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feat: add Euler CFG++ and Euler-A CFG++ samplers (#1354)
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Daniele
GitHub
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@ -162,10 +162,10 @@ Generation Options:
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-s, --seed RNG seed (default: 42, use random seed for < 0)
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--sampling-method sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m,
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dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s,
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er_sde] (default: euler for Flux/SD3/Wan, euler_a otherwise)
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er_sde, euler_cfg_pp, euler_a_cfg_pp] (default: euler for Flux/SD3/Wan, euler_a otherwise)
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--high-noise-sampling-method (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a,
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dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep,
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res_2s, er_sde] default: euler for Flux/SD3/Wan, euler_a otherwise
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res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp] default: euler for Flux/SD3/Wan, euler_a otherwise
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--scheduler denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits,
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smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent], default:
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discrete
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@ -1244,12 +1244,12 @@ ArgOptions SDGenerationParams::get_options() {
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on_seed_arg},
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{"",
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"--sampling-method",
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"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] "
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"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]"
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"(default: euler for Flux/SD3/Wan, euler_a otherwise)",
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on_sample_method_arg},
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{"",
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"--high-noise-sampling-method",
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"(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]"
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"(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]"
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" default: euler for Flux/SD3/Wan, euler_a otherwise",
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on_high_noise_sample_method_arg},
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{"",
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@ -264,10 +264,10 @@ Default Generation Options:
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-s, --seed RNG seed (default: 42, use random seed for < 0)
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--sampling-method sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m,
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dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s,
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er_sde] (default: euler for Flux/SD3/Wan, euler_a otherwise)
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er_sde, euler_cfg_pp, euler_a_cfg_pp] (default: euler for Flux/SD3/Wan, euler_a otherwise)
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--high-noise-sampling-method (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a,
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dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep,
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res_2s, er_sde] default: euler for Flux/SD3/Wan, euler_a otherwise
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res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp] default: euler for Flux/SD3/Wan, euler_a otherwise
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--scheduler denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits,
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smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent], default:
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discrete
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@ -67,6 +67,10 @@ static enum sample_method_t get_sdapi_sample_method(std::string name) {
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{"k_res_multistep", RES_MULTISTEP_SAMPLE_METHOD},
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{"res 2s", RES_2S_SAMPLE_METHOD},
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{"k_res_2s", RES_2S_SAMPLE_METHOD},
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{"euler_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
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{"k_euler_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
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{"euler_a_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
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{"k_euler_a_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
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};
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auto it = hardcoded.find(name);
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return it != hardcoded.end() ? it->second : SAMPLE_METHOD_COUNT;
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@ -51,6 +51,8 @@ enum sample_method_t {
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RES_MULTISTEP_SAMPLE_METHOD,
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RES_2S_SAMPLE_METHOD,
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ER_SDE_SAMPLE_METHOD,
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EULER_CFG_PP_SAMPLE_METHOD,
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EULER_A_CFG_PP_SAMPLE_METHOD,
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SAMPLE_METHOD_COUNT
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};
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104
src/denoiser.hpp
104
src/denoiser.hpp
@ -752,7 +752,7 @@ struct Flux2FlowDenoiser : public FluxFlowDenoiser {
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}
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};
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typedef std::function<sd::Tensor<float>(const sd::Tensor<float>&, float, int)> denoise_cb_t;
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typedef std::function<sd::Tensor<float>(const sd::Tensor<float>&, float, int, sd::Tensor<float>*)> denoise_cb_t;
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static std::pair<float, float> get_ancestral_step(float sigma_from,
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float sigma_to,
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@ -828,7 +828,7 @@ static sd::Tensor<float> sample_euler_ancestral(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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float sigma = sigmas[i];
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auto denoised_opt = model(x, sigma, i + 1);
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auto denoised_opt = model(x, sigma, i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -851,7 +851,7 @@ static sd::Tensor<float> sample_euler_flow(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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float sigma = sigmas[i];
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auto denoised_opt = model(x, sigma, i + 1);
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auto denoised_opt = model(x, sigma, i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -873,7 +873,7 @@ static sd::Tensor<float> sample_euler(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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float sigma = sigmas[i];
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auto denoised_opt = model(x, sigma, i + 1);
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auto denoised_opt = model(x, sigma, i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -889,7 +889,7 @@ static sd::Tensor<float> sample_heun(denoise_cb_t model,
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const std::vector<float>& sigmas) {
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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auto denoised_opt = model(x, sigmas[i], -(i + 1));
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auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -900,7 +900,7 @@ static sd::Tensor<float> sample_heun(denoise_cb_t model,
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x += d * dt;
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} else {
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sd::Tensor<float> x2 = x + d * dt;
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auto denoised2_opt = model(x2, sigmas[i + 1], i + 1);
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auto denoised2_opt = model(x2, sigmas[i + 1], i + 1, nullptr);
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if (denoised2_opt.empty()) {
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return {};
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}
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@ -917,7 +917,7 @@ static sd::Tensor<float> sample_dpm2(denoise_cb_t model,
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const std::vector<float>& sigmas) {
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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auto denoised_opt = model(x, sigmas[i], -(i + 1));
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auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -930,7 +930,7 @@ static sd::Tensor<float> sample_dpm2(denoise_cb_t model,
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float dt_1 = sigma_mid - sigmas[i];
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float dt_2 = sigmas[i + 1] - sigmas[i];
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sd::Tensor<float> x2 = x + d * dt_1;
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auto denoised2_opt = model(x2, sigma_mid, i + 1);
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auto denoised2_opt = model(x2, sigma_mid, i + 1, nullptr);
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if (denoised2_opt.empty()) {
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return {};
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}
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@ -951,7 +951,7 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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auto denoised_opt = model(x, sigmas[i], -(i + 1));
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auto denoised_opt = model(x, sigmas[i], -(i + 1), nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -967,7 +967,7 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral(denoise_cb_t model,
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float s = t + 0.5f * h;
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float sigma_s = sigma_fn(s);
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sd::Tensor<float> x2 = (sigma_s / sigma_fn(t)) * x - (exp(-h * 0.5f) - 1) * denoised;
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auto denoised2_opt = model(x2, sigma_s, i + 1);
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auto denoised2_opt = model(x2, sigma_s, i + 1, nullptr);
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if (denoised2_opt.empty()) {
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return {};
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}
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@ -994,7 +994,7 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
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bool opt_first_step = (1.0 - sigma < 1e-6);
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auto denoised_opt = model(x, sigma, (opt_first_step ? 1 : -1) * (i + 1));
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auto denoised_opt = model(x, sigma, (opt_first_step ? 1 : -1) * (i + 1), nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1023,8 +1023,8 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
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// so sigma_s = 1 = sigma, and sigma_s_i_ratio = sigma_s / sigma = 1
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// u = (x*sigma_s_i_ratio)+(denoised*(1.0f-sigma_s_i_ratio))
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// = (x*1)+(denoised*0) = x
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// so D_i = model(u, sigma_s, i + 1)
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// = model(x, sigma, i + 1)
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// so D_i = model(u, sigma_s, i + 1, nullptr)
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// = model(x, sigma, i + 1, nullptr)
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// = denoised
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D_i = denoised;
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@ -1057,7 +1057,7 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
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float sigma_s_i_ratio = sigma_s / sigma;
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sd::Tensor<float> u = (x * sigma_s_i_ratio) + (denoised * (1.0f - sigma_s_i_ratio));
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auto denoised2_opt = model(u, sigma_s, i + 1);
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auto denoised2_opt = model(u, sigma_s, i + 1, nullptr);
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if (denoised2_opt.empty()) {
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return {};
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}
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@ -1084,7 +1084,7 @@ static sd::Tensor<float> sample_dpmpp_2m(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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auto denoised_opt = model(x, sigmas[i], i + 1);
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auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1116,7 +1116,7 @@ static sd::Tensor<float> sample_dpmpp_2m_v2(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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auto denoised_opt = model(x, sigmas[i], i + 1);
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auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1151,7 +1151,7 @@ static sd::Tensor<float> sample_lcm(denoise_cb_t model,
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bool is_flow_denoiser) {
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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auto denoised_opt = model(x, sigmas[i], i + 1);
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auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1177,7 +1177,7 @@ static sd::Tensor<float> sample_ipndm(denoise_cb_t model,
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float sigma = sigmas[i];
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float sigma_next = sigmas[i + 1];
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auto denoised_opt = model(x, sigma, i + 1);
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auto denoised_opt = model(x, sigma, i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1221,7 +1221,7 @@ static sd::Tensor<float> sample_ipndm_v(denoise_cb_t model,
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float sigma = sigmas[i];
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float t_next = sigmas[i + 1];
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auto denoised_opt = model(x, sigma, i + 1);
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auto denoised_opt = model(x, sigma, i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1283,7 +1283,7 @@ static sd::Tensor<float> sample_res_multistep(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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auto denoised_opt = model(x, sigmas[i], i + 1);
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auto denoised_opt = model(x, sigmas[i], i + 1, nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1360,7 +1360,7 @@ static sd::Tensor<float> sample_res_2s(denoise_cb_t model,
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float sigma_from = sigmas[i];
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float sigma_to = sigmas[i + 1];
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auto denoised_opt = model(x, sigma_from, -(i + 1));
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auto denoised_opt = model(x, sigma_from, -(i + 1), nullptr);
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if (denoised_opt.empty()) {
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return {};
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}
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@ -1386,7 +1386,7 @@ static sd::Tensor<float> sample_res_2s(denoise_cb_t model,
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sd::Tensor<float> eps1 = denoised - x0;
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sd::Tensor<float> x2 = x0 + eps1 * (h * a21);
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auto denoised2_opt = model(x2, sigma_c2, i + 1);
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auto denoised2_opt = model(x2, sigma_c2, i + 1, nullptr);
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if (denoised2_opt.empty()) {
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return {};
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}
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@ -1463,7 +1463,7 @@ static sd::Tensor<float> sample_er_sde(denoise_cb_t model,
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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sd::Tensor<float> denoised = model(x, sigmas[i], i + 1);
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sd::Tensor<float> denoised = model(x, sigmas[i], i + 1, nullptr);
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if (denoised.empty()) {
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return {};
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}
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@ -1549,7 +1549,7 @@ static sd::Tensor<float> sample_ddim_trailing(denoise_cb_t model,
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float sigma = sigmas[i];
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float sigma_to = sigmas[i + 1];
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auto model_output_opt = model(x, sigma, i + 1);
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auto model_output_opt = model(x, sigma, i + 1, nullptr);
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if (model_output_opt.empty()) {
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return {};
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}
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@ -1621,7 +1621,7 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
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int timestep_s = (int)floor((1 - eta) * prev_timestep);
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float sigma = sigmas[i];
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auto model_output_opt = model(x, sigma, i + 1);
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auto model_output_opt = model(x, sigma, i + 1, nullptr);
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if (model_output_opt.empty()) {
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return {};
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}
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@ -1649,6 +1649,56 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
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return x;
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}
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static sd::Tensor<float> sample_euler_cfg_pp(denoise_cb_t model,
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sd::Tensor<float> x,
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const std::vector<float>& sigmas) {
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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float sigma = sigmas[i];
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sd::Tensor<float> uncond_denoised;
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auto denoised_opt = model(x, sigma, i + 1, &uncond_denoised);
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if (denoised_opt.empty() || uncond_denoised.empty()) {
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return {};
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}
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sd::Tensor<float> denoised = std::move(denoised_opt);
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sd::Tensor<float> d = (x - uncond_denoised) / sigma;
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x = denoised + d * sigmas[i + 1];
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}
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return x;
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}
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static sd::Tensor<float> sample_euler_ancestral_cfg_pp(denoise_cb_t model,
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sd::Tensor<float> x,
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const std::vector<float>& sigmas,
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std::shared_ptr<RNG> rng,
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float eta) {
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int steps = static_cast<int>(sigmas.size()) - 1;
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for (int i = 0; i < steps; i++) {
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float sigma = sigmas[i];
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sd::Tensor<float> uncond_denoised;
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auto denoised_opt = model(x, sigma, i + 1, &uncond_denoised);
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if (denoised_opt.empty() || uncond_denoised.empty()) {
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return {};
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}
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sd::Tensor<float> denoised = std::move(denoised_opt);
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sd::Tensor<float> d = (x - uncond_denoised) / sigma;
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auto [sigma_down, sigma_up] = get_ancestral_step(sigmas[i], sigmas[i + 1], eta);
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x = denoised + d * sigma_down;
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if (sigmas[i + 1] > 0) {
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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,
|
||||
@ -1694,6 +1744,10 @@ 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 {};
|
||||
}
|
||||
|
||||
@ -73,6 +73,8 @@ const char* sampling_methods_str[] = {
|
||||
"Res Multistep",
|
||||
"Res 2s",
|
||||
"ER-SDE",
|
||||
"Euler CFG++",
|
||||
"Euler A CFG++",
|
||||
};
|
||||
|
||||
/*================================================== Helper Functions ================================================*/
|
||||
@ -1600,6 +1602,15 @@ public:
|
||||
cache_params,
|
||||
denoiser.get(),
|
||||
sigmas);
|
||||
|
||||
// Spectrum cache is not supported for CFG++ samplers
|
||||
if (method == EULER_CFG_PP_SAMPLE_METHOD || method == EULER_A_CFG_PP_SAMPLE_METHOD) {
|
||||
if (cache_runtime.spectrum_enabled) {
|
||||
LOG_WARN("Spectrum cache requested but not supported for CFG++ samplers");
|
||||
cache_runtime.spectrum_enabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
size_t steps = sigmas.size() - 1;
|
||||
bool has_skiplayer = slg_scale != 0.0f && !skip_layers.empty();
|
||||
if (has_skiplayer && !sd_version_is_dit(version)) {
|
||||
@ -1614,7 +1625,7 @@ public:
|
||||
sd::Tensor<float> denoised = x_t;
|
||||
SamplePreviewContext preview = prepare_sample_preview_context();
|
||||
|
||||
auto denoise = [&](const sd::Tensor<float>& x, float sigma, int step) -> sd::Tensor<float> {
|
||||
auto denoise = [&](const sd::Tensor<float>& x, float sigma, int step, sd::Tensor<float>* out_uncond_denoised = nullptr) -> sd::Tensor<float> {
|
||||
if (step == 1 || step == -1) {
|
||||
pretty_progress(0, (int)steps, 0);
|
||||
}
|
||||
@ -1637,15 +1648,17 @@ public:
|
||||
}
|
||||
|
||||
if (cache_runtime.spectrum_enabled && cache_runtime.spectrum.should_predict()) {
|
||||
cache_runtime.spectrum.predict(&denoised);
|
||||
if (!denoise_mask.empty()) {
|
||||
denoised = denoised * denoise_mask + init_latent * (1.0f - denoise_mask);
|
||||
if (out_uncond_denoised == nullptr) {
|
||||
cache_runtime.spectrum.predict(&denoised);
|
||||
if (!denoise_mask.empty()) {
|
||||
denoised = denoised * denoise_mask + init_latent * (1.0f - denoise_mask);
|
||||
}
|
||||
if (sd_should_preview_denoised() && preview.callback != nullptr) {
|
||||
preview_image(step, denoised, version, preview.mode, preview.callback, preview.data, false);
|
||||
}
|
||||
report_sample_progress(step, steps, t0);
|
||||
return denoised;
|
||||
}
|
||||
if (sd_should_preview_denoised() && preview.callback != nullptr) {
|
||||
preview_image(step, denoised, version, preview.mode, preview.callback, preview.data, false);
|
||||
}
|
||||
report_sample_progress(step, steps, t0);
|
||||
return denoised;
|
||||
}
|
||||
|
||||
if (sd_should_preview_noisy() && preview.callback != nullptr) {
|
||||
@ -1768,6 +1781,10 @@ public:
|
||||
latent_result += (cond_out - skip_cond_out) * slg_scale;
|
||||
}
|
||||
denoised = latent_result * c_out + x * c_skip;
|
||||
if (out_uncond_denoised != nullptr) {
|
||||
sd::Tensor<float> base_uncond = !uncond_out.empty() ? uncond_out : cond_out;
|
||||
*out_uncond_denoised = base_uncond * c_out + x * c_skip;
|
||||
}
|
||||
if (cache_runtime.spectrum_enabled) {
|
||||
cache_runtime.spectrum.update(denoised);
|
||||
}
|
||||
@ -1965,6 +1982,8 @@ const char* sample_method_to_str[] = {
|
||||
"res_multistep",
|
||||
"res_2s",
|
||||
"er_sde",
|
||||
"euler_cfg_pp",
|
||||
"euler_a_cfg_pp",
|
||||
};
|
||||
|
||||
const char* sd_sample_method_name(enum sample_method_t sample_method) {
|
||||
@ -2527,6 +2546,7 @@ static float resolve_eta(sd_ctx_t* sd_ctx,
|
||||
case EULER_A_SAMPLE_METHOD:
|
||||
case DPMPP2S_A_SAMPLE_METHOD:
|
||||
case ER_SDE_SAMPLE_METHOD:
|
||||
case EULER_A_CFG_PP_SAMPLE_METHOD:
|
||||
return 1.0f;
|
||||
default:;
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user