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#include "runtime/guidance.h"
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <string>
#include <utility>
#include "core/util.h"
namespace sd::guidance {
static bool has_tensor(const sd::Tensor<float>* tensor) {
return tensor != nullptr && !tensor->empty();
}
bool is_adaptive_projected_guidance_enabled(const AdaptiveProjectedGuidanceParams& params) {
return params.eta != 1.0f || params.momentum != 0.0f || params.norm_threshold > 0.0f;
}
AdaptiveProjectedGuidanceParams parse_adaptive_projected_guidance_args(const char* extra_sample_args) {
AdaptiveProjectedGuidanceParams params;
for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "extra sample arg")) {
float parsed = 0.0f;
if (key == "apg_eta") {
if (!parse_strict_float(value, parsed)) {
LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
continue;
}
params.eta = parsed;
} else if (key == "apg_momentum") {
if (!parse_strict_float(value, parsed)) {
LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
continue;
}
params.momentum = parsed;
} else if (key == "apg_norm_threshold") {
if (!parse_strict_float(value, parsed)) {
LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
continue;
}
params.norm_threshold = parsed;
} else if (key == "apg_norm_threshold_smoothing") {
if (!parse_strict_float(value, parsed)) {
LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
continue;
}
params.norm_threshold_smoothing = parsed;
}
}
return params;
}
bool parse_skip_layer_guidance_uncond_arg(const char* extra_sample_args) {
bool uncond = false;
for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "extra sample arg")) {
if (key == "slg_uncond") {
if (!parse_strict_bool(value, uncond)) {
LOG_WARN("ignoring invalid SLG extra sample arg '%s=%s'", key.c_str(), value.c_str());
}
}
}
return uncond;
}
ClassifierFreeGuidance::ClassifierFreeGuidance(float guidance_scale,
float image_guidance_scale)
: guidance_scale_(guidance_scale),
image_guidance_scale_(image_guidance_scale) {
}
GuiderOutput ClassifierFreeGuidance::forward(const GuidanceInput& input,
GuiderOutput previous) const {
(void)previous;
GuiderOutput output;
if (!has_tensor(input.pred_cond)) {
return output;
}
const sd::Tensor<float>& pred_cond = *input.pred_cond;
output.pred = pred_cond;
if (has_tensor(input.pred_uncond)) {
const sd::Tensor<float>& pred_uncond = *input.pred_uncond;
if (has_tensor(input.pred_img_uncond)) {
const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
output.pred = pred_img_uncond +
image_guidance_scale_ * (pred_uncond - pred_img_uncond) +
guidance_scale_ * (pred_cond - pred_uncond);
} else {
output.pred = pred_uncond + guidance_scale_ * (pred_cond - pred_uncond);
}
} else if (has_tensor(input.pred_img_uncond)) {
const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
output.pred = pred_img_uncond + guidance_scale_ * (pred_cond - pred_img_uncond);
}
return output;
}
AdaptiveProjectedGuidance::AdaptiveProjectedGuidance(float guidance_scale,
float image_guidance_scale,
AdaptiveProjectedGuidanceParams params)
: guidance_scale_(guidance_scale),
image_guidance_scale_(image_guidance_scale),
params_(params) {
}
static sd::Tensor<float> calculate_guidance_delta(const sd::Tensor<float>& pred_cond,
const sd::Tensor<float>* pred_uncond,
const sd::Tensor<float>* pred_img_uncond,
float guidance_scale,
float image_guidance_scale) {
if (pred_img_uncond != nullptr) {
if (pred_uncond != nullptr && guidance_scale == 1.0f) {
return *pred_uncond - *pred_img_uncond;
}
if (pred_uncond != nullptr) {
return pred_cond +
(*pred_uncond * (image_guidance_scale - guidance_scale) +
*pred_img_uncond * (1.0f - image_guidance_scale)) /
(guidance_scale - 1.0f);
}
return pred_cond - *pred_img_uncond;
}
return pred_cond - *pred_uncond;
}
GuiderOutput AdaptiveProjectedGuidance::forward(const GuidanceInput& input,
GuiderOutput previous) const {
(void)previous;
GuiderOutput output;
if (!has_tensor(input.pred_cond)) {
return output;
}
const sd::Tensor<float>& pred_cond = *input.pred_cond;
output.pred = pred_cond;
if (has_tensor(input.pred_uncond)) {
const sd::Tensor<float>& pred_uncond = *input.pred_uncond;
if (has_tensor(input.pred_img_uncond)) {
const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
output.pred = pred_img_uncond +
image_guidance_scale_ * (pred_uncond - pred_img_uncond) +
guidance_scale_ * (pred_cond - pred_uncond);
} else {
output.pred = pred_uncond + guidance_scale_ * (pred_cond - pred_uncond);
}
} else if (has_tensor(input.pred_img_uncond)) {
const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
output.pred = pred_img_uncond + guidance_scale_ * (pred_cond - pred_img_uncond);
}
if (!has_tensor(input.pred_uncond) && !has_tensor(input.pred_img_uncond)) {
return output;
}
const sd::Tensor<float>* pred_uncond = input.pred_uncond;
const sd::Tensor<float>* pred_img_uncond = input.pred_img_uncond;
sd::Tensor<float> deltas = calculate_guidance_delta(pred_cond,
pred_uncond,
pred_img_uncond,
guidance_scale_,
image_guidance_scale_);
if (params_.momentum != 0.0f) {
if (momentum_buffer_.shape() != deltas.shape()) {
momentum_buffer_ = sd::Tensor<float>::zeros_like(deltas);
}
deltas += params_.momentum * momentum_buffer_;
momentum_buffer_ = deltas;
}
float diff_norm = 0.0f;
const int standard_res = 2 * 1024 / 8; // Use SDXL as the standard resolution (1024x1024, 8x8 patches, 4=2x2 channels)
if (params_.norm_threshold > 0.0f) {
diff_norm = std::sqrt((deltas * deltas).sum()) * standard_res / std::sqrt(static_cast<float>(deltas.numel()));
}
float apg_scale_factor = 1.0f;
if (params_.norm_threshold > 0.0f) {
if (diff_norm > 0.0f) {
if (params_.norm_threshold_smoothing <= 0.0f) {
apg_scale_factor = std::min(1.0f, params_.norm_threshold / diff_norm);
} else {
float x = params_.norm_threshold / diff_norm;
apg_scale_factor = x / std::pow(1.0f + std::pow(x, 1.0f / params_.norm_threshold_smoothing),
params_.norm_threshold_smoothing);
}
}
}
deltas *= apg_scale_factor;
if (params_.eta != 1.0f) {
float cond_norm_sq = (pred_cond * pred_cond).sum();
if (cond_norm_sq != 0.0f) {
float projection_scale = (pred_cond * deltas).sum() / cond_norm_sq;
deltas += (params_.eta - 1.0f) * (projection_scale * pred_cond);
}
}
output.pred = pred_cond;
if (pred_uncond != nullptr) {
if (guidance_scale_ != 1.0f) {
output.pred = pred_cond + (guidance_scale_ - 1.0f) * deltas;
} else if (pred_img_uncond != nullptr) {
output.pred = pred_cond + (image_guidance_scale_ - 1.0f) * deltas;
}
} else if (pred_img_uncond != nullptr) {
output.pred = *pred_img_uncond + guidance_scale_ * deltas;
}
return output;
}
SkipLayerGuidance::SkipLayerGuidance(std::vector<int> layers,
float scale,
float start,
float stop)
: layers_(std::move(layers)),
scale_(scale),
start_(start),
stop_(stop) {
}
bool SkipLayerGuidance::is_enabled_for_step(const GuidanceInput& input) const {
if (layers_.empty() || input.schedule_size == 0) {
return false;
}
int start_step = static_cast<int>(start_ * static_cast<float>(input.schedule_size));
int stop_step = static_cast<int>(stop_ * static_cast<float>(input.schedule_size));
return input.step > start_step && input.step < stop_step;
}
const std::vector<int>& SkipLayerGuidance::layers() const {
return layers_;
}
GuiderOutput SkipLayerGuidance::forward(const GuidanceInput& input,
GuiderOutput output) const {
if (scale_ == 0.0f || !is_enabled_for_step(input) || !input.predict_skip_layer) {
return output;
}
if (output.pred.empty() || !has_tensor(input.pred_cond)) {
return GuiderOutput();
}
output.pred_skip_layer = input.predict_skip_layer();
if (output.pred_skip_layer.empty()) {
return GuiderOutput();
}
output.pred += (*input.pred_cond - output.pred_skip_layer) * scale_;
return output;
}
} // namespace sd::guidance