upstream-archive/stable-diffusion.cpp
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refactor: unify the naming style of ggml extension functions (#921)

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leejet
2025-10-28 23:26:48 +08:00
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parent 77eb95f8e4
commit dd75fc081c
20 changed files with 600 additions and 601 deletions
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62
preprocessing.hpp
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@@ -28,7 +28,7 @@ void gaussian_kernel(struct ggml_tensor* kernel) {
for (int x = 0; x < kernel->ne[1]; x++) {
float gy = -ks_mid + x;
float k_ = expf(-((gx * gx + gy * gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
ggml_tensor_set_f32(kernel, k_, x, y);
ggml_ext_tensor_set_f32(kernel, k_, x, y);
}
}
}
@@ -36,11 +36,11 @@ void gaussian_kernel(struct ggml_tensor* 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_tensor_get_f32(rgb_img, ix, iy);
float g = ggml_tensor_get_f32(rgb_img, ix, iy, 1);
float b = ggml_tensor_get_f32(rgb_img, ix, iy, 2);
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);
float gray = 0.2989f * r + 0.5870f * g + 0.1140f * b;
ggml_tensor_set_f32(grayscale, gray, ix, iy);
ggml_ext_tensor_set_f32(grayscale, gray, ix, iy);
}
}
}
@@ -81,37 +81,37 @@ void normalize_tensor(struct ggml_tensor* g) {
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_tensor_get_f32(D, ix, iy) * 180.0f / M_PI_;
float angle = ggml_ext_tensor_get_f32(D, ix, iy) * 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_tensor_get_f32(G, ix, iy + 1);
r = ggml_tensor_get_f32(G, ix, iy - 1);
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_tensor_get_f32(G, ix + 1, iy - 1);
r = ggml_tensor_get_f32(G, ix - 1, iy + 1);
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_tensor_get_f32(G, ix + 1, iy);
r = ggml_tensor_get_f32(G, ix - 1, iy);
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_tensor_get_f32(G, ix - 1, iy - 1);
r = ggml_tensor_get_f32(G, ix + 1, iy + 1);
q = ggml_ext_tensor_get_f32(G, ix - 1, iy - 1);
r = ggml_ext_tensor_get_f32(G, ix + 1, iy + 1);
}
float cur = ggml_tensor_get_f32(G, ix, iy);
float cur = ggml_ext_tensor_get_f32(G, ix, iy);
if ((cur >= q) && (cur >= r)) {
ggml_tensor_set_f32(result, cur, ix, iy);
ggml_ext_tensor_set_f32(result, cur, ix, iy);
} else {
ggml_tensor_set_f32(result, 0.0f, ix, iy);
ggml_ext_tensor_set_f32(result, 0.0f, ix, iy);
}
}
}
@@ -138,9 +138,9 @@ void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float lo
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_tensor_set_f32(img, ggml_tensor_get_f32(img, ix, iy), ix, iy);
ggml_ext_tensor_set_f32(img, ggml_ext_tensor_get_f32(img, ix, iy), ix, iy);
} else {
ggml_tensor_set_f32(img, 0.0f, ix, iy);
ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
}
}
}
@@ -148,14 +148,14 @@ void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float lo
// 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_tensor_get_f32(img, ix, iy);
float imd_v = ggml_ext_tensor_get_f32(img, ix, iy);
if (imd_v == weak) {
if (ggml_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix + 1, iy) == strong ||
ggml_tensor_get_f32(img, ix, iy - 1) == strong || ggml_tensor_get_f32(img, ix, iy + 1) == strong ||
ggml_tensor_get_f32(img, ix - 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix - 1, iy) == strong) {
ggml_tensor_set_f32(img, strong, ix, iy);
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_tensor_set_f32(img, 0.0f, ix, iy);
ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
}
}
}
@@ -198,7 +198,7 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
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_tensor(img, image);
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);
@@ -211,14 +211,14 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
// to RGB channels
for (int iy = 0; iy < img.height; iy++) {
for (int ix = 0; ix < img.width; ix++) {
float gray = ggml_tensor_get_f32(image_gray, ix, iy);
float gray = ggml_ext_tensor_get_f32(image_gray, ix, iy);
gray = inverse ? 1.0f - gray : gray;
ggml_tensor_set_f32(image, gray, ix, iy);
ggml_tensor_set_f32(image, gray, ix, iy, 1);
ggml_tensor_set_f32(image, gray, ix, iy, 2);
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);
}
}
sd_tensor_to_image(image, img.data);
ggml_tensor_to_sd_image(image, img.data);
ggml_free(work_ctx);
return true;
}