wehub-resource-sync
75 lines
2.9 KiB
C++
75 lines
2.9 KiB
C++
#include <iostream>
|
|
#include "diffusion/stable_diffusion.hpp"
|
|
#define MNN_OPEN_TIME_TRACE
|
|
#include <MNN/AutoTime.hpp>
|
|
#include <MNN/expr/ExecutorScope.hpp>
|
|
using namespace MNN::DIFFUSION;
|
|
|
|
int main(int argc, const char* argv[]) {
|
|
if (argc < 9) {
|
|
MNN_PRINT("=====================================================================================================================\n");
|
|
MNN_PRINT("Usage: ./diffusion_demo <resource_path> <model_type> <memory_mode> <backend_type> <iteration_num> <random_seed> <output_image_name> <prompt_text>\n");
|
|
MNN_PRINT("=====================================================================================================================\n");
|
|
return 0;
|
|
}
|
|
|
|
auto resource_path = argv[1];
|
|
auto model_type = (DiffusionModelType)atoi(argv[2]);
|
|
auto memory_mode = atoi(argv[3]);
|
|
auto backend_type = (MNNForwardType)atoi(argv[4]);
|
|
auto iteration_num = atoi(argv[5]);
|
|
auto random_seed = atoi(argv[6]);
|
|
auto img_name = argv[7];
|
|
|
|
std::string input_text;
|
|
for (int i = 8; i < argc; ++i) {
|
|
input_text += argv[i];
|
|
if (i < argc - 1) {
|
|
input_text += " ";
|
|
}
|
|
}
|
|
|
|
MNN_PRINT("Model resource path: %s\n", resource_path);
|
|
if(model_type == STABLE_DIFFUSION_1_5) {
|
|
MNN_PRINT("Model type is stable diffusion 1.5\n");
|
|
} else if (model_type == STABLE_DIFFUSION_TAIYI_CHINESE) {
|
|
MNN_PRINT("Model type is stable diffusion taiyi chinese version\n");
|
|
} else {
|
|
MNN_PRINT("Error: Model type %d not supported, please check\n", (int)model_type);
|
|
}
|
|
|
|
if(memory_mode == 1) {
|
|
MNN_PRINT("(Memory Enough) All Diffusion models will be initialized when application enter. with fast initialization\n");
|
|
} else {
|
|
MNN_PRINT("(Memory Lack) Each diffusion model will be initialized when using, freed after using. with slow initialization\n");
|
|
}
|
|
MNN_PRINT("Backend type: %d\n", (int)backend_type);
|
|
MNN_PRINT("Output image name: %s\n", img_name);
|
|
MNN_PRINT("Prompt text: %s\n", input_text.c_str());
|
|
|
|
|
|
std::unique_ptr<Diffusion> diffusion(Diffusion::createDiffusion(resource_path, model_type, backend_type, memory_mode));
|
|
|
|
diffusion->load();
|
|
|
|
// callback to show progress
|
|
auto progressDisplay = [](int progress) {
|
|
std::cout << "Progress: " << progress << "%" << std::endl;
|
|
};
|
|
diffusion->run(input_text, img_name, iteration_num, random_seed, progressDisplay);
|
|
|
|
/*
|
|
when need multi text-generation-image:
|
|
if you choose memory lack mode, need diffusion load with each diffusion run.
|
|
if you choose memory enough mode, just start another diffusion run, only need diffusion load in first time.
|
|
*/
|
|
while(0) {
|
|
if(memory_mode != 1) {
|
|
diffusion->load();
|
|
}
|
|
|
|
diffusion->run("a big horse", "demo_2.jpg", 20, 42, progressDisplay);
|
|
}
|
|
return 0;
|
|
}
|