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Nov 22, 2025
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refactor: optimize the handling of sample method #999

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8722d02
refactor: optimize the handling of sample method
leejet Nov 22, 2025
7774118
fix sampling_methods_str
leejet Nov 22, 2025
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28 changes: 14 additions & 14 deletions denoiser.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -619,7 +619,7 @@ static void sample_k_diffusion(sample_method_t method,
size_t steps = sigmas.size() - 1;
// sample_euler_ancestral
switch (method) {
case EULER_A: {
case EULER_A_SAMPLE_METHOD: {
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);

Expand Down Expand Up @@ -672,7 +672,7 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case EULER: // Implemented without any sigma churn
case EULER_SAMPLE_METHOD: // Implemented without any sigma churn
{
struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);

Expand Down Expand Up @@ -705,7 +705,7 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case HEUN: {
case HEUN_SAMPLE_METHOD: {
struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* x2 = ggml_dup_tensor(work_ctx, x);

Expand Down Expand Up @@ -755,7 +755,7 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case DPM2: {
case DPM2_SAMPLE_METHOD: {
struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* x2 = ggml_dup_tensor(work_ctx, x);

Expand Down Expand Up @@ -807,7 +807,7 @@ static void sample_k_diffusion(sample_method_t method,
}

} break;
case DPMPP2S_A: {
case DPMPP2S_A_SAMPLE_METHOD: {
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* x2 = ggml_dup_tensor(work_ctx, x);

Expand Down Expand Up @@ -871,7 +871,7 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case DPMPP2M: // DPM++ (2M) from Karras et al (2022)
case DPMPP2M_SAMPLE_METHOD: // DPM++ (2M) from Karras et al (2022)
{
struct ggml_tensor* old_denoised = ggml_dup_tensor(work_ctx, x);

Expand Down Expand Up @@ -910,7 +910,7 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case DPMPP2Mv2: // Modified DPM++ (2M) from https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457
case DPMPP2Mv2_SAMPLE_METHOD: // Modified DPM++ (2M) from https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457
{
struct ggml_tensor* old_denoised = ggml_dup_tensor(work_ctx, x);

Expand Down Expand Up @@ -953,7 +953,7 @@ 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
case IPNDM_SAMPLE_METHOD: // iPNDM sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
{
int max_order = 4;
ggml_tensor* x_next = x;
Expand Down Expand Up @@ -1028,7 +1028,7 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case IPNDM_V: // iPNDM_v sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
case IPNDM_V_SAMPLE_METHOD: // 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;
Expand Down Expand Up @@ -1102,7 +1102,7 @@ static void sample_k_diffusion(sample_method_t method,
d_cur = ggml_dup_tensor(work_ctx, x_next);
}
} break;
case LCM: // Latent Consistency Models
case LCM_SAMPLE_METHOD: // Latent Consistency Models
{
struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);
Expand Down Expand Up @@ -1137,8 +1137,8 @@ static void sample_k_diffusion(sample_method_t method,
}
}
} break;
case DDIM_TRAILING: // Denoising Diffusion Implicit Models
// with the "trailing" timestep spacing
case DDIM_TRAILING_SAMPLE_METHOD: // Denoising Diffusion Implicit Models
// with the "trailing" timestep spacing
{
// See J. Song et al., "Denoising Diffusion Implicit
// Models", arXiv:2010.02502 [cs.LG]
Expand Down Expand Up @@ -1331,8 +1331,8 @@ static void sample_k_diffusion(sample_method_t method,
// factor c_in.
}
} break;
case TCD: // Strategic Stochastic Sampling (Algorithm 4) in
// Trajectory Consistency Distillation
case TCD_SAMPLE_METHOD: // Strategic Stochastic Sampling (Algorithm 4) in
// Trajectory Consistency Distillation
{
// See J. Zheng et al., "Trajectory Consistency
// Distillation: Improved Latent Consistency Distillation
Expand Down
6 changes: 5 additions & 1 deletion examples/cli/main.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1902,10 +1902,14 @@ int main(int argc, const char* argv[]) {
return 1;
}

if (params.sample_params.sample_method == SAMPLE_METHOD_DEFAULT) {
if (params.sample_params.sample_method == SAMPLE_METHOD_COUNT) {
params.sample_params.sample_method = sd_get_default_sample_method(sd_ctx);
}

if (params.high_noise_sample_params.sample_method == SAMPLE_METHOD_COUNT) {
params.high_noise_sample_params.sample_method = sd_get_default_sample_method(sd_ctx);
}

if (params.sample_params.scheduler == SCHEDULER_COUNT) {
params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx);
}
Expand Down
57 changes: 33 additions & 24 deletions stable-diffusion.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -47,8 +47,8 @@ const char* model_version_to_str[] = {
};

const char* sampling_methods_str[] = {
"default",
"Euler",
"Euler A",
"Heun",
"DPM2",
"DPM++ (2s)",
Expand All @@ -59,7 +59,6 @@ const char* sampling_methods_str[] = {
"LCM",
"DDIM \"trailing\"",
"TCD",
"Euler A",
};

/*================================================== Helper Functions ================================================*/
Expand Down Expand Up @@ -2228,8 +2227,8 @@ enum rng_type_t str_to_rng_type(const char* str) {
}

const char* sample_method_to_str[] = {
"default",
"euler",
"euler_a",
"heun",
"dpm2",
"dpm++2s_a",
Expand All @@ -2240,7 +2239,6 @@ const char* sample_method_to_str[] = {
"lcm",
"ddim_trailing",
"tcd",
"euler_a",
};

const char* sd_sample_method_name(enum sample_method_t sample_method) {
Expand Down Expand Up @@ -2468,7 +2466,7 @@ void sd_sample_params_init(sd_sample_params_t* sample_params) {
sample_params->guidance.slg.layer_end = 0.2f;
sample_params->guidance.slg.scale = 0.f;
sample_params->scheduler = SCHEDULER_COUNT;
sample_params->sample_method = SAMPLE_METHOD_DEFAULT;
sample_params->sample_method = SAMPLE_METHOD_COUNT;
sample_params->sample_steps = 20;
}

Expand Down Expand Up @@ -2626,19 +2624,19 @@ void free_sd_ctx(sd_ctx_t* sd_ctx) {

enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx) {
if (sd_ctx != nullptr && sd_ctx->sd != nullptr) {
SDVersion version = sd_ctx->sd->version;
if (sd_version_is_dit(version))
return EULER;
else
return EULER_A;
if (sd_version_is_dit(sd_ctx->sd->version)) {
return EULER_SAMPLE_METHOD;
}
}
return SAMPLE_METHOD_COUNT;
return EULER_A_SAMPLE_METHOD;
}

enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx) {
auto edm_v_denoiser = std::dynamic_pointer_cast<EDMVDenoiser>(sd_ctx->sd->denoiser);
if (edm_v_denoiser) {
return EXPONENTIAL_SCHEDULER;
if (sd_ctx != nullptr && sd_ctx->sd != nullptr) {
auto edm_v_denoiser = std::dynamic_pointer_cast<EDMVDenoiser>(sd_ctx->sd->denoiser);
if (edm_v_denoiser) {
return EXPONENTIAL_SCHEDULER;
}
}
return DISCRETE_SCHEDULER;
}
Expand Down Expand Up @@ -2826,7 +2824,6 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
int C = sd_ctx->sd->get_latent_channel();
int W = width / sd_ctx->sd->get_vae_scale_factor();
int H = height / sd_ctx->sd->get_vae_scale_factor();
LOG_INFO("sampling using %s method", sampling_methods_str[sample_method]);

struct ggml_tensor* control_latent = nullptr;
if (sd_version_is_control(sd_ctx->sd->version) && image_hint != nullptr) {
Expand Down Expand Up @@ -3055,10 +3052,15 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
sd_ctx->sd->rng->manual_seed(seed);
sd_ctx->sd->sampler_rng->manual_seed(seed);

int sample_steps = sd_img_gen_params->sample_params.sample_steps;

size_t t0 = ggml_time_ms();

enum sample_method_t sample_method = sd_img_gen_params->sample_params.sample_method;
if (sample_method == SAMPLE_METHOD_COUNT) {
sample_method = sd_get_default_sample_method(sd_ctx);
}
LOG_INFO("sampling using %s method", sampling_methods_str[sample_method]);

int sample_steps = sd_img_gen_params->sample_params.sample_steps;
std::vector<float> sigmas = sd_ctx->sd->denoiser->get_sigmas(sample_steps, sd_img_gen_params->sample_params.scheduler, sd_ctx->sd->version);

ggml_tensor* init_latent = nullptr;
Expand Down Expand Up @@ -3247,11 +3249,6 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
LOG_INFO("encode_first_stage completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
}

enum sample_method_t sample_method = sd_img_gen_params->sample_params.sample_method;
if (sample_method == SAMPLE_METHOD_DEFAULT) {
sample_method = sd_get_default_sample_method(sd_ctx);
}

sd_image_t* result_images = generate_image_internal(sd_ctx,
work_ctx,
init_latent,
Expand Down Expand Up @@ -3301,6 +3298,12 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s

int vae_scale_factor = sd_ctx->sd->get_vae_scale_factor();

enum sample_method_t sample_method = sd_vid_gen_params->sample_params.sample_method;
if (sample_method == SAMPLE_METHOD_COUNT) {
sample_method = sd_get_default_sample_method(sd_ctx);
}
LOG_INFO("sampling using %s method", sampling_methods_str[sample_method]);

int high_noise_sample_steps = 0;
if (sd_ctx->sd->high_noise_diffusion_model) {
high_noise_sample_steps = sd_vid_gen_params->high_noise_sample_params.sample_steps;
Expand Down Expand Up @@ -3569,6 +3572,12 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
// High Noise Sample
if (high_noise_sample_steps > 0) {
LOG_DEBUG("sample(high noise) %dx%dx%d", W, H, T);
enum sample_method_t high_noise_sample_method = sd_vid_gen_params->high_noise_sample_params.sample_method;
if (high_noise_sample_method == SAMPLE_METHOD_COUNT) {
high_noise_sample_method = sd_get_default_sample_method(sd_ctx);
}
LOG_INFO("sampling(high noise) using %s method", sampling_methods_str[high_noise_sample_method]);

int64_t sampling_start = ggml_time_ms();

std::vector<float> high_noise_sigmas = std::vector<float>(sigmas.begin(), sigmas.begin() + high_noise_sample_steps + 1);
Expand All @@ -3587,7 +3596,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
sd_vid_gen_params->high_noise_sample_params.guidance,
sd_vid_gen_params->high_noise_sample_params.eta,
sd_vid_gen_params->high_noise_sample_params.shifted_timestep,
sd_vid_gen_params->high_noise_sample_params.sample_method,
high_noise_sample_method,
high_noise_sigmas,
-1,
{},
Expand Down Expand Up @@ -3624,7 +3633,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
sd_vid_gen_params->sample_params.guidance,
sd_vid_gen_params->sample_params.eta,
sd_vid_gen_params->sample_params.shifted_timestep,
sd_vid_gen_params->sample_params.sample_method,
sample_method,
sigmas,
-1,
{},
Expand Down
25 changes: 12 additions & 13 deletions stable-diffusion.h
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Original file line number Diff line number Diff line change
Expand Up @@ -36,19 +36,18 @@ enum rng_type_t {
};

enum sample_method_t {
SAMPLE_METHOD_DEFAULT,
EULER,
HEUN,
DPM2,
DPMPP2S_A,
DPMPP2M,
DPMPP2Mv2,
IPNDM,
IPNDM_V,
LCM,
DDIM_TRAILING,
TCD,
EULER_A,
EULER_SAMPLE_METHOD,
EULER_A_SAMPLE_METHOD,
HEUN_SAMPLE_METHOD,
DPM2_SAMPLE_METHOD,
DPMPP2S_A_SAMPLE_METHOD,
DPMPP2M_SAMPLE_METHOD,
DPMPP2Mv2_SAMPLE_METHOD,
IPNDM_SAMPLE_METHOD,
IPNDM_V_SAMPLE_METHOD,
LCM_SAMPLE_METHOD,
DDIM_TRAILING_SAMPLE_METHOD,
TCD_SAMPLE_METHOD,
SAMPLE_METHOD_COUNT
};

Expand Down
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