alphard/stable-diffusion.cpp
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feat: add ipndm and ipndm_v samplers (#344)

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Daniele 2024-08-27 16:03:41 +00:00 committed by GitHub
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5 changed files with 160 additions and 2 deletions
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2
README.md
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@ -217,7 +217,7 @@ arguments:
1.0 corresponds to full destruction of information in init image
-H, --height H image height, in pixel space (default: 512)
-W, --width W image width, in pixel space (default: 512)
--sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, lcm}
--sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm}
sampling method (default: "euler_a")
--steps STEPS number of sample steps (default: 20)
--rng {std_default, cuda} RNG (default: cuda)

152
denoiser.hpp
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@ -800,6 +800,158 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case IPNDM: // iPNDM sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
{
int max_order = 4;
ggml_tensor* x_next = x;
std::vector<ggml_tensor*> buffer_model;
for (int i = 0; i < steps; i++) {
float sigma = sigmas[i];
float sigma_next = sigmas[i + 1];
ggml_tensor* x_cur = x_next;
float* vec_x_cur = (float*)x_cur->data;
float* vec_x_next = (float*)x_next->data;
// Denoising step
ggml_tensor* denoised = model(x_cur, sigma, i + 1);
float* vec_denoised = (float*)denoised->data;
// d_cur = (x_cur - denoised) / sigma
struct ggml_tensor* d_cur = ggml_dup_tensor(work_ctx, x_cur);
float* vec_d_cur = (float*)d_cur->data;
for (int j = 0; j < ggml_nelements(d_cur); j++) {
vec_d_cur[j] = (vec_x_cur[j] - vec_denoised[j]) / sigma;
}
int order = std::min(max_order, i + 1);
// Calculate vec_x_next based on the order
switch (order) {
case 1: // First Euler step
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x_next[j] = vec_x_cur[j] + (sigma_next - sigma) * vec_d_cur[j];
}
break;
case 2: // Use one history point
{
float* vec_d_prev1 = (float*)buffer_model.back()->data;
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x_next[j] = vec_x_cur[j] + (sigma_next - sigma) * (3 * vec_d_cur[j] - vec_d_prev1[j]) / 2;
}
}
break;
case 3: // Use two history points
{
float* vec_d_prev1 = (float*)buffer_model.back()->data;
float* vec_d_prev2 = (float*)buffer_model[buffer_model.size() - 2]->data;
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x_next[j] = vec_x_cur[j] + (sigma_next - sigma) * (23 * vec_d_cur[j] - 16 * vec_d_prev1[j] + 5 * vec_d_prev2[j]) / 12;
}
}
break;
case 4: // Use three history points
{
float* vec_d_prev1 = (float*)buffer_model.back()->data;
float* vec_d_prev2 = (float*)buffer_model[buffer_model.size() - 2]->data;
float* vec_d_prev3 = (float*)buffer_model[buffer_model.size() - 3]->data;
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x_next[j] = vec_x_cur[j] + (sigma_next - sigma) * (55 * vec_d_cur[j] - 59 * vec_d_prev1[j] + 37 * vec_d_prev2[j] - 9 * vec_d_prev3[j]) / 24;
}
}
break;
}
// Manage buffer_model
if (buffer_model.size() == max_order - 1) {
// Shift elements to the left
for (int k = 0; k < max_order - 2; k++) {
buffer_model[k] = buffer_model[k + 1];
}
buffer_model.back() = d_cur; // Replace the last element with d_cur
} else {
buffer_model.push_back(d_cur);
}
}
} break;
case IPNDM_V: // iPNDM_v sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
{
int max_order = 4;
std::vector<ggml_tensor*> buffer_model;
ggml_tensor* x_next = x;
for (int i = 0; i < steps; i++) {
float sigma = sigmas[i];
float t_next = sigmas[i + 1];
// Denoising step
ggml_tensor* denoised = model(x, sigma, i + 1);
float* vec_denoised = (float*)denoised->data;
struct ggml_tensor* d_cur = ggml_dup_tensor(work_ctx, x);
float* vec_d_cur = (float*)d_cur->data;
float* vec_x = (float*)x->data;
// d_cur = (x - denoised) / sigma
for (int j = 0; j < ggml_nelements(d_cur); j++) {
vec_d_cur[j] = (vec_x[j] - vec_denoised[j]) / sigma;
}
int order = std::min(max_order, i + 1);
float h_n = t_next - sigma;
float h_n_1 = (i > 0) ? (sigma - sigmas[i - 1]) : h_n;
switch (order) {
case 1: // First Euler step
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x[j] += vec_d_cur[j] * h_n;
}
break;
case 2: {
float* vec_d_prev1 = (float*)buffer_model.back()->data;
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x[j] += h_n * ((2 + (h_n / h_n_1)) * vec_d_cur[j] - (h_n / h_n_1) * vec_d_prev1[j]) / 2;
}
break;
}
case 3: {
float h_n_2 = (i > 1) ? (sigmas[i - 1] - sigmas[i - 2]) : h_n_1;
float* vec_d_prev1 = (float*)buffer_model.back()->data;
float* vec_d_prev2 = (buffer_model.size() > 1) ? (float*)buffer_model[buffer_model.size() - 2]->data : vec_d_prev1;
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x[j] += h_n * ((23 * vec_d_cur[j] - 16 * vec_d_prev1[j] + 5 * vec_d_prev2[j]) / 12);
}
break;
}
case 4: {
float h_n_2 = (i > 1) ? (sigmas[i - 1] - sigmas[i - 2]) : h_n_1;
float h_n_3 = (i > 2) ? (sigmas[i - 2] - sigmas[i - 3]) : h_n_2;
float* vec_d_prev1 = (float*)buffer_model.back()->data;
float* vec_d_prev2 = (buffer_model.size() > 1) ? (float*)buffer_model[buffer_model.size() - 2]->data : vec_d_prev1;
float* vec_d_prev3 = (buffer_model.size() > 2) ? (float*)buffer_model[buffer_model.size() - 3]->data : vec_d_prev2;
for (int j = 0; j < ggml_nelements(x_next); j++) {
vec_x[j] += h_n * ((55 * vec_d_cur[j] - 59 * vec_d_prev1[j] + 37 * vec_d_prev2[j] - 9 * vec_d_prev3[j]) / 24);
}
break;
}
}
// Manage buffer_model
if (buffer_model.size() == max_order - 1) {
buffer_model.erase(buffer_model.begin());
}
buffer_model.push_back(d_cur);
// Prepare the next d tensor
d_cur = ggml_dup_tensor(work_ctx, x_next);
}
} break;
case LCM: // Latent Consistency Models
{
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);

4
examples/cli/main.cpp
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@ -36,6 +36,8 @@ const char* sample_method_str[] = {
"dpm++2s_a",
"dpm++2m",
"dpm++2mv2",
"ipndm",
"ipndm_v",
"lcm",
};
@ -195,7 +197,7 @@ void print_usage(int argc, const char* argv[]) {
printf(" 1.0 corresponds to full destruction of information in init image\n");
printf(" -H, --height H image height, in pixel space (default: 512)\n");
printf(" -W, --width W image width, in pixel space (default: 512)\n");
printf(" --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, lcm}\n");
printf(" --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm}\n");
printf(" sampling method (default: \"euler_a\")\n");
printf(" --steps STEPS number of sample steps (default: 20)\n");
printf(" --rng {std_default, cuda} RNG (default: cuda)\n");

2
stable-diffusion.cpp
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@ -41,6 +41,8 @@ const char* sampling_methods_str[] = {
"DPM++ (2s)",
"DPM++ (2M)",
"modified DPM++ (2M)",
"iPNDM",
"iPNDM_v",
"LCM",
};

2
stable-diffusion.h
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@ -41,6 +41,8 @@ enum sample_method_t {
DPMPP2S_A,
DPMPP2M,
DPMPP2Mv2,
IPNDM,
IPNDM_V,
LCM,
N_SAMPLE_METHODS
};