upstream-archive/stable-diffusion.cpp
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feat: denoise strength as starting noise level (#1738)

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stduhpf
2026-07-05 10:24:46 +02:00
committed by GitHub
parent 38a51f8105
commit c60b36af0f
2 changed files with 60 additions and 3 deletions
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14
src/runtime/denoiser.hpp
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@@ -1013,6 +1013,7 @@ struct Denoiser {
const sd::Tensor<float>& latent) = 0;
virtual sd::Tensor<float> inverse_noise_scaling(float sigma,
const sd::Tensor<float>& latent) = 0;
virtual float noise_level_to_sigma(float noise_level) = 0;
virtual std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) {
auto bound_t_to_sigma = std::bind(&Denoiser::t_to_sigma, this, std::placeholders::_1);
@@ -1160,6 +1161,10 @@ struct CompVisDenoiser : public Denoiser {
SD_UNUSED(sigma);
return latent;
}
float noise_level_to_sigma(float noise_level) {
return noise_level / (1.0f - noise_level);
}
};
struct CompVisVDenoiser : public CompVisDenoiser {
@@ -1247,6 +1252,10 @@ struct DiscreteFlowDenoiser : public Denoiser {
sd::Tensor<float> inverse_noise_scaling(float sigma, const sd::Tensor<float>& latent) override {
return latent * (1.0f / (1.0f - sigma));
}
float noise_level_to_sigma(float noise_level) {
return noise_level;
}
};
struct FluxFlowDenoiser : public DiscreteFlowDenoiser {
@@ -1384,6 +1393,11 @@ struct MiniT2IFlowDenoiser : public Denoiser {
return latent;
}
float noise_level_to_sigma(float noise_level) {
SD_UNUSED(noise_level);
return 1.0f;
}
std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) override {
SD_UNUSED(image_seq_len);
SD_UNUSED(scheduler_type);

49
src/stable-diffusion.cpp
View File

@@ -4319,13 +4319,56 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
LOG_INFO("IMG2IMG");
if (request->strength < 1.f) {
size_t t_enc = static_cast<size_t>(plan->sample_steps * request->strength);
if (t_enc == static_cast<size_t>(plan->sample_steps)) {
t_enc--;
bool strength_as_noise_level = false;
bool force_first_sigma = false;
for (const auto& [key, value] : parse_key_value_args(sd_img_gen_params->sample_params.extra_sample_args, "img2img arg")) {
if (key == "strength_as_noise_level") {
if (!parse_strict_bool(value, strength_as_noise_level)) {
LOG_WARN("ignoring invalid img2img sample arg '%s=%s'", key.c_str(), value.c_str());
}
} else if (key == "force_first_sigma") {
if (!parse_strict_bool(value, force_first_sigma)) {
LOG_WARN("ignoring invalid img2img sample arg '%s=%s'", key.c_str(), value.c_str());
}
}
}
size_t t_enc;
float target_sigma = -1;
if (!strength_as_noise_level) {
t_enc = static_cast<size_t>(plan->sample_steps * request->strength);
if (t_enc == static_cast<size_t>(plan->sample_steps)) {
t_enc--;
}
} else {
LOG_DEBUG("Interpreting denoise strength as relative noise level");
// assume x_noised = K * (x * (1-noise_level) + noise * noise_level) = K * lerp(x, noise, noise_level)
// K = 1, noise_level = sigma for flow models
// K = 1+sigma, noise_level=sigma/(1+sigma) for diffusion models
float target_noise_level = request->strength;
target_sigma = sd_ctx->sd->denoiser->noise_level_to_sigma(target_noise_level);
size_t start_index = 0;
for (size_t i = 0; i < plan->sigmas.size(); ++i) {
if (plan->sigmas[i] <= target_sigma) {
start_index = i;
break;
}
}
if (start_index >= plan->sigmas.size() - 1) {
start_index = plan->sigmas.size() - 2; // Leave at least 1 step
}
t_enc = plan->sample_steps - start_index - 1;
}
LOG_INFO("target t_enc is %zu steps", t_enc);
std::vector<float> sigma_sched;
sigma_sched.assign(plan->sigmas.begin() + plan->sample_steps - t_enc - 1, plan->sigmas.end());
if (target_sigma > 0 && force_first_sigma && strength_as_noise_level) {
LOG_DEBUG("force_first_sigma to %.4f (from %.4f)", target_sigma, sigma_sched[0]);
sigma_sched[0] = target_sigma;
}
plan->sigmas = std::move(sigma_sched);
plan->sample_steps = static_cast<int>(plan->sigmas.size() - 1);
}