DarthAffe/stable-diffusion.cpp
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chore: format code

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leejet committed 2026-08-19 23:06:07 +08:00
1 parent 16304cc3fd
commit 97d2990807
6 files changed
+17 -19
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src/runtime/denoiser.hpp
+4 -4
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@@ -2586,7 +2586,7 @@ static sd::Tensor<float> sample_lms(denoise_cb_t model,
// modified with "history shift" value, which seemingly needs less steps
int divisions = 1000;
int max_order = 4;
int shift = 1; // 4, 0 - original; 4, 1 - PR #1843; 3, 1 - smoother image
int shift = 1; // 4, 0 - original; 4, 1 - PR #1843; 3, 1 - smoother image
for (const auto& [key, value] : extra_sample_args) {
int parsed = 0;
if (key == "lms_max_order") {
@@ -2624,7 +2624,7 @@ static sd::Tensor<float> sample_lms(denoise_cb_t model,
auto linear_multistep_coeff = [=](const int order, const int m, const int j) -> float {
if (!divisions)
return sigmas[m + 1] - sigmas[m]; // delta / 0 * 0
#define LMS_PRECISION float // when divisions > 30 millions, the double precision fixes noise
#define LMS_PRECISION float // when divisions > 30 millions, the double precision fixes noise
const LMS_PRECISION a = sigmas[m], dx = (sigmas[m + 1] - a) / divisions, s = sigmas[m - j];
const LMS_PRECISION b0 = a + 0.5f * dx; // using Riemann middle integral
LMS_PRECISION sum = 0.0f;
@@ -2672,8 +2672,8 @@ static sd::Tensor<float> sample_lms(denoise_cb_t model,
int hist_max = hist.size() - 1;
for (int c = 2; c <= order; c++)
x += hist[std::min(hist_max, hist_size_p1 - c + shift)] * lms_coeff[c - 1];
// max_order == 4 => hist[] index = 2, 1, 0
// shift == 1 => hist[] index = 2, 2, 1
// max_order == 4 => hist[] index = 2, 1, 0
// shift == 1 => hist[] index = 2, 2, 1
}
if (hist_size_p1 == max_order) {
hist.erase(hist.begin());