8124588cf1
* set ggml url to FSSRepo/ggml * ggml-alloc integration * offload all functions to gpu * gguf format + native converter * merge custom vae to a model * full offload to gpu * improve pretty progress --------- Co-authored-by: leejet <leejet714@gmail.com>
391 lines
14 KiB
C++
391 lines
14 KiB
C++
#include "common.h"
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <string>
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#include <thread>
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#include <unordered_set>
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#include <vector>
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#if defined(__APPLE__) && defined(__MACH__)
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#include <sys/sysctl.h>
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#include <sys/types.h>
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#endif
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#if !defined(_WIN32)
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#include <sys/ioctl.h>
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#include <unistd.h>
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#endif
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// get_num_physical_cores is copy from
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// https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
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// LICENSE: https://github.com/ggerganov/llama.cpp/blob/master/LICENSE
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int32_t get_num_physical_cores() {
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#ifdef __linux__
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// enumerate the set of thread siblings, num entries is num cores
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std::unordered_set<std::string> siblings;
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for (uint32_t cpu = 0; cpu < UINT32_MAX; ++cpu) {
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std::ifstream thread_siblings("/sys/devices/system/cpu" + std::to_string(cpu) + "/topology/thread_siblings");
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if (!thread_siblings.is_open()) {
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break; // no more cpus
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}
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std::string line;
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if (std::getline(thread_siblings, line)) {
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siblings.insert(line);
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}
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}
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if (siblings.size() > 0) {
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return static_cast<int32_t>(siblings.size());
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}
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#elif defined(__APPLE__) && defined(__MACH__)
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int32_t num_physical_cores;
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size_t len = sizeof(num_physical_cores);
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int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
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if (result == 0) {
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return num_physical_cores;
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}
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#elif defined(_WIN32)
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// TODO: Implement
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#endif
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unsigned int n_threads = std::thread::hardware_concurrency();
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return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
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}
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const char* rng_type_to_str[] = {
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"std_default",
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"cuda",
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};
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// Names of the sampler method, same order as enum sample_method in stable-diffusion.h
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const char* sample_method_str[] = {
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"euler_a",
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"euler",
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"heun",
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"dpm2",
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"dpm++2s_a",
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"dpm++2m",
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"dpm++2mv2",
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"lcm",
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};
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// Names of the sigma schedule overrides, same order as sample_schedule in stable-diffusion.h
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const char* schedule_str[] = {
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"default",
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"discrete",
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"karras"};
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const char* modes_str[] = {
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"txt2img",
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"img2img"};
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void print_params(SDParams params) {
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printf("Option: \n");
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printf(" n_threads: %d\n", params.n_threads);
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printf(" mode: %s\n", modes_str[params.mode]);
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printf(" model_path: %s\n", params.model_path.c_str());
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printf(" output_path: %s\n", params.output_path.c_str());
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printf(" init_img: %s\n", params.input_path.c_str());
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printf(" prompt: %s\n", params.prompt.c_str());
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printf(" negative_prompt: %s\n", params.negative_prompt.c_str());
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printf(" cfg_scale: %.2f\n", params.cfg_scale);
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printf(" width: %d\n", params.width);
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printf(" height: %d\n", params.height);
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printf(" sample_method: %s\n", sample_method_str[params.sample_method]);
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printf(" schedule: %s\n", schedule_str[params.schedule]);
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printf(" sample_steps: %d\n", params.sample_steps);
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printf(" strength: %.2f\n", params.strength);
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printf(" rng: %s\n", rng_type_to_str[params.rng_type]);
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printf(" seed: %ld\n", params.seed);
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printf(" batch_count: %d\n", params.batch_count);
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}
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void print_usage(int argc, const char* argv[]) {
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printf("usage: %s [arguments]\n", argv[0]);
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printf("\n");
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printf("arguments:\n");
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printf(" -h, --help show this help message and exit\n");
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printf(" -M, --mode [txt2img or img2img] generation mode (default: txt2img)\n");
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printf(" -t, --threads N number of threads to use during computation (default: -1).\n");
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printf(" If threads <= 0, then threads will be set to the number of CPU physical cores\n");
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printf(" -m, --model [MODEL] path to model\n");
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printf(" --lora-model-dir [DIR] lora model directory\n");
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printf(" -i, --init-img [IMAGE] path to the input image, required by img2img\n");
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printf(" -o, --output OUTPUT path to write result image to (default: ./output.png)\n");
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printf(" -p, --prompt [PROMPT] the prompt to render\n");
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printf(" -n, --negative-prompt PROMPT the negative prompt (default: \"\")\n");
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printf(" --cfg-scale SCALE unconditional guidance scale: (default: 7.0)\n");
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printf(" --strength STRENGTH strength for noising/unnoising (default: 0.75)\n");
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printf(" 1.0 corresponds to full destruction of information in init image\n");
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printf(" -H, --height H image height, in pixel space (default: 512)\n");
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printf(" -W, --width W image width, in pixel space (default: 512)\n");
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printf(" --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, lcm}\n");
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printf(" sampling method (default: \"euler_a\")\n");
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printf(" --steps STEPS number of sample steps (default: 20)\n");
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printf(" --rng {std_default, cuda} RNG (default: cuda)\n");
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printf(" -s SEED, --seed SEED RNG seed (default: 42, use random seed for < 0)\n");
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printf(" -b, --batch-count COUNT number of images to generate.\n");
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printf(" --schedule {discrete, karras} Denoiser sigma schedule (default: discrete)\n");
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printf(" -v, --verbose print extra info\n");
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}
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void parse_args(int argc, const char** argv, SDParams& params) {
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bool invalid_arg = false;
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std::string arg;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg == "-t" || arg == "--threads") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.n_threads = std::stoi(argv[i]);
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} else if (arg == "-M" || arg == "--mode") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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const char* mode_selected = argv[i];
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int mode_found = -1;
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for (int d = 0; d < MODE_COUNT; d++) {
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if (!strcmp(mode_selected, modes_str[d])) {
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mode_found = d;
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}
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}
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if (mode_found == -1) {
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fprintf(stderr, "error: invalid mode %s, must be one of [txt2img, img2img]\n",
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mode_selected);
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exit(1);
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}
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params.mode = (sd_mode)mode_found;
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} else if (arg == "-m" || arg == "--model") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.model_path = argv[i];
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} else if (arg == "--lora-model-dir") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.lora_model_dir = argv[i];
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} else if (arg == "-i" || arg == "--init-img") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.input_path = argv[i];
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} else if (arg == "-o" || arg == "--output") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.output_path = argv[i];
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} else if (arg == "-p" || arg == "--prompt") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.prompt = argv[i];
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} else if (arg == "-n" || arg == "--negative-prompt") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.negative_prompt = argv[i];
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} else if (arg == "--cfg-scale") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.cfg_scale = std::stof(argv[i]);
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} else if (arg == "--strength") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.strength = std::stof(argv[i]);
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} else if (arg == "-H" || arg == "--height") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.height = std::stoi(argv[i]);
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} else if (arg == "-W" || arg == "--width") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.width = std::stoi(argv[i]);
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} else if (arg == "--steps") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.sample_steps = std::stoi(argv[i]);
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} else if (arg == "-b" || arg == "--batch-count") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.batch_count = std::stoi(argv[i]);
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} else if (arg == "--rng") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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std::string rng_type_str = argv[i];
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if (rng_type_str == "std_default") {
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params.rng_type = STD_DEFAULT_RNG;
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} else if (rng_type_str == "cuda") {
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params.rng_type = CUDA_RNG;
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} else {
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invalid_arg = true;
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break;
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}
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} else if (arg == "--schedule") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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const char* schedule_selected = argv[i];
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int schedule_found = -1;
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for (int d = 0; d < N_SCHEDULES; d++) {
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if (!strcmp(schedule_selected, schedule_str[d])) {
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schedule_found = d;
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}
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}
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if (schedule_found == -1) {
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invalid_arg = true;
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break;
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}
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params.schedule = (Schedule)schedule_found;
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} else if (arg == "-s" || arg == "--seed") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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params.seed = std::stoll(argv[i]);
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} else if (arg == "--sampling-method") {
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if (++i >= argc) {
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invalid_arg = true;
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break;
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}
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const char* sample_method_selected = argv[i];
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int sample_method_found = -1;
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for (int m = 0; m < N_SAMPLE_METHODS; m++) {
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if (!strcmp(sample_method_selected, sample_method_str[m])) {
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sample_method_found = m;
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}
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}
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if (sample_method_found == -1) {
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invalid_arg = true;
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break;
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}
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params.sample_method = (SampleMethod)sample_method_found;
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} else if (arg == "-h" || arg == "--help") {
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print_usage(argc, argv);
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exit(0);
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} else if (arg == "-v" || arg == "--verbose") {
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params.verbose = true;
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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print_usage(argc, argv);
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exit(1);
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}
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}
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if (invalid_arg) {
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fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
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print_usage(argc, argv);
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exit(1);
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}
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if (params.n_threads <= 0) {
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params.n_threads = get_num_physical_cores();
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}
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if (params.prompt.length() == 0) {
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fprintf(stderr, "error: the following arguments are required: prompt\n");
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print_usage(argc, argv);
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exit(1);
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}
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if (params.model_path.length() == 0) {
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fprintf(stderr, "error: the following arguments are required: model_path\n");
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print_usage(argc, argv);
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exit(1);
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}
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if (params.mode == IMG2IMG && params.input_path.length() == 0) {
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fprintf(stderr, "error: when using the img2img mode, the following arguments are required: init-img\n");
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print_usage(argc, argv);
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exit(1);
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}
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if (params.output_path.length() == 0) {
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fprintf(stderr, "error: the following arguments are required: output_path\n");
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print_usage(argc, argv);
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exit(1);
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}
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if (params.width <= 0 || params.width % 64 != 0) {
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fprintf(stderr, "error: the width must be a multiple of 64\n");
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exit(1);
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}
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if (params.height <= 0 || params.height % 64 != 0) {
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fprintf(stderr, "error: the height must be a multiple of 64\n");
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exit(1);
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}
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if (params.sample_steps <= 0) {
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fprintf(stderr, "error: the sample_steps must be greater than 0\n");
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exit(1);
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}
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if (params.strength < 0.f || params.strength > 1.f) {
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fprintf(stderr, "error: can only work with strength in [0.0, 1.0]\n");
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exit(1);
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}
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if (params.seed < 0) {
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srand((int)time(NULL));
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params.seed = rand();
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}
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}
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std::string basename(const std::string& path) {
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size_t pos = path.find_last_of('/');
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if (pos != std::string::npos) {
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return path.substr(pos + 1);
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}
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pos = path.find_last_of('\\');
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if (pos != std::string::npos) {
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return path.substr(pos + 1);
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}
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return path;
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}
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const char* get_image_params(SDParams params, int seed) {
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std::string parameter_string = params.prompt + "\n";
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if (params.negative_prompt.size() != 0) {
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parameter_string += "Negative prompt: " + params.negative_prompt + "\n";
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}
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parameter_string += "Steps: " + std::to_string(params.sample_steps) + ", ";
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parameter_string += "CFG scale: " + std::to_string(params.cfg_scale) + ", ";
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parameter_string += "Seed: " + std::to_string(seed) + ", ";
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parameter_string += "Size: " + std::to_string(params.width) + "x" + std::to_string(params.height) + ", ";
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parameter_string += "Model: " + basename(params.model_path) + ", ";
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parameter_string += "RNG: " + std::string(rng_type_to_str[params.rng_type]) + ", ";
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parameter_string += "Sampler: " + std::string(sample_method_str[params.sample_method]);
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if (params.schedule == KARRAS) {
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parameter_string += " karras";
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}
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parameter_string += ", ";
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parameter_string += "Version: stable-diffusion.cpp";
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return parameter_string.c_str();
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} |