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Merged
leejet merged 5 commits into from
Feb 1, 2026
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feat: add res_multistep, res_2s and bong tangent scheduler #1234

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5d7c1f8
add res multistep
rmatif Jan 29, 2026
9843170
add res_2s
rmatif Jan 29, 2026
de617eb
add bong tangent
rmatif Jan 29, 2026
3ddab19
fix res_2s model call
rmatif Jan 29, 2026
9945c05
format code
leejet Feb 1, 2026
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305 changes: 305 additions & 0 deletions denoiser.hpp
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Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
#ifndef __DENOISER_HPP__
#define __DENOISER_HPP__

#include <cmath>

#include "ggml_extend.hpp"
#include "gits_noise.inl"

Expand Down Expand Up @@ -351,6 +353,95 @@ struct SmoothStepScheduler : SigmaScheduler {
}
};

struct BongTangentScheduler : SigmaScheduler {
static constexpr float kPi = 3.14159265358979323846f;

static std::vector<float> get_bong_tangent_sigmas(int steps, float slope, float pivot, float start, float end) {
std::vector<float> sigmas;
if (steps <= 0) {
return sigmas;
}

float smax = ((2.0f / kPi) * atanf(-slope * (0.0f - pivot)) + 1.0f) * 0.5f;
float smin = ((2.0f / kPi) * atanf(-slope * ((float)(steps - 1) - pivot)) + 1.0f) * 0.5f;
float srange = smax - smin;
float sscale = start - end;

sigmas.reserve(steps);

if (fabsf(srange) < 1e-8f) {
if (steps == 1) {
sigmas.push_back(start);
return sigmas;
}
for (int i = 0; i < steps; ++i) {
float t = (float)i / (float)(steps - 1);
sigmas.push_back(start + (end - start) * t);
}
return sigmas;
}

float inv_srange = 1.0f / srange;
for (int x = 0; x < steps; ++x) {
float v = ((2.0f / kPi) * atanf(-slope * ((float)x - pivot)) + 1.0f) * 0.5f;
float sigma = ((v - smin) * inv_srange) * sscale + end;
sigmas.push_back(sigma);
}

return sigmas;
}

std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t /*t_to_sigma*/) override {
std::vector<float> result;
if (n == 0) {
return result;
}

float start = sigma_max;
float end = sigma_min;
float middle = sigma_min + (sigma_max - sigma_min) * 0.5f;

float pivot_1 = 0.6f;
float pivot_2 = 0.6f;
float slope_1 = 0.2f;
float slope_2 = 0.2f;

int steps = static_cast<int>(n) + 2;
int midpoint = static_cast<int>(((float)steps * pivot_1 + (float)steps * pivot_2) * 0.5f);
int pivot_1_i = static_cast<int>((float)steps * pivot_1);
int pivot_2_i = static_cast<int>((float)steps * pivot_2);

float slope_scale = (float)steps / 40.0f;
slope_1 = slope_1 / slope_scale;
slope_2 = slope_2 / slope_scale;

int stage_2_len = steps - midpoint;
int stage_1_len = steps - stage_2_len;

std::vector<float> sigmas_1 = get_bong_tangent_sigmas(stage_1_len, slope_1, (float)pivot_1_i, start, middle);
std::vector<float> sigmas_2 = get_bong_tangent_sigmas(stage_2_len, slope_2, (float)(pivot_2_i - stage_1_len), middle, end);

if (!sigmas_1.empty()) {
sigmas_1.pop_back();
}

result.reserve(n + 1);
result.insert(result.end(), sigmas_1.begin(), sigmas_1.end());
result.insert(result.end(), sigmas_2.begin(), sigmas_2.end());

if (result.size() < n + 1) {
while (result.size() < n + 1) {
result.push_back(end);
}
} else if (result.size() > n + 1) {
result.resize(n + 1);
}

result[n] = 0.0f;
return result;
}
};

struct KLOptimalScheduler : SigmaScheduler {
std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
std::vector<float> sigmas;
Expand Down Expand Up @@ -431,6 +522,10 @@ struct Denoiser {
LOG_INFO("get_sigmas with SmoothStep scheduler");
scheduler = std::make_shared<SmoothStepScheduler>();
break;
case BONG_TANGENT_SCHEDULER:
LOG_INFO("get_sigmas with bong_tangent scheduler");
scheduler = std::make_shared<BongTangentScheduler>();
break;
case KL_OPTIMAL_SCHEDULER:
LOG_INFO("get_sigmas with KL Optimal scheduler");
scheduler = std::make_shared<KLOptimalScheduler>();
Expand Down Expand Up @@ -1634,6 +1729,216 @@ static bool sample_k_diffusion(sample_method_t method,
}
}
} break;
case RES_MULTISTEP_SAMPLE_METHOD: // Res Multistep sampler
{
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* old_denoised = ggml_dup_tensor(work_ctx, x);

bool have_old_sigma = false;
float old_sigma_down = 0.0f;

auto t_fn = [](float sigma) -> float { return -logf(sigma); };
auto sigma_fn = [](float t) -> float { return expf(-t); };
auto phi1_fn = [](float t) -> float {
if (fabsf(t) < 1e-6f) {
return 1.0f + t * 0.5f + (t * t) / 6.0f;
}
return (expf(t) - 1.0f) / t;
};
auto phi2_fn = [&](float t) -> float {
if (fabsf(t) < 1e-6f) {
return 0.5f + t / 6.0f + (t * t) / 24.0f;
}
float phi1_val = phi1_fn(t);
return (phi1_val - 1.0f) / t;
};

for (int i = 0; i < steps; i++) {
ggml_tensor* denoised = model(x, sigmas[i], i + 1);
if (denoised == nullptr) {
return false;
}

float sigma_from = sigmas[i];
float sigma_to = sigmas[i + 1];
float sigma_up = 0.0f;
float sigma_down = sigma_to;

if (eta > 0.0f) {
float sigma_from_sq = sigma_from * sigma_from;
float sigma_to_sq = sigma_to * sigma_to;
if (sigma_from_sq > 0.0f) {
float term = sigma_to_sq * (sigma_from_sq - sigma_to_sq) / sigma_from_sq;
if (term > 0.0f) {
sigma_up = eta * std::sqrt(term);
}
}
sigma_up = std::min(sigma_up, sigma_to);
float sigma_down_sq = sigma_to_sq - sigma_up * sigma_up;
sigma_down = sigma_down_sq > 0.0f ? std::sqrt(sigma_down_sq) : 0.0f;
}

if (sigma_down == 0.0f || !have_old_sigma) {
float dt = sigma_down - sigma_from;
float* vec_x = (float*)x->data;
float* vec_denoised = (float*)denoised->data;

for (int j = 0; j < ggml_nelements(x); j++) {
float d = (vec_x[j] - vec_denoised[j]) / sigma_from;
vec_x[j] = vec_x[j] + d * dt;
}
} else {
float t = t_fn(sigma_from);
float t_old = t_fn(old_sigma_down);
float t_next = t_fn(sigma_down);
float t_prev = t_fn(sigmas[i - 1]);
float h = t_next - t;
float c2 = (t_prev - t_old) / h;

float phi1_val = phi1_fn(-h);
float phi2_val = phi2_fn(-h);
float b1 = phi1_val - phi2_val / c2;
float b2 = phi2_val / c2;

if (!std::isfinite(b1)) {
b1 = 0.0f;
}
if (!std::isfinite(b2)) {
b2 = 0.0f;
}

float sigma_h = sigma_fn(h);
float* vec_x = (float*)x->data;
float* vec_denoised = (float*)denoised->data;
float* vec_old_denoised = (float*)old_denoised->data;

for (int j = 0; j < ggml_nelements(x); j++) {
vec_x[j] = sigma_h * vec_x[j] + h * (b1 * vec_denoised[j] + b2 * vec_old_denoised[j]);
}
}

if (sigmas[i + 1] > 0 && sigma_up > 0.0f) {
ggml_ext_im_set_randn_f32(noise, rng);
float* vec_x = (float*)x->data;
float* vec_noise = (float*)noise->data;

for (int j = 0; j < ggml_nelements(x); j++) {
vec_x[j] = vec_x[j] + vec_noise[j] * sigma_up;
}
}

float* vec_old_denoised = (float*)old_denoised->data;
float* vec_denoised = (float*)denoised->data;
for (int j = 0; j < ggml_nelements(x); j++) {
vec_old_denoised[j] = vec_denoised[j];
}

old_sigma_down = sigma_down;
have_old_sigma = true;
}
} break;
case RES_2S_SAMPLE_METHOD: // Res 2s sampler
{
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* x0 = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* x2 = ggml_dup_tensor(work_ctx, x);

const float c2 = 0.5f;
auto t_fn = [](float sigma) -> float { return -logf(sigma); };
auto phi1_fn = [](float t) -> float {
if (fabsf(t) < 1e-6f) {
return 1.0f + t * 0.5f + (t * t) / 6.0f;
}
return (expf(t) - 1.0f) / t;
};
auto phi2_fn = [&](float t) -> float {
if (fabsf(t) < 1e-6f) {
return 0.5f + t / 6.0f + (t * t) / 24.0f;
}
float phi1_val = phi1_fn(t);
return (phi1_val - 1.0f) / t;
};

for (int i = 0; i < steps; i++) {
float sigma_from = sigmas[i];
float sigma_to = sigmas[i + 1];

ggml_tensor* denoised = model(x, sigma_from, -(i + 1));
if (denoised == nullptr) {
return false;
}

float sigma_up = 0.0f;
float sigma_down = sigma_to;
if (eta > 0.0f) {
float sigma_from_sq = sigma_from * sigma_from;
float sigma_to_sq = sigma_to * sigma_to;
if (sigma_from_sq > 0.0f) {
float term = sigma_to_sq * (sigma_from_sq - sigma_to_sq) / sigma_from_sq;
if (term > 0.0f) {
sigma_up = eta * std::sqrt(term);
}
}
sigma_up = std::min(sigma_up, sigma_to);
float sigma_down_sq = sigma_to_sq - sigma_up * sigma_up;
sigma_down = sigma_down_sq > 0.0f ? std::sqrt(sigma_down_sq) : 0.0f;
}

float* vec_x = (float*)x->data;
float* vec_x0 = (float*)x0->data;
for (int j = 0; j < ggml_nelements(x); j++) {
vec_x0[j] = vec_x[j];
}

if (sigma_down == 0.0f || sigma_from == 0.0f) {
float* vec_denoised = (float*)denoised->data;
for (int j = 0; j < ggml_nelements(x); j++) {
vec_x[j] = vec_denoised[j];
}
} else {
float t = t_fn(sigma_from);
float t_next = t_fn(sigma_down);
float h = t_next - t;

float a21 = c2 * phi1_fn(-h * c2);
float phi1_val = phi1_fn(-h);
float phi2_val = phi2_fn(-h);
float b2 = phi2_val / c2;
float b1 = phi1_val - b2;

float sigma_c2 = expf(-(t + h * c2));

float* vec_denoised = (float*)denoised->data;
float* vec_x2 = (float*)x2->data;
for (int j = 0; j < ggml_nelements(x); j++) {
float eps1 = vec_denoised[j] - vec_x0[j];
vec_x2[j] = vec_x0[j] + h * a21 * eps1;
}

ggml_tensor* denoised2 = model(x2, sigma_c2, i + 1);
if (denoised2 == nullptr) {
return false;
}
float* vec_denoised2 = (float*)denoised2->data;

for (int j = 0; j < ggml_nelements(x); j++) {
float eps1 = vec_denoised[j] - vec_x0[j];
float eps2 = vec_denoised2[j] - vec_x0[j];
vec_x[j] = vec_x0[j] + h * (b1 * eps1 + b2 * eps2);
}
}

if (sigmas[i + 1] > 0 && sigma_up > 0.0f) {
ggml_ext_im_set_randn_f32(noise, rng);
float* vec_x = (float*)x->data;
float* vec_noise = (float*)noise->data;

for (int j = 0; j < ggml_nelements(x); j++) {
vec_x[j] = vec_x[j] + vec_noise[j] * sigma_up;
}
}
}
} break;

default:
LOG_ERROR("Attempting to sample with nonexisting sample method %i", method);
Expand Down
14 changes: 7 additions & 7 deletions examples/cli/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -107,14 +107,14 @@ Generation Options:
medium
--skip-layer-start <float> SLG enabling point (default: 0.01)
--skip-layer-end <float> SLG disabling point (default: 0.2)
--eta <float> eta in DDIM, only for DDIM and TCD (default: 0)
--eta <float> eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
--high-noise-cfg-scale <float> (high noise) unconditional guidance scale: (default: 7.0)
--high-noise-img-cfg-scale <float> (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
--high-noise-guidance <float> (high noise) distilled guidance scale for models with guidance input (default: 3.5)
--high-noise-slg-scale <float> (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
--high-noise-skip-layer-start <float> (high noise) SLG enabling point (default: 0.01)
--high-noise-skip-layer-end <float> (high noise) SLG disabling point (default: 0.2)
--high-noise-eta <float> (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
--high-noise-eta <float> (high noise) eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
--strength <float> strength for noising/unnoising (default: 0.75)
--pm-style-strength <float>
--control-strength <float> strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
Expand All @@ -123,12 +123,12 @@ Generation Options:
--increase-ref-index automatically increase the indices of references images based on the order they are listed (starting with 1).
--disable-auto-resize-ref-image disable auto resize of ref images
-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] (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] default: euler for Flux/SD3/Wan, euler_a otherwise
--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] (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] 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], default: discrete
kl_optimal, lcm, bong_tangent], default: discrete
--sigmas custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
--skip-layers layers to skip for SLG steps (default: [7,8,9])
--high-noise-skip-layers (high noise) layers to skip for SLG steps (default: [7,8,9])
Expand Down
6 changes: 3 additions & 3 deletions examples/common/common.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1478,17 +1478,17 @@ struct SDGenerationParams {
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] "
"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] "
"(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]"
"(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]"
" default: euler for Flux/SD3/Wan, euler_a otherwise",
on_high_noise_sample_method_arg},
{"",
"--scheduler",
"denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm], default: discrete",
"denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent], default: discrete",
on_scheduler_arg},
{"",
"--sigmas",
Expand Down
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