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Merged
leejet merged 2 commits into from
May 14, 2026
Merged

feat: add Euler CFG++ and Euler-A CFG++ samplers #1354

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6be4c50
feat: add Euler CFG++ and Euler-A CFG++ samplers
daniandtheweb May 5, 2026
2e1a489
fix: fixed typo
daniandtheweb May 9, 2026
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4 changes: 2 additions & 2 deletions examples/cli/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -160,10 +160,10 @@ Generation Options:
-s, --seed RNG seed (default: 42, use random seed for < 0)
--sampling-method sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m,
dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s,
er_sde] (default: euler for Flux/SD3/Wan, euler_a otherwise)
er_sde, euler_cfg_pp, euler_a_cfg_pp] (default: euler for Flux/SD3/Wan, euler_a otherwise)
--high-noise-sampling-method (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a,
dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep,
res_2s, er_sde] default: euler for Flux/SD3/Wan, euler_a otherwise
res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp] default: euler for Flux/SD3/Wan, euler_a otherwise
--scheduler denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits,
smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent], default:
discrete
Expand Down
4 changes: 2 additions & 2 deletions examples/common/common.cpp
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Expand Up @@ -1237,12 +1237,12 @@ ArgOptions SDGenerationParams::get_options() {
on_seed_arg},
{"",
"--sampling-method",
"sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde] "
"sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
"(default: euler for Flux/SD3/Wan, euler_a otherwise)",
on_sample_method_arg},
{"",
"--high-noise-sampling-method",
"(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde]"
"(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
" default: euler for Flux/SD3/Wan, euler_a otherwise",
on_high_noise_sample_method_arg},
{"",
Expand Down
4 changes: 2 additions & 2 deletions examples/server/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -262,10 +262,10 @@ Default Generation Options:
-s, --seed RNG seed (default: 42, use random seed for < 0)
--sampling-method sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m,
dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s,
er_sde] (default: euler for Flux/SD3/Wan, euler_a otherwise)
er_sde, euler_cfg_pp, euler_a_cfg_pp] (default: euler for Flux/SD3/Wan, euler_a otherwise)
--high-noise-sampling-method (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a,
dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep,
res_2s, er_sde] default: euler for Flux/SD3/Wan, euler_a otherwise
res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp] default: euler for Flux/SD3/Wan, euler_a otherwise
--scheduler denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits,
smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent], default:
discrete
Expand Down
2 changes: 1 addition & 1 deletion examples/server/frontend
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Submodule frontend updated from 797ccf to 1a3417
4 changes: 4 additions & 0 deletions examples/server/routes_sdapi.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,10 @@ static enum sample_method_t get_sdapi_sample_method(std::string name) {
{"k_res_multistep", RES_MULTISTEP_SAMPLE_METHOD},
{"res 2s", RES_2S_SAMPLE_METHOD},
{"k_res_2s", RES_2S_SAMPLE_METHOD},
{"euler_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
{"k_euler_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
{"euler_a_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
{"k_euler_a_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
};
auto it = hardcoded.find(name);
return it != hardcoded.end() ? it->second : SAMPLE_METHOD_COUNT;
Expand Down
2 changes: 2 additions & 0 deletions include/stable-diffusion.h
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,8 @@ enum sample_method_t {
RES_MULTISTEP_SAMPLE_METHOD,
RES_2S_SAMPLE_METHOD,
ER_SDE_SAMPLE_METHOD,
EULER_CFG_PP_SAMPLE_METHOD,
EULER_A_CFG_PP_SAMPLE_METHOD,
SAMPLE_METHOD_COUNT
};

Expand Down
106 changes: 80 additions & 26 deletions src/denoiser.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -752,7 +752,7 @@ struct Flux2FlowDenoiser : public FluxFlowDenoiser {
}
};

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

static std::pair<float, float> get_ancestral_step(float sigma_from,
float sigma_to,
Expand Down Expand Up @@ -816,7 +816,7 @@ static sd::Tensor<float> sample_euler_ancestral(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);
auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -839,7 +839,7 @@ static sd::Tensor<float> sample_euler_flow(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);
auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -861,7 +861,7 @@ 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);
auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -877,7 +877,7 @@ 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));
auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -888,7 +888,7 @@ static sd::Tensor<float> sample_heun(denoise_cb_t model,
x += d * dt;
} else {
sd::Tensor<float> x2 = x + d * dt;
auto denoised2_opt = model(x2, sigmas[i + 1], i + 1);
auto denoised2_opt = model(x2, sigmas[i + 1], i + 1, nullptr);
if (denoised2_opt.empty()) {
return {};
}
Expand All @@ -905,7 +905,7 @@ 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));
auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -918,7 +918,7 @@ 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);
auto denoised2_opt = model(x2, sigma_mid, i + 1, nullptr);
if (denoised2_opt.empty()) {
return {};
}
Expand All @@ -939,7 +939,7 @@ 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));
auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -955,7 +955,7 @@ 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);
auto denoised2_opt = model(x2, sigma_s, i + 1, nullptr);
if (denoised2_opt.empty()) {
return {};
}
Expand All @@ -982,7 +982,7 @@ 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));
auto denoised_opt = model(x, sigma, (opt_first_step ? 1 : -1) * (i + 1), nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1011,8 +1011,8 @@ 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)
// = model(x, sigma, i + 1)
// so D_i = model(u, sigma_s, i + 1, nullptr)
// = model(x, sigma, i + 1, nullptr)
// = denoised
D_i = denoised;

Expand Down Expand Up @@ -1045,7 +1045,7 @@ 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);
auto denoised2_opt = model(u, sigma_s, i + 1, nullptr);
if (denoised2_opt.empty()) {
return {};
}
Expand All @@ -1072,7 +1072,7 @@ 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);
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1104,7 +1104,7 @@ 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);
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1139,7 +1139,7 @@ static sd::Tensor<float> sample_lcm(denoise_cb_t model,
bool is_flow_denoiser) {
int steps = static_cast<int>(sigmas.size()) - 1;
for (int i = 0; i < steps; i++) {
auto denoised_opt = model(x, sigmas[i], i + 1);
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -1165,7 +1165,7 @@ 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);
auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1209,7 +1209,7 @@ 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);
auto denoised_opt = model(x, sigma, i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -1235,7 +1235,7 @@ static sd::Tensor<float> sample_ipndm_v(denoise_cb_t model,
break;
}

if (hist.size() == static_cast<size_t>(max_order - 1)) {
if (hist.size() == static_cast<size_t>(max_order - 1), nullptr) {

@masamaru-san masamaru-san May 9, 2026

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Because this is a comma expression, the if statement will always evaluate to nullptr (= false). Is that okay?

@daniandtheweb daniandtheweb May 9, 2026

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I must have mistakenly added it during the refactor, thanks for noticing, I've just fixed that.

@masamaru-san masamaru-san May 9, 2026 •
edited
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Thanks.

edit: I'm not a Contributor so that plz ignore my comments.

hist.erase(hist.begin());
}
hist.push_back(std::move(d_cur));
Expand Down Expand Up @@ -1270,7 +1270,7 @@ 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);
auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1342,7 +1342,7 @@ 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));
auto denoised_opt = model(x, sigma_from, -(i + 1), nullptr);
if (denoised_opt.empty()) {
return {};
}
Expand All @@ -1368,7 +1368,7 @@ 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);
auto denoised2_opt = model(x2, sigma_c2, i + 1, nullptr);
if (denoised2_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1442,7 +1442,7 @@ 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);
sd::Tensor<float> denoised = model(x, sigmas[i], i + 1, nullptr);
if (denoised.empty()) {
return {};
}
Expand Down Expand Up @@ -1528,7 +1528,7 @@ static sd::Tensor<float> sample_ddim_trailing(denoise_cb_t model,
float sigma = sigmas[i];
float sigma_to = sigmas[i + 1];

auto model_output_opt = model(x, sigma, i + 1);
auto model_output_opt = model(x, sigma, i + 1, nullptr);
if (model_output_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1600,7 +1600,7 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
int timestep_s = (int)floor((1 - eta) * prev_timestep);
float sigma = sigmas[i];

auto model_output_opt = model(x, sigma, i + 1);
auto model_output_opt = model(x, sigma, i + 1, nullptr);
if (model_output_opt.empty()) {
return {};
}
Expand Down Expand Up @@ -1628,6 +1628,56 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
return x;
}

static sd::Tensor<float> sample_euler_cfg_pp(denoise_cb_t model,
sd::Tensor<float> x,
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()) {
return {};
}

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

x = denoised + d * sigmas[i + 1];
}
return x;
}

static sd::Tensor<float> sample_euler_ancestral_cfg_pp(denoise_cb_t model,
sd::Tensor<float> x,
const std::vector<float>& sigmas,
std::shared_ptr<RNG> rng,
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()) {
return {};
}

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

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

x = denoised + d * sigma_down;

if (sigmas[i + 1] > 0) {
x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
}
}
return x;
}

// k diffusion reverse ODE: dx = (x - D(x;\sigma)) / \sigma dt; \sigma(t) = t
static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
denoise_cb_t model,
Expand Down Expand Up @@ -1673,6 +1723,10 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
return sample_ddim_trailing(model, std::move(x), sigmas, rng, eta);
case TCD_SAMPLE_METHOD:
return sample_tcd(model, std::move(x), sigmas, rng, eta);
case EULER_CFG_PP_SAMPLE_METHOD:
return sample_euler_cfg_pp(model, std::move(x), sigmas);
case EULER_A_CFG_PP_SAMPLE_METHOD:
return sample_euler_ancestral_cfg_pp(model, std::move(x), sigmas, rng, eta);
default:
return {};
}
Expand Down
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