124 lines
4.4 KiB
C++
124 lines
4.4 KiB
C++
#include "esrgan.hpp"
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#include "ggml_extend.hpp"
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#include "model.h"
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#include "stable-diffusion.h"
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struct UpscalerGGML {
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ggml_backend_t backend = NULL; // general backend
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ggml_type model_data_type = GGML_TYPE_F16;
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std::shared_ptr<ESRGAN> esrgan_upscaler;
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std::string esrgan_path;
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int n_threads;
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UpscalerGGML(int n_threads)
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: n_threads(n_threads) {
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}
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bool load_from_file(const std::string& esrgan_path) {
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#ifdef SD_USE_CUBLAS
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LOG_DEBUG("Using CUDA backend");
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backend = ggml_backend_cuda_init(0);
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#endif
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#ifdef SD_USE_METAL
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LOG_DEBUG("Using Metal backend");
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ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
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backend = ggml_backend_metal_init();
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#endif
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if (!backend) {
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LOG_DEBUG("Using CPU backend");
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backend = ggml_backend_cpu_init();
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}
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LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
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esrgan_upscaler = std::make_shared<ESRGAN>(backend, model_data_type);
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if (!esrgan_upscaler->load_from_file(esrgan_path)) {
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return false;
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}
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return true;
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}
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sd_image_t upscale(sd_image_t input_image, uint32_t upscale_factor) {
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// upscale_factor, unused for RealESRGAN_x4plus_anime_6B.pth
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sd_image_t upscaled_image = {0, 0, 0, NULL};
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int output_width = (int)input_image.width * esrgan_upscaler->scale;
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int output_height = (int)input_image.height * esrgan_upscaler->scale;
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LOG_INFO("upscaling from (%i x %i) to (%i x %i)",
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input_image.width, input_image.height, output_width, output_height);
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struct ggml_init_params params;
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params.mem_size = output_width * output_height * 3 * sizeof(float) * 2;
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params.mem_size += 2 * ggml_tensor_overhead();
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params.mem_buffer = NULL;
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params.no_alloc = false;
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// draft context
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struct ggml_context* upscale_ctx = ggml_init(params);
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if (!upscale_ctx) {
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LOG_ERROR("ggml_init() failed");
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return upscaled_image;
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}
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LOG_DEBUG("upscale work buffer size: %.2f MB", params.mem_size / 1024.f / 1024.f);
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ggml_tensor* input_image_tensor = ggml_new_tensor_4d(upscale_ctx, GGML_TYPE_F32, input_image.width, input_image.height, 3, 1);
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sd_image_to_tensor(input_image.data, input_image_tensor);
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ggml_tensor* upscaled = ggml_new_tensor_4d(upscale_ctx, GGML_TYPE_F32, output_width, output_height, 3, 1);
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auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
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esrgan_upscaler->compute(n_threads, in, &out);
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};
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int64_t t0 = ggml_time_ms();
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sd_tiling(input_image_tensor, upscaled, esrgan_upscaler->scale, esrgan_upscaler->tile_size, 0.25f, on_tiling);
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esrgan_upscaler->free_compute_buffer();
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ggml_tensor_clamp(upscaled, 0.f, 1.f);
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uint8_t* upscaled_data = sd_tensor_to_image(upscaled);
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ggml_free(upscale_ctx);
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int64_t t3 = ggml_time_ms();
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LOG_INFO("input_image_tensor upscaled, taking %.2fs", (t3 - t0) / 1000.0f);
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upscaled_image = {
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(uint32_t)output_width,
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(uint32_t)output_height,
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3,
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upscaled_data,
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};
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return upscaled_image;
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}
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};
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struct upscaler_ctx_t {
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UpscalerGGML* upscaler = NULL;
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};
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upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
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int n_threads,
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enum sd_type_t wtype) {
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upscaler_ctx_t* upscaler_ctx = (upscaler_ctx_t*)malloc(sizeof(upscaler_ctx_t));
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if (upscaler_ctx == NULL) {
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return NULL;
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}
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std::string esrgan_path(esrgan_path_c_str);
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upscaler_ctx->upscaler = new UpscalerGGML(n_threads);
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if (upscaler_ctx->upscaler == NULL) {
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return NULL;
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}
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if (!upscaler_ctx->upscaler->load_from_file(esrgan_path)) {
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delete upscaler_ctx->upscaler;
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upscaler_ctx->upscaler = NULL;
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free(upscaler_ctx);
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return NULL;
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}
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return upscaler_ctx;
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}
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sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_t upscale_factor) {
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return upscaler_ctx->upscaler->upscale(input_image, upscale_factor);
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}
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void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx) {
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if (upscaler_ctx->upscaler != NULL) {
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delete upscaler_ctx->upscaler;
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upscaler_ctx->upscaler = NULL;
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}
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free(upscaler_ctx);
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}
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