& id_pixel_values_tensor,
+                             const sd::Tensor<float>& prompt_embeds_tensor,
                              std::vector<bool>& class_tokens_mask,
-        struct ggml_tensor* id_embeds) {
+                             const sd::Tensor<float>& id_embeds_tensor = {}) {
         ctm.clear();
         ctmf16.clear();
         ctmpos.clear();
@@ -458,20 +457,20 @@ public:
 
         auto runner_ctx = get_context();
 
-        struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+        ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+
+        ggml_tensor* id_pixel_values = make_input(id_pixel_values_tensor);
+        ggml_tensor* prompt_embeds   = make_input(prompt_embeds_tensor);
+        ggml_tensor* id_embeds       = make_optional_input(id_embeds_tensor);
 
         int64_t hidden_size = prompt_embeds->ne[0];
         int64_t seq_length  = prompt_embeds->ne[1];
         ggml_type type      = GGML_TYPE_F32;
 
-        struct ggml_tensor* class_tokens_mask_d = ggml_new_tensor_1d(runner_ctx.ggml_ctx, type, class_tokens_mask.size());
+        ggml_tensor* class_tokens_mask_d = ggml_new_tensor_1d(runner_ctx.ggml_ctx, type, class_tokens_mask.size());
 
-        struct ggml_tensor* id_pixel_values_d = to_backend(id_pixel_values);
-        struct ggml_tensor* prompt_embeds_d   = to_backend(prompt_embeds);
-        struct ggml_tensor* id_embeds_d       = to_backend(id_embeds);
-
-        struct ggml_tensor* left  = nullptr;
-        struct ggml_tensor* right = nullptr;
+        ggml_tensor* left  = nullptr;
+        ggml_tensor* right = nullptr;
         for (int i = 0; i < class_tokens_mask.size(); i++) {
             if (class_tokens_mask[i]) {
                 // printf(" 1,");
@@ -495,7 +494,7 @@ public:
             right = ggml_new_tensor_3d(runner_ctx.ggml_ctx, type,
                                        hidden_size, seq_length - ctmpos[ctmpos.size() - 1] - 1, 1);
         }
-        struct ggml_tensor* class_tokens_mask_pos = ggml_new_tensor_1d(runner_ctx.ggml_ctx, GGML_TYPE_I32, ctmpos.size());
+        ggml_tensor* class_tokens_mask_pos = ggml_new_tensor_1d(runner_ctx.ggml_ctx, GGML_TYPE_I32, ctmpos.size());
 
         {
             if (type == GGML_TYPE_F16)
@@ -526,21 +525,21 @@ public:
                 }
             }
         }
-        struct ggml_tensor* updated_prompt_embeds = nullptr;
+        ggml_tensor* updated_prompt_embeds = nullptr;
         if (pm_version == PM_VERSION_1)
             updated_prompt_embeds = id_encoder.forward(&runner_ctx,
-                                                       id_pixel_values_d,
-                                                       prompt_embeds_d,
+                                                       id_pixel_values,
+                                                       prompt_embeds,
                                                        class_tokens_mask_d,
                                                        class_tokens_mask_pos,
                                                        left, right);
         else if (pm_version == PM_VERSION_2)
             updated_prompt_embeds = id_encoder2.forward(&runner_ctx,
-                                                        id_pixel_values_d,
-                                                        prompt_embeds_d,
+                                                        id_pixel_values,
+                                                        prompt_embeds,
                                                         class_tokens_mask_d,
                                                         class_tokens_mask_pos,
-                                                        id_embeds_d,
+                                                        id_embeds,
                                                         left, right);
 
         ggml_build_forward_expand(gf, updated_prompt_embeds);
@@ -548,25 +547,21 @@ public:
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* id_pixel_values,
-                 struct ggml_tensor* prompt_embeds,
-                 struct ggml_tensor* id_embeds,
-                 std::vector<bool>& class_tokens_mask,
-                 struct ggml_tensor** updated_prompt_embeds,
-                 ggml_context* output_ctx) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            // return build_graph(compute_allocr, id_pixel_values, prompt_embeds, class_tokens_mask);
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<float>& id_pixel_values,
+                              const sd::Tensor<float>& prompt_embeds,
+                              const sd::Tensor<float>& id_embeds,
+                              std::vector<bool>& class_tokens_mask) {
+        auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds);
         };
 
-        // GGMLRunner::compute(get_graph, n_threads, updated_prompt_embeds);
-        return GGMLRunner::compute(get_graph, n_threads, true, updated_prompt_embeds, output_ctx);
+        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true));
     }
 };
 
 struct PhotoMakerIDEmbed : public GGMLRunner {
-    std::map<std::string, struct ggml_tensor*> tensors;
+    std::map<std::string, ggml_tensor*> tensors;
     std::string file_path;
     ModelLoader* model_loader;
     bool load_failed = false;
@@ -606,7 +601,7 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
             }
             if (dry_run) {
                 std::lock_guard<std::mutex> lock(tensor_mutex);
-                struct ggml_tensor* real = ggml_new_tensor(params_ctx,
+                ggml_tensor* real = ggml_new_tensor(params_ctx,
                                                     tensor_storage.type,
                                                     tensor_storage.n_dims,
                                                     tensor_storage.ne);
@@ -629,8 +624,8 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
         return true;
     }
 
-    struct ggml_tensor* get() {
-        std::map<std::string, struct ggml_tensor*>::iterator pos;
+    ggml_tensor* get() {
+        std::map<std::string, ggml_tensor*>::iterator pos;
         pos = tensors.find("pmid.id_embeds");
         if (pos != tensors.end())
             return pos->second;

src/preprocessing.hpp

@@ -1,179 +1,241 @@
 #ifndef __PREPROCESSING_HPP__
 #define __PREPROCESSING_HPP__
 
+#include <cmath>
+#include <limits>
+
 #include "ggml_extend.hpp"
+
 #define M_PI_ 3.14159265358979323846f
 
-void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml_tensor* kernel, int padding) {
-    struct ggml_init_params params;
-    params.mem_size                 = 80 * input->ne[0] * input->ne[1];  // 20M for 512x512
-    params.mem_buffer               = nullptr;
-    params.no_alloc                 = false;
-    struct ggml_context* ctx0       = ggml_init(params);
-    struct ggml_tensor* kernel_fp16 = ggml_new_tensor_4d(ctx0, GGML_TYPE_F16, kernel->ne[0], kernel->ne[1], 1, 1);
-    ggml_fp32_to_fp16_row((float*)kernel->data, (ggml_fp16_t*)kernel_fp16->data, ggml_nelements(kernel));
-    ggml_tensor* h  = ggml_conv_2d(ctx0, kernel_fp16, input, 1, 1, padding, padding, 1, 1);
-    ggml_cgraph* gf = ggml_new_graph(ctx0);
-    ggml_build_forward_expand(gf, ggml_cpy(ctx0, h, output));
-    ggml_graph_compute_with_ctx(ctx0, gf, 1);
-    ggml_free(ctx0);
+static inline int64_t preprocessing_offset_4d(const sd::Tensor<float>& tensor, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
+    const auto& shape = tensor.shape();
+    int64_t n0        = shape.size() > 0 ? shape[0] : 1;
+    int64_t n1        = shape.size() > 1 ? shape[1] : 1;
+    int64_t n2        = shape.size() > 2 ? shape[2] : 1;
+    return ((i3 * n2 + i2) * n1 + i1) * n0 + i0;
 }
 
-void gaussian_kernel(struct ggml_tensor* kernel) {
-    int ks_mid   = static_cast<int>(kernel->ne[0] / 2);
+static inline float preprocessing_get_4d(const sd::Tensor<float>& tensor, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
+    return tensor.values()[static_cast<size_t>(preprocessing_offset_4d(tensor, i0, i1, i2, i3))];
+}
+
+static inline void preprocessing_set_4d(sd::Tensor<float>& tensor, float value, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
+    tensor.values()[static_cast<size_t>(preprocessing_offset_4d(tensor, i0, i1, i2, i3))] = value;
+}
+
+static inline sd::Tensor<float> sd_image_to_preprocessing_tensor(sd_image_t image) {
+    sd::Tensor<float> tensor({static_cast<int64_t>(image.width), static_cast<int64_t>(image.height), static_cast<int64_t>(image.channel), 1});
+    for (uint32_t y = 0; y < image.height; ++y) {
+        for (uint32_t x = 0; x < image.width; ++x) {
+            for (uint32_t c = 0; c < image.channel; ++c) {
+                preprocessing_set_4d(tensor, sd_image_get_f32(image, x, y, c), x, y, c, 0);
+            }
+        }
+    }
+    return tensor;
+}
+
+static inline void preprocessing_tensor_to_sd_image(const sd::Tensor<float>& tensor, uint8_t* image_data) {
+    GGML_ASSERT(tensor.dim() == 4);
+    GGML_ASSERT(tensor.shape()[3] == 1);
+    GGML_ASSERT(image_data != nullptr);
+
+    int width   = static_cast<int>(tensor.shape()[0]);
+    int height  = static_cast<int>(tensor.shape()[1]);
+    int channel = static_cast<int>(tensor.shape()[2]);
+    for (int y = 0; y < height; ++y) {
+        for (int x = 0; x < width; ++x) {
+            for (int c = 0; c < channel; ++c) {
+                float value                               = preprocessing_get_4d(tensor, x, y, c, 0);
+                value                                     = std::min(1.0f, std::max(0.0f, value));
+                image_data[(y * width + x) * channel + c] = static_cast<uint8_t>(std::round(value * 255.0f));
+            }
+        }
+    }
+}
+
+static inline sd::Tensor<float> gaussian_kernel_tensor(int kernel_size) {
+    sd::Tensor<float> kernel({kernel_size, kernel_size, 1, 1});
+    int ks_mid   = kernel_size / 2;
     float sigma  = 1.4f;
-    float normal = 1.f / (2.0f * M_PI_ * powf(sigma, 2.0f));
-    for (int y = 0; y < kernel->ne[0]; y++) {
+    float normal = 1.f / (2.0f * M_PI_ * std::pow(sigma, 2.0f));
+    for (int y = 0; y < kernel_size; ++y) {
         float gx = static_cast<float>(-ks_mid + y);
-        for (int x = 0; x < kernel->ne[1]; x++) {
+        for (int x = 0; x < kernel_size; ++x) {
             float gy = static_cast<float>(-ks_mid + x);
-            float k_ = expf(-((gx * gx + gy * gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
-            ggml_ext_tensor_set_f32(kernel, k_, x, y);
+            float k  = std::exp(-((gx * gx + gy * gy) / (2.0f * std::pow(sigma, 2.0f)))) * normal;
+            preprocessing_set_4d(kernel, k, x, y, 0, 0);
         }
     }
+    return kernel;
 }
 
-void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
-    for (int iy = 0; iy < rgb_img->ne[1]; iy++) {
-        for (int ix = 0; ix < rgb_img->ne[0]; ix++) {
-            float r    = ggml_ext_tensor_get_f32(rgb_img, ix, iy);
-            float g    = ggml_ext_tensor_get_f32(rgb_img, ix, iy, 1);
-            float b    = ggml_ext_tensor_get_f32(rgb_img, ix, iy, 2);
+static inline sd::Tensor<float> convolve_tensor(const sd::Tensor<float>& input, const sd::Tensor<float>& kernel, int padding) {
+    GGML_ASSERT(input.dim() == 4);
+    GGML_ASSERT(kernel.dim() == 4);
+    GGML_ASSERT(input.shape()[3] == 1);
+    GGML_ASSERT(kernel.shape()[2] == 1);
+    GGML_ASSERT(kernel.shape()[3] == 1);
+
+    sd::Tensor<float> output(input.shape());
+    int64_t width    = input.shape()[0];
+    int64_t height   = input.shape()[1];
+    int64_t channels = input.shape()[2];
+    int64_t kernel_w = kernel.shape()[0];
+    int64_t kernel_h = kernel.shape()[1];
+
+    for (int64_t c = 0; c < channels; ++c) {
+        for (int64_t y = 0; y < height; ++y) {
+            for (int64_t x = 0; x < width; ++x) {
+                float sum = 0.0f;
+                for (int64_t ky = 0; ky < kernel_h; ++ky) {
+                    int64_t iy = y + ky - padding;
+                    if (iy < 0 || iy >= height) {
+                        continue;
+                    }
+                    for (int64_t kx = 0; kx < kernel_w; ++kx) {
+                        int64_t ix = x + kx - padding;
+                        if (ix < 0 || ix >= width) {
+                            continue;
+                        }
+                        sum += preprocessing_get_4d(input, ix, iy, c, 0) * preprocessing_get_4d(kernel, kx, ky, 0, 0);
+                    }
+                }
+                preprocessing_set_4d(output, sum, x, y, c, 0);
+            }
+        }
+    }
+    return output;
+}
+
+static inline sd::Tensor<float> grayscale_tensor(const sd::Tensor<float>& rgb_img) {
+    GGML_ASSERT(rgb_img.dim() == 4);
+    GGML_ASSERT(rgb_img.shape()[2] >= 3);
+    sd::Tensor<float> grayscale({rgb_img.shape()[0], rgb_img.shape()[1], 1, rgb_img.shape()[3]});
+    for (int64_t iy = 0; iy < rgb_img.shape()[1]; ++iy) {
+        for (int64_t ix = 0; ix < rgb_img.shape()[0]; ++ix) {
+            float r    = preprocessing_get_4d(rgb_img, ix, iy, 0, 0);
+            float g    = preprocessing_get_4d(rgb_img, ix, iy, 1, 0);
+            float b    = preprocessing_get_4d(rgb_img, ix, iy, 2, 0);
             float gray = 0.2989f * r + 0.5870f * g + 0.1140f * b;
-            ggml_ext_tensor_set_f32(grayscale, gray, ix, iy);
+            preprocessing_set_4d(grayscale, gray, ix, iy, 0, 0);
         }
     }
+    return grayscale;
 }
 
-void prop_hypot(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
-    int n_elements = static_cast<int>(ggml_nelements(h));
-    float* dx      = (float*)x->data;
-    float* dy      = (float*)y->data;
-    float* dh      = (float*)h->data;
-    for (int i = 0; i < n_elements; i++) {
-        dh[i] = sqrtf(dx[i] * dx[i] + dy[i] * dy[i]);
+static inline sd::Tensor<float> tensor_hypot(const sd::Tensor<float>& x, const sd::Tensor<float>& y) {
+    sd::tensor_check_same_shape(x, y);
+    sd::Tensor<float> out(x.shape());
+    for (int64_t i = 0; i < out.numel(); ++i) {
+        out[i] = std::sqrt(x[i] * x[i] + y[i] * y[i]);
     }
+    return out;
 }
 
-void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
-    int n_elements = static_cast<int>(ggml_nelements(h));
-    float* dx      = (float*)x->data;
-    float* dy      = (float*)y->data;
-    float* dh      = (float*)h->data;
-    for (int i = 0; i < n_elements; i++) {
-        dh[i] = atan2f(dy[i], dx[i]);
+static inline sd::Tensor<float> tensor_arctan2(const sd::Tensor<float>& x, const sd::Tensor<float>& y) {
+    sd::tensor_check_same_shape(x, y);
+    sd::Tensor<float> out(x.shape());
+    for (int64_t i = 0; i < out.numel(); ++i) {
+        out[i] = std::atan2(y[i], x[i]);
     }
+    return out;
 }
 
-void normalize_tensor(struct ggml_tensor* g) {
-    int n_elements = static_cast<int>(ggml_nelements(g));
-    float* dg      = (float*)g->data;
-    float max      = -INFINITY;
-    for (int i = 0; i < n_elements; i++) {
-        max = dg[i] > max ? dg[i] : max;
+static inline void normalize_tensor(sd::Tensor<float>* g) {
+    GGML_ASSERT(g != nullptr);
+    if (g->empty()) {
+        return;
     }
-    max = 1.0f / max;
-    for (int i = 0; i < n_elements; i++) {
-        dg[i] *= max;
+    float max_value = -std::numeric_limits<float>::infinity();
+    for (int64_t i = 0; i < g->numel(); ++i) {
+        max_value = std::max(max_value, (*g)[i]);
     }
+    if (max_value == 0.0f || !std::isfinite(max_value)) {
+        return;
+    }
+    *g *= (1.0f / max_value);
 }
 
-void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struct ggml_tensor* D) {
-    for (int iy = 1; iy < result->ne[1] - 1; iy++) {
-        for (int ix = 1; ix < result->ne[0] - 1; ix++) {
-            float angle = ggml_ext_tensor_get_f32(D, ix, iy) * 180.0f / M_PI_;
-            angle       = angle < 0.0f ? angle += 180.0f : angle;
+static inline sd::Tensor<float> non_max_supression(const sd::Tensor<float>& G, const sd::Tensor<float>& D) {
+    GGML_ASSERT(G.shape() == D.shape());
+    sd::Tensor<float> result = sd::Tensor<float>::zeros(G.shape());
+    for (int64_t iy = 1; iy < result.shape()[1] - 1; ++iy) {
+        for (int64_t ix = 1; ix < result.shape()[0] - 1; ++ix) {
+            float angle = preprocessing_get_4d(D, ix, iy, 0, 0) * 180.0f / M_PI_;
+            angle       = angle < 0.0f ? angle + 180.0f : angle;
             float q     = 1.0f;
             float r     = 1.0f;
 
-            // angle 0
-            if ((0 >= angle && angle < 22.5f) || (157.5f >= angle && angle <= 180)) {
-                q = ggml_ext_tensor_get_f32(G, ix, iy + 1);
-                r = ggml_ext_tensor_get_f32(G, ix, iy - 1);
-            }
-            // angle 45
-            else if (22.5f >= angle && angle < 67.5f) {
-                q = ggml_ext_tensor_get_f32(G, ix + 1, iy - 1);
-                r = ggml_ext_tensor_get_f32(G, ix - 1, iy + 1);
-            }
-            // angle 90
-            else if (67.5f >= angle && angle < 112.5) {
-                q = ggml_ext_tensor_get_f32(G, ix + 1, iy);
-                r = ggml_ext_tensor_get_f32(G, ix - 1, iy);
-            }
-            // angle 135
-            else if (112.5 >= angle && angle < 157.5f) {
-                q = ggml_ext_tensor_get_f32(G, ix - 1, iy - 1);
-                r = ggml_ext_tensor_get_f32(G, ix + 1, iy + 1);
+            if ((0 >= angle && angle < 22.5f) || (157.5f >= angle && angle <= 180.0f)) {
+                q = preprocessing_get_4d(G, ix, iy + 1, 0, 0);
+                r = preprocessing_get_4d(G, ix, iy - 1, 0, 0);
+            } else if (22.5f >= angle && angle < 67.5f) {
+                q = preprocessing_get_4d(G, ix + 1, iy - 1, 0, 0);
+                r = preprocessing_get_4d(G, ix - 1, iy + 1, 0, 0);
+            } else if (67.5f >= angle && angle < 112.5f) {
+                q = preprocessing_get_4d(G, ix + 1, iy, 0, 0);
+                r = preprocessing_get_4d(G, ix - 1, iy, 0, 0);
+            } else if (112.5f >= angle && angle < 157.5f) {
+                q = preprocessing_get_4d(G, ix - 1, iy - 1, 0, 0);
+                r = preprocessing_get_4d(G, ix + 1, iy + 1, 0, 0);
             }
 
-            float cur = ggml_ext_tensor_get_f32(G, ix, iy);
-            if ((cur >= q) && (cur >= r)) {
-                ggml_ext_tensor_set_f32(result, cur, ix, iy);
-            } else {
-                ggml_ext_tensor_set_f32(result, 0.0f, ix, iy);
-            }
+            float cur = preprocessing_get_4d(G, ix, iy, 0, 0);
+            preprocessing_set_4d(result, (cur >= q && cur >= r) ? cur : 0.0f, ix, iy, 0, 0);
         }
     }
+    return result;
 }
 
-void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float low_threshold, float weak, float strong) {
-    int n_elements = static_cast<int>(ggml_nelements(img));
-    float* imd     = (float*)img->data;
-    float max      = -INFINITY;
-    for (int i = 0; i < n_elements; i++) {
-        max = imd[i] > max ? imd[i] : max;
+static inline void threshold_hystersis(sd::Tensor<float>* img, float high_threshold, float low_threshold, float weak, float strong) {
+    GGML_ASSERT(img != nullptr);
+    if (img->empty()) {
+        return;
     }
-    float ht = max * high_threshold;
+    float max_value = -std::numeric_limits<float>::infinity();
+    for (int64_t i = 0; i < img->numel(); ++i) {
+        max_value = std::max(max_value, (*img)[i]);
+    }
+
+    float ht = max_value * high_threshold;
     float lt = ht * low_threshold;
-    for (int i = 0; i < n_elements; i++) {
-        float img_v = imd[i];
-        if (img_v >= ht) {  // strong pixel
-            imd[i] = strong;
-        } else if (img_v <= ht && img_v >= lt) {  // strong pixel
-            imd[i] = weak;
+    for (int64_t i = 0; i < img->numel(); ++i) {
+        float img_v = (*img)[i];
+        if (img_v >= ht) {
+            (*img)[i] = strong;
+        } else if (img_v <= ht && img_v >= lt) {
+            (*img)[i] = weak;
         }
     }
 
-    for (int iy = 0; iy < img->ne[1]; iy++) {
-        for (int ix = 0; ix < img->ne[0]; ix++) {
-            if (ix >= 3 && ix <= img->ne[0] - 3 && iy >= 3 && iy <= img->ne[1] - 3) {
-                ggml_ext_tensor_set_f32(img, ggml_ext_tensor_get_f32(img, ix, iy), ix, iy);
-            } else {
-                ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
+    for (int64_t iy = 0; iy < img->shape()[1]; ++iy) {
+        for (int64_t ix = 0; ix < img->shape()[0]; ++ix) {
+            if (!(ix >= 3 && ix <= img->shape()[0] - 3 && iy >= 3 && iy <= img->shape()[1] - 3)) {
+                preprocessing_set_4d(*img, 0.0f, ix, iy, 0, 0);
             }
         }
     }
 
-    // hysteresis
-    for (int iy = 1; iy < img->ne[1] - 1; iy++) {
-        for (int ix = 1; ix < img->ne[0] - 1; ix++) {
-            float imd_v = ggml_ext_tensor_get_f32(img, ix, iy);
+    for (int64_t iy = 1; iy < img->shape()[1] - 1; ++iy) {
+        for (int64_t ix = 1; ix < img->shape()[0] - 1; ++ix) {
+            float imd_v = preprocessing_get_4d(*img, ix, iy, 0, 0);
             if (imd_v == weak) {
-                if (ggml_ext_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix + 1, iy) == strong ||
-                    ggml_ext_tensor_get_f32(img, ix, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix, iy + 1) == strong ||
-                    ggml_ext_tensor_get_f32(img, ix - 1, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix - 1, iy) == strong) {
-                    ggml_ext_tensor_set_f32(img, strong, ix, iy);
-                } else {
-                    ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
-                }
+                bool has_strong_neighbor =
+                    preprocessing_get_4d(*img, ix + 1, iy - 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix + 1, iy, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix, iy - 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix, iy + 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix - 1, iy - 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix - 1, iy, 0, 0) == strong;
+                preprocessing_set_4d(*img, has_strong_neighbor ? strong : 0.0f, ix, iy, 0, 0);
             }
         }
     }
 }
 
 bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold, float weak, float strong, bool inverse) {
-    struct ggml_init_params params;
-    params.mem_size               = static_cast<size_t>(40 * img.width * img.height);  // 10MB for 512x512
-    params.mem_buffer             = nullptr;
-    params.no_alloc               = false;
-    struct ggml_context* work_ctx = ggml_init(params);
-
-    if (!work_ctx) {
-        LOG_ERROR("ggml_init() failed");
-        return false;
-    }
-
     float kX[9] = {
         -1, 0, 1,
         -2, 0, 2,
@@ -184,42 +246,32 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
         0, 0, 0,
         -1, -2, -1};
 
-    // generate kernel
-    int kernel_size             = 5;
-    struct ggml_tensor* gkernel = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, kernel_size, kernel_size, 1, 1);
-    struct ggml_tensor* sf_kx   = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
-    memcpy(sf_kx->data, kX, ggml_nbytes(sf_kx));
-    struct ggml_tensor* sf_ky = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
-    memcpy(sf_ky->data, kY, ggml_nbytes(sf_ky));
-    gaussian_kernel(gkernel);
-    struct ggml_tensor* image      = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, img.width, img.height, 3, 1);
-    struct ggml_tensor* image_gray = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, img.width, img.height, 1, 1);
-    struct ggml_tensor* iX         = ggml_dup_tensor(work_ctx, image_gray);
-    struct ggml_tensor* iY         = ggml_dup_tensor(work_ctx, image_gray);
-    struct ggml_tensor* G          = ggml_dup_tensor(work_ctx, image_gray);
-    struct ggml_tensor* tetha      = ggml_dup_tensor(work_ctx, image_gray);
-    sd_image_to_ggml_tensor(img, image);
-    grayscale(image, image_gray);
-    convolve(image_gray, image_gray, gkernel, 2);
-    convolve(image_gray, iX, sf_kx, 1);
-    convolve(image_gray, iY, sf_ky, 1);
-    prop_hypot(iX, iY, G);
-    normalize_tensor(G);
-    prop_arctan2(iX, iY, tetha);
-    non_max_supression(image_gray, G, tetha);
-    threshold_hystersis(image_gray, high_threshold, low_threshold, weak, strong);
-    // to RGB channels
-    for (uint32_t iy = 0; iy < img.height; iy++) {
-        for (uint32_t ix = 0; ix < img.width; ix++) {
-            float gray = ggml_ext_tensor_get_f32(image_gray, ix, iy);
+    sd::Tensor<float> gkernel = gaussian_kernel_tensor(5);
+    sd::Tensor<float> sf_kx({3, 3, 1, 1}, std::vector<float>(kX, kX + 9));
+    sd::Tensor<float> sf_ky({3, 3, 1, 1}, std::vector<float>(kY, kY + 9));
+
+    sd::Tensor<float> image      = sd_image_to_preprocessing_tensor(img);
+    sd::Tensor<float> image_gray = grayscale_tensor(image);
+    image_gray                   = convolve_tensor(image_gray, gkernel, 2);
+    sd::Tensor<float> iX         = convolve_tensor(image_gray, sf_kx, 1);
+    sd::Tensor<float> iY         = convolve_tensor(image_gray, sf_ky, 1);
+    sd::Tensor<float> G          = tensor_hypot(iX, iY);
+    normalize_tensor(&G);
+    sd::Tensor<float> theta = tensor_arctan2(iX, iY);
+    image_gray              = non_max_supression(G, theta);
+    threshold_hystersis(&image_gray, high_threshold, low_threshold, weak, strong);
+
+    for (uint32_t iy = 0; iy < img.height; ++iy) {
+        for (uint32_t ix = 0; ix < img.width; ++ix) {
+            float gray = preprocessing_get_4d(image_gray, ix, iy, 0, 0);
             gray       = inverse ? 1.0f - gray : gray;
-            ggml_ext_tensor_set_f32(image, gray, ix, iy);
-            ggml_ext_tensor_set_f32(image, gray, ix, iy, 1);
-            ggml_ext_tensor_set_f32(image, gray, ix, iy, 2);
+            for (uint32_t c = 0; c < img.channel; ++c) {
+                preprocessing_set_4d(image, gray, ix, iy, c, 0);
             }
         }
-    ggml_tensor_to_sd_image(image, img.data);
-    ggml_free(work_ctx);
+    }
+
+    preprocessing_tensor_to_sd_image(image, img.data);
     return true;
 }
 

src/qwen_image.hpp

@@ -26,9 +26,9 @@ namespace Qwen {
             blocks["linear_2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, out_dim, sample_proj_bias));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* sample,
-                                    struct ggml_tensor* condition = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* sample,
+                             ggml_tensor* condition = nullptr) {
             if (condition != nullptr) {
                 auto cond_proj = std::dynamic_pointer_cast<Linear>(blocks["cond_proj"]);
                 sample         = ggml_add(ctx->ggml_ctx, sample, cond_proj->forward(ctx, condition));
@@ -49,8 +49,8 @@ namespace Qwen {
             blocks["timestep_embedder"] = std::shared_ptr<GGMLBlock>(new TimestepEmbedding(256, embedding_dim));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* timesteps) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* timesteps) {
             // timesteps: [N,]
             // return: [N, embedding_dim]
             auto timestep_embedder = std::dynamic_pointer_cast<TimestepEmbedding>(blocks["timestep_embedder"]);
@@ -107,10 +107,10 @@ namespace Qwen {
         }
 
         std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                      struct ggml_tensor* img,
-                                                      struct ggml_tensor* txt,
-                                                      struct ggml_tensor* pe,
-                                                      struct ggml_tensor* mask = nullptr) {
+                                                      ggml_tensor* img,
+                                                      ggml_tensor* txt,
+                                                      ggml_tensor* pe,
+                                                      ggml_tensor* mask = nullptr) {
             // 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]
@@ -249,11 +249,11 @@ namespace Qwen {
         }
 
         virtual std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                              struct ggml_tensor* img,
-                                                              struct ggml_tensor* txt,
-                                                              struct ggml_tensor* t_emb,
-                                                              struct ggml_tensor* pe,
-                                                              struct ggml_tensor* modulate_index = nullptr) {
+                                                              ggml_tensor* img,
+                                                              ggml_tensor* txt,
+                                                              ggml_tensor* t_emb,
+                                                              ggml_tensor* pe,
+                                                              ggml_tensor* modulate_index = nullptr) {
             // 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]
@@ -325,9 +325,9 @@ namespace Qwen {
             blocks["linear"] = std::shared_ptr<GGMLBlock>(new Linear(conditioning_embedding_dim, embedding_dim * 2, bias));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* c) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* c) {
             // x: [N, n_token, hidden_size]
             // c: [N, hidden_size]
             // return: [N, n_token, patch_size * patch_size * out_channels]
@@ -389,12 +389,12 @@ namespace Qwen {
             blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, params.patch_size * params.patch_size * params.out_channels));
         }
 
-        struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* timestep,
-                                         struct ggml_tensor* context,
-                                         struct ggml_tensor* pe,
-                                         struct ggml_tensor* modulate_index = nullptr) {
+        ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
+                                  ggml_tensor* x,
+                                  ggml_tensor* timestep,
+                                  ggml_tensor* context,
+                                  ggml_tensor* pe,
+                                  ggml_tensor* modulate_index = nullptr) {
             auto time_text_embed = std::dynamic_pointer_cast<QwenTimestepProjEmbeddings>(blocks["time_text_embed"]);
             auto txt_norm        = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm"]);
             auto img_in          = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
@@ -429,13 +429,13 @@ namespace Qwen {
             return img;
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* timestep,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* pe,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe,
                              std::vector<ggml_tensor*> ref_latents = {},
-                                    struct ggml_tensor* modulate_index    = nullptr) {
+                             ggml_tensor* modulate_index           = nullptr) {
             // Forward pass of DiT.
             // x: [N, C, H, W]
             // timestep: [N,]
@@ -521,24 +521,25 @@ namespace Qwen {
             return "qwen_image";
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
             qwen_image.get_param_tensors(tensors, prefix);
         }
 
-        struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                        struct ggml_tensor* timesteps,
-                                        struct ggml_tensor* context,
-                                        std::vector<ggml_tensor*> ref_latents = {},
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor,
+                                 const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
                                  bool increase_ref_index                                  = false) {
+            ggml_cgraph* gf        = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
             GGML_ASSERT(x->ne[3] == 1);
-            struct ggml_cgraph* gf = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
-
-            x         = to_backend(x);
-            context   = to_backend(context);
-            timesteps = to_backend(timesteps);
-
-            for (int i = 0; i < ref_latents.size(); i++) {
-                ref_latents[i] = to_backend(ref_latents[i]);
+            GGML_ASSERT(!context_tensor.empty());
+            ggml_tensor* context = make_input(context_tensor);
+            std::vector<ggml_tensor*> ref_latents;
+            ref_latents.reserve(ref_latents_tensor.size());
+            for (const auto& ref_latent_tensor : ref_latents_tensor) {
+                ref_latents.push_back(make_input(ref_latent_tensor));
             }
 
             pe_vec      = Rope::gen_qwen_image_pe(static_cast<int>(x->ne[1]),
@@ -587,7 +588,7 @@ namespace Qwen {
 
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* out = qwen_image.forward(&runner_ctx,
+            ggml_tensor* out = qwen_image.forward(&runner_ctx,
                                                   x,
                                                   timesteps,
                                                   context,
@@ -600,54 +601,59 @@ namespace Qwen {
             return gf;
         }
 
-        bool compute(int n_threads,
-                     struct ggml_tensor* x,
-                     struct ggml_tensor* timesteps,
-                     struct ggml_tensor* context,
-                     std::vector<ggml_tensor*> ref_latents = {},
-                     bool increase_ref_index               = false,
-                     struct ggml_tensor** output           = nullptr,
-                     struct ggml_context* output_ctx       = nullptr) {
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context,
+                                  const std::vector<sd::Tensor<float>>& ref_latents = {},
+                                  bool increase_ref_index                           = false) {
             // x: [N, in_channels, h, w]
             // timesteps: [N, ]
             // context: [N, max_position, hidden_size]
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+            auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
             };
 
-            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
         }
 
         void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
             params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
             params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);
 
             {
-                // auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 16, 1);
+                // auto x = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 16, 16, 16, 1);
                 // ggml_set_f32(x, 0.01f);
-                auto x = load_tensor_from_file(work_ctx, "./qwen_image_x.bin");
-                print_ggml_tensor(x);
+                auto x = sd::load_tensor_from_file_as_tensor<float>("./qwen_image_x.bin");
+                print_sd_tensor(x);
 
                 std::vector<float> timesteps_vec(1, 1000.f);
-                auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
+                auto timesteps = sd::Tensor<float>::from_vector(timesteps_vec);
 
-                // auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 3584, 256, 1);
+                // auto context = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 3584, 256, 1);
                 // ggml_set_f32(context, 0.01f);
-                auto context = load_tensor_from_file(work_ctx, "./qwen_image_context.bin");
-                print_ggml_tensor(context);
+                auto context = sd::load_tensor_from_file_as_tensor<float>("./qwen_image_context.bin");
+                print_sd_tensor(context);
 
-                struct ggml_tensor* out = nullptr;
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                compute(8, x, timesteps, context, {}, false, &out, work_ctx);
+                auto out_opt = compute(8,
+                                       x,
+                                       timesteps,
+                                       context,
+                                       {},
+                                       false);
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("qwen_image test done in %lldms", t1 - t0);
             }
         }

src/rope.hpp

@@ -600,9 +600,9 @@ namespace Rope {
         return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
     }
 
-    __STATIC_INLINE__ struct ggml_tensor* apply_rope(struct ggml_context* ctx,
-                                                     struct ggml_tensor* x,
-                                                     struct ggml_tensor* pe,
+    __STATIC_INLINE__ ggml_tensor* apply_rope(ggml_context* ctx,
+                                              ggml_tensor* x,
+                                              ggml_tensor* pe,
                                               bool rope_interleaved = true) {
         // x: [N, L, n_head, d_head]
         // pe: [L, d_head/2, 2, 2], [[cos, -sin], [sin, cos]]
@@ -641,12 +641,12 @@ namespace Rope {
         return x_out;
     }
 
-    __STATIC_INLINE__ struct ggml_tensor* attention(GGMLRunnerContext* ctx,
-                                                    struct ggml_tensor* q,
-                                                    struct ggml_tensor* k,
-                                                    struct ggml_tensor* v,
-                                                    struct ggml_tensor* pe,
-                                                    struct ggml_tensor* mask,
+    __STATIC_INLINE__ ggml_tensor* attention(GGMLRunnerContext* ctx,
+                                             ggml_tensor* q,
+                                             ggml_tensor* k,
+                                             ggml_tensor* v,
+                                             ggml_tensor* pe,
+                                             ggml_tensor* mask,
                                              float kv_scale        = 1.0f,
                                              bool rope_interleaved = true) {
         // q,k,v: [N, L, n_head, d_head]

src/sample-cache.cpp

@@ -0,0 +1,361 @@
+#include "sample-cache.h"
+
+namespace sd_sample {
+
+    static float get_cache_reuse_threshold(const sd_cache_params_t& params) {
+        float reuse_threshold = params.reuse_threshold;
+        if (reuse_threshold == INFINITY) {
+            if (params.mode == SD_CACHE_EASYCACHE) {
+                reuse_threshold = 0.2f;
+            } else if (params.mode == SD_CACHE_UCACHE) {
+                reuse_threshold = 1.0f;
+            }
+        }
+        return std::max(0.0f, reuse_threshold);
+    }
+
+    bool SampleCacheRuntime::easycache_enabled() const {
+        return mode == SampleCacheMode::EASYCACHE;
+    }
+
+    bool SampleCacheRuntime::ucache_enabled() const {
+        return mode == SampleCacheMode::UCACHE;
+    }
+
+    bool SampleCacheRuntime::cachedit_enabled() const {
+        return mode == SampleCacheMode::CACHEDIT;
+    }
+
+    static bool has_valid_cache_percent_range(const sd_cache_params_t& cache_params) {
+        if (cache_params.mode != SD_CACHE_EASYCACHE && cache_params.mode != SD_CACHE_UCACHE) {
+            return true;
+        }
+
+        return cache_params.start_percent >= 0.0f &&
+               cache_params.start_percent < 1.0f &&
+               cache_params.end_percent > 0.0f &&
+               cache_params.end_percent <= 1.0f &&
+               cache_params.start_percent < cache_params.end_percent;
+    }
+
+    static void init_easycache_runtime(SampleCacheRuntime& runtime,
+                                       SDVersion version,
+                                       const sd_cache_params_t& cache_params,
+                                       Denoiser* denoiser) {
+        if (!sd_version_is_dit(version)) {
+            LOG_WARN("EasyCache requested but not supported for this model type");
+            return;
+        }
+
+        EasyCacheConfig config;
+        config.enabled         = true;
+        config.reuse_threshold = get_cache_reuse_threshold(cache_params);
+        config.start_percent   = cache_params.start_percent;
+        config.end_percent     = cache_params.end_percent;
+
+        runtime.easycache.init(config, denoiser);
+        if (!runtime.easycache.enabled()) {
+            LOG_WARN("EasyCache requested but could not be initialized for this run");
+            return;
+        }
+
+        runtime.mode = SampleCacheMode::EASYCACHE;
+        LOG_INFO("EasyCache enabled - threshold: %.3f, start: %.2f, end: %.2f",
+                 config.reuse_threshold,
+                 config.start_percent,
+                 config.end_percent);
+    }
+
+    static void init_ucache_runtime(SampleCacheRuntime& runtime,
+                                    SDVersion version,
+                                    const sd_cache_params_t& cache_params,
+                                    Denoiser* denoiser,
+                                    const std::vector<float>& sigmas) {
+        if (!sd_version_is_unet(version)) {
+            LOG_WARN("UCache requested but not supported for this model type (only UNET models)");
+            return;
+        }
+
+        UCacheConfig config;
+        config.enabled                = true;
+        config.reuse_threshold        = get_cache_reuse_threshold(cache_params);
+        config.start_percent          = cache_params.start_percent;
+        config.end_percent            = cache_params.end_percent;
+        config.error_decay_rate       = std::max(0.0f, std::min(1.0f, cache_params.error_decay_rate));
+        config.use_relative_threshold = cache_params.use_relative_threshold;
+        config.reset_error_on_compute = cache_params.reset_error_on_compute;
+
+        runtime.ucache.init(config, denoiser);
+        if (!runtime.ucache.enabled()) {
+            LOG_WARN("UCache requested but could not be initialized for this run");
+            return;
+        }
+
+        runtime.ucache.set_sigmas(sigmas);
+        runtime.mode = SampleCacheMode::UCACHE;
+        LOG_INFO("UCache enabled - threshold: %.3f, start: %.2f, end: %.2f, decay: %.2f, relative: %s, reset: %s",
+                 config.reuse_threshold,
+                 config.start_percent,
+                 config.end_percent,
+                 config.error_decay_rate,
+                 config.use_relative_threshold ? "true" : "false",
+                 config.reset_error_on_compute ? "true" : "false");
+    }
+
+    static void init_cachedit_runtime(SampleCacheRuntime& runtime,
+                                      SDVersion version,
+                                      const sd_cache_params_t& cache_params,
+                                      const std::vector<float>& sigmas) {
+        if (!sd_version_is_dit(version)) {
+            LOG_WARN("CacheDIT requested but not supported for this model type (only DiT models)");
+            return;
+        }
+
+        DBCacheConfig dbcfg;
+        dbcfg.enabled                     = (cache_params.mode == SD_CACHE_DBCACHE || cache_params.mode == SD_CACHE_CACHE_DIT);
+        dbcfg.Fn_compute_blocks           = cache_params.Fn_compute_blocks;
+        dbcfg.Bn_compute_blocks           = cache_params.Bn_compute_blocks;
+        dbcfg.residual_diff_threshold     = cache_params.residual_diff_threshold;
+        dbcfg.max_warmup_steps            = cache_params.max_warmup_steps;
+        dbcfg.max_cached_steps            = cache_params.max_cached_steps;
+        dbcfg.max_continuous_cached_steps = cache_params.max_continuous_cached_steps;
+        if (cache_params.scm_mask != nullptr && strlen(cache_params.scm_mask) > 0) {
+            dbcfg.steps_computation_mask = parse_scm_mask(cache_params.scm_mask);
+        }
+        dbcfg.scm_policy_dynamic = cache_params.scm_policy_dynamic;
+
+        TaylorSeerConfig tcfg;
+        tcfg.enabled             = (cache_params.mode == SD_CACHE_TAYLORSEER || cache_params.mode == SD_CACHE_CACHE_DIT);
+        tcfg.n_derivatives       = cache_params.taylorseer_n_derivatives;
+        tcfg.skip_interval_steps = cache_params.taylorseer_skip_interval;
+
+        runtime.cachedit.init(dbcfg, tcfg);
+        if (!runtime.cachedit.enabled()) {
+            LOG_WARN("CacheDIT requested but could not be initialized for this run");
+            return;
+        }
+
+        runtime.cachedit.set_sigmas(sigmas);
+        runtime.mode = SampleCacheMode::CACHEDIT;
+        LOG_INFO("CacheDIT enabled - mode: %s, Fn: %d, Bn: %d, threshold: %.3f, warmup: %d",
+                 cache_params.mode == SD_CACHE_CACHE_DIT ? "DBCache+TaylorSeer" : (cache_params.mode == SD_CACHE_DBCACHE ? "DBCache" : "TaylorSeer"),
+                 dbcfg.Fn_compute_blocks,
+                 dbcfg.Bn_compute_blocks,
+                 dbcfg.residual_diff_threshold,
+                 dbcfg.max_warmup_steps);
+    }
+
+    static void init_spectrum_runtime(SampleCacheRuntime& runtime,
+                                      SDVersion version,
+                                      const sd_cache_params_t& cache_params,
+                                      const std::vector<float>& sigmas) {
+        if (!sd_version_is_unet(version) && !sd_version_is_dit(version)) {
+            LOG_WARN("Spectrum requested but not supported for this model type (only UNET and DiT models)");
+            return;
+        }
+
+        SpectrumConfig config;
+        config.w            = cache_params.spectrum_w;
+        config.m            = cache_params.spectrum_m;
+        config.lam          = cache_params.spectrum_lam;
+        config.window_size  = cache_params.spectrum_window_size;
+        config.flex_window  = cache_params.spectrum_flex_window;
+        config.warmup_steps = cache_params.spectrum_warmup_steps;
+        config.stop_percent = cache_params.spectrum_stop_percent;
+
+        size_t total_steps = sigmas.size() > 0 ? sigmas.size() - 1 : 0;
+        runtime.spectrum.init(config, total_steps);
+        runtime.spectrum_enabled = true;
+
+        LOG_INFO("Spectrum enabled - w: %.2f, m: %d, lam: %.2f, window: %d, flex: %.2f, warmup: %d, stop: %.0f%%",
+                 config.w, config.m, config.lam,
+                 config.window_size, config.flex_window,
+                 config.warmup_steps, config.stop_percent * 100.0f);
+    }
+
+    SampleCacheRuntime init_sample_cache_runtime(SDVersion version,
+                                                 const sd_cache_params_t* cache_params,
+                                                 Denoiser* denoiser,
+                                                 const std::vector<float>& sigmas) {
+        SampleCacheRuntime runtime;
+        if (cache_params == nullptr || cache_params->mode == SD_CACHE_DISABLED) {
+            return runtime;
+        }
+
+        if (!has_valid_cache_percent_range(*cache_params)) {
+            LOG_WARN("Cache disabled due to invalid percent range (start=%.3f, end=%.3f)",
+                     cache_params->start_percent,
+                     cache_params->end_percent);
+            return runtime;
+        }
+
+        switch (cache_params->mode) {
+            case SD_CACHE_EASYCACHE:
+                init_easycache_runtime(runtime, version, *cache_params, denoiser);
+                break;
+            case SD_CACHE_UCACHE:
+                init_ucache_runtime(runtime, version, *cache_params, denoiser, sigmas);
+                break;
+            case SD_CACHE_DBCACHE:
+            case SD_CACHE_TAYLORSEER:
+            case SD_CACHE_CACHE_DIT:
+                init_cachedit_runtime(runtime, version, *cache_params, sigmas);
+                break;
+            case SD_CACHE_SPECTRUM:
+                init_spectrum_runtime(runtime, version, *cache_params, sigmas);
+                break;
+            default:
+                break;
+        }
+
+        return runtime;
+    }
+
+    SampleStepCacheDispatcher::SampleStepCacheDispatcher(SampleCacheRuntime& runtime, int step, float sigma)
+        : runtime(runtime), step(step), sigma(sigma), step_index(step > 0 ? (step - 1) : -1) {
+        if (step_index < 0) {
+            return;
+        }
+
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                runtime.easycache.begin_step(step_index, sigma);
+                break;
+            case SampleCacheMode::UCACHE:
+                runtime.ucache.begin_step(step_index, sigma);
+                break;
+            case SampleCacheMode::CACHEDIT:
+                runtime.cachedit.begin_step(step_index, sigma);
+                break;
+            case SampleCacheMode::NONE:
+                break;
+        }
+    }
+
+    bool SampleStepCacheDispatcher::before_condition(const void* condition,
+                                                     const sd::Tensor<float>& input,
+                                                     sd::Tensor<float>* output) {
+        if (step_index < 0 || condition == nullptr || output == nullptr) {
+            return false;
+        }
+
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                return runtime.easycache.before_condition(condition, input, output, sigma, step_index);
+            case SampleCacheMode::UCACHE:
+                return runtime.ucache.before_condition(condition, input, output, sigma, step_index);
+            case SampleCacheMode::CACHEDIT:
+                return runtime.cachedit.before_condition(condition, input, output, sigma, step_index);
+            case SampleCacheMode::NONE:
+                return false;
+        }
+
+        return false;
+    }
+
+    void SampleStepCacheDispatcher::after_condition(const void* condition,
+                                                    const sd::Tensor<float>& input,
+                                                    const sd::Tensor<float>& output) {
+        if (step_index < 0 || condition == nullptr) {
+            return;
+        }
+
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                runtime.easycache.after_condition(condition, input, output);
+                break;
+            case SampleCacheMode::UCACHE:
+                runtime.ucache.after_condition(condition, input, output);
+                break;
+            case SampleCacheMode::CACHEDIT:
+                runtime.cachedit.after_condition(condition, input, output);
+                break;
+            case SampleCacheMode::NONE:
+                break;
+        }
+    }
+
+    bool SampleStepCacheDispatcher::is_step_skipped() const {
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                return runtime.easycache.is_step_skipped();
+            case SampleCacheMode::UCACHE:
+                return runtime.ucache.is_step_skipped();
+            case SampleCacheMode::CACHEDIT:
+                return runtime.cachedit.is_step_skipped();
+            case SampleCacheMode::NONE:
+                return false;
+        }
+
+        return false;
+    }
+
+    void log_sample_cache_summary(const SampleCacheRuntime& runtime, size_t total_steps) {
+        if (runtime.easycache_enabled()) {
+            if (runtime.easycache.total_steps_skipped > 0 && total_steps > 0) {
+                if (runtime.easycache.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - runtime.easycache.total_steps_skipped);
+                    LOG_INFO("EasyCache skipped %d/%zu steps (%.2fx estimated speedup)",
+                             runtime.easycache.total_steps_skipped,
+                             total_steps,
+                             speedup);
+                } else {
+                    LOG_INFO("EasyCache skipped %d/%zu steps",
+                             runtime.easycache.total_steps_skipped,
+                             total_steps);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("EasyCache completed without skipping steps");
+            }
+        }
+
+        if (runtime.ucache_enabled()) {
+            if (runtime.ucache.total_steps_skipped > 0 && total_steps > 0) {
+                if (runtime.ucache.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - runtime.ucache.total_steps_skipped);
+                    LOG_INFO("UCache skipped %d/%zu steps (%.2fx estimated speedup)",
+                             runtime.ucache.total_steps_skipped,
+                             total_steps,
+                             speedup);
+                } else {
+                    LOG_INFO("UCache skipped %d/%zu steps",
+                             runtime.ucache.total_steps_skipped,
+                             total_steps);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("UCache completed without skipping steps");
+            }
+        }
+
+        if (runtime.cachedit_enabled()) {
+            if (runtime.cachedit.total_steps_skipped > 0 && total_steps > 0) {
+                if (runtime.cachedit.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - runtime.cachedit.total_steps_skipped);
+                    LOG_INFO("CacheDIT skipped %d/%zu steps (%.2fx estimated speedup)",
+                             runtime.cachedit.total_steps_skipped,
+                             total_steps,
+                             speedup);
+                } else {
+                    LOG_INFO("CacheDIT skipped %d/%zu steps",
+                             runtime.cachedit.total_steps_skipped,
+                             total_steps);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("CacheDIT completed without skipping steps");
+            }
+        }
+
+        if (runtime.spectrum_enabled && runtime.spectrum.total_steps_skipped > 0 && total_steps > 0) {
+            double speedup = static_cast<double>(total_steps) /
+                             static_cast<double>(total_steps - runtime.spectrum.total_steps_skipped);
+            LOG_INFO("Spectrum skipped %d/%zu steps (%.2fx estimated speedup)",
+                     runtime.spectrum.total_steps_skipped,
+                     total_steps,
+                     speedup);
+        }
+    }
+
+}  // namespace sd_sample

src/sample-cache.h

@@ -0,0 +1,61 @@
+#ifndef __SAMPLE_CACHE_H__
+#define __SAMPLE_CACHE_H__
+
+#include <vector>
+
+#include "cache_dit.hpp"
+#include "denoiser.hpp"
+#include "easycache.hpp"
+#include "model.h"
+#include "spectrum.hpp"
+#include "tensor.hpp"
+#include "ucache.hpp"
+#include "util.h"
+
+namespace sd_sample {
+
+    enum class SampleCacheMode {
+        NONE,
+        EASYCACHE,
+        UCACHE,
+        CACHEDIT,
+    };
+
+    struct SampleCacheRuntime {
+        SampleCacheMode mode = SampleCacheMode::NONE;
+
+        EasyCacheState easycache;
+        UCacheState ucache;
+        CacheDitConditionState cachedit;
+        SpectrumState spectrum;
+
+        bool spectrum_enabled = false;
+
+        bool easycache_enabled() const;
+        bool ucache_enabled() const;
+        bool cachedit_enabled() const;
+    };
+
+    struct SampleStepCacheDispatcher {
+        SampleCacheRuntime& runtime;
+        int step;
+        float sigma;
+        int step_index;
+
+        SampleStepCacheDispatcher(SampleCacheRuntime& runtime, int step, float sigma);
+
+        bool before_condition(const void* condition, const sd::Tensor<float>& input, sd::Tensor<float>* output);
+        void after_condition(const void* condition, const sd::Tensor<float>& input, const sd::Tensor<float>& output);
+        bool is_step_skipped() const;
+    };
+
+    SampleCacheRuntime init_sample_cache_runtime(SDVersion version,
+                                                 const sd_cache_params_t* cache_params,
+                                                 Denoiser* denoiser,
+                                                 const std::vector<float>& sigmas);
+
+    void log_sample_cache_summary(const SampleCacheRuntime& runtime, size_t total_steps);
+
+}  // namespace sd_sample
+
+#endif  // __SAMPLE_CACHE_H__

src/spectrum.hpp

@@ -0,0 +1,187 @@
+#ifndef __SPECTRUM_HPP__
+#define __SPECTRUM_HPP__
+
+#include <cmath>
+#include <cstring>
+#include <vector>
+
+#include "ggml_extend.hpp"
+#include "tensor.hpp"
+
+struct SpectrumConfig {
+    float w            = 0.40f;
+    int m              = 3;
+    float lam          = 1.0f;
+    int window_size    = 2;
+    float flex_window  = 0.50f;
+    int warmup_steps   = 4;
+    float stop_percent = 0.9f;
+};
+
+struct SpectrumState {
+    SpectrumConfig config;
+    int cnt                 = 0;
+    int num_cached          = 0;
+    float curr_ws           = 2.0f;
+    int K                   = 6;
+    int stop_step           = 0;
+    int total_steps_skipped = 0;
+
+    std::vector<std::vector<float>> H_buf;
+    std::vector<float> T_buf;
+
+    void init(const SpectrumConfig& cfg, size_t total_steps) {
+        config              = cfg;
+        cnt                 = 0;
+        num_cached          = 0;
+        curr_ws             = (float)cfg.window_size;
+        K                   = std::max(cfg.m + 1, 6);
+        stop_step           = (int)(cfg.stop_percent * (float)total_steps);
+        total_steps_skipped = 0;
+        H_buf.clear();
+        T_buf.clear();
+    }
+
+    float taus(int step_cnt) const {
+        return (step_cnt / 50.0f) * 2.0f - 1.0f;
+    }
+
+    bool should_predict() {
+        if (cnt < config.warmup_steps)
+            return false;
+        if (stop_step > 0 && cnt >= stop_step)
+            return false;
+        if ((int)H_buf.size() < 2)
+            return false;
+
+        int ws = std::max(1, (int)std::floor(curr_ws));
+        return (num_cached + 1) % ws != 0;
+    }
+
+    void update(const sd::Tensor<float>& denoised) {
+        H_buf.emplace_back(denoised.data(), denoised.data() + denoised.numel());
+        T_buf.push_back(taus(cnt));
+
+        while ((int)H_buf.size() > K) {
+            H_buf.erase(H_buf.begin());
+            T_buf.erase(T_buf.begin());
+        }
+
+        if (cnt >= config.warmup_steps)
+            curr_ws += config.flex_window;
+
+        num_cached = 0;
+        cnt++;
+    }
+
+    void predict(sd::Tensor<float>* denoised) {
+        GGML_ASSERT(denoised != nullptr);
+        int64_t F    = (int64_t)H_buf[0].size();
+        int K_curr   = (int)H_buf.size();
+        int M1       = config.m + 1;
+        float tau_at = taus(cnt);
+
+        std::vector<float> X(K_curr * M1);
+        for (int i = 0; i < K_curr; i++) {
+            X[i * M1] = 1.0f;
+            if (M1 > 1)
+                X[i * M1 + 1] = T_buf[i];
+            for (int j = 2; j < M1; j++)
+                X[i * M1 + j] = 2.0f * T_buf[i] * X[i * M1 + j - 1] - X[i * M1 + j - 2];
+        }
+
+        std::vector<float> x_star(M1);
+        x_star[0] = 1.0f;
+        if (M1 > 1)
+            x_star[1] = tau_at;
+        for (int j = 2; j < M1; j++)
+            x_star[j] = 2.0f * tau_at * x_star[j - 1] - x_star[j - 2];
+
+        std::vector<float> XtX(M1 * M1, 0.0f);
+        for (int i = 0; i < M1; i++) {
+            for (int j = 0; j < M1; j++) {
+                float sum = 0.0f;
+                for (int k = 0; k < K_curr; k++)
+                    sum += X[k * M1 + i] * X[k * M1 + j];
+                XtX[i * M1 + j] = sum + (i == j ? config.lam : 0.0f);
+            }
+        }
+
+        std::vector<float> L(M1 * M1, 0.0f);
+        if (!cholesky_decompose(XtX.data(), L.data(), M1)) {
+            float trace = 0.0f;
+            for (int i = 0; i < M1; i++)
+                trace += XtX[i * M1 + i];
+            for (int i = 0; i < M1; i++)
+                XtX[i * M1 + i] += 1e-4f * trace / M1;
+            cholesky_decompose(XtX.data(), L.data(), M1);
+        }
+
+        std::vector<float> v(M1);
+        cholesky_solve(L.data(), x_star.data(), v.data(), M1);
+
+        std::vector<float> weights(K_curr, 0.0f);
+        for (int k = 0; k < K_curr; k++)
+            for (int j = 0; j < M1; j++)
+                weights[k] += X[k * M1 + j] * v[j];
+
+        float* out          = denoised->data();
+        float w_cheb        = config.w;
+        float w_taylor      = 1.0f - w_cheb;
+        const float* h_last = H_buf.back().data();
+        const float* h_prev = H_buf[H_buf.size() - 2].data();
+
+        for (int64_t f = 0; f < F; f++) {
+            float pred_cheb = 0.0f;
+            for (int k = 0; k < K_curr; k++)
+                pred_cheb += weights[k] * H_buf[k][f];
+
+            float pred_taylor = h_last[f] + 0.5f * (h_last[f] - h_prev[f]);
+
+            out[f] = w_taylor * pred_taylor + w_cheb * pred_cheb;
+        }
+
+        num_cached++;
+        total_steps_skipped++;
+        cnt++;
+    }
+
+private:
+    static bool cholesky_decompose(const float* A, float* L, int n) {
+        std::memset(L, 0, n * n * sizeof(float));
+        for (int i = 0; i < n; i++) {
+            for (int j = 0; j <= i; j++) {
+                float sum = 0.0f;
+                for (int k = 0; k < j; k++)
+                    sum += L[i * n + k] * L[j * n + k];
+                if (i == j) {
+                    float diag = A[i * n + i] - sum;
+                    if (diag <= 0.0f)
+                        return false;
+                    L[i * n + j] = std::sqrt(diag);
+                } else {
+                    L[i * n + j] = (A[i * n + j] - sum) / L[j * n + j];
+                }
+            }
+        }
+        return true;
+    }
+
+    static void cholesky_solve(const float* L, const float* b, float* x, int n) {
+        std::vector<float> y(n);
+        for (int i = 0; i < n; i++) {
+            float sum = 0.0f;
+            for (int j = 0; j < i; j++)
+                sum += L[i * n + j] * y[j];
+            y[i] = (b[i] - sum) / L[i * n + i];
+        }
+        for (int i = n - 1; i >= 0; i--) {
+            float sum = 0.0f;
+            for (int j = i + 1; j < n; j++)
+                sum += L[j * n + i] * x[j];
+            x[i] = (y[i] - sum) / L[i * n + i];
+        }
+    }
+};
+
+#endif  // __SPECTRUM_HPP__

src/t5.hpp

@@ -1,4 +1,4 @@
-#ifndef __T5_HPP__
+#ifndef __T5_HPP__
 #define __T5_HPP__
 
 #include <cfloat>
@@ -211,9 +211,9 @@ protected:
         // implementation. It's based on the following three ideas:
         //
         // 1. Because it uses the *unigram* model:
-        //     best_score(x1, x2, …, xt) = best_score(x1, x2, …, x{t-1}) + score(xt)
+        //     best_score(x1, x2, ... xt) = best_score(x1, x2, ... x{t-1}) + score(xt)
         // Deciding the best path (and score) can be decoupled into two isolated
-        // terms: (a) the best path ended before the last token `best_score(x1, x2, …,
+        // terms: (a) the best path ended before the last token `best_score(x1, x2, ...)`
         // x{t-1})`, and (b) the last token and its `score(xt)`. The two terms are
         // not related to each other at all.
         //
@@ -227,7 +227,7 @@ protected:
         // position, where n is the input length and k is the maximum number of tokens
         // that can be recognized starting at each position.
         //
-        // 2. Again, because it uses the *unigram* model, we don’t need to actually
+        // 2. Again, because it uses the *unigram* model, we don't need to actually
         // store the lattice nodes. We still recognize all the tokens and lattice
         // nodes from the input, but along identifying them, we use and discard them
         // on the fly. There is no need to actually store them for best path Viterbi
@@ -462,7 +462,7 @@ protected:
     int64_t hidden_size;
     float eps;
 
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         enum ggml_type wtype = GGML_TYPE_F32;
         params["weight"]     = ggml_new_tensor_1d(ctx, wtype, hidden_size);
     }
@@ -473,8 +473,8 @@ public:
         : hidden_size(hidden_size),
           eps(eps) {}
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
-        struct ggml_tensor* w = params["weight"];
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+        ggml_tensor* w = params["weight"];
         x              = ggml_rms_norm(ctx->ggml_ctx, x, eps);
         x              = ggml_mul(ctx->ggml_ctx, x, w);
         return x;
@@ -488,7 +488,7 @@ public:
         blocks["wo"] = std::shared_ptr<GGMLBlock>(new Linear(ff_dim, model_dim, false));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         // x: [N, n_token, model_dim]
         auto wi = std::dynamic_pointer_cast<Linear>(blocks["wi"]);
         auto wo = std::dynamic_pointer_cast<Linear>(blocks["wo"]);
@@ -510,7 +510,7 @@ public:
         blocks["wo"] = std::shared_ptr<GGMLBlock>(new Linear(ff_dim, model_dim, false, false, false, scale));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         // x: [N, n_token, model_dim]
         auto wi_0 = std::dynamic_pointer_cast<Linear>(blocks["wi_0"]);
         auto wi_1 = std::dynamic_pointer_cast<Linear>(blocks["wi_1"]);
@@ -531,7 +531,7 @@ public:
         blocks["layer_norm"]     = std::shared_ptr<GGMLBlock>(new T5LayerNorm(model_dim));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         // x: [N, n_token, model_dim]
         auto DenseReluDense = std::dynamic_pointer_cast<T5DenseGatedActDense>(blocks["DenseReluDense"]);
         auto layer_norm     = std::dynamic_pointer_cast<T5LayerNorm>(blocks["layer_norm"]);
@@ -570,8 +570,8 @@ public:
         }
     }
 
-    struct ggml_tensor* compute_bias(GGMLRunnerContext* ctx,
-                                     struct ggml_tensor* relative_position_bucket) {
+    ggml_tensor* compute_bias(GGMLRunnerContext* ctx,
+                              ggml_tensor* relative_position_bucket) {
         auto relative_attention_bias = std::dynamic_pointer_cast<Embedding>(blocks["relative_attention_bias"]);
 
         auto values = relative_attention_bias->forward(ctx, relative_position_bucket);            // shape (query_length, key_length, num_heads)
@@ -580,11 +580,11 @@ public:
     }
 
     // x: [N, n_token, model_dim]
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                                struct ggml_tensor* x,
-                                                                struct ggml_tensor* past_bias                = nullptr,
-                                                                struct ggml_tensor* mask                     = nullptr,
-                                                                struct ggml_tensor* relative_position_bucket = nullptr) {
+    std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                  ggml_tensor* x,
+                                                  ggml_tensor* past_bias                = nullptr,
+                                                  ggml_tensor* mask                     = nullptr,
+                                                  ggml_tensor* relative_position_bucket = nullptr) {
         auto q_proj   = std::dynamic_pointer_cast<Linear>(blocks["q"]);
         auto k_proj   = std::dynamic_pointer_cast<Linear>(blocks["k"]);
         auto v_proj   = std::dynamic_pointer_cast<Linear>(blocks["v"]);
@@ -629,11 +629,11 @@ public:
         blocks["layer_norm"]    = std::shared_ptr<GGMLBlock>(new T5LayerNorm(model_dim));
     }
 
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                                struct ggml_tensor* x,
-                                                                struct ggml_tensor* past_bias                = nullptr,
-                                                                struct ggml_tensor* mask                     = nullptr,
-                                                                struct ggml_tensor* relative_position_bucket = nullptr) {
+    std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                  ggml_tensor* x,
+                                                  ggml_tensor* past_bias                = nullptr,
+                                                  ggml_tensor* mask                     = nullptr,
+                                                  ggml_tensor* relative_position_bucket = nullptr) {
         // x: [N, n_token, model_dim]
         auto SelfAttention = std::dynamic_pointer_cast<T5Attention>(blocks["SelfAttention"]);
         auto layer_norm    = std::dynamic_pointer_cast<T5LayerNorm>(blocks["layer_norm"]);
@@ -655,11 +655,11 @@ public:
         blocks["layer.1"] = std::shared_ptr<GGMLBlock>(new T5LayerFF(model_dim, ff_dim));
     }
 
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                                struct ggml_tensor* x,
-                                                                struct ggml_tensor* past_bias                = nullptr,
-                                                                struct ggml_tensor* mask                     = nullptr,
-                                                                struct ggml_tensor* relative_position_bucket = nullptr) {
+    std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                  ggml_tensor* x,
+                                                  ggml_tensor* past_bias                = nullptr,
+                                                  ggml_tensor* mask                     = nullptr,
+                                                  ggml_tensor* relative_position_bucket = nullptr) {
         // x: [N, n_token, model_dim]
         auto layer_0 = std::dynamic_pointer_cast<T5LayerSelfAttention>(blocks["layer.0"]);
         auto layer_1 = std::dynamic_pointer_cast<T5LayerFF>(blocks["layer.1"]);
@@ -690,11 +690,11 @@ public:
         blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new T5LayerNorm(model_dim));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* past_bias                = nullptr,
-                                struct ggml_tensor* attention_mask           = nullptr,
-                                struct ggml_tensor* relative_position_bucket = nullptr) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* past_bias                = nullptr,
+                         ggml_tensor* attention_mask           = nullptr,
+                         ggml_tensor* relative_position_bucket = nullptr) {
         // x: [N, n_token, model_dim]
         for (int i = 0; i < num_layers; i++) {
             auto block = std::dynamic_pointer_cast<T5Block>(blocks["block." + std::to_string(i)]);
@@ -737,11 +737,11 @@ public:
                                                                      params.model_dim));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* past_bias                = nullptr,
-                                struct ggml_tensor* attention_mask           = nullptr,
-                                struct ggml_tensor* relative_position_bucket = nullptr) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* past_bias                = nullptr,
+                         ggml_tensor* attention_mask           = nullptr,
+                         ggml_tensor* relative_position_bucket = nullptr) {
         // input_ids: [N, n_token]
 
         auto shared  = std::dynamic_pointer_cast<Embedding>(blocks["shared"]);
@@ -776,14 +776,14 @@ struct T5Runner : public GGMLRunner {
         return "t5";
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         model.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* relative_position_bucket,
-                                struct ggml_tensor* attention_mask = nullptr) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* relative_position_bucket,
+                         ggml_tensor* attention_mask = nullptr) {
         size_t N       = input_ids->ne[1];
         size_t n_token = input_ids->ne[0];
 
@@ -791,12 +791,11 @@ struct T5Runner : public GGMLRunner {
         return hidden_states;
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* attention_mask = nullptr) {
-        struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
-
-        input_ids      = to_backend(input_ids);
-        attention_mask = to_backend(attention_mask);
+    ggml_cgraph* build_graph(const sd::Tensor<int32_t>& input_ids_tensor,
+                             const sd::Tensor<float>& attention_mask_tensor = {}) {
+        ggml_cgraph* gf             = ggml_new_graph(compute_ctx);
+        ggml_tensor* input_ids      = make_input(input_ids_tensor);
+        ggml_tensor* attention_mask = attention_mask_tensor.empty() ? nullptr : make_input(attention_mask_tensor);
 
         relative_position_bucket_vec = compute_relative_position_bucket(static_cast<int>(input_ids->ne[0]), static_cast<int>(input_ids->ne[0]));
 
@@ -814,22 +813,20 @@ struct T5Runner : public GGMLRunner {
         set_backend_tensor_data(relative_position_bucket, relative_position_bucket_vec.data());
 
         auto runner_ctx            = get_context();
-        struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, relative_position_bucket, attention_mask);
+        ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, relative_position_bucket, attention_mask);
 
         ggml_build_forward_expand(gf, hidden_states);
 
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* input_ids,
-                 struct ggml_tensor* attention_mask,
-                 ggml_tensor** output,
-                 ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<int32_t>& input_ids,
+                              const sd::Tensor<float>& attention_mask) {
+        auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(input_ids, attention_mask);
         };
-        return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true), 3);
     }
 
     static std::vector<int> _relative_position_bucket(const std::vector<int>& relative_position,
@@ -912,7 +909,7 @@ struct T5Embedder {
         : model(backend, offload_params_to_cpu, tensor_storage_map, prefix, is_umt5), tokenizer(is_umt5) {
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         model.get_param_tensors(tensors, prefix);
     }
 
@@ -962,17 +959,16 @@ struct T5Embedder {
     }
 
     void test() {
-        struct ggml_init_params params;
+        ggml_init_params params;
         params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
         params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
-        struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != nullptr);
+        ggml_context* ctx = ggml_init(params);
+        GGML_ASSERT(ctx != nullptr);
 
         {
             std::string text("a lovely cat");
-            // 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);
@@ -981,15 +977,17 @@ struct T5Embedder {
                 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 = nullptr;
+            auto input_ids      = sd::Tensor<int32_t>::from_vector(tokens);
+            auto attention_mask = sd::Tensor<float>::from_vector(masks);
+            sd::Tensor<float> out;
 
             int64_t t0   = ggml_time_ms();
-            model.compute(8, input_ids, attention_mask, &out, work_ctx);
+            auto out_opt = model.compute(8, input_ids, attention_mask);
             int64_t t1   = ggml_time_ms();
 
-            print_ggml_tensor(out);
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
             LOG_DEBUG("t5 test done in %lldms", t1 - t0);
         }
     }

src/tae.hpp

@@ -37,7 +37,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         // x: [n, n_in, h, w]
         // return: [n, n_out, h, w]
 
@@ -107,7 +107,7 @@ public:
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, z_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         // x: [n, in_channels, h, w]
         // return: [n, z_channels, h/8, w/8]
 
@@ -157,7 +157,7 @@ public:
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) override {
         // z: [n, z_channels, h, w]
         // return: [n, out_channels, h*8, w*8]
 
@@ -192,7 +192,7 @@ public:
         blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels * stride, channels, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         auto conv = std::dynamic_pointer_cast<UnaryBlock>(blocks["conv"]);
         auto h    = x;
         if (stride != 1) {
@@ -212,7 +212,7 @@ public:
         blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels * stride, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
         auto conv = std::dynamic_pointer_cast<UnaryBlock>(blocks["conv"]);
         auto h    = conv->forward(ctx, x);
         if (stride != 1) {
@@ -236,7 +236,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* past) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* past) {
         // x: [n, channels, h, w]
         auto conv0 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.0"]);
         auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.2"]);
@@ -260,8 +260,8 @@ public:
     }
 };
 
-struct ggml_tensor* patchify(struct ggml_context* ctx,
-                             struct ggml_tensor* x,
+ggml_tensor* patchify(ggml_context* ctx,
+                      ggml_tensor* x,
                       int64_t patch_size,
                       int64_t b = 1) {
     // x: [f, b*c, h*q, w*r]
@@ -289,8 +289,8 @@ struct ggml_tensor* patchify(struct ggml_context* ctx,
     return x;
 }
 
-struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                               struct ggml_tensor* x,
+ggml_tensor* unpatchify(ggml_context* ctx,
+                        ggml_tensor* x,
                         int64_t patch_size,
                         int64_t b = 1) {
     // x: [f, b*c*r*q, h, w]
@@ -339,7 +339,7 @@ public:
         blocks[std::to_string(index)] = std::shared_ptr<GGMLBlock>(new Conv2d(hidden, z_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) override {
         auto first_conv = std::dynamic_pointer_cast<Conv2d>(blocks["0"]);
 
         if (patch_size > 1) {
@@ -396,7 +396,7 @@ public:
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels[num_layers], out_channels * patch_size * patch_size, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) override {
         auto first_conv = std::dynamic_pointer_cast<Conv2d>(blocks["1"]);
 
         // Clamp()
@@ -442,10 +442,12 @@ protected:
     bool decode_only;
     SDVersion version;
 
+public:
+    int z_channels = 16;
+
 public:
     TAEHV(bool decode_only = true, SDVersion version = VERSION_WAN2)
         : decode_only(decode_only), version(version) {
-        int z_channels = 16;
         int patch = 1;
         if (version == VERSION_WAN2_2_TI2V) {
             z_channels = 48;
@@ -457,7 +459,7 @@ public:
         }
     }
 
-    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+    ggml_tensor* decode(GGMLRunnerContext* ctx, ggml_tensor* z) {
         auto decoder = std::dynamic_pointer_cast<TinyVideoDecoder>(blocks["decoder"]);
         if (sd_version_is_wan(version)) {
             // (W, H, C, T) -> (W, H, T, C)
@@ -471,7 +473,7 @@ public:
         return result;
     }
 
-    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* encode(GGMLRunnerContext* ctx, ggml_tensor* x) {
         auto encoder = std::dynamic_pointer_cast<TinyVideoEncoder>(blocks["encoder"]);
         // (W, H, T, C) -> (W, H, C, T)
         x                  = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
@@ -494,10 +496,12 @@ protected:
     bool decode_only;
     bool taef2 = false;
 
+public:
+    int z_channels = 4;
+
 public:
     TAESD(bool decode_only = true, SDVersion version = VERSION_SD1)
         : decode_only(decode_only) {
-        int z_channels       = 4;
         bool use_midblock_gn = false;
         taef2                = sd_version_is_flux2(version);
 
@@ -515,7 +519,7 @@ public:
         }
     }
 
-    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+    ggml_tensor* decode(GGMLRunnerContext* ctx, ggml_tensor* z) {
         auto decoder = std::dynamic_pointer_cast<TinyDecoder>(blocks["decoder.layers"]);
         if (taef2) {
             z = unpatchify(ctx->ggml_ctx, z, 2);
@@ -523,7 +527,7 @@ public:
         return decoder->forward(ctx, z);
     }
 
-    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* encode(GGMLRunnerContext* ctx, ggml_tensor* x) {
         auto encoder = std::dynamic_pointer_cast<TinyEncoder>(blocks["encoder.layers"]);
         auto z       = encoder->forward(ctx, x);
         if (taef2) {
@@ -533,20 +537,7 @@ public:
     }
 };
 
-struct TinyAutoEncoder : public GGMLRunner {
-    TinyAutoEncoder(ggml_backend_t backend, bool offload_params_to_cpu)
-        : GGMLRunner(backend, offload_params_to_cpu) {}
-    virtual bool compute(const int n_threads,
-                         struct ggml_tensor* z,
-                         bool decode_graph,
-                         struct ggml_tensor** output,
-                         struct ggml_context* output_ctx = nullptr) = 0;
-
-    virtual bool load_from_file(const std::string& file_path, int n_threads)                                      = 0;
-    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
-};
-
-struct TinyImageAutoEncoder : public TinyAutoEncoder {
+struct TinyImageAutoEncoder : public VAE {
     TAESD taesd;
     bool decode_only = false;
 
@@ -558,7 +549,8 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
                          SDVersion version = VERSION_SD1)
         : decode_only(decoder_only),
           taesd(decoder_only, version),
-          TinyAutoEncoder(backend, offload_params_to_cpu) {
+          VAE(version, backend, offload_params_to_cpu) {
+        scale_input = false;
         taesd.init(params_ctx, tensor_storage_map, prefix);
     }
 
@@ -566,60 +558,48 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
         return "taesd";
     }
 
-    bool load_from_file(const std::string& file_path, int n_threads) {
-        LOG_INFO("loading taesd from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> taesd_tensors;
-        taesd.get_param_tensors(taesd_tensors);
-        std::set<std::string> ignore_tensors;
-        if (decode_only) {
-            ignore_tensors.insert("encoder.");
-        }
-
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file_and_convert_name(file_path)) {
-            LOG_ERROR("init taesd model loader from file failed: '%s'", file_path.c_str());
-            return false;
-        }
-
-        bool success = model_loader.load_tensors(taesd_tensors, ignore_tensors, n_threads);
-
-        if (!success) {
-            LOG_ERROR("load tae tensors from model loader failed");
-            return false;
-        }
-
-        LOG_INFO("taesd model loaded");
-        return success;
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         taesd.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
-        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
-        z                       = to_backend(z);
+    sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
+        SD_UNUSED(rng);
+        return vae_output;
+    }
+
+    sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return taesd.z_channels;
+    }
+
+    ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
+        ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
+        ggml_tensor* z   = make_input(z_tensor);
         auto runner_ctx  = get_context();
-        struct ggml_tensor* out = decode_graph ? taesd.decode(&runner_ctx, z) : taesd.encode(&runner_ctx, z);
+        ggml_tensor* out = decode_graph ? taesd.decode(&runner_ctx, z) : taesd.encode(&runner_ctx, z);
         ggml_build_forward_expand(gf, out);
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(z, decode_graph);
+    sd::Tensor<float> _compute(const int n_threads,
+                               const sd::Tensor<float>& z_tensor,
+                               bool decode_graph) override {
+        auto get_graph = [&]() -> ggml_cgraph* {
+            return build_graph(z_tensor, decode_graph);
         };
 
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z_tensor.dim());
     }
 };
 
-struct TinyVideoAutoEncoder : public TinyAutoEncoder {
+struct TinyVideoAutoEncoder : public VAE {
     TAEHV taehv;
     bool decode_only = false;
 
@@ -631,7 +611,8 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
                          SDVersion version = VERSION_WAN2)
         : decode_only(decoder_only),
           taehv(decoder_only, version),
-          TinyAutoEncoder(backend, offload_params_to_cpu) {
+          VAE(version, backend, offload_params_to_cpu) {
+        scale_input = false;
         taehv.init(params_ctx, tensor_storage_map, prefix);
     }
 
@@ -639,56 +620,44 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
         return "taehv";
     }
 
-    bool load_from_file(const std::string& file_path, int n_threads) {
-        LOG_INFO("loading taehv from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> taehv_tensors;
-        taehv.get_param_tensors(taehv_tensors);
-        std::set<std::string> ignore_tensors;
-        if (decode_only) {
-            ignore_tensors.insert("encoder.");
-        }
-
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
-            LOG_ERROR("init taehv model loader from file failed: '%s'", file_path.c_str());
-            return false;
-        }
-
-        bool success = model_loader.load_tensors(taehv_tensors, ignore_tensors, n_threads);
-
-        if (!success) {
-            LOG_ERROR("load tae tensors from model loader failed");
-            return false;
-        }
-
-        LOG_INFO("taehv model loaded");
-        return success;
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         taehv.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
-        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
-        z                       = to_backend(z);
+    sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
+        SD_UNUSED(rng);
+        return vae_output;
+    }
+
+    sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return taehv.z_channels;
+    }
+
+    ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
+        ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
+        ggml_tensor* z   = make_input(z_tensor);
         auto runner_ctx  = get_context();
-        struct ggml_tensor* out = decode_graph ? taehv.decode(&runner_ctx, z) : taehv.encode(&runner_ctx, z);
+        ggml_tensor* out = decode_graph ? taehv.decode(&runner_ctx, z) : taehv.encode(&runner_ctx, z);
         ggml_build_forward_expand(gf, out);
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(z, decode_graph);
+    sd::Tensor<float> _compute(const int n_threads,
+                               const sd::Tensor<float>& z_tensor,
+                               bool decode_graph) override {
+        auto get_graph = [&]() -> ggml_cgraph* {
+            return build_graph(z_tensor, decode_graph);
         };
 
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z_tensor.dim());
     }
 };
 

src/tensor_ggml.hpp

@@ -0,0 +1,127 @@
+#ifndef __SD_TENSOR_GGML_HPP__
+#define __SD_TENSOR_GGML_HPP__
+
+#include <array>
+#include <cstring>
+#include <fstream>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+#include "ggml.h"
+#include "tensor.hpp"
+
+namespace sd {
+
+    template <typename T>
+    struct GGMLTypeTraits;
+
+    template <>
+    struct GGMLTypeTraits<float> {
+        static constexpr ggml_type type = GGML_TYPE_F32;
+    };
+
+    template <>
+    struct GGMLTypeTraits<ggml_fp16_t> {
+        static constexpr ggml_type type = GGML_TYPE_F16;
+    };
+
+    template <>
+    struct GGMLTypeTraits<int32_t> {
+        static constexpr ggml_type type = GGML_TYPE_I32;
+    };
+
+    template <>
+    struct GGMLTypeTraits<int64_t> {
+        static constexpr ggml_type type = GGML_TYPE_I64;
+    };
+
+    inline std::vector<int64_t> shape_from_ggml(const ggml_tensor* tensor) {
+        std::vector<int64_t> shape;
+        shape.reserve(static_cast<size_t>(ggml_n_dims(tensor)));
+        for (int i = 0; i < ggml_n_dims(tensor); ++i) {
+            shape.push_back(tensor->ne[i]);
+        }
+        return shape;
+    }
+
+    template <typename T>
+    inline Tensor<T> make_sd_tensor_from_ggml(const ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return {};
+        }
+        if (tensor->type != GGMLTypeTraits<T>::type) {
+            GGML_ABORT("ggml tensor type does not match sd::Tensor type");
+        }
+        Tensor<T> result(shape_from_ggml(tensor));
+        if (tensor->buffer != nullptr) {
+            ggml_backend_tensor_get(tensor, result.data(), 0, ggml_nbytes(tensor));
+        } else {
+            std::memcpy(result.data(), tensor->data, ggml_nbytes(tensor));
+        }
+        return result;
+    }
+
+    template <typename T>
+    inline ggml_tensor* make_ggml_tensor(ggml_context* ctx, const Tensor<T>& tensor, bool copy_data = true) {
+        GGML_ASSERT(tensor.dim() > 0 && tensor.dim() <= 5);
+
+        int n_dims = std::min(static_cast<int>(tensor.dim()), GGML_MAX_DIMS);
+
+        std::array<int64_t, GGML_MAX_DIMS> ne = {1, 1, 1, 1};
+        for (int64_t i = 0; i < n_dims; ++i) {
+            ne[static_cast<size_t>(i)] = tensor.shape()[static_cast<size_t>(i)];
+        }
+
+        if (tensor.dim() == 5) {
+            ne[3] *= tensor.shape()[4];
+        }
+
+        ggml_tensor* result = ggml_new_tensor(ctx, GGMLTypeTraits<T>::type, n_dims, ne.data());
+        if (copy_data && tensor.numel() > 0) {
+            std::memcpy(result->data, tensor.data(), static_cast<size_t>(ggml_nbytes(result)));
+        }
+        return result;
+    }
+
+    template <typename T>
+    inline Tensor<T> load_tensor_from_file_as_tensor(const std::string& file_path) {
+        std::ifstream file(file_path, std::ios::binary);
+        if (!file.is_open()) {
+            throw std::runtime_error("failed to open tensor file: " + file_path);
+        }
+
+        int32_t n_dims = 0;
+        int32_t length = 0;
+        int32_t ttype  = 0;
+        file.read(reinterpret_cast<char*>(&n_dims), sizeof(n_dims));
+        file.read(reinterpret_cast<char*>(&length), sizeof(length));
+        file.read(reinterpret_cast<char*>(&ttype), sizeof(ttype));
+        if (!file.good()) {
+            throw std::runtime_error("incomplete tensor file header: " + file_path);
+        }
+        if (static_cast<ggml_type>(ttype) != GGMLTypeTraits<T>::type) {
+            throw std::invalid_argument("tensor file type does not match requested sd::Tensor type");
+        }
+
+        std::vector<int64_t> shape(4, 1);
+        for (int i = 0; i < n_dims; ++i) {
+            int32_t dim = 1;
+            file.read(reinterpret_cast<char*>(&dim), sizeof(dim));
+            shape[static_cast<size_t>(i)] = dim;
+        }
+        std::string name(static_cast<size_t>(length), '\0');
+        file.read(name.data(), length);
+
+        shape.resize(static_cast<size_t>(n_dims));
+        Tensor<T> tensor(shape);
+        file.read(reinterpret_cast<char*>(tensor.data()), static_cast<std::streamsize>(tensor.numel() * sizeof(T)));
+        if (!file.good()) {
+            throw std::runtime_error("incomplete tensor file data: " + file_path);
+        }
+        return tensor;
+    }
+
+}  // namespace sd
+
+#endif

src/tokenize_util.cpp

@@ -1,4 +1,4 @@
-#include <algorithm>
+#include <algorithm>
 #include <iostream>
 #include <string>
 #include <vector>
@@ -982,7 +982,7 @@ std::vector<std::string> split_with_special_tokens(
 }
 
 // int main() {
-//     std::string text = "I'm testing C++ token_split function. 你好，世界! 123";
+//     std::string text = "I'm testing C++ token_split function. Hello world 123";
 //     auto tokens = token_split(text);
 
 //     for (const auto& t : tokens) {

src/ucache.hpp

@@ -6,8 +6,10 @@
 #include <unordered_map>
 #include <vector>
 
+#include "condition_cache_utils.hpp"
 #include "denoiser.hpp"
 #include "ggml_extend.hpp"
+#include "tensor.hpp"
 
 struct UCacheConfig {
     bool enabled                = false;
@@ -36,8 +38,8 @@ struct UCacheState {
     bool initial_step            = true;
     bool skip_current_step       = false;
     bool step_active             = false;
-    const SDCondition* anchor_condition = nullptr;
-    std::unordered_map<const SDCondition*, UCacheCacheEntry> cache_diffs;
+    const void* anchor_condition = nullptr;
+    std::unordered_map<const void*, UCacheCacheEntry> cache_diffs;
     std::vector<float> prev_input;
     std::vector<float> prev_output;
     float output_prev_norm                = 0.0f;
@@ -233,43 +235,30 @@ struct UCacheState {
         return base_threshold * multiplier;
     }
 
-    bool has_cache(const SDCondition* cond) const {
+    bool has_cache(const void* cond) const {
         auto it = cache_diffs.find(cond);
         return it != cache_diffs.end() && !it->second.diff.empty();
     }
 
-    void update_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void update_cache(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         UCacheCacheEntry& entry = cache_diffs[cond];
-        size_t ne               = static_cast<size_t>(ggml_nelements(output));
-        entry.diff.resize(ne);
-        float* out_data = (float*)output->data;
-        float* in_data  = (float*)input->data;
-
-        for (size_t i = 0; i < ne; ++i) {
-            entry.diff[i] = out_data[i] - in_data[i];
-        }
+        sd::store_condition_cache_diff(&entry.diff, input, output);
     }
 
-    void apply_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void apply_cache(const void* cond, const sd::Tensor<float>& input, sd::Tensor<float>* output) {
         auto it = cache_diffs.find(cond);
         if (it == cache_diffs.end() || it->second.diff.empty()) {
             return;
         }
-
-        copy_ggml_tensor(output, input);
-        float* out_data                = (float*)output->data;
-        const std::vector<float>& diff = it->second.diff;
-        for (size_t i = 0; i < diff.size(); ++i) {
-            out_data[i] += diff[i];
-        }
+        sd::apply_condition_cache_diff(it->second.diff, input, output);
     }
 
-    bool before_condition(const SDCondition* cond,
-                          ggml_tensor* input,
-                          ggml_tensor* output,
+    bool before_condition(const void* cond,
+                          const sd::Tensor<float>& input,
+                          sd::Tensor<float>* output,
                           float sigma,
                           int step_index) {
-        if (!enabled() || step_index < 0) {
+        if (!enabled() || step_index < 0 || output == nullptr) {
             return false;
         }
         if (step_index != current_step_index) {
@@ -302,12 +291,12 @@ struct UCacheState {
             return false;
         }
 
-        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        size_t ne = static_cast<size_t>(input.numel());
         if (prev_input.size() != ne) {
             return false;
         }
 
-        float* input_data = (float*)input->data;
+        const float* input_data = input.data();
         last_input_change       = 0.0f;
         for (size_t i = 0; i < ne; ++i) {
             last_input_change += std::fabs(input_data[i] - prev_input[i]);
@@ -354,7 +343,7 @@ struct UCacheState {
         return false;
     }
 
-    void after_condition(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void after_condition(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
         if (!step_is_active()) {
             return;
         }
@@ -367,15 +356,15 @@ struct UCacheState {
         steps_computed_since_active++;
         consecutive_skipped_steps = 0;
 
-        size_t ne      = static_cast<size_t>(ggml_nelements(input));
-        float* in_data = (float*)input->data;
+        size_t ne            = static_cast<size_t>(input.numel());
+        const float* in_data = input.data();
         prev_input.resize(ne);
         for (size_t i = 0; i < ne; ++i) {
             prev_input[i] = in_data[i];
         }
         has_prev_input = true;
 
-        float* out_data     = (float*)output->data;
+        const float* out_data = output.data();
         float output_change   = 0.0f;
         if (has_prev_output && prev_output.size() == ne) {
             for (size_t i = 0; i < ne; ++i) {

src/unet.hpp

@@ -60,9 +60,9 @@ public:
         blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* context,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* context,
                          int timesteps) {
         // x: [N, in_channels, h, w] aka [b*t, in_channels, h, w], t == timesteps
         // context: [N, max_position(aka n_context), hidden_size(aka context_dim)] aka [b*t, n_context, context_dim], t == timesteps
@@ -388,10 +388,10 @@ public:
         blocks["out.2"] = std::shared_ptr<GGMLBlock>(new Conv2d(model_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* resblock_forward(std::string name,
+    ggml_tensor* resblock_forward(std::string name,
                                   GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* emb,
+                                  ggml_tensor* x,
+                                  ggml_tensor* emb,
                                   int num_video_frames) {
         if (version == VERSION_SVD) {
             auto block = std::dynamic_pointer_cast<VideoResBlock>(blocks[name]);
@@ -404,10 +404,10 @@ public:
         }
     }
 
-    struct ggml_tensor* attention_layer_forward(std::string name,
+    ggml_tensor* attention_layer_forward(std::string name,
                                          GGMLRunnerContext* ctx,
-                                                struct ggml_tensor* x,
-                                                struct ggml_tensor* context,
+                                         ggml_tensor* x,
+                                         ggml_tensor* context,
                                          int timesteps) {
         if (version == VERSION_SVD) {
             auto block = std::dynamic_pointer_cast<SpatialVideoTransformer>(blocks[name]);
@@ -420,14 +420,14 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* timesteps,
-                                struct ggml_tensor* context,
-                                struct ggml_tensor* c_concat              = nullptr,
-                                struct ggml_tensor* y                     = nullptr,
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* timesteps,
+                         ggml_tensor* context,
+                         ggml_tensor* c_concat              = nullptr,
+                         ggml_tensor* y                     = nullptr,
                          int num_video_frames               = -1,
-                                std::vector<struct ggml_tensor*> controls = {},
+                         std::vector<ggml_tensor*> controls = {},
                          float control_strength             = 0.f) {
         // x: [N, in_channels, h, w] or [N, in_channels/2, h, w]
         // timesteps: [N,]
@@ -480,7 +480,7 @@ public:
         }
 
         // input_blocks
-        std::vector<struct ggml_tensor*> hs;
+        std::vector<ggml_tensor*> hs;
 
         // input block 0
         auto h = input_blocks_0_0->forward(ctx, x);
@@ -605,37 +605,38 @@ struct UNetModelRunner : public GGMLRunner {
         return "unet";
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         unet.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                    struct ggml_tensor* timesteps,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* c_concat              = nullptr,
-                                    struct ggml_tensor* y                     = nullptr,
+    ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                             const sd::Tensor<float>& timesteps_tensor,
+                             const sd::Tensor<float>& context_tensor               = {},
+                             const sd::Tensor<float>& c_concat_tensor              = {},
+                             const sd::Tensor<float>& y_tensor                     = {},
                              int num_video_frames                                  = -1,
-                                    std::vector<struct ggml_tensor*> controls = {},
+                             const std::vector<sd::Tensor<float>>& controls_tensor = {},
                              float control_strength                                = 0.f) {
-        struct ggml_cgraph* gf = new_graph_custom(UNET_GRAPH_SIZE);
+        ggml_cgraph* gf = new_graph_custom(UNET_GRAPH_SIZE);
+
+        ggml_tensor* x         = make_input(x_tensor);
+        ggml_tensor* timesteps = make_input(timesteps_tensor);
+        ggml_tensor* context   = make_optional_input(context_tensor);
+        ggml_tensor* c_concat  = make_optional_input(c_concat_tensor);
+        ggml_tensor* y         = make_optional_input(y_tensor);
+        std::vector<ggml_tensor*> controls;
+        controls.reserve(controls_tensor.size());
+        for (const auto& control_tensor : controls_tensor) {
+            controls.push_back(make_input(control_tensor));
+        }
 
         if (num_video_frames == -1) {
             num_video_frames = static_cast<int>(x->ne[3]);
         }
 
-        x         = to_backend(x);
-        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]);
-        }
-
         auto runner_ctx = get_context();
 
-        struct ggml_tensor* out = unet.forward(&runner_ctx,
+        ggml_tensor* out = unet.forward(&runner_ctx,
                                         x,
                                         timesteps,
                                         context,
@@ -650,37 +651,35 @@ struct UNetModelRunner : public GGMLRunner {
         return gf;
     }
 
-    bool 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,
+    sd::Tensor<float> compute(int n_threads,
+                              const sd::Tensor<float>& x,
+                              const sd::Tensor<float>& timesteps,
+                              const sd::Tensor<float>& context               = {},
+                              const sd::Tensor<float>& c_concat              = {},
+                              const sd::Tensor<float>& y                     = {},
                               int num_video_frames                           = -1,
-                 std::vector<struct ggml_tensor*> controls = {},
-                 float control_strength                    = 0.f,
-                 struct ggml_tensor** output               = nullptr,
-                 struct ggml_context* output_ctx           = nullptr) {
+                              const std::vector<sd::Tensor<float>>& controls = {},
+                              float control_strength                         = 0.f) {
         // x: [N, in_channels, h, w]
         // timesteps: [N, ]
         // context: [N, max_position, hidden_size]([N, 77, 768]) or [1, max_position, hidden_size]
         // c_concat: [N, in_channels, h, w] or [1, in_channels, h, w]
         // y: [N, adm_in_channels] or [1, adm_in_channels]
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+        auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength);
         };
 
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
     }
 
     void test() {
-        struct ggml_init_params params;
+        ggml_init_params params;
         params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
         params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
-        struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != nullptr);
+        ggml_context* ctx = ggml_init(params);
+        GGML_ASSERT(ctx != nullptr);
 
         {
             // CPU, num_video_frames = 1, x{num_video_frames, 8, 8, 8}: Pass
@@ -689,27 +688,37 @@ struct UNetModelRunner : public GGMLRunner {
             // CUDA, num_video_frames = 3, x{num_video_frames, 8, 8, 8}: nan
             int num_video_frames = 3;
 
-            auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 8, num_video_frames);
+            sd::Tensor<float> x({8, 8, 8, num_video_frames});
             std::vector<float> timesteps_vec(num_video_frames, 999.f);
-            auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
-            ggml_set_f32(x, 0.5f);
+            auto timesteps = sd::Tensor<float>::from_vector(timesteps_vec);
+            x.fill_(0.5f);
             // print_ggml_tensor(x);
 
-            auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 1024, 1, num_video_frames);
-            ggml_set_f32(context, 0.5f);
+            sd::Tensor<float> context({1024, 1, num_video_frames});
+            context.fill_(0.5f);
             // print_ggml_tensor(context);
 
-            auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, num_video_frames);
-            ggml_set_f32(y, 0.5f);
+            sd::Tensor<float> y({768, num_video_frames});
+            y.fill_(0.5f);
             // print_ggml_tensor(y);
 
-            struct ggml_tensor* out = nullptr;
+            sd::Tensor<float> out;
 
             int64_t t0   = ggml_time_ms();
-            compute(8, x, timesteps, context, nullptr, y, num_video_frames, {}, 0.f, &out, work_ctx);
+            auto out_opt = compute(8,
+                                   x,
+                                   timesteps,
+                                   context,
+                                   {},
+                                   y,
+                                   num_video_frames,
+                                   {},
+                                   0.f);
             int64_t t1   = ggml_time_ms();
 
-            print_ggml_tensor(out);
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
             LOG_DEBUG("unet test done in %lldms", t1 - t0);
         }
     }

src/upscaler.cpp

@@ -2,6 +2,7 @@
 #include "ggml_extend.hpp"
 #include "model.h"
 #include "stable-diffusion.h"
+#include "util.h"
 
 struct UpscalerGGML {
     ggml_backend_t backend    = nullptr;  // general backend
@@ -64,6 +65,39 @@ struct UpscalerGGML {
         return true;
     }
 
+    sd::Tensor<float> upscale_tensor(const sd::Tensor<float>& input_tensor) {
+        sd::Tensor<float> upscaled;
+        if (tile_size <= 0 || (input_tensor.shape()[0] <= tile_size && input_tensor.shape()[1] <= tile_size)) {
+            upscaled = esrgan_upscaler->compute(n_threads, input_tensor);
+        } else {
+            auto on_processing = [&](const sd::Tensor<float>& input_tile) -> sd::Tensor<float> {
+                auto output_tile = esrgan_upscaler->compute(n_threads, input_tile);
+                if (output_tile.empty()) {
+                    LOG_ERROR("esrgan compute failed while processing a tile");
+                    return {};
+                }
+                return output_tile;
+            };
+
+            upscaled = process_tiles_2d(input_tensor,
+                                        static_cast<int>(input_tensor.shape()[0] * esrgan_upscaler->scale),
+                                        static_cast<int>(input_tensor.shape()[1] * esrgan_upscaler->scale),
+                                        esrgan_upscaler->scale,
+                                        tile_size,
+                                        tile_size,
+                                        0.25f,
+                                        false,
+                                        false,
+                                        on_processing);
+        }
+        esrgan_upscaler->free_compute_buffer();
+        if (upscaled.empty()) {
+            LOG_ERROR("esrgan compute failed");
+            return {};
+        }
+        return upscaled;
+    }
+
     sd_image_t upscale(sd_image_t input_image, uint32_t upscale_factor) {
         // upscale_factor, unused for RealESRGAN_x4plus_anime_6B.pth
         sd_image_t upscaled_image = {0, 0, 0, nullptr};
@@ -72,39 +106,17 @@ struct UpscalerGGML {
         LOG_INFO("upscaling from (%i x %i) to (%i x %i)",
                  input_image.width, input_image.height, output_width, output_height);
 
-        struct ggml_init_params params;
-        params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1G
-        params.mem_buffer = nullptr;
-        params.no_alloc   = false;
-
-        // draft context
-        struct ggml_context* upscale_ctx = ggml_init(params);
-        if (!upscale_ctx) {
-            LOG_ERROR("ggml_init() failed");
+        sd::Tensor<float> input_tensor = sd_image_to_tensor(input_image);
+        sd::Tensor<float> upscaled;
+        int64_t t0 = ggml_time_ms();
+        upscaled   = upscale_tensor(input_tensor);
+        if (upscaled.empty()) {
             return upscaled_image;
         }
-        // LOG_DEBUG("upscale work buffer size: %.2f MB", params.mem_size / 1024.f / 1024.f);
-        ggml_tensor* input_image_tensor = ggml_new_tensor_4d(upscale_ctx, GGML_TYPE_F32, input_image.width, input_image.height, 3, 1);
-        sd_image_to_ggml_tensor(input_image, input_image_tensor);
-
-        ggml_tensor* upscaled = ggml_new_tensor_4d(upscale_ctx, GGML_TYPE_F32, output_width, output_height, 3, 1);
-        auto on_tiling        = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-            return esrgan_upscaler->compute(n_threads, in, &out);
-        };
-        int64_t t0 = ggml_time_ms();
-        sd_tiling(input_image_tensor, upscaled, esrgan_upscaler->scale, esrgan_upscaler->tile_size, 0.25f, on_tiling);
-        esrgan_upscaler->free_compute_buffer();
-        ggml_ext_tensor_clamp_inplace(upscaled, 0.f, 1.f);
-        uint8_t* upscaled_data = ggml_tensor_to_sd_image(upscaled);
-        ggml_free(upscale_ctx);
+        sd_image_t upscaled_data = tensor_to_sd_image(upscaled);
         int64_t t3               = ggml_time_ms();
         LOG_INFO("input_image_tensor upscaled, taking %.2fs", (t3 - t0) / 1000.0f);
-        upscaled_image = {
-            (uint32_t)output_width,
-            (uint32_t)output_height,
-            3,
-            upscaled_data,
-        };
+        upscaled_image = upscaled_data;
         return upscaled_image;
     }
 };

src/util.cpp

@@ -479,158 +479,96 @@ const char* sd_get_system_info() {
     return buffer;
 }
 
-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;
-    converted_image.height  = image.height;
-    converted_image.channel = image.channel;
+sd_image_t tensor_to_sd_image(const sd::Tensor<float>& tensor, int frame_index) {
+    const auto& shape = tensor.shape();
+    GGML_ASSERT(shape.size() == 4 || shape.size() == 5);
+    int width     = static_cast<int>(shape[0]);
+    int height    = static_cast<int>(shape[1]);
+    int channel   = static_cast<int>(shape[shape.size() == 5 ? 3 : 2]);
+    uint8_t* data = (uint8_t*)malloc(static_cast<size_t>(width * height * channel));
+    GGML_ASSERT(data != nullptr);
 
-    // Allocate memory for float data
-    converted_image.data = (float*)malloc(image.width * image.height * image.channel * sizeof(float));
-
-    for (uint32_t i = 0; i < image.width * image.height * image.channel; i++) {
-        // Convert uint8_t to float
-        converted_image.data[i] = (float)image.data[i];
-    }
-
-    return converted_image;
-}
-
-// Function to perform double linear interpolation
-float interpolate(float v1, float v2, float v3, float v4, float x_ratio, float y_ratio) {
-    return v1 * (1 - x_ratio) * (1 - y_ratio) + v2 * x_ratio * (1 - y_ratio) + v3 * (1 - x_ratio) * y_ratio + v4 * x_ratio * y_ratio;
-}
-
-sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int target_height) {
-    sd_image_f32_t resized_image;
-    resized_image.width   = target_width;
-    resized_image.height  = target_height;
-    resized_image.channel = image.channel;
-
-    // Allocate memory for resized float data
-    resized_image.data = (float*)malloc(target_width * target_height * image.channel * sizeof(float));
-
-    for (int y = 0; y < target_height; y++) {
-        for (int x = 0; x < target_width; x++) {
-            float original_x = (float)x * image.width / target_width;
-            float original_y = (float)y * image.height / target_height;
-
-            uint32_t x1 = (uint32_t)original_x;
-            uint32_t y1 = (uint32_t)original_y;
-            uint32_t x2 = std::min(x1 + 1, image.width - 1);
-            uint32_t y2 = std::min(y1 + 1, image.height - 1);
-
-            for (uint32_t k = 0; k < image.channel; k++) {
-                float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
-                float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
-                float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
-                float v4 = *(image.data + y2 * image.width * image.channel + x2 * image.channel + k);
-
-                float x_ratio = original_x - x1;
-                float y_ratio = original_y - y1;
-
-                float value = interpolate(v1, v2, v3, v4, x_ratio, y_ratio);
-
-                *(resized_image.data + y * target_width * image.channel + x * image.channel + k) = value;
+    for (int iw = 0; iw < width; ++iw) {
+        for (int ih = 0; ih < height; ++ih) {
+            for (int ic = 0; ic < channel; ++ic) {
+                float value                            = shape.size() == 5 ? tensor.index(iw, ih, frame_index, ic, 0)
+                                                                           : tensor.index(iw, ih, ic, frame_index);
+                value                                  = std::clamp(value, 0.0f, 1.0f);
+                data[(ih * width + iw) * channel + ic] = static_cast<uint8_t>(std::round(value * 255.0f));
             }
         }
     }
-
-    return resized_image;
+    return {
+        static_cast<uint32_t>(width),
+        static_cast<uint32_t>(height),
+        static_cast<uint32_t>(channel),
+        data,
+    };
 }
 
-void normalize_sd_image_f32_t(sd_image_f32_t image, float means[3], float stds[3]) {
-    for (uint32_t y = 0; y < image.height; y++) {
-        for (uint32_t x = 0; x < image.width; x++) {
-            for (uint32_t k = 0; k < image.channel; k++) {
-                int index         = (y * image.width + x) * image.channel + k;
-                image.data[index] = (image.data[index] - means[k]) / stds[k];
+sd::Tensor<float> sd_image_to_tensor(sd_image_t image,
+                                     int target_width,
+                                     int target_height,
+                                     bool scale) {
+    sd::Tensor<float> tensor = sd::zeros<float>({static_cast<int64_t>(image.width),
+                                                 static_cast<int64_t>(image.height),
+                                                 static_cast<int64_t>(image.channel),
+                                                 1});
+    for (uint32_t iw = 0; iw < image.width; ++iw) {
+        for (uint32_t ih = 0; ih < image.height; ++ih) {
+            for (uint32_t ic = 0; ic < image.channel; ++ic) {
+                tensor.index(iw, ih, ic, 0) = sd_image_get_f32(image, iw, ih, ic, scale);
             }
         }
     }
+    if (target_width >= 0 && target_height >= 0 &&
+        (tensor.shape()[0] != target_width || tensor.shape()[1] != target_height)) {
+        tensor = sd::ops::interpolate(tensor,
+                                      {target_width,
+                                       target_height,
+                                       tensor.shape()[2],
+                                       tensor.shape()[3]});
+    }
+    return tensor;
 }
 
 // Constants for means and std
 float means[3] = {0.48145466f, 0.4578275f, 0.40821073f};
 float stds[3]  = {0.26862954f, 0.26130258f, 0.27577711f};
 
-// Function to clip and preprocess sd_image_f32_t
-sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int target_height) {
-    float width_scale  = (float)target_width / image.width;
-    float height_scale = (float)target_height / image.height;
+sd::Tensor<float> clip_preprocess(const sd::Tensor<float>& image, int target_width, int target_height) {
+    GGML_ASSERT(image.dim() == 4);
+    GGML_ASSERT(image.shape()[2] == 3);
+    GGML_ASSERT(image.shape()[3] == 1);
+    GGML_ASSERT(target_width > 0 && target_height > 0);
 
+    float width_scale  = static_cast<float>(target_width) / static_cast<float>(image.shape()[0]);
+    float height_scale = static_cast<float>(target_height) / static_cast<float>(image.shape()[1]);
     float scale        = std::fmax(width_scale, height_scale);
 
-    // Interpolation
-    int resized_width   = (int)(scale * image.width);
-    int resized_height  = (int)(scale * image.height);
-    float* resized_data = (float*)malloc(resized_width * resized_height * image.channel * sizeof(float));
+    int64_t resized_width  = static_cast<int64_t>(scale * static_cast<float>(image.shape()[0]));
+    int64_t resized_height = static_cast<int64_t>(scale * static_cast<float>(image.shape()[1]));
 
-    for (int y = 0; y < resized_height; y++) {
-        for (int x = 0; x < resized_width; x++) {
-            float original_x = (float)x * image.width / resized_width;
-            float original_y = (float)y * image.height / resized_height;
+    sd::Tensor<float> resized = sd::ops::interpolate(
+        image,
+        {resized_width, resized_height, image.shape()[2], image.shape()[3]});
 
-            uint32_t x1 = (uint32_t)original_x;
-            uint32_t y1 = (uint32_t)original_y;
-            uint32_t x2 = std::min(x1 + 1, image.width - 1);
-            uint32_t y2 = std::min(y1 + 1, image.height - 1);
+    int64_t h_offset = std::max<int64_t>((resized_height - target_height) / 2, 0);
+    int64_t w_offset = std::max<int64_t>((resized_width - target_width) / 2, 0);
 
-            for (uint32_t k = 0; k < image.channel; k++) {
-                float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
-                float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
-                float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
-                float v4 = *(image.data + y2 * image.width * image.channel + x2 * image.channel + k);
-
-                float x_ratio = original_x - x1;
-                float y_ratio = original_y - y1;
-
-                float value = interpolate(v1, v2, v3, v4, x_ratio, y_ratio);
-
-                *(resized_data + y * resized_width * image.channel + x * image.channel + k) = value;
+    sd::Tensor<float> cropped({target_width, target_height, image.shape()[2], image.shape()[3]});
+    for (int64_t y = 0; y < target_height; ++y) {
+        for (int64_t x = 0; x < target_width; ++x) {
+            for (int64_t c = 0; c < image.shape()[2]; ++c) {
+                cropped.index(x, y, c, 0) = resized.index(x + w_offset, y + h_offset, c, 0);
             }
         }
     }
 
-    // Clip and preprocess
-    int h_offset = std::max((int)(resized_height - target_height) / 2, 0);
-    int w_offset = std::max((int)(resized_width - target_width) / 2, 0);
-
-    sd_image_f32_t result;
-    result.width   = target_width;
-    result.height  = target_height;
-    result.channel = image.channel;
-    result.data    = (float*)malloc(target_height * target_width * image.channel * sizeof(float));
-
-    for (uint32_t k = 0; k < image.channel; k++) {
-        for (uint32_t i = 0; i < result.height; i++) {
-            for (uint32_t j = 0; j < result.width; j++) {
-                int src_y = std::min(static_cast<int>(i + h_offset), resized_height - 1);
-                int src_x = std::min(static_cast<int>(j + w_offset), resized_width - 1);
-                *(result.data + i * result.width * image.channel + j * image.channel + k) =
-                    fmin(fmax(*(resized_data + src_y * resized_width * image.channel + src_x * image.channel + k), 0.0f), 255.0f) / 255.0f;
-            }
-        }
-    }
-
-    // Free allocated memory
-    free(resized_data);
-
-    // Normalize
-    for (uint32_t k = 0; k < image.channel; k++) {
-        for (uint32_t i = 0; i < result.height; i++) {
-            for (uint32_t j = 0; j < result.width; j++) {
-                // *(result.data + i * size * image.channel + j * image.channel + k) = 0.5f;
-                int offset  = i * result.width * image.channel + j * image.channel + k;
-                float value = *(result.data + offset);
-                value       = (value - means[k]) / stds[k];
-                // value = 0.5f;
-                *(result.data + offset) = value;
-            }
-        }
-    }
-
-    return result;
+    sd::Tensor<float> normalized = sd::ops::clamp(cropped, 0.0f, 1.0f);
+    sd::Tensor<float> mean({1, 1, 3, 1}, {means[0], means[1], means[2]});
+    sd::Tensor<float> std({1, 1, 3, 1}, {stds[0], stds[1], stds[2]});
+    return (normalized - mean) / std;
 }
 
 // Ref: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cad87bf4e3e0b0a759afa94e933527c3123d59bc/modules/prompt_parser.py#L345

src/util.h

@@ -7,6 +7,7 @@
 #include <vector>
 
 #include "stable-diffusion.h"
+#include "tensor.hpp"
 
 #define SAFE_STR(s) ((s) ? (s) : "")
 #define BOOL_STR(b) ((b) ? "true" : "false")
@@ -29,20 +30,14 @@ std::string utf32_to_utf8(const std::u32string& utf32_str);
 std::u32string unicode_value_to_utf32(int unicode_value);
 // std::string sd_basename(const std::string& path);
 
-typedef struct {
-    uint32_t width;
-    uint32_t height;
-    uint32_t channel;
-    float* data;
-} sd_image_f32_t;
+sd_image_t tensor_to_sd_image(const sd::Tensor<float>& tensor, int frame_index = 0);
 
-void normalize_sd_image_f32_t(sd_image_f32_t image, float means[3], float stds[3]);
+sd::Tensor<float> sd_image_to_tensor(sd_image_t image,
+                                     int target_width  = -1,
+                                     int target_height = -1,
+                                     bool scale        = true);
 
-sd_image_f32_t sd_image_t_to_sd_image_f32_t(sd_image_t image);
-
-sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int target_height);
-
-sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int target_height);
+sd::Tensor<float> clip_preprocess(const sd::Tensor<float>& image, int target_width, int target_height);
 
 class MmapWrapper {
 public:

src/wan.hpp

@@ -25,7 +25,7 @@ namespace WAN {
         std::tuple<int, int, int> dilation;
         bool bias;
 
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             params["weight"] = ggml_new_tensor_4d(ctx,
                                                   GGML_TYPE_F16,
                                                   std::get<2>(kernel_size),
@@ -53,11 +53,11 @@ namespace WAN {
               dilation(std::move(dilation)),
               bias(bias) {}
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* cache_x = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* cache_x = nullptr) {
             // x: [N*IC, ID, IH, IW]
             // result: x: [N*OC, ID, IH, IW]
-            struct ggml_tensor* w = params["weight"];
-            struct ggml_tensor* b = nullptr;
+            ggml_tensor* w = params["weight"];
+            ggml_tensor* b = nullptr;
             if (bias) {
                 b = params["bias"];
             }
@@ -86,7 +86,7 @@ namespace WAN {
     protected:
         int64_t dim;
 
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             ggml_type wtype = GGML_TYPE_F32;
             auto iter       = tensor_storage_map.find(prefix + "gamma");
             if (iter != tensor_storage_map.end()) {
@@ -100,11 +100,11 @@ namespace WAN {
         RMS_norm(int64_t dim)
             : dim(dim) {}
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
             // x: [N*IC, ID, IH, IW], IC == dim
             // assert N == 1
 
-            struct ggml_tensor* w = params["gamma"];
+            ggml_tensor* w = params["gamma"];
             w              = ggml_reshape_1d(ctx->ggml_ctx, w, ggml_nelements(w));
             auto h         = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 3, 0, 1, 2));  // [ID, IH, IW, N*IC]
             h              = ggml_rms_norm(ctx->ggml_ctx, h, 1e-12f);
@@ -148,10 +148,10 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t b,
-                                    std::vector<struct ggml_tensor*>& feat_cache,
+                             std::vector<ggml_tensor*>& feat_cache,
                              int& feat_idx,
                              int chunk_idx) {
             // x: [b*c, t, h, w]
@@ -254,8 +254,8 @@ namespace WAN {
             GGML_ASSERT(in_channels * factor % out_channels == 0);
             group_size = in_channels * factor / out_channels;
         }
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t B = 1) {
             // x: [B*IC, T, H, W]
             // return: [B*OC, T/factor_t, H/factor_s, W/factor_s]
@@ -301,8 +301,8 @@ namespace WAN {
             GGML_ASSERT(out_channels * factor % in_channels == 0);
             repeats = out_channels * factor / in_channels;
         }
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              bool first_chunk = false,
                              int64_t B        = 1) {
             // x: [B*IC, T, H, W]
@@ -356,14 +356,14 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t b,
-                                    std::vector<struct ggml_tensor*>& feat_cache,
+                             std::vector<ggml_tensor*>& feat_cache,
                              int& feat_idx) {
             // x: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
-            struct ggml_tensor* h = x;
+            ggml_tensor* h = x;
             if (in_dim != out_dim) {
                 auto shortcut = std::dynamic_pointer_cast<CausalConv3d>(blocks["shortcut"]);
 
@@ -430,15 +430,15 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t b,
-                                    std::vector<struct ggml_tensor*>& feat_cache,
+                             std::vector<ggml_tensor*>& feat_cache,
                              int& feat_idx,
                              int chunk_idx) {
             // x: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
-            struct ggml_tensor* x_copy = x;
+            ggml_tensor* x_copy = x;
 
             auto avg_shortcut = std::dynamic_pointer_cast<AvgDown3D>(blocks["avg_shortcut"]);
 
@@ -492,15 +492,15 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t b,
-                                    std::vector<struct ggml_tensor*>& feat_cache,
+                             std::vector<ggml_tensor*>& feat_cache,
                              int& feat_idx,
                              int chunk_idx) {
             // x: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
-            struct ggml_tensor* x_copy = x;
+            ggml_tensor* x_copy = x;
 
             int i = 0;
             for (; i < mult; i++) {
@@ -537,8 +537,8 @@ namespace WAN {
             blocks["proj"]   = std::shared_ptr<GGMLBlock>(new Conv2d(dim, dim, {1, 1}));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t b) {
             // x: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
@@ -659,10 +659,10 @@ namespace WAN {
             blocks["head.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, z_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t b,
-                                    std::vector<struct ggml_tensor*>& feat_cache,
+                             std::vector<ggml_tensor*>& feat_cache,
                              int& feat_idx,
                              int chunk_idx) {
             // x: [b*c, t, h, w]
@@ -830,10 +830,10 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
                              int64_t b,
-                                    std::vector<struct ggml_tensor*>& feat_cache,
+                             std::vector<ggml_tensor*>& feat_cache,
                              int& feat_idx,
                              int chunk_idx) {
             // x: [b*c, t, h, w]
@@ -934,16 +934,16 @@ namespace WAN {
 
         int _conv_num = 33;
         int _conv_idx = 0;
-        std::vector<struct ggml_tensor*> _feat_map;
+        std::vector<ggml_tensor*> _feat_map;
         int _enc_conv_num = 28;
         int _enc_conv_idx = 0;
-        std::vector<struct ggml_tensor*> _enc_feat_map;
+        std::vector<ggml_tensor*> _enc_feat_map;
 
         void clear_cache() {
             _conv_idx     = 0;
-            _feat_map     = std::vector<struct ggml_tensor*>(_conv_num, nullptr);
+            _feat_map     = std::vector<ggml_tensor*>(_conv_num, nullptr);
             _enc_conv_idx = 0;
-            _enc_feat_map = std::vector<struct ggml_tensor*>(_enc_conv_num, nullptr);
+            _enc_feat_map = std::vector<ggml_tensor*>(_enc_conv_num, nullptr);
         }
 
     public:
@@ -966,8 +966,8 @@ namespace WAN {
             blocks["conv2"]   = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim, z_dim, {1, 1, 1}));
         }
 
-        struct ggml_tensor* patchify(struct ggml_context* ctx,
-                                     struct ggml_tensor* x,
+        ggml_tensor* patchify(ggml_context* ctx,
+                              ggml_tensor* x,
                               int64_t patch_size,
                               int64_t b = 1) {
             // x: [b*c, f, h*q, w*r]
@@ -993,8 +993,8 @@ namespace WAN {
             return x;
         }
 
-        struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
+        ggml_tensor* unpatchify(ggml_context* ctx,
+                                ggml_tensor* x,
                                 int64_t patch_size,
                                 int64_t b = 1) {
             // x: [b*c*r*q, f, h, w]
@@ -1019,8 +1019,8 @@ namespace WAN {
             return x;
         }
 
-        struct ggml_tensor* encode(GGMLRunnerContext* ctx,
-                                   struct ggml_tensor* x,
+        ggml_tensor* encode(GGMLRunnerContext* ctx,
+                            ggml_tensor* x,
                             int64_t b = 1) {
             // x: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
@@ -1037,7 +1037,7 @@ namespace WAN {
 
             int64_t t     = x->ne[2];
             int64_t iter_ = 1 + (t - 1) / 4;
-            struct ggml_tensor* out;
+            ggml_tensor* out;
             for (int i = 0; i < iter_; i++) {
                 _enc_conv_idx = 0;
                 if (i == 0) {
@@ -1055,8 +1055,8 @@ namespace WAN {
             return mu;
         }
 
-        struct ggml_tensor* decode(GGMLRunnerContext* ctx,
-                                   struct ggml_tensor* z,
+        ggml_tensor* decode(GGMLRunnerContext* ctx,
+                            ggml_tensor* z,
                             int64_t b = 1) {
             // z: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
@@ -1068,7 +1068,7 @@ namespace WAN {
 
             int64_t iter_ = z->ne[2];
             auto x        = conv2->forward(ctx, z);
-            struct ggml_tensor* out;
+            ggml_tensor* out;
             for (int i = 0; i < iter_; i++) {
                 _conv_idx = 0;
                 if (i == 0) {
@@ -1087,8 +1087,8 @@ namespace WAN {
             return out;
         }
 
-        struct ggml_tensor* decode_partial(GGMLRunnerContext* ctx,
-                                           struct ggml_tensor* z,
+        ggml_tensor* decode_partial(GGMLRunnerContext* ctx,
+                                    ggml_tensor* z,
                                     int i,
                                     int64_t b = 1) {
             // z: [b*c, t, h, w]
@@ -1109,6 +1109,7 @@ namespace WAN {
     };
 
     struct WanVAERunner : public VAE {
+        float scale_factor = 1.0f;
         bool decode_only   = true;
         WanVAE ae;
 
@@ -1118,7 +1119,7 @@ namespace WAN {
                      const std::string prefix                       = "",
                      bool decode_only                               = false,
                      SDVersion version                              = VERSION_WAN2)
-            : decode_only(decode_only), ae(decode_only, version == VERSION_WAN2_2_TI2V), VAE(backend, offload_params_to_cpu) {
+            : decode_only(decode_only), ae(decode_only, version == VERSION_WAN2_2_TI2V), VAE(version, backend, offload_params_to_cpu) {
             ae.init(params_ctx, tensor_storage_map, prefix);
         }
 
@@ -1126,26 +1127,82 @@ namespace WAN {
             return "wan_vae";
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) override {
             ae.get_param_tensors(tensors, prefix);
         }
 
-        struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
-            struct ggml_cgraph* gf = new_graph_custom(10240 * z->ne[2]);
+        sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
+            SD_UNUSED(rng);
+            return vae_output;
+        }
 
-            z = to_backend(z);
+        std::pair<sd::Tensor<float>, sd::Tensor<float>> get_latents_mean_std(const sd::Tensor<float>& latents) {
+            int channel_dim = latents.dim() == 5 ? 3 : 2;
+            std::vector<int64_t> stats_shape(static_cast<size_t>(latents.dim()), 1);
+            if (latents.shape()[channel_dim] == 16) {  // Wan2.1 VAE
+                stats_shape[static_cast<size_t>(channel_dim)] = 16;
+
+                auto mean_tensor = sd::Tensor<float>::from_vector({-0.7571f, -0.7089f, -0.9113f, 0.1075f, -0.1745f, 0.9653f, -0.1517f, 1.5508f,
+                                                                   0.4134f, -0.0715f, 0.5517f, -0.3632f, -0.1922f, -0.9497f, 0.2503f, -0.2921f});
+                mean_tensor.reshape_(stats_shape);
+                auto std_tensor = sd::Tensor<float>::from_vector({2.8184f, 1.4541f, 2.3275f, 2.6558f, 1.2196f, 1.7708f, 2.6052f, 2.0743f,
+                                                                  3.2687f, 2.1526f, 2.8652f, 1.5579f, 1.6382f, 1.1253f, 2.8251f, 1.9160f});
+                std_tensor.reshape_(stats_shape);
+                return {std::move(mean_tensor), std::move(std_tensor)};
+            }
+            if (latents.shape()[channel_dim] == 48) {  // Wan2.2 VAE
+                stats_shape[static_cast<size_t>(channel_dim)] = 48;
+
+                auto mean_tensor = sd::Tensor<float>::from_vector({-0.2289f, -0.0052f, -0.1323f, -0.2339f, -0.2799f, 0.0174f, 0.1838f, 0.1557f,
+                                                                   -0.1382f, 0.0542f, 0.2813f, 0.0891f, 0.1570f, -0.0098f, 0.0375f, -0.1825f,
+                                                                   -0.2246f, -0.1207f, -0.0698f, 0.5109f, 0.2665f, -0.2108f, -0.2158f, 0.2502f,
+                                                                   -0.2055f, -0.0322f, 0.1109f, 0.1567f, -0.0729f, 0.0899f, -0.2799f, -0.1230f,
+                                                                   -0.0313f, -0.1649f, 0.0117f, 0.0723f, -0.2839f, -0.2083f, -0.0520f, 0.3748f,
+                                                                   0.0152f, 0.1957f, 0.1433f, -0.2944f, 0.3573f, -0.0548f, -0.1681f, -0.0667f});
+                mean_tensor.reshape_(stats_shape);
+                auto std_tensor = sd::Tensor<float>::from_vector({0.4765f, 1.0364f, 0.4514f, 1.1677f, 0.5313f, 0.4990f, 0.4818f, 0.5013f,
+                                                                  0.8158f, 1.0344f, 0.5894f, 1.0901f, 0.6885f, 0.6165f, 0.8454f, 0.4978f,
+                                                                  0.5759f, 0.3523f, 0.7135f, 0.6804f, 0.5833f, 1.4146f, 0.8986f, 0.5659f,
+                                                                  0.7069f, 0.5338f, 0.4889f, 0.4917f, 0.4069f, 0.4999f, 0.6866f, 0.4093f,
+                                                                  0.5709f, 0.6065f, 0.6415f, 0.4944f, 0.5726f, 1.2042f, 0.5458f, 1.6887f,
+                                                                  0.3971f, 1.0600f, 0.3943f, 0.5537f, 0.5444f, 0.4089f, 0.7468f, 0.7744f});
+                std_tensor.reshape_(stats_shape);
+                return {std::move(mean_tensor), std::move(std_tensor)};
+            }
+            GGML_ABORT("unexpected latent channel dimension %lld for version %d",
+                       (long long)latents.shape()[channel_dim],
+                       version);
+        }
+
+        sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents);
+            return (latents * std_tensor) / scale_factor + mean_tensor;
+        }
+
+        sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
+            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents);
+            return ((latents - mean_tensor) * scale_factor) / std_tensor;
+        }
+
+        int get_encoder_output_channels(int input_channels) {
+            return static_cast<int>(ae.z_dim);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
+            ggml_cgraph* gf = new_graph_custom(10240 * z_tensor.shape()[2]);
+            ggml_tensor* z  = make_input(z_tensor);
 
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);
+            ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);
 
             ggml_build_forward_expand(gf, out);
 
             return gf;
         }
 
-        struct ggml_cgraph* build_graph_partial(struct ggml_tensor* z, bool decode_graph, int i) {
-            struct ggml_cgraph* gf = new_graph_custom(20480);
+        ggml_cgraph* build_graph_partial(const sd::Tensor<float>& z_tensor, bool decode_graph, int i) {
+            ggml_cgraph* gf = new_graph_custom(20480);
 
             ae.clear_cache();
 
@@ -1154,11 +1211,11 @@ namespace WAN {
                 ae._feat_map[feat_idx] = feat_cache;
             }
 
-            z = to_backend(z);
+            ggml_tensor* z = make_input(z_tensor);
 
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* out = decode_graph ? ae.decode_partial(&runner_ctx, z, i) : ae.encode(&runner_ctx, z);
+            ggml_tensor* out = decode_graph ? ae.decode_partial(&runner_ctx, z, i) : ae.encode(&runner_ctx, z);
 
             for (size_t feat_idx = 0; feat_idx < ae._feat_map.size(); feat_idx++) {
                 ggml_tensor* feat_cache = ae._feat_map[feat_idx];
@@ -1173,86 +1230,85 @@ namespace WAN {
             return gf;
         }
 
-        bool compute(const int n_threads,
-                     struct ggml_tensor* z,
-                     bool decode_graph,
-                     struct ggml_tensor** output,
-                     struct ggml_context* output_ctx = nullptr) override {
+        sd::Tensor<float> _compute(const int n_threads,
+                                   const sd::Tensor<float>& z,
+                                   bool decode_graph) override {
             if (true) {
-                auto get_graph = [&]() -> struct ggml_cgraph* {
+                sd::Tensor<float> input;
+                if (z.dim() == 4) {
+                    input = z.unsqueeze(2);
+                }
+                auto get_graph = [&]() -> ggml_cgraph* {
+                    if (input.empty()) {
                         return build_graph(z, decode_graph);
+                    } else {
+                        return build_graph(input, decode_graph);
+                    }
                 };
-                return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+                auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true),
+                                                              input.empty() ? z.dim() : input.dim());
+                if (!result.empty() && z.dim() == 4) {
+                    result.squeeze_(2);
+                }
+                return result;
             } else {  // chunk 1 result is weird
                 ae.clear_cache();
-                int64_t t      = z->ne[2];
+                int64_t t      = z.shape()[2];
                 int i          = 0;
-                auto get_graph = [&]() -> struct ggml_cgraph* {
+                auto get_graph = [&]() -> ggml_cgraph* {
                     return build_graph_partial(z, decode_graph, i);
                 };
-                struct ggml_tensor* out = nullptr;
-                bool res                = GGMLRunner::compute(get_graph, n_threads, true, &out, output_ctx);
+                auto out_opt = GGMLRunner::compute<float>(get_graph, n_threads, true);
+                if (!out_opt.has_value()) {
+                    return {};
+                }
+                sd::Tensor<float> out = std::move(*out_opt);
                 ae.clear_cache();
                 if (t == 1) {
-                    *output = out;
-                    return res;
+                    return out;
                 }
 
-                *output = ggml_new_tensor_4d(output_ctx, GGML_TYPE_F32, out->ne[0], out->ne[1], (t - 1) * 4 + 1, out->ne[3]);
-
-                auto copy_to_output = [&]() {
-                    for (int64_t i3 = 0; i3 < out->ne[3]; i3++) {
-                        for (int64_t i2 = 0; i2 < out->ne[2]; i2++) {
-                            for (int64_t i1 = 0; i1 < out->ne[1]; i1++) {
-                                for (int64_t i0 = 0; i0 < out->ne[0]; i0++) {
-                                    float value    = ggml_ext_tensor_get_f32(out, i0, i1, i2, i3);
-                                    int64_t offset = (i == 0) ? 0 : (1 + (i - 1) * 4);
-                                    ggml_ext_tensor_set_f32(*output, value, i0, i1, offset + i2, i3);
-                                }
-                            }
-                        }
-                    }
-                };
-
-                copy_to_output();
-
-                out = ggml_new_tensor_4d(output_ctx, GGML_TYPE_F32, out->ne[0], out->ne[1], 4, out->ne[3]);
+                sd::Tensor<float> output = std::move(out);
 
                 for (i = 1; i < t; i++) {
-                    res = res || GGMLRunner::compute(get_graph, n_threads, true, &out);
+                    auto chunk_opt = GGMLRunner::compute<float>(get_graph, n_threads, true);
+                    if (!chunk_opt.has_value()) {
+                        return {};
+                    }
+                    out = std::move(*chunk_opt);
                     ae.clear_cache();
-                    copy_to_output();
+                    output = sd::ops::concat(output, out, 2);
                 }
                 free_cache_ctx_and_buffer();
-                return res;
+                return output;
             }
         }
 
         void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
             params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1G
             params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);
 
             if (true) {
                 // cpu f32, pass
                 // cpu f16, pass
                 // cuda f16, pass
                 // cuda f32, pass
-                auto z = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 104, 60, 2, 16);
-                ggml_set_f32(z, 0.5f);
-                z = load_tensor_from_file(work_ctx, "wan_vae_z.bin");
-                print_ggml_tensor(z);
-                struct ggml_tensor* out = nullptr;
+                auto z = sd::load_tensor_from_file_as_tensor<float>("wan_vae_z.bin");
+                print_sd_tensor(z);
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                compute(8, z, true, &out, work_ctx);
+                auto out_opt = _compute(8, z, true);
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("decode test done in %ldms", t1 - t0);
             }
         };
@@ -1314,10 +1370,10 @@ namespace WAN {
             }
         }
 
-        virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                            struct ggml_tensor* x,
-                                            struct ggml_tensor* pe,
-                                            struct ggml_tensor* mask = nullptr) {
+        virtual ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                     ggml_tensor* x,
+                                     ggml_tensor* pe,
+                                     ggml_tensor* mask = nullptr) {
             // x: [N, n_token, dim]
             // pe: [n_token, d_head/2, 2, 2]
             // return [N, n_token, dim]
@@ -1355,9 +1411,9 @@ namespace WAN {
                           bool qk_norm = true,
                           float eps    = 1e-6)
             : WanSelfAttention(dim, num_heads, qk_norm, eps) {}
-        virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                            struct ggml_tensor* x,
-                                            struct ggml_tensor* context,
+        virtual ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                     ggml_tensor* x,
+                                     ggml_tensor* context,
                                      int64_t context_img_len) = 0;
     };
 
@@ -1368,9 +1424,9 @@ namespace WAN {
                              bool qk_norm = true,
                              float eps    = 1e-6)
             : WanCrossAttention(dim, num_heads, qk_norm, eps) {}
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* context,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* context,
                              int64_t context_img_len) override {
             // x: [N, n_token, dim]
             // context: [N, n_context, dim]
@@ -1416,9 +1472,9 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* context,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* context,
                              int64_t context_img_len) override {
             // x: [N, n_token, dim]
             // context: [N, context_img_len + context_txt_len, dim]
@@ -1464,7 +1520,7 @@ namespace WAN {
         }
     };
 
-    static struct ggml_tensor* modulate_add(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* e) {
+    static ggml_tensor* modulate_add(ggml_context* ctx, ggml_tensor* x, ggml_tensor* e) {
         // x: [N, n_token, dim]
         // e: [N, 1, dim] or [N, T, 1, dim]
         if (ggml_n_dims(e) == 3) {
@@ -1478,7 +1534,7 @@ namespace WAN {
         return x;
     }
 
-    static struct ggml_tensor* modulate_mul(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* e) {
+    static ggml_tensor* modulate_mul(ggml_context* ctx, ggml_tensor* x, ggml_tensor* e) {
         // x: [N, n_token, dim]
         // e: [N, 1, dim] or [N, T, 1, dim]
         if (ggml_n_dims(e) == 3) {
@@ -1496,7 +1552,7 @@ namespace WAN {
     protected:
         int64_t dim;
 
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
             params["modulation"] = ggml_new_tensor_3d(ctx, wtype, dim, 6, 1);
         }
@@ -1530,11 +1586,11 @@ namespace WAN {
             blocks["ffn.2"] = std::shared_ptr<GGMLBlock>(new Linear(ffn_dim, dim));
         }
 
-        virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                            struct ggml_tensor* x,
-                                            struct ggml_tensor* e,
-                                            struct ggml_tensor* pe,
-                                            struct ggml_tensor* context,
+        virtual ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                     ggml_tensor* x,
+                                     ggml_tensor* e,
+                                     ggml_tensor* pe,
+                                     ggml_tensor* context,
                                      int64_t context_img_len = 257) {
             // x: [N, n_token, dim]
             // e: [N, 6, dim] or [N, T, 6, dim]
@@ -1584,7 +1640,7 @@ namespace WAN {
     class VaceWanAttentionBlock : public WanAttentionBlock {
     protected:
         int block_id;
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
             params["modulation"] = ggml_new_tensor_3d(ctx, wtype, dim, 6, 1);
         }
@@ -1606,11 +1662,11 @@ namespace WAN {
         }
 
         std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                      struct ggml_tensor* c,
-                                                      struct ggml_tensor* x,
-                                                      struct ggml_tensor* e,
-                                                      struct ggml_tensor* pe,
-                                                      struct ggml_tensor* context,
+                                                      ggml_tensor* c,
+                                                      ggml_tensor* x,
+                                                      ggml_tensor* e,
+                                                      ggml_tensor* pe,
+                                                      ggml_tensor* context,
                                                       int64_t context_img_len = 257) {
             // x: [N, n_token, dim]
             // e: [N, 6, dim] or [N, T, 6, dim]
@@ -1636,7 +1692,7 @@ namespace WAN {
     protected:
         int64_t dim;
 
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
             params["modulation"] = ggml_new_tensor_3d(ctx, wtype, dim, 2, 1);
         }
@@ -1653,9 +1709,9 @@ namespace WAN {
             blocks["head"] = std::shared_ptr<GGMLBlock>(new Linear(dim, out_dim));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* e) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* e) {
             // x: [N, n_token, dim]
             // e: [N, dim] or [N, T, dim]
             // return [N, n_token, out_dim]
@@ -1683,7 +1739,7 @@ namespace WAN {
         int64_t in_dim;
         int64_t flf_pos_embed_token_number;
 
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             if (flf_pos_embed_token_number > 0) {
                 params["emb_pos"] = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, in_dim, flf_pos_embed_token_number, 1);
             }
@@ -1701,8 +1757,8 @@ namespace WAN {
             blocks["proj.4"] = std::shared_ptr<GGMLBlock>(new LayerNorm(out_dim));
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* image_embeds) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* image_embeds) {
             if (flf_pos_embed_token_number > 0) {
                 auto emb_pos = params["emb_pos"];
 
@@ -1821,8 +1877,8 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
-                                              struct ggml_tensor* x) {
+        ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
+                                       ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
             int64_t T = x->ne[2];
@@ -1834,8 +1890,8 @@ namespace WAN {
             return x;
         }
 
-        struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
+        ggml_tensor* unpatchify(ggml_context* ctx,
+                                ggml_tensor* x,
                                 int64_t t_len,
                                 int64_t h_len,
                                 int64_t w_len) {
@@ -1861,13 +1917,13 @@ namespace WAN {
             return x;
         }
 
-        struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* timestep,
-                                         struct ggml_tensor* context,
-                                         struct ggml_tensor* pe,
-                                         struct ggml_tensor* clip_fea     = nullptr,
-                                         struct ggml_tensor* vace_context = nullptr,
+        ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
+                                  ggml_tensor* x,
+                                  ggml_tensor* timestep,
+                                  ggml_tensor* context,
+                                  ggml_tensor* pe,
+                                  ggml_tensor* clip_fea     = nullptr,
+                                  ggml_tensor* vace_context = nullptr,
                                   float vace_strength       = 1.f,
                                   int64_t N                 = 1) {
             // x: [N*C, T, H, W], C => in_dim
@@ -1955,14 +2011,14 @@ namespace WAN {
             return x;
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* timestep,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* clip_fea        = nullptr,
-                                    struct ggml_tensor* time_dim_concat = nullptr,
-                                    struct ggml_tensor* vace_context    = nullptr,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe,
+                             ggml_tensor* clip_fea        = nullptr,
+                             ggml_tensor* time_dim_concat = nullptr,
+                             ggml_tensor* vace_context    = nullptr,
                              float vace_strength          = 1.f,
                              int64_t N                    = 1) {
             // Forward pass of DiT.
@@ -2129,27 +2185,27 @@ namespace WAN {
             return desc;
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
             wan.get_param_tensors(tensors, prefix);
         }
 
-        struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                        struct ggml_tensor* timesteps,
-                                        struct ggml_tensor* context,
-                                        struct ggml_tensor* clip_fea        = nullptr,
-                                        struct ggml_tensor* c_concat        = nullptr,
-                                        struct ggml_tensor* time_dim_concat = nullptr,
-                                        struct ggml_tensor* vace_context    = nullptr,
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor         = {},
+                                 const sd::Tensor<float>& clip_fea_tensor        = {},
+                                 const sd::Tensor<float>& c_concat_tensor        = {},
+                                 const sd::Tensor<float>& time_dim_concat_tensor = {},
+                                 const sd::Tensor<float>& vace_context_tensor    = {},
                                  float vace_strength                             = 1.f) {
-            struct ggml_cgraph* gf = new_graph_custom(WAN_GRAPH_SIZE);
+            ggml_cgraph* gf = new_graph_custom(WAN_GRAPH_SIZE);
 
-            x               = to_backend(x);
-            timesteps       = to_backend(timesteps);
-            context         = to_backend(context);
-            clip_fea        = to_backend(clip_fea);
-            c_concat        = to_backend(c_concat);
-            time_dim_concat = to_backend(time_dim_concat);
-            vace_context    = to_backend(vace_context);
+            ggml_tensor* x               = make_input(x_tensor);
+            ggml_tensor* timesteps       = make_input(timesteps_tensor);
+            ggml_tensor* context         = make_optional_input(context_tensor);
+            ggml_tensor* clip_fea        = make_optional_input(clip_fea_tensor);
+            ggml_tensor* c_concat        = make_optional_input(c_concat_tensor);
+            ggml_tensor* time_dim_concat = make_optional_input(time_dim_concat_tensor);
+            ggml_tensor* vace_context    = make_optional_input(vace_context_tensor);
 
             pe_vec      = Rope::gen_wan_pe(static_cast<int>(x->ne[2]),
                                            static_cast<int>(x->ne[1]),
@@ -2174,7 +2230,7 @@ namespace WAN {
 
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* out = wan.forward(&runner_ctx,
+            ggml_tensor* out = wan.forward(&runner_ctx,
                                            x,
                                            timesteps,
                                            context,
@@ -2189,60 +2245,60 @@ namespace WAN {
             return gf;
         }
 
-        bool compute(int n_threads,
-                     struct ggml_tensor* x,
-                     struct ggml_tensor* timesteps,
-                     struct ggml_tensor* context,
-                     struct ggml_tensor* clip_fea        = nullptr,
-                     struct ggml_tensor* c_concat        = nullptr,
-                     struct ggml_tensor* time_dim_concat = nullptr,
-                     struct ggml_tensor* vace_context    = nullptr,
-                     float vace_strength                 = 1.f,
-                     struct ggml_tensor** output         = nullptr,
-                     struct ggml_context* output_ctx     = nullptr) {
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context         = {},
+                                  const sd::Tensor<float>& clip_fea        = {},
+                                  const sd::Tensor<float>& c_concat        = {},
+                                  const sd::Tensor<float>& time_dim_concat = {},
+                                  const sd::Tensor<float>& vace_context    = {},
+                                  float vace_strength                      = 1.f) {
+            auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, clip_fea, c_concat, time_dim_concat, vace_context, vace_strength);
             };
 
-            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
         }
 
         void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
             params.mem_size   = static_cast<size_t>(200 * 1024 * 1024);  // 200 MB
             params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);
 
             {
                 // cpu f16: pass
                 // cuda f16: pass
                 // cpu q8_0: pass
-                // auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 104, 60, 1, 16);
+                // auto x = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 104, 60, 1, 16);
                 // ggml_set_f32(x, 0.01f);
-                auto x = load_tensor_from_file(work_ctx, "wan_dit_x.bin");
-                print_ggml_tensor(x);
+                auto x = sd::load_tensor_from_file_as_tensor<float>("wan_dit_x.bin");
+                print_sd_tensor(x);
 
                 std::vector<float> timesteps_vec(3, 1000.f);
                 timesteps_vec[0] = 0.f;
-                auto timesteps   = vector_to_ggml_tensor(work_ctx, timesteps_vec);
+                auto timesteps   = sd::Tensor<float>::from_vector(timesteps_vec);
 
-                // auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 4096, 512, 1);
+                // auto context = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 4096, 512, 1);
                 // ggml_set_f32(context, 0.01f);
-                auto context = load_tensor_from_file(work_ctx, "wan_dit_context.bin");
-                print_ggml_tensor(context);
-                // auto clip_fea = load_tensor_from_file(work_ctx, "wan_dit_clip_fea.bin");
+                auto context = sd::load_tensor_from_file_as_tensor<float>("wan_dit_context.bin");
+                print_sd_tensor(context);
+                // auto clip_fea = load_tensor_from_file(ctx, "wan_dit_clip_fea.bin");
                 // print_ggml_tensor(clip_fea);
 
-                struct ggml_tensor* out = nullptr;
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                compute(8, x, timesteps, context, nullptr, nullptr, nullptr, nullptr, 1.f, &out, work_ctx);
+                auto out_opt = compute(8, x, timesteps, context, {}, {}, {}, {}, 1.f);
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("wan test done in %lldms", t1 - t0);
             }
         }

src/z_image.hpp

@@ -42,10 +42,10 @@ namespace ZImage {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask = nullptr) {
             // x: [N, n_token, hidden_size]
             int64_t n_token = x->ne[1];
             int64_t N       = x->ne[2];
@@ -124,23 +124,23 @@ namespace ZImage {
             blocks["w3"] = std::make_shared<Linear>(dim, hidden_dim, false);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto w1 = std::dynamic_pointer_cast<Linear>(blocks["w1"]);
             auto w2 = std::dynamic_pointer_cast<Linear>(blocks["w2"]);
             auto w3 = std::dynamic_pointer_cast<Linear>(blocks["w3"]);
 
             auto x1 = w1->forward(ctx, x);
             auto x3 = w3->forward(ctx, x);
-            x       = ggml_mul(ctx->ggml_ctx, ggml_silu(ctx->ggml_ctx, x1), x3);
+            x       = ggml_swiglu_split(ctx->ggml_ctx, x1, x3);
             x       = w2->forward(ctx, x);
 
             return x;
         }
     };
 
-    __STATIC_INLINE__ struct ggml_tensor* modulate(struct ggml_context* ctx,
-                                                   struct ggml_tensor* x,
-                                                   struct ggml_tensor* scale) {
+    __STATIC_INLINE__ ggml_tensor* modulate(ggml_context* ctx,
+                                            ggml_tensor* x,
+                                            ggml_tensor* scale) {
         // x: [N, L, C]
         // scale: [N, C]
         scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
@@ -175,11 +175,11 @@ namespace ZImage {
             }
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask        = nullptr,
-                                    struct ggml_tensor* adaln_input = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask        = nullptr,
+                             ggml_tensor* adaln_input = nullptr) {
             auto attention       = std::dynamic_pointer_cast<JointAttention>(blocks["attention"]);
             auto feed_forward    = std::dynamic_pointer_cast<FeedForward>(blocks["feed_forward"]);
             auto attention_norm1 = std::dynamic_pointer_cast<RMSNorm>(blocks["attention_norm1"]);
@@ -241,9 +241,9 @@ namespace ZImage {
             blocks["adaLN_modulation.1"] = std::make_shared<Linear>(MIN(hidden_size, ADALN_EMBED_DIM), hidden_size);
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* c) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* c) {
             // x: [N, n_token, hidden_size]
             // c: [N, hidden_size]
             // return: [N, n_token, patch_size * patch_size * out_channels]
@@ -284,7 +284,7 @@ namespace ZImage {
     protected:
         ZImageParams z_image_params;
 
-        void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             params["cap_pad_token"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, z_image_params.hidden_size);
             params["x_pad_token"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, z_image_params.hidden_size);
         }
@@ -346,11 +346,11 @@ namespace ZImage {
             blocks["final_layer"] = std::make_shared<FinalLayer>(z_image_params.hidden_size, z_image_params.patch_size, z_image_params.out_channels);
         }
 
-        struct ggml_tensor* forward_core(GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* timestep,
-                                         struct ggml_tensor* context,
-                                         struct ggml_tensor* pe) {
+        ggml_tensor* forward_core(GGMLRunnerContext* ctx,
+                                  ggml_tensor* x,
+                                  ggml_tensor* timestep,
+                                  ggml_tensor* context,
+                                  ggml_tensor* pe) {
             auto x_embedder     = std::dynamic_pointer_cast<Linear>(blocks["x_embedder"]);
             auto t_embedder     = std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder"]);
             auto cap_embedder_0 = std::dynamic_pointer_cast<RMSNorm>(blocks["cap_embedder.0"]);
@@ -414,11 +414,11 @@ namespace ZImage {
             return img;
         }
 
-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* timestep,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* pe,
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe,
                              std::vector<ggml_tensor*> ref_latents = {}) {
             // Forward pass of DiT.
             // x: [N, C, H, W]
@@ -477,24 +477,25 @@ namespace ZImage {
             return "z_image";
         }
 
-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
             z_image.get_param_tensors(tensors, prefix);
         }
 
-        struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                        struct ggml_tensor* timesteps,
-                                        struct ggml_tensor* context,
-                                        std::vector<ggml_tensor*> ref_latents = {},
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor,
+                                 const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
                                  bool increase_ref_index                                  = false) {
+            ggml_cgraph* gf        = new_graph_custom(Z_IMAGE_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
             GGML_ASSERT(x->ne[3] == 1);
-            struct ggml_cgraph* gf = new_graph_custom(Z_IMAGE_GRAPH_SIZE);
-
-            x         = to_backend(x);
-            context   = to_backend(context);
-            timesteps = to_backend(timesteps);
-
-            for (int i = 0; i < ref_latents.size(); i++) {
-                ref_latents[i] = to_backend(ref_latents[i]);
+            GGML_ASSERT(!context_tensor.empty());
+            ggml_tensor* context = make_input(context_tensor);
+            std::vector<ggml_tensor*> ref_latents;
+            ref_latents.reserve(ref_latents_tensor.size());
+            for (const auto& ref_latent_tensor : ref_latents_tensor) {
+                ref_latents.push_back(make_input(ref_latent_tensor));
             }
 
             pe_vec      = Rope::gen_z_image_pe(static_cast<int>(x->ne[1]),
@@ -518,7 +519,7 @@ namespace ZImage {
             set_backend_tensor_data(pe, pe_vec.data());
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* out = z_image.forward(&runner_ctx,
+            ggml_tensor* out = z_image.forward(&runner_ctx,
                                                x,
                                                timesteps,
                                                context,
@@ -530,54 +531,59 @@ namespace ZImage {
             return gf;
         }
 
-        bool compute(int n_threads,
-                     struct ggml_tensor* x,
-                     struct ggml_tensor* timesteps,
-                     struct ggml_tensor* context,
-                     std::vector<ggml_tensor*> ref_latents = {},
-                     bool increase_ref_index               = false,
-                     struct ggml_tensor** output           = nullptr,
-                     struct ggml_context* output_ctx       = nullptr) {
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context,
+                                  const std::vector<sd::Tensor<float>>& ref_latents = {},
+                                  bool increase_ref_index                           = false) {
             // x: [N, in_channels, h, w]
             // timesteps: [N, ]
             // context: [N, max_position, hidden_size]
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+            auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
             };
 
-            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
         }
 
         void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
             params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
             params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);
 
             {
-                // auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 16, 1);
+                // auto x = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 16, 16, 16, 1);
                 // ggml_set_f32(x, 0.01f);
-                auto x = load_tensor_from_file(work_ctx, "./z_image_x.bin");
-                print_ggml_tensor(x);
+                auto x = sd::load_tensor_from_file_as_tensor<float>("./z_image_x.bin");
+                print_sd_tensor(x);
 
                 std::vector<float> timesteps_vec(1, 0.f);
-                auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
+                auto timesteps = sd::Tensor<float>::from_vector(timesteps_vec);
 
-                // auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 2560, 256, 1);
+                // auto context = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 2560, 256, 1);
                 // ggml_set_f32(context, 0.01f);
-                auto context = load_tensor_from_file(work_ctx, "./z_image_context.bin");
-                print_ggml_tensor(context);
+                auto context = sd::load_tensor_from_file_as_tensor<float>("./z_image_context.bin");
+                print_sd_tensor(context);
 
-                struct ggml_tensor* out = nullptr;
+                sd::Tensor<float> out;
 
                 int64_t t0   = ggml_time_ms();
-                compute(8, x, timesteps, context, {}, false, &out, work_ctx);
+                auto out_opt = compute(8,
+                                       x,
+                                       timesteps,
+                                       context,
+                                       {},
+                                       false);
                 int64_t t1   = ggml_time_ms();
 
-                print_ggml_tensor(out);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
                 LOG_DEBUG("z_image test done in %lldms", t1 - t0);
             }
         }