mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2026-09-19 09:15:18 +02:00
sd: sync to master-560-e8323ca (#2082)
* sd: sync to master-540-f16a110 * tae post-merge fixes * build fixes * restore image mask for non-inpainting models * sd: sync to master-551-99c1de3 * avoid nlohmann/json.hpp include diffs * Euler A now works on Flux * sd: sync to master-555-7397dda avi_writer.h got removed upstream, but I've simply kept the local copy for now. * sd: sync to master-558-8afbeb6 * sd: sync to master-560-e8323ca
This commit is contained in:
Wagner Bruna
GitHub
+122
-146
@@ -351,9 +351,40 @@ std::vector<std::string> split_string(const std::string& str, char delimiter) {
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return result;
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}
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// { kcpp
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static int sdloglevel = 0; //-1 = hide all, 0 = normal, 1 = showall
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static bool sdquiet = false;
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// } kcpp
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static std::string build_progress_bar(int step, int steps) {
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std::string progress = " |";
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int max_progress = 50;
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int32_t current = 0;
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if (steps > 0) {
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current = (int32_t)(step * 1.f * max_progress / steps);
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}
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for (int i = 0; i < 50; i++) {
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if (i > current) {
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progress += " ";
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} else if (i == current && i != max_progress - 1) {
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progress += ">";
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} else {
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progress += "=";
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}
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}
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progress += "|";
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return progress;
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}
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static void print_progress_line(int step, int steps, const std::string& speed_text) {
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if (step == 0) {
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return;
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}
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std::string progress = build_progress_bar(step, steps);
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const char* lf = (step == steps ? "\n" : "");
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printf("\r%s %i/%i - %s\033[K%s", progress.c_str(), step, steps, speed_text.c_str(), lf);
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fflush(stdout); // for linux
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}
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void pretty_progress(int step, int steps, float time) {
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if (sd_progress_cb) {
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sd_progress_cb(step, steps, time, sd_progress_cb_data);
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@@ -366,29 +397,36 @@ void pretty_progress(int step, int steps, float time) {
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{
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return;
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}
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std::string progress = " |";
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int max_progress = 50;
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int32_t current = (int32_t)(step * 1.f * max_progress / steps);
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for (int i = 0; i < 50; i++) {
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if (i > current) {
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progress += " ";
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} else if (i == current && i != max_progress - 1) {
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progress += ">";
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} else {
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progress += "=";
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}
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}
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progress += "|";
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const char* lf = (step == steps ? "\n" : "");
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const char* unit = "s/it";
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float speed = time;
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if (speed < 1.0f && speed > 0.f) {
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speed = 1.0f / speed;
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unit = "it/s";
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}
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printf("\r%s %i/%i - %.2f%s\033[K%s", progress.c_str(), step, steps, speed, unit, lf);
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fflush(stdout); // for linux
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print_progress_line(step, steps, sd_format("%.2f%s", speed, unit));
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}
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void pretty_bytes_progress(int step, int steps, uint64_t bytes_processed, float elapsed_seconds) {
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if (sd_progress_cb) {
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float time = elapsed_seconds / (step + 1e-6f);
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sd_progress_cb(step, steps, time, sd_progress_cb_data);
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return;
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}
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if (step == 0) {
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return;
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}
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double bytes_per_second = 0.0;
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if (elapsed_seconds > 0.0f) {
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bytes_per_second = bytes_processed / (double)elapsed_seconds;
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}
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double speed_mb = bytes_per_second / (1024.0 * 1024.0);
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if (speed_mb >= 1024.0) {
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print_progress_line(step, steps, sd_format("%.2fGB/s", speed_mb / 1024.0));
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} else {
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print_progress_line(step, steps, sd_format("%.2fMB/s", speed_mb));
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}
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}
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std::string ltrim(const std::string& s) {
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@@ -523,158 +561,96 @@ const char* sd_get_system_info() {
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return buffer;
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}
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sd_image_f32_t sd_image_t_to_sd_image_f32_t(sd_image_t image) {
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sd_image_f32_t converted_image;
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converted_image.width = image.width;
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converted_image.height = image.height;
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converted_image.channel = image.channel;
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sd_image_t tensor_to_sd_image(const sd::Tensor<float>& tensor, int frame_index) {
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const auto& shape = tensor.shape();
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GGML_ASSERT(shape.size() == 4 || shape.size() == 5);
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int width = static_cast<int>(shape[0]);
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int height = static_cast<int>(shape[1]);
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int channel = static_cast<int>(shape[shape.size() == 5 ? 3 : 2]);
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uint8_t* data = (uint8_t*)malloc(static_cast<size_t>(width * height * channel));
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GGML_ASSERT(data != nullptr);
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// Allocate memory for float data
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converted_image.data = (float*)malloc(image.width * image.height * image.channel * sizeof(float));
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for (uint32_t i = 0; i < image.width * image.height * image.channel; i++) {
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// Convert uint8_t to float
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converted_image.data[i] = (float)image.data[i];
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}
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return converted_image;
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}
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// Function to perform double linear interpolation
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float interpolate(float v1, float v2, float v3, float v4, float x_ratio, float y_ratio) {
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return v1 * (1 - x_ratio) * (1 - y_ratio) + v2 * x_ratio * (1 - y_ratio) + v3 * (1 - x_ratio) * y_ratio + v4 * x_ratio * y_ratio;
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}
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sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int target_height) {
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sd_image_f32_t resized_image;
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resized_image.width = target_width;
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resized_image.height = target_height;
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resized_image.channel = image.channel;
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// Allocate memory for resized float data
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resized_image.data = (float*)malloc(target_width * target_height * image.channel * sizeof(float));
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for (int y = 0; y < target_height; y++) {
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for (int x = 0; x < target_width; x++) {
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float original_x = (float)x * image.width / target_width;
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float original_y = (float)y * image.height / target_height;
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uint32_t x1 = (uint32_t)original_x;
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uint32_t y1 = (uint32_t)original_y;
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uint32_t x2 = std::min(x1 + 1, image.width - 1);
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uint32_t y2 = std::min(y1 + 1, image.height - 1);
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for (uint32_t k = 0; k < image.channel; k++) {
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float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
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float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
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float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
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float v4 = *(image.data + y2 * image.width * image.channel + x2 * image.channel + k);
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float x_ratio = original_x - x1;
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float y_ratio = original_y - y1;
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float value = interpolate(v1, v2, v3, v4, x_ratio, y_ratio);
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*(resized_image.data + y * target_width * image.channel + x * image.channel + k) = value;
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for (int iw = 0; iw < width; ++iw) {
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for (int ih = 0; ih < height; ++ih) {
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for (int ic = 0; ic < channel; ++ic) {
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float value = shape.size() == 5 ? tensor.index(iw, ih, frame_index, ic, 0)
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: tensor.index(iw, ih, ic, frame_index);
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value = std::clamp(value, 0.0f, 1.0f);
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data[(ih * width + iw) * channel + ic] = static_cast<uint8_t>(std::round(value * 255.0f));
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}
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}
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}
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return resized_image;
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return {
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static_cast<uint32_t>(width),
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static_cast<uint32_t>(height),
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static_cast<uint32_t>(channel),
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data,
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};
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}
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void normalize_sd_image_f32_t(sd_image_f32_t image, float means[3], float stds[3]) {
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for (uint32_t y = 0; y < image.height; y++) {
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for (uint32_t x = 0; x < image.width; x++) {
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for (uint32_t k = 0; k < image.channel; k++) {
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int index = (y * image.width + x) * image.channel + k;
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image.data[index] = (image.data[index] - means[k]) / stds[k];
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sd::Tensor<float> sd_image_to_tensor(sd_image_t image,
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int target_width,
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int target_height,
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bool scale) {
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sd::Tensor<float> tensor = sd::zeros<float>({static_cast<int64_t>(image.width),
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static_cast<int64_t>(image.height),
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static_cast<int64_t>(image.channel),
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1});
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for (uint32_t iw = 0; iw < image.width; ++iw) {
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for (uint32_t ih = 0; ih < image.height; ++ih) {
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for (uint32_t ic = 0; ic < image.channel; ++ic) {
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tensor.index(iw, ih, ic, 0) = sd_image_get_f32(image, iw, ih, ic, scale);
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}
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}
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}
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if (target_width >= 0 && target_height >= 0 &&
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(tensor.shape()[0] != target_width || tensor.shape()[1] != target_height)) {
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tensor = sd::ops::interpolate(tensor,
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{target_width,
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target_height,
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tensor.shape()[2],
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tensor.shape()[3]});
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}
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return tensor;
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}
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// Constants for means and std
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float means[3] = {0.48145466f, 0.4578275f, 0.40821073f};
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float stds[3] = {0.26862954f, 0.26130258f, 0.27577711f};
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// Function to clip and preprocess sd_image_f32_t
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sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int target_height) {
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float width_scale = (float)target_width / image.width;
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float height_scale = (float)target_height / image.height;
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sd::Tensor<float> clip_preprocess(const sd::Tensor<float>& image, int target_width, int target_height) {
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GGML_ASSERT(image.dim() == 4);
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GGML_ASSERT(image.shape()[2] == 3);
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GGML_ASSERT(image.shape()[3] == 1);
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GGML_ASSERT(target_width > 0 && target_height > 0);
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float scale = std::fmax(width_scale, height_scale);
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float width_scale = static_cast<float>(target_width) / static_cast<float>(image.shape()[0]);
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float height_scale = static_cast<float>(target_height) / static_cast<float>(image.shape()[1]);
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float scale = std::fmax(width_scale, height_scale);
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// Interpolation
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int resized_width = (int)(scale * image.width);
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int resized_height = (int)(scale * image.height);
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float* resized_data = (float*)malloc(resized_width * resized_height * image.channel * sizeof(float));
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int64_t resized_width = static_cast<int64_t>(scale * static_cast<float>(image.shape()[0]));
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int64_t resized_height = static_cast<int64_t>(scale * static_cast<float>(image.shape()[1]));
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for (int y = 0; y < resized_height; y++) {
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for (int x = 0; x < resized_width; x++) {
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float original_x = (float)x * image.width / resized_width;
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float original_y = (float)y * image.height / resized_height;
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sd::Tensor<float> resized = sd::ops::interpolate(
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image,
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{resized_width, resized_height, image.shape()[2], image.shape()[3]});
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uint32_t x1 = (uint32_t)original_x;
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uint32_t y1 = (uint32_t)original_y;
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uint32_t x2 = std::min(x1 + 1, image.width - 1);
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uint32_t y2 = std::min(y1 + 1, image.height - 1);
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int64_t h_offset = std::max<int64_t>((resized_height - target_height) / 2, 0);
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int64_t w_offset = std::max<int64_t>((resized_width - target_width) / 2, 0);
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for (uint32_t k = 0; k < image.channel; k++) {
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float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
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float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
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float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
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float v4 = *(image.data + y2 * image.width * image.channel + x2 * image.channel + k);
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float x_ratio = original_x - x1;
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float y_ratio = original_y - y1;
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float value = interpolate(v1, v2, v3, v4, x_ratio, y_ratio);
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*(resized_data + y * resized_width * image.channel + x * image.channel + k) = value;
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sd::Tensor<float> cropped({target_width, target_height, image.shape()[2], image.shape()[3]});
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for (int64_t y = 0; y < target_height; ++y) {
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for (int64_t x = 0; x < target_width; ++x) {
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for (int64_t c = 0; c < image.shape()[2]; ++c) {
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cropped.index(x, y, c, 0) = resized.index(x + w_offset, y + h_offset, c, 0);
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}
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}
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}
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// Clip and preprocess
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int h_offset = std::max((int)(resized_height - target_height) / 2, 0);
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int w_offset = std::max((int)(resized_width - target_width) / 2, 0);
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sd_image_f32_t result;
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result.width = target_width;
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result.height = target_height;
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result.channel = image.channel;
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result.data = (float*)malloc(target_height * target_width * image.channel * sizeof(float));
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for (uint32_t k = 0; k < image.channel; k++) {
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for (uint32_t i = 0; i < result.height; i++) {
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for (uint32_t j = 0; j < result.width; j++) {
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int src_y = std::min(static_cast<int>(i + h_offset), resized_height - 1);
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int src_x = std::min(static_cast<int>(j + w_offset), resized_width - 1);
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*(result.data + i * result.width * image.channel + j * image.channel + k) =
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fmin(fmax(*(resized_data + src_y * resized_width * image.channel + src_x * image.channel + k), 0.0f), 255.0f) / 255.0f;
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}
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}
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}
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// Free allocated memory
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free(resized_data);
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// Normalize
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for (uint32_t k = 0; k < image.channel; k++) {
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for (uint32_t i = 0; i < result.height; i++) {
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for (uint32_t j = 0; j < result.width; j++) {
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// *(result.data + i * size * image.channel + j * image.channel + k) = 0.5f;
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int offset = i * result.width * image.channel + j * image.channel + k;
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float value = *(result.data + offset);
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value = (value - means[k]) / stds[k];
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// value = 0.5f;
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*(result.data + offset) = value;
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}
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}
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}
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return result;
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sd::Tensor<float> normalized = sd::ops::clamp(cropped, 0.0f, 1.0f);
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sd::Tensor<float> mean({1, 1, 3, 1}, {means[0], means[1], means[2]});
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sd::Tensor<float> std({1, 1, 3, 1}, {stds[0], stds[1], stds[2]});
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return (normalized - mean) / std;
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}
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// Ref: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cad87bf4e3e0b0a759afa94e933527c3123d59bc/modules/prompt_parser.py#L345
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Block a user