DarthAffe/stable-diffusion.cpp
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refactor: simplify DPM++ (2S) Ancestral (#667)

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vmobilis 2025-09-16 18:05:25 +03:00 committed by GitHub
parent 8909523e92
commit 97ad3e7ff9
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1 changed files with 6 additions and 15 deletions
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21
denoiser.hpp
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@ -770,7 +770,6 @@ static void sample_k_diffusion(sample_method_t method,
} break;
case DPMPP2S_A: {
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* x2 = ggml_dup_tensor(work_ctx, x);
for (int i = 0; i < steps; i++) {
@ -785,22 +784,15 @@ static void sample_k_diffusion(sample_method_t method,
auto sigma_fn = [](float t) -> float { return exp(-t); };
if (sigma_down == 0) {
// Euler step
float* vec_d = (float*)d->data;
// d = (x - denoised) / sigmas[i];
// dt = sigma_down - sigmas[i];
// x += d * dt;
// => x = denoised
float* vec_x = (float*)x->data;
float* vec_denoised = (float*)denoised->data;
for (int j = 0; j < ggml_nelements(d); j++) {
vec_d[j] = (vec_x[j] - vec_denoised[j]) / sigmas[i];
}
// TODO: If sigma_down == 0, isn't this wrong?
// But
// https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py#L525
// has this exactly the same way.
float dt = sigma_down - sigmas[i];
for (int j = 0; j < ggml_nelements(d); j++) {
vec_x[j] = vec_x[j] + vec_d[j] * dt;
for (int j = 0; j < ggml_nelements(x); j++) {
vec_x[j] = vec_denoised[j];
}
} else {
// DPM-Solver++(2S)
@ -809,7 +801,6 @@ static void sample_k_diffusion(sample_method_t method,
float h = t_next - t;
float s = t + 0.5f * h;
float* vec_d = (float*)d->data;
float* vec_x = (float*)x->data;
float* vec_x2 = (float*)x2->data;
float* vec_denoised = (float*)denoised->data;