upstream-archive/stable-diffusion.cpp
1
0
Fork 0
mirror of https://github.com/leejet/stable-diffusion.cpp.git synced 2026-08-03 00:30:41 -05:00
Code Issues Packages Projects Releases Wiki Activity

style: remove redundant struct qualifiers for consistent C/C++ type usage (#1349)

Browse Source
This commit is contained in:
leejet
2026-03-16 22:17:22 +08:00
committed by GitHub
parent 997bb11fb6
commit 84cbd88df1
32 changed files with 1692 additions and 1693 deletions
Download Patch File Download Diff File Expand all files Collapse all files

58
src/preprocessing.hpp
View File

@@ -4,13 +4,13 @@
#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);
void convolve(ggml_tensor* input, ggml_tensor* output, ggml_tensor* kernel, int padding) {
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;
ggml_context* ctx0 = ggml_init(params);
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);
@@ -19,7 +19,7 @@ void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml
ggml_free(ctx0);
}
void gaussian_kernel(struct ggml_tensor* kernel) {
void gaussian_kernel(ggml_tensor* kernel) {
int ks_mid = static_cast<int>(kernel->ne[0] / 2);
float sigma = 1.4f;
float normal = 1.f / (2.0f * M_PI_ * powf(sigma, 2.0f));
@@ -33,7 +33,7 @@ void gaussian_kernel(struct ggml_tensor* kernel) {
}
}
void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
void grayscale(ggml_tensor* rgb_img, 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);
@@ -45,7 +45,7 @@ void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
}
}
void prop_hypot(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
void prop_hypot(ggml_tensor* x, ggml_tensor* y, ggml_tensor* h) {
int n_elements = static_cast<int>(ggml_nelements(h));
float* dx = (float*)x->data;
float* dy = (float*)y->data;
@@ -55,7 +55,7 @@ void prop_hypot(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor
}
}
void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
void prop_arctan2(ggml_tensor* x, ggml_tensor* y, ggml_tensor* h) {
int n_elements = static_cast<int>(ggml_nelements(h));
float* dx = (float*)x->data;
float* dy = (float*)y->data;
@@ -65,7 +65,7 @@ void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tens
}
}
void normalize_tensor(struct ggml_tensor* g) {
void normalize_tensor(ggml_tensor* g) {
int n_elements = static_cast<int>(ggml_nelements(g));
float* dg = (float*)g->data;
float max = -INFINITY;
@@ -78,7 +78,7 @@ void normalize_tensor(struct ggml_tensor* g) {
}
}
void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struct ggml_tensor* D) {
void non_max_supression(ggml_tensor* result, ggml_tensor* G, 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_;
@@ -117,7 +117,7 @@ void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struc
}
}
void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float low_threshold, float weak, float strong) {
void threshold_hystersis(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;
@@ -163,11 +163,11 @@ void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float lo
}
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);
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;
ggml_context* work_ctx = ggml_init(params);
if (!work_ctx) {
LOG_ERROR("ggml_init() failed");
@@ -185,19 +185,19 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
-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);
int kernel_size = 5;
ggml_tensor* gkernel = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, kernel_size, kernel_size, 1, 1);
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);
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);
ggml_tensor* image = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, img.width, img.height, 3, 1);
ggml_tensor* image_gray = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, img.width, img.height, 1, 1);
ggml_tensor* iX = ggml_dup_tensor(work_ctx, image_gray);
ggml_tensor* iY = ggml_dup_tensor(work_ctx, image_gray);
ggml_tensor* G = ggml_dup_tensor(work_ctx, image_gray);
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);