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refactor: split guidance composition #1506

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Merged
leejet merged 1 commit into from
May 17, 2026
+284 −119
Merged

refactor: split guidance composition #1506

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153 changes: 77 additions & 76 deletions src/denoiser.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,13 @@
#include <algorithm>
#include <cctype>
#include <cmath>
#include <functional>
#include <string>
#include <utility>

#include "ggml_extend.hpp"
#include "gits_noise.inl"
#include "guidance.h"
#include "tensor.hpp"

/*================================================= CompVisDenoiser ==================================================*/
Expand Down Expand Up @@ -894,7 +896,7 @@ struct Flux2FlowDenoiser : public FluxFlowDenoiser {
}
};

typedef std::function<sd::Tensor<float>(const sd::Tensor<float>&, float, int, sd::Tensor<float>*)> denoise_cb_t;
typedef std::function<sd::guidance::GuiderOutput(const sd::Tensor<float>&, float, int)> denoise_cb_t;

static std::pair<float, float> get_ancestral_step(float sigma_from,
float sigma_to,
Expand Down Expand Up @@ -972,11 +974,11 @@ static sd::Tensor<float> sample_euler_ancestral(denoise_cb_t model,
for (int i = 0; i < steps; i++) {
float sigma = sigmas[i];
float sigma_to = sigmas[i + 1];
auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigma, i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
if (sigma_to == 0.f) {
x = denoised;
} else if (eta == 0.f) {
Expand All @@ -1003,11 +1005,11 @@ static sd::Tensor<float> sample_euler(denoise_cb_t model,
int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
float sigma = sigmas[i];
auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigma, i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
sd::Tensor<float> d = (x - denoised) / sigma;
x += d * (sigmas[i + 1] - sigma);
}
Expand All @@ -1019,22 +1021,22 @@ static sd::Tensor<float> sample_heun(denoise_cb_t model,
const std::vector<float>& sigmas) {
int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigmas[i], -(i + 1));
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
sd::Tensor<float> d = (x - denoised) / sigmas[i];
float dt = sigmas[i + 1] - sigmas[i];
if (sigmas[i + 1] == 0) {
x += d * dt;
} else {
sd::Tensor<float> x2 = x + d * dt;
auto denoised2_opt = model(x2, sigmas[i + 1], i + 1, nullptr);
if (denoised2_opt.empty()) {
auto denoised2_opt = model(x2, sigmas[i + 1], i + 1);
if (denoised2_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised2 = std::move(denoised2_opt);
sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
d = (d + (x2 - denoised2) / sigmas[i + 1]) / 2.0f;
x += d * dt;
}
Expand All @@ -1047,11 +1049,11 @@ static sd::Tensor<float> sample_dpm2(denoise_cb_t model,
const std::vector<float>& sigmas) {
int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigmas[i], -(i + 1));
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
sd::Tensor<float> d = (x - denoised) / sigmas[i];
if (sigmas[i + 1] == 0) {
x += d * (sigmas[i + 1] - sigmas[i]);
Expand All @@ -1060,11 +1062,11 @@ static sd::Tensor<float> sample_dpm2(denoise_cb_t model,
float dt_1 = sigma_mid - sigmas[i];
float dt_2 = sigmas[i + 1] - sigmas[i];
sd::Tensor<float> x2 = x + d * dt_1;
auto denoised2_opt = model(x2, sigma_mid, i + 1, nullptr);
if (denoised2_opt.empty()) {
auto denoised2_opt = model(x2, sigma_mid, i + 1);
if (denoised2_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised2 = std::move(denoised2_opt);
sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
x += ((x2 - denoised2) / sigma_mid) * dt_2;
}
}
Expand All @@ -1081,11 +1083,11 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral(denoise_cb_t model,

int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigmas[i], -(i + 1));
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
auto [sigma_down, sigma_up] = get_ancestral_step(sigmas[i], sigmas[i + 1], eta);

if (sigma_down == 0) {
Expand All @@ -1097,11 +1099,11 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral(denoise_cb_t model,
float s = t + 0.5f * h;
float sigma_s = sigma_fn(s);
sd::Tensor<float> x2 = (sigma_s / sigma_fn(t)) * x - (exp(-h * 0.5f) - 1) * denoised;
auto denoised2_opt = model(x2, sigma_s, i + 1, nullptr);
if (denoised2_opt.empty()) {
auto denoised2_opt = model(x2, sigma_s, i + 1);
if (denoised2_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised2 = std::move(denoised2_opt);
sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (exp(-h) - 1) * denoised2;
}

Expand All @@ -1124,11 +1126,11 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,

bool opt_first_step = (1.0 - sigma < 1e-6);

auto denoised_opt = model(x, sigma, (opt_first_step ? 1 : -1) * (i + 1), nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigma, (opt_first_step ? 1 : -1) * (i + 1));
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);

if (sigma_to == 0.0f) {
// Euler method (final step, no noise)
Expand All @@ -1153,8 +1155,8 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
// so sigma_s = 1 = sigma, and sigma_s_i_ratio = sigma_s / sigma = 1
// u = (x*sigma_s_i_ratio)+(denoised*(1.0f-sigma_s_i_ratio))
// = (x*1)+(denoised*0) = x
// so D_i = model(u, sigma_s, i + 1, nullptr)
// = model(x, sigma, i + 1, nullptr)
// so D_i = model(u, sigma_s, i + 1)
// = model(x, sigma, i + 1)
// = denoised
D_i = denoised;

Expand Down Expand Up @@ -1187,11 +1189,11 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
float sigma_s_i_ratio = sigma_s / sigma;
sd::Tensor<float> u = (x * sigma_s_i_ratio) + (denoised * (1.0f - sigma_s_i_ratio));

auto denoised2_opt = model(u, sigma_s, i + 1, nullptr);
if (denoised2_opt.empty()) {
auto denoised2_opt = model(u, sigma_s, i + 1);
if (denoised2_opt.pred.empty()) {
return {};
}
D_i = std::move(denoised2_opt);
D_i = std::move(denoised2_opt.pred);
}

float sigma_down_i_ratio = sigma_down / sigma;
Expand All @@ -1214,11 +1216,11 @@ static sd::Tensor<float> sample_dpmpp_2m(denoise_cb_t model,

int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigmas[i], i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
float t = t_fn(sigmas[i]);
float t_next = t_fn(sigmas[i + 1]);
float h = t_next - t;
Expand Down Expand Up @@ -1246,11 +1248,11 @@ static sd::Tensor<float> sample_dpmpp_2m_v2(denoise_cb_t model,

int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigmas[i], i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
float t = t_fn(sigmas[i]);
float t_next = t_fn(sigmas[i + 1]);
float h = t_next - t;
Expand Down Expand Up @@ -1354,11 +1356,11 @@ static sd::Tensor<float> sample_lcm(denoise_cb_t model,

int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigmas[i], i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
x = std::move(denoised_opt);
x = std::move(denoised_opt.pred);
if (sigmas[i + 1] > 0) {
if (is_flow_denoiser) {
x *= (1 - sigmas[i + 1]);
Expand Down Expand Up @@ -1400,11 +1402,11 @@ static sd::Tensor<float> sample_ipndm(denoise_cb_t model,
float sigma = sigmas[i];
float sigma_next = sigmas[i + 1];

auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigma, i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);

sd::Tensor<float> d_cur = (x - denoised) / sigma;
int order = std::min(max_order, i + 1);
Expand Down Expand Up @@ -1444,11 +1446,11 @@ static sd::Tensor<float> sample_ipndm_v(denoise_cb_t model,
float sigma = sigmas[i];
float t_next = sigmas[i + 1];

auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigma, i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);

sd::Tensor<float> d_cur = (x - denoised) / sigma;
int order = std::min(max_order, i + 1);
Expand Down Expand Up @@ -1506,11 +1508,11 @@ static sd::Tensor<float> sample_res_multistep(denoise_cb_t model,

int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigmas[i], i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);

float sigma_from = sigmas[i];
float sigma_to = sigmas[i + 1];
Expand Down Expand Up @@ -1583,11 +1585,11 @@ static sd::Tensor<float> sample_res_2s(denoise_cb_t model,
float sigma_from = sigmas[i];
float sigma_to = sigmas[i + 1];

auto denoised_opt = model(x, sigma_from, -(i + 1), nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigma_from, -(i + 1));
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);

auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step(sigma_from, sigma_to, eta, is_flow_denoiser);

Expand All @@ -1609,11 +1611,11 @@ static sd::Tensor<float> sample_res_2s(denoise_cb_t model,
sd::Tensor<float> eps1 = denoised - x0;
sd::Tensor<float> x2 = x0 + eps1 * (h * a21);

auto denoised2_opt = model(x2, sigma_c2, i + 1, nullptr);
if (denoised2_opt.empty()) {
auto denoised2_opt = model(x2, sigma_c2, i + 1);
if (denoised2_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised2 = std::move(denoised2_opt);
sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
sd::Tensor<float> eps2 = denoised2 - x0;
x = x0 + h * (b1 * eps1 + b2 * eps2);
}
Expand Down Expand Up @@ -1686,10 +1688,11 @@ static sd::Tensor<float> sample_er_sde(denoise_cb_t model,

int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
sd::Tensor<float> denoised = model(x, sigmas[i], i + 1, nullptr);
if (denoised.empty()) {
auto denoised_opt = model(x, sigmas[i], i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt.pred);

int stage_used = std::min(max_stage, i + 1);

Expand Down Expand Up @@ -1804,11 +1807,11 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
int timestep_s = (int)floor((1 - eta) * prev_timestep);
float sigma = sigmas[i];

auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
auto denoised_opt = model(x, sigma, i + 1);
if (denoised_opt.pred.empty()) {
return {};
}
sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
sd::Tensor<float> d = (x - denoised) / sigma;

float alpha_prod_t = 1.0f / (sigma * sigma + 1.0f);
Expand All @@ -1833,16 +1836,15 @@ static sd::Tensor<float> sample_euler_cfg_pp(denoise_cb_t model,
const std::vector<float>& sigmas) {
int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
float sigma = sigmas[i];
sd::Tensor<float> uncond_denoised;

auto denoised_opt = model(x, sigma, i + 1, &uncond_denoised);
if (denoised_opt.empty() || uncond_denoised.empty()) {
float sigma = sigmas[i];
auto denoised_opt = model(x, sigma, i + 1);
if (denoised_opt.pred.empty() || denoised_opt.pred_uncond.empty()) {
return {};
}

sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> d = (x - uncond_denoised) / sigma;
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
sd::Tensor<float> uncond_denoised = std::move(denoised_opt.pred_uncond);
sd::Tensor<float> d = (x - uncond_denoised) / sigma;

x = denoised + d * sigmas[i + 1];
}
Expand All @@ -1856,16 +1858,15 @@ static sd::Tensor<float> sample_euler_ancestral_cfg_pp(denoise_cb_t model,
float eta) {
int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
float sigma = sigmas[i];
sd::Tensor<float> uncond_denoised;

auto denoised_opt = model(x, sigma, i + 1, &uncond_denoised);
if (denoised_opt.empty() || uncond_denoised.empty()) {
float sigma = sigmas[i];
auto denoised_opt = model(x, sigma, i + 1);
if (denoised_opt.pred.empty() || denoised_opt.pred_uncond.empty()) {
return {};
}

sd::Tensor<float> denoised = std::move(denoised_opt);
sd::Tensor<float> d = (x - uncond_denoised) / sigma;
sd::Tensor<float> denoised = std::move(denoised_opt.pred);
sd::Tensor<float> uncond_denoised = std::move(denoised_opt.pred_uncond);
sd::Tensor<float> d = (x - uncond_denoised) / sigma;

auto [sigma_down, sigma_up] = get_ancestral_step(sigmas[i], sigmas[i + 1], eta);

Expand Down
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