upstream-archive/stable-diffusion.cpp
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chore: eliminate compilation warnings under MSVC (#1170)

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leejet
2026-01-04 22:26:57 +08:00
committed by GitHub
parent 2cef4badb8
commit b90b1ee9cf
30 changed files with 420 additions and 461 deletions
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20
preprocessing.hpp
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@@ -2,7 +2,7 @@
#define __PREPROCESSING_HPP__
#include "ggml_extend.hpp"
#define M_PI_ 3.14159265358979323846
#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;
@@ -20,13 +20,13 @@ void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml
}
void gaussian_kernel(struct ggml_tensor* kernel) {
int ks_mid = kernel->ne[0] / 2;
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));
for (int y = 0; y < kernel->ne[0]; y++) {
float gx = -ks_mid + y;
float gx = static_cast<float>(-ks_mid + y);
for (int x = 0; x < kernel->ne[1]; x++) {
float gy = -ks_mid + 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);
}
@@ -46,7 +46,7 @@ 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) {
int n_elements = ggml_nelements(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;
@@ -56,7 +56,7 @@ 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) {
int n_elements = ggml_nelements(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;
@@ -66,7 +66,7 @@ void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tens
}
void normalize_tensor(struct ggml_tensor* g) {
int n_elements = ggml_nelements(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++) {
@@ -118,7 +118,7 @@ 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) {
int n_elements = ggml_nelements(img);
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++) {
@@ -209,8 +209,8 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
non_max_supression(image_gray, G, tetha);
threshold_hystersis(image_gray, high_threshold, low_threshold, weak, strong);
// to RGB channels
for (int iy = 0; iy < img.height; iy++) {
for (int ix = 0; ix < img.width; ix++) {
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);
gray = inverse ? 1.0f - gray : gray;
ggml_ext_tensor_set_f32(image, gray, ix, iy);