chore: fix windows build and release

Some SDXL LoRAs (eg. PCM) can exceed 12k nodes.

.github/workflows/build.yml

@@ -146,7 +146,7 @@ jobs:
             sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip
 
   windows-latest-cmake:
-    runs-on: windows-2019
+    runs-on: windows-2025
 
     env:
       VULKAN_VERSION: 1.3.261.1

CMakeLists.txt

@@ -28,6 +28,7 @@ option(SD_CUDA                       "sd: cuda backend" OFF)
 option(SD_HIPBLAS                    "sd: rocm backend" OFF)
 option(SD_METAL                      "sd: metal backend" OFF)
 option(SD_VULKAN                     "sd: vulkan backend" OFF)
+option(SD_OPENCL                     "sd: opencl backend" OFF)
 option(SD_SYCL                       "sd: sycl backend" OFF)
 option(SD_MUSA                       "sd: musa backend" OFF)
 option(SD_FAST_SOFTMAX               "sd: x1.5 faster softmax, indeterministic (sometimes, same seed don't generate same image), cuda only" OFF)
@@ -52,6 +53,12 @@ if (SD_VULKAN)
     add_definitions(-DSD_USE_VULKAN)
 endif ()
 
+if (SD_OPENCL)
+    message("-- Use OpenCL as backend stable-diffusion")
+    set(GGML_OPENCL ON)
+    add_definitions(-DSD_USE_OPENCL)
+endif ()
+
 if (SD_HIPBLAS)
     message("-- Use HIPBLAS as backend stable-diffusion")
     set(GGML_HIP ON)

Dockerfile.musa

@@ -2,14 +2,17 @@ ARG MUSA_VERSION=rc3.1.1
 
 FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu22.04 as build
 
-RUN apt-get update && apt-get install -y cmake
+RUN apt-get update && apt-get install -y ccache cmake git
 
 WORKDIR /sd.cpp
 
 COPY . .
 
 RUN mkdir build && cd build && \
-    cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ \
+        -DCMAKE_C_FLAGS="${CMAKE_C_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
+        -DCMAKE_CXX_FLAGS="${CMAKE_CXX_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
+        -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release && \
     cmake --build . --config Release
 
 FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu22.04 as runtime

README.md

@@ -22,7 +22,7 @@ Inference of Stable Diffusion and Flux in pure C/C++
 - Accelerated memory-efficient CPU inference
     - Only requires ~2.3GB when using txt2img with fp16 precision to generate a 512x512 image, enabling Flash Attention just requires ~1.8GB.
 - AVX, AVX2 and AVX512 support for x86 architectures
-- Full CUDA, Metal, Vulkan and SYCL backend for GPU acceleration.
+- Full CUDA, Metal, Vulkan, OpenCL and SYCL backend for GPU acceleration.
 - Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs models
     - No need to convert to `.ggml` or `.gguf` anymore!
 - Flash Attention for memory usage optimization
@@ -160,6 +160,73 @@ cmake .. -DSD_VULKAN=ON
 cmake --build . --config Release
 ```
 
+##### Using OpenCL (for Adreno GPU)
+
+Currently, it supports only Adreno GPUs and is primarily optimized for Q4_0 type
+
+To build for Windows ARM please refers to [Windows 11 Arm64
+](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/OPENCL.md#windows-11-arm64)
+
+Building for Android:
+
+  Android NDK:
+       Download and install the Android NDK from the [official Android developer site](https://developer.android.com/ndk/downloads).
+
+Setup OpenCL Dependencies for NDK:
+
+You need to provide OpenCL headers and the ICD loader library to your NDK sysroot.
+
+*   OpenCL Headers:
+    ```bash
+    # In a temporary working directory
+    git clone https://github.com/KhronosGroup/OpenCL-Headers
+    cd OpenCL-Headers
+    # Replace <YOUR_NDK_PATH> with your actual NDK installation path
+    # e.g., cp -r CL /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
+    sudo cp -r CL <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
+    cd ..
+    ```
+
+*   OpenCL ICD Loader:
+    ```bash
+    # In the same temporary working directory
+    git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
+    cd OpenCL-ICD-Loader
+    mkdir build_ndk && cd build_ndk
+
+    # Replace <YOUR_NDK_PATH> in the CMAKE_TOOLCHAIN_FILE and OPENCL_ICD_LOADER_HEADERS_DIR
+    cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
+      -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
+      -DOPENCL_ICD_LOADER_HEADERS_DIR=<YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
+      -DANDROID_ABI=arm64-v8a \
+      -DANDROID_PLATFORM=24 \
+      -DANDROID_STL=c++_shared
+
+    ninja
+    # Replace <YOUR_NDK_PATH>
+    # e.g., cp libOpenCL.so /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
+    sudo cp libOpenCL.so <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
+    cd ../..
+    ```
+
+Build `stable-diffusion.cpp` for Android with OpenCL:
+
+```bash
+mkdir build-android && cd build-android
+
+# Replace <YOUR_NDK_PATH> with your actual NDK installation path
+# e.g., -DCMAKE_TOOLCHAIN_FILE=/path/to/android-ndk-r26c/build/cmake/android.toolchain.cmake
+cmake .. -G Ninja \
+  -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
+  -DANDROID_ABI=arm64-v8a \
+  -DANDROID_PLATFORM=android-28 \
+  -DGGML_OPENMP=OFF \
+  -DSD_OPENCL=ON
+
+ninja
+```
+*(Note: Don't forget to include `LD_LIBRARY_PATH=/vendor/lib64` in your command line before running the binary)*
+
 ##### Using SYCL
 
 Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and [Intel® oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before start. More details and steps can refer to [llama.cpp SYCL backend](https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md#linux).

common.hpp

@@ -56,7 +56,7 @@ public:
         // x: [N, channels, h, w]
         auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-        x = ggml_upscale(ctx, x, 2);  // [N, channels, h*2, w*2]
+        x = ggml_upscale(ctx, x, 2, GGML_SCALE_MODE_NEAREST);  // [N, channels, h*2, w*2]
         x = conv->forward(ctx, x);    // [N, out_channels, h*2, w*2]
         return x;
     }

esrgan.hpp

@@ -130,8 +130,8 @@ public:
         body_feat = conv_body->forward(ctx, body_feat);
         feat      = ggml_add(ctx, feat, body_feat);
         // upsample
-        feat     = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2)));
-        feat     = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2)));
+        feat     = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+        feat     = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
         auto out = conv_last->forward(ctx, lrelu(ctx, conv_hr->forward(ctx, feat)));
         return out;
     }

ggml_extend.hpp

@@ -39,6 +39,10 @@
 #include "ggml-vulkan.h"
 #endif
 
+#ifdef SD_USE_OPENCL
+#include "ggml-opencl.h"
+#endif
+
 #ifdef SD_USE_SYCL
 #include "ggml-sycl.h"
 #endif
@@ -113,7 +117,8 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_kronecker(ggml_context* ctx, struct g
                                      a->ne[0] * b->ne[0],
                                      a->ne[1] * b->ne[1],
                                      a->ne[2] * b->ne[2],
-                                     a->ne[3] * b->ne[3]),
+                                     a->ne[3] * b->ne[3],
+                                     GGML_SCALE_MODE_NEAREST), 
                     b);
 }
 
@@ -597,6 +602,8 @@ typedef std::function<void(ggml_tensor*, ggml_tensor*, bool)> on_tile_process;
 
 // Tiling
 __STATIC_INLINE__ void sd_tiling(ggml_tensor* input, ggml_tensor* output, const int scale, const int tile_size, const float tile_overlap_factor, on_tile_process on_processing) {
+    output = ggml_set_f32(output, 0);
+
     int input_width   = (int)input->ne[0];
     int input_height  = (int)input->ne[1];
     int output_width  = (int)output->ne[0];

lora.hpp

@@ -3,7 +3,7 @@
 
 #include "ggml_extend.hpp"
 
-#define LORA_GRAPH_SIZE 10240
+#define LORA_GRAPH_SIZE 15360
 
 struct LoraModel : public GGMLRunner {
     enum lora_t {

model.cpp

@@ -26,6 +26,10 @@
 #include "ggml-vulkan.h"
 #endif
 
+#ifdef SD_USE_OPENCL
+#include "ggml-opencl.h"
+#endif
+
 #define ST_HEADER_SIZE_LEN 8
 
 uint64_t read_u64(uint8_t* buffer) {

stable-diffusion.cpp

@@ -181,6 +181,14 @@ public:
             LOG_WARN("Failed to initialize Vulkan backend");
         }
 #endif
+#ifdef SD_USE_OPENCL
+        LOG_DEBUG("Using OpenCL backend");
+        // ggml_log_set(ggml_log_callback_default, nullptr); // Optional ggml logs
+        backend = ggml_backend_opencl_init();
+        if (!backend) {
+            LOG_WARN("Failed to initialize OpenCL backend");
+        }
+#endif
 #ifdef SD_USE_SYCL
         LOG_DEBUG("Using SYCL backend");
         backend = ggml_backend_sycl_init(0);
@@ -276,10 +284,10 @@ public:
             model_loader.set_wtype_override(GGML_TYPE_F32, "vae.");
         }
 
-        LOG_INFO("Weight type:                 %s", model_wtype != SD_TYPE_COUNT ? ggml_type_name(model_wtype) : "??");
-        LOG_INFO("Conditioner weight type:     %s", conditioner_wtype != SD_TYPE_COUNT ? ggml_type_name(conditioner_wtype) : "??");
-        LOG_INFO("Diffusion model weight type: %s", diffusion_model_wtype != SD_TYPE_COUNT ? ggml_type_name(diffusion_model_wtype) : "??");
-        LOG_INFO("VAE weight type:             %s", vae_wtype != SD_TYPE_COUNT ? ggml_type_name(vae_wtype) : "??");
+        LOG_INFO("Weight type:                 %s", model_wtype != GGML_TYPE_COUNT ? ggml_type_name(model_wtype) : "??");
+        LOG_INFO("Conditioner weight type:     %s", conditioner_wtype != GGML_TYPE_COUNT ? ggml_type_name(conditioner_wtype) : "??");
+        LOG_INFO("Diffusion model weight type: %s", diffusion_model_wtype != GGML_TYPE_COUNT ? ggml_type_name(diffusion_model_wtype) : "??");
+        LOG_INFO("VAE weight type:             %s", vae_wtype != GGML_TYPE_COUNT ? ggml_type_name(vae_wtype) : "??");
 
         LOG_DEBUG("ggml tensor size = %d bytes", (int)sizeof(ggml_tensor));
 

tae.hpp

@@ -149,7 +149,7 @@ public:
                 if (i == 1) {
                     h = ggml_relu_inplace(ctx, h);
                 } else {
-                    h = ggml_upscale(ctx, h, 2);
+                    h = ggml_upscale(ctx, h, 2, GGML_SCALE_MODE_NEAREST);
                 }
                 continue;
             }

upscaler.cpp

@@ -28,6 +28,10 @@ struct UpscalerGGML {
         LOG_DEBUG("Using Vulkan backend");
         backend = ggml_backend_vk_init(0);
 #endif
+#ifdef SD_USE_OPENCL
+        LOG_DEBUG("Using OpenCL backend");
+        backend = ggml_backend_opencl_init();
+#endif
 #ifdef SD_USE_SYCL
         LOG_DEBUG("Using SYCL backend");
         backend = ggml_backend_sycl_init(0);