#include <iostream>
#include <map>
#include <random>
#include <regex>
#include <sstream>
#include <string>
#include <vector>

#include <json.hpp>
using json = nlohmann::json;

#if defined(_WIN32)
#define NOMINMAX
#include <windows.h>
#endif  // _WIN32

#define SAFE_STR(s) ((s) ? (s) : "")
#define BOOL_STR(b) ((b) ? "true" : "false")

const char* modes_str[] = {
    "img_gen",
    "vid_gen",
    "convert",
    "upscale",
};
#define SD_ALL_MODES_STR "img_gen, vid_gen, convert, upscale"

enum SDMode {
    IMG_GEN,
    VID_GEN,
    CONVERT,
    UPSCALE,
    MODE_COUNT
};

#if defined(_WIN32)
static std::string utf16_to_utf8(const std::wstring& wstr) {
    if (wstr.empty())
        return {};
    int size_needed = WideCharToMultiByte(CP_UTF8, 0, wstr.data(), (int)wstr.size(),
                                          nullptr, 0, nullptr, nullptr);
    if (size_needed <= 0)
        throw std::runtime_error("UTF-16 to UTF-8 conversion failed");

    std::string utf8(size_needed, 0);
    WideCharToMultiByte(CP_UTF8, 0, wstr.data(), (int)wstr.size(),
                        (char*)utf8.data(), size_needed, nullptr, nullptr);
    return utf8;
}

static std::string argv_to_utf8(int index, const char** argv) {
    int argc;
    wchar_t** argv_w = CommandLineToArgvW(GetCommandLineW(), &argc);
    if (!argv_w)
        throw std::runtime_error("Failed to parse command line");

    std::string result;
    if (index < argc) {
        result = utf16_to_utf8(argv_w[index]);
    }
    LocalFree(argv_w);
    return result;
}

#else  // Linux / macOS
static std::string argv_to_utf8(int index, const char** argv) {
    return std::string(argv[index]);
}

#endif

struct StringOption {
    std::string short_name;
    std::string long_name;
    std::string desc;
    std::string* target;
};

struct IntOption {
    std::string short_name;
    std::string long_name;
    std::string desc;
    int* target;
};

struct FloatOption {
    std::string short_name;
    std::string long_name;
    std::string desc;
    float* target;
};

struct BoolOption {
    std::string short_name;
    std::string long_name;
    std::string desc;
    bool keep_true;
    bool* target;
};

struct ManualOption {
    std::string short_name;
    std::string long_name;
    std::string desc;
    std::function<int(int argc, const char** argv, int index)> cb;
};

struct ArgOptions {
    std::vector<StringOption> string_options;
    std::vector<IntOption> int_options;
    std::vector<FloatOption> float_options;
    std::vector<BoolOption> bool_options;
    std::vector<ManualOption> manual_options;

    static std::string wrap_text(const std::string& text, size_t width, size_t indent) {
        std::ostringstream oss;
        size_t line_len = 0;
        size_t pos      = 0;

        while (pos < text.size()) {
            // Preserve manual newlines
            if (text[pos] == '\n') {
                oss << '\n'
                    << std::string(indent, ' ');
                line_len = indent;
                ++pos;
                continue;
            }

            // Add the character
            oss << text[pos];
            ++line_len;
            ++pos;

            // If the current line exceeds width, try to break at the last space
            if (line_len >= width) {
                std::string current = oss.str();
                size_t back         = current.size();

                // Find the last space (for a clean break)
                while (back > 0 && current[back - 1] != ' ' && current[back - 1] != '\n')
                    --back;

                // If found a space to break on
                if (back > 0 && current[back - 1] != '\n') {
                    std::string before = current.substr(0, back - 1);
                    std::string after  = current.substr(back);
                    oss.str("");
                    oss.clear();
                    oss << before << "\n"
                        << std::string(indent, ' ') << after;
                } else {
                    // If no space found, just break at width
                    oss << "\n"
                        << std::string(indent, ' ');
                }
                line_len = indent;
            }
        }

        return oss.str();
    }

    void print() const {
        constexpr size_t max_line_width = 120;

        struct Entry {
            std::string names;
            std::string desc;
        };
        std::vector<Entry> entries;

        auto add_entry = [&](const std::string& s, const std::string& l,
                             const std::string& desc, const std::string& hint = "") {
            std::ostringstream ss;
            if (!s.empty())
                ss << s;
            if (!s.empty() && !l.empty())
                ss << ", ";
            if (!l.empty())
                ss << l;
            if (!hint.empty())
                ss << " " << hint;
            entries.push_back({ss.str(), desc});
        };

        for (auto& o : string_options)
            add_entry(o.short_name, o.long_name, o.desc, "<string>");
        for (auto& o : int_options)
            add_entry(o.short_name, o.long_name, o.desc, "<int>");
        for (auto& o : float_options)
            add_entry(o.short_name, o.long_name, o.desc, "<float>");
        for (auto& o : bool_options)
            add_entry(o.short_name, o.long_name, o.desc, "");
        for (auto& o : manual_options)
            add_entry(o.short_name, o.long_name, o.desc);

        size_t max_name_width = 0;
        for (auto& e : entries)
            max_name_width = std::max(max_name_width, e.names.size());

        for (auto& e : entries) {
            size_t indent            = 2 + max_name_width + 4;
            size_t desc_width        = (max_line_width > indent ? max_line_width - indent : 40);
            std::string wrapped_desc = wrap_text(e.desc, max_line_width, indent);
            std::cout << "  " << std::left << std::setw(static_cast<int>(max_name_width) + 4)
                      << e.names << wrapped_desc << "\n";
        }
    }
};

static bool parse_options(int argc, const char** argv, const std::vector<ArgOptions>& options_list) {
    bool invalid_arg = false;
    std::string arg;

    auto match_and_apply = [&](auto& opts, auto&& apply_fn) -> bool {
        for (auto& option : opts) {
            if ((option.short_name.size() > 0 && arg == option.short_name) ||
                (option.long_name.size() > 0 && arg == option.long_name)) {
                apply_fn(option);
                return true;
            }
        }
        return false;
    };

    for (int i = 1; i < argc; i++) {
        arg            = argv[i];
        bool found_arg = false;

        for (auto& options : options_list) {
            if (match_and_apply(options.string_options, [&](auto& option) {
                    if (++i >= argc) {
                        invalid_arg = true;
                        return;
                    }
                    *option.target = argv_to_utf8(i, argv);
                    found_arg      = true;
                }))
                break;

            if (match_and_apply(options.int_options, [&](auto& option) {
                    if (++i >= argc) {
                        invalid_arg = true;
                        return;
                    }
                    *option.target = std::stoi(argv[i]);
                    found_arg      = true;
                }))
                break;

            if (match_and_apply(options.float_options, [&](auto& option) {
                    if (++i >= argc) {
                        invalid_arg = true;
                        return;
                    }
                    *option.target = std::stof(argv[i]);
                    found_arg      = true;
                }))
                break;

            if (match_and_apply(options.bool_options, [&](auto& option) {
                    *option.target = option.keep_true ? true : false;
                    found_arg      = true;
                }))
                break;

            if (match_and_apply(options.manual_options, [&](auto& option) {
                    int ret = option.cb(argc, argv, i);
                    if (ret < 0) {
                        invalid_arg = true;
                        return;
                    }
                    i += ret;
                    found_arg = true;
                }))
                break;
        }

        if (invalid_arg) {
            fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
            return false;
        }
        if (!found_arg) {
            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
            return false;
        }
    }

    return true;
}

struct SDContextParams {
    int n_threads = -1;
    std::string model_path;
    std::string clip_l_path;
    std::string clip_g_path;
    std::string clip_vision_path;
    std::string t5xxl_path;
    std::string llm_path;
    std::string llm_vision_path;
    std::string diffusion_model_path;
    std::string high_noise_diffusion_model_path;
    std::string vae_path;
    std::string taesd_path;
    std::string esrgan_path;
    std::string control_net_path;
    std::string embedding_dir;
    std::string photo_maker_path;
    sd_type_t wtype = SD_TYPE_COUNT;
    std::string tensor_type_rules;
    std::string lora_model_dir;

    rng_type_t rng_type         = CUDA_RNG;
    rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
    bool offload_params_to_cpu  = false;
    bool control_net_cpu        = false;
    bool clip_on_cpu            = false;
    bool vae_on_cpu             = false;
    bool diffusion_flash_attn   = false;
    bool diffusion_conv_direct  = false;
    bool vae_conv_direct        = false;

    bool chroma_use_dit_mask = true;
    bool chroma_use_t5_mask  = false;
    int chroma_t5_mask_pad   = 1;

    prediction_t prediction           = DEFAULT_PRED;
    lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;

    sd_tiling_params_t vae_tiling_params = {false, 0, 0, 0.5f, 0.0f, 0.0f};
    bool force_sdxl_vae_conv_scale       = false;

    float flow_shift = INFINITY;

    ArgOptions get_options() {
        ArgOptions options;
        options.string_options = {
            {"-m",
             "--model",
             "path to full model",
             &model_path},
            {"",
             "--clip_l",
             "path to the clip-l text encoder", &clip_l_path},
            {"", "--clip_g",
             "path to the clip-g text encoder",
             &clip_g_path},
            {"",
             "--clip_vision",
             "path to the clip-vision encoder",
             &clip_vision_path},
            {"",
             "--t5xxl",
             "path to the t5xxl text encoder",
             &t5xxl_path},
            {"",
             "--llm",
             "path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)",
             &llm_path},
            {"",
             "--llm_vision",
             "path to the llm vit",
             &llm_vision_path},
            {"",
             "--qwen2vl",
             "alias of --llm. Deprecated.",
             &llm_path},
            {"",
             "--qwen2vl_vision",
             "alias of --llm_vision. Deprecated.",
             &llm_vision_path},
            {"",
             "--diffusion-model",
             "path to the standalone diffusion model",
             &diffusion_model_path},
            {"",
             "--high-noise-diffusion-model",
             "path to the standalone high noise diffusion model",
             &high_noise_diffusion_model_path},
            {"",
             "--vae",
             "path to standalone vae model",
             &vae_path},
            {"",
             "--taesd",
             "path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)",
             &taesd_path},
            {"",
             "--control-net",
             "path to control net model",
             &control_net_path},
            {"",
             "--embd-dir",
             "embeddings directory",
             &embedding_dir},
            {"",
             "--lora-model-dir",
             "lora model directory",
             &lora_model_dir},

            {"",
             "--tensor-type-rules",
             "weight type per tensor pattern (example: \"^vae\\.=f16,model\\.=q8_0\")",
             &tensor_type_rules},
            {"",
             "--photo-maker",
             "path to PHOTOMAKER model",
             &photo_maker_path},
            {"",
             "--upscale-model",
             "path to esrgan model.",
             &esrgan_path},
        };

        options.int_options = {
            {"-t",
             "--threads",
             "number of threads to use during computation (default: -1). "
             "If threads <= 0, then threads will be set to the number of CPU physical cores",
             &n_threads},
            {"",
             "--chroma-t5-mask-pad",
             "t5 mask pad size of chroma",
             &chroma_t5_mask_pad},
        };

        options.float_options = {
            {"",
             "--vae-tile-overlap",
             "tile overlap for vae tiling, in fraction of tile size (default: 0.5)",
             &vae_tiling_params.target_overlap},
            {"",
             "--flow-shift",
             "shift value for Flow models like SD3.x or WAN (default: auto)",
             &flow_shift},
        };

        options.bool_options = {
            {"",
             "--vae-tiling",
             "process vae in tiles to reduce memory usage",
             true, &vae_tiling_params.enabled},
            {"",
             "--force-sdxl-vae-conv-scale",
             "force use of conv scale on sdxl vae",
             true, &force_sdxl_vae_conv_scale},
            {"",
             "--offload-to-cpu",
             "place the weights in RAM to save VRAM, and automatically load them into VRAM when needed",
             true, &offload_params_to_cpu},
            {"",
             "--control-net-cpu",
             "keep controlnet in cpu (for low vram)",
             true, &control_net_cpu},
            {"",
             "--clip-on-cpu",
             "keep clip in cpu (for low vram)",
             true, &clip_on_cpu},
            {"",
             "--vae-on-cpu",
             "keep vae in cpu (for low vram)",
             true, &vae_on_cpu},
            {"",
             "--diffusion-fa",
             "use flash attention in the diffusion model",
             true, &diffusion_flash_attn},
            {"",
             "--diffusion-conv-direct",
             "use ggml_conv2d_direct in the diffusion model",
             true, &diffusion_conv_direct},
            {"",
             "--vae-conv-direct",
             "use ggml_conv2d_direct in the vae model",
             true, &vae_conv_direct},
            {"",
             "--chroma-disable-dit-mask",
             "disable dit mask for chroma",
             false, &chroma_use_dit_mask},
            {"",
             "--chroma-enable-t5-mask",
             "enable t5 mask for chroma",
             true, &chroma_use_t5_mask},
        };

        auto on_type_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg = argv[index];
            wtype           = str_to_sd_type(arg);
            if (wtype == SD_TYPE_COUNT) {
                fprintf(stderr, "error: invalid weight format %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_rng_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg = argv[index];
            rng_type        = str_to_rng_type(arg);
            if (rng_type == RNG_TYPE_COUNT) {
                fprintf(stderr, "error: invalid rng type %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_sampler_rng_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg  = argv[index];
            sampler_rng_type = str_to_rng_type(arg);
            if (sampler_rng_type == RNG_TYPE_COUNT) {
                fprintf(stderr, "error: invalid sampler rng type %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_prediction_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg = argv[index];
            prediction      = str_to_prediction(arg);
            if (prediction == PREDICTION_COUNT) {
                fprintf(stderr, "error: invalid prediction type %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_lora_apply_mode_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg = argv[index];
            lora_apply_mode = str_to_lora_apply_mode(arg);
            if (lora_apply_mode == LORA_APPLY_MODE_COUNT) {
                fprintf(stderr, "error: invalid lora apply model %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_tile_size_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            std::string tile_size_str = argv[index];
            size_t x_pos              = tile_size_str.find('x');
            try {
                if (x_pos != std::string::npos) {
                    std::string tile_x_str        = tile_size_str.substr(0, x_pos);
                    std::string tile_y_str        = tile_size_str.substr(x_pos + 1);
                    vae_tiling_params.tile_size_x = std::stoi(tile_x_str);
                    vae_tiling_params.tile_size_y = std::stoi(tile_y_str);
                } else {
                    vae_tiling_params.tile_size_x = vae_tiling_params.tile_size_y = std::stoi(tile_size_str);
                }
            } catch (const std::invalid_argument&) {
                return -1;
            } catch (const std::out_of_range&) {
                return -1;
            }
            return 1;
        };

        auto on_relative_tile_size_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            std::string rel_size_str = argv[index];
            size_t x_pos             = rel_size_str.find('x');
            try {
                if (x_pos != std::string::npos) {
                    std::string rel_x_str        = rel_size_str.substr(0, x_pos);
                    std::string rel_y_str        = rel_size_str.substr(x_pos + 1);
                    vae_tiling_params.rel_size_x = std::stof(rel_x_str);
                    vae_tiling_params.rel_size_y = std::stof(rel_y_str);
                } else {
                    vae_tiling_params.rel_size_x = vae_tiling_params.rel_size_y = std::stof(rel_size_str);
                }
            } catch (const std::invalid_argument&) {
                return -1;
            } catch (const std::out_of_range&) {
                return -1;
            }
            return 1;
        };

        options.manual_options = {
            {"",
             "--type",
             "weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). "
             "If not specified, the default is the type of the weight file",
             on_type_arg},
            {"",
             "--rng",
             "RNG, one of [std_default, cuda, cpu], default: cuda(sd-webui), cpu(comfyui)",
             on_rng_arg},
            {"",
             "--sampler-rng",
             "sampler RNG, one of [std_default, cuda, cpu]. If not specified, use --rng",
             on_sampler_rng_arg},
            {"",
             "--prediction",
             "prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]",
             on_prediction_arg},
            {"",
             "--lora-apply-mode",
             "the way to apply LoRA, one of [auto, immediately, at_runtime], default is auto. "
             "In auto mode, if the model weights contain any quantized parameters, the at_runtime mode will be used; otherwise, immediately will be used."
             "The immediately mode may have precision and compatibility issues with quantized parameters, "
             "but it usually offers faster inference speed and, in some cases, lower memory usage. "
             "The at_runtime mode, on the other hand, is exactly the opposite.",
             on_lora_apply_mode_arg},
            {"",
             "--vae-tile-size",
             "tile size for vae tiling, format [X]x[Y] (default: 32x32)",
             on_tile_size_arg},
            {"",
             "--vae-relative-tile-size",
             "relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1 (overrides --vae-tile-size)",
             on_relative_tile_size_arg},
        };

        return options;
    }

    bool process_and_check(SDMode mode) {
        if (mode != UPSCALE && model_path.length() == 0 && diffusion_model_path.length() == 0) {
            fprintf(stderr, "error: the following arguments are required: model_path/diffusion_model\n");
            return false;
        }

        if (mode == UPSCALE) {
            if (esrgan_path.length() == 0) {
                fprintf(stderr, "error: upscale mode needs an upscaler model (--upscale-model)\n");
                return false;
            }
        }

        if (n_threads <= 0) {
            n_threads = sd_get_num_physical_cores();
        }

        return true;
    }

    std::string to_string() const {
        std::ostringstream oss;
        oss << "SDContextParams {\n"
            << "  n_threads: " << n_threads << ",\n"
            << "  model_path: \"" << model_path << "\",\n"
            << "  clip_l_path: \"" << clip_l_path << "\",\n"
            << "  clip_g_path: \"" << clip_g_path << "\",\n"
            << "  clip_vision_path: \"" << clip_vision_path << "\",\n"
            << "  t5xxl_path: \"" << t5xxl_path << "\",\n"
            << "  llm_path: \"" << llm_path << "\",\n"
            << "  llm_vision_path: \"" << llm_vision_path << "\",\n"
            << "  diffusion_model_path: \"" << diffusion_model_path << "\",\n"
            << "  high_noise_diffusion_model_path: \"" << high_noise_diffusion_model_path << "\",\n"
            << "  vae_path: \"" << vae_path << "\",\n"
            << "  taesd_path: \"" << taesd_path << "\",\n"
            << "  esrgan_path: \"" << esrgan_path << "\",\n"
            << "  control_net_path: \"" << control_net_path << "\",\n"
            << "  embedding_dir: \"" << embedding_dir << "\",\n"
            << "  wtype: " << sd_type_name(wtype) << ",\n"
            << "  tensor_type_rules: \"" << tensor_type_rules << "\",\n"
            << "  lora_model_dir: \"" << lora_model_dir << "\",\n"
            << "  photo_maker_path: \"" << photo_maker_path << "\",\n"
            << "  rng_type: " << sd_rng_type_name(rng_type) << ",\n"
            << "  sampler_rng_type: " << sd_rng_type_name(sampler_rng_type) << ",\n"
            << "  flow_shift: " << (std::isinf(flow_shift) ? "INF" : std::to_string(flow_shift)) << "\n"
            << "  offload_params_to_cpu: " << (offload_params_to_cpu ? "true" : "false") << ",\n"
            << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
            << "  clip_on_cpu: " << (clip_on_cpu ? "true" : "false") << ",\n"
            << "  vae_on_cpu: " << (vae_on_cpu ? "true" : "false") << ",\n"
            << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
            << "  diffusion_conv_direct: " << (diffusion_conv_direct ? "true" : "false") << ",\n"
            << "  vae_conv_direct: " << (vae_conv_direct ? "true" : "false") << ",\n"
            << "  chroma_use_dit_mask: " << (chroma_use_dit_mask ? "true" : "false") << ",\n"
            << "  chroma_use_t5_mask: " << (chroma_use_t5_mask ? "true" : "false") << ",\n"
            << "  chroma_t5_mask_pad: " << chroma_t5_mask_pad << ",\n"
            << "  prediction: " << sd_prediction_name(prediction) << ",\n"
            << "  lora_apply_mode: " << sd_lora_apply_mode_name(lora_apply_mode) << ",\n"
            << "  vae_tiling_params: { "
            << vae_tiling_params.enabled << ", "
            << vae_tiling_params.tile_size_x << ", "
            << vae_tiling_params.tile_size_y << ", "
            << vae_tiling_params.target_overlap << ", "
            << vae_tiling_params.rel_size_x << ", "
            << vae_tiling_params.rel_size_y << " },\n"
            << "  force_sdxl_vae_conv_scale: " << (force_sdxl_vae_conv_scale ? "true" : "false") << "\n"
            << "}";
        return oss.str();
    }

    sd_ctx_params_t to_sd_ctx_params_t(bool vae_decode_only, bool free_params_immediately, bool taesd_preview) {
        sd_ctx_params_t sd_ctx_params = {
            model_path.c_str(),
            clip_l_path.c_str(),
            clip_g_path.c_str(),
            clip_vision_path.c_str(),
            t5xxl_path.c_str(),
            llm_path.c_str(),
            llm_vision_path.c_str(),
            diffusion_model_path.c_str(),
            high_noise_diffusion_model_path.c_str(),
            vae_path.c_str(),
            taesd_path.c_str(),
            control_net_path.c_str(),
            lora_model_dir.c_str(),
            embedding_dir.c_str(),
            photo_maker_path.c_str(),
            tensor_type_rules.c_str(),
            vae_decode_only,
            free_params_immediately,
            n_threads,
            wtype,
            rng_type,
            sampler_rng_type,
            prediction,
            lora_apply_mode,
            offload_params_to_cpu,
            clip_on_cpu,
            control_net_cpu,
            vae_on_cpu,
            diffusion_flash_attn,
            taesd_preview,
            diffusion_conv_direct,
            vae_conv_direct,
            force_sdxl_vae_conv_scale,
            chroma_use_dit_mask,
            chroma_use_t5_mask,
            chroma_t5_mask_pad,
            flow_shift,
        };
        return sd_ctx_params;
    }
};

template <typename T>
static std::string vec_to_string(const std::vector<T>& v) {
    std::ostringstream oss;
    oss << "[";
    for (size_t i = 0; i < v.size(); i++) {
        oss << v[i];
        if (i + 1 < v.size())
            oss << ", ";
    }
    oss << "]";
    return oss.str();
}

static std::string vec_str_to_string(const std::vector<std::string>& v) {
    std::ostringstream oss;
    oss << "[";
    for (size_t i = 0; i < v.size(); i++) {
        oss << "\"" << v[i] << "\"";
        if (i + 1 < v.size())
            oss << ", ";
    }
    oss << "]";
    return oss.str();
}

struct SDGenerationParams {
    std::string prompt;
    std::string negative_prompt;
    int clip_skip   = -1;  // <= 0 represents unspecified
    int width       = 512;
    int height      = 512;
    int batch_count = 1;
    std::string init_image_path;
    std::string end_image_path;
    std::string mask_image_path;
    std::string control_image_path;
    std::vector<std::string> ref_image_paths;
    std::string control_video_path;
    bool auto_resize_ref_image = true;
    bool increase_ref_index    = false;

    std::vector<int> skip_layers = {7, 8, 9};
    sd_sample_params_t sample_params;

    std::vector<int> high_noise_skip_layers = {7, 8, 9};
    sd_sample_params_t high_noise_sample_params;

    std::string easycache_option;
    sd_easycache_params_t easycache_params;

    float moe_boundary  = 0.875f;
    int video_frames    = 1;
    int fps             = 16;
    float vace_strength = 1.f;

    float strength         = 0.75f;
    float control_strength = 0.9f;

    int64_t seed = 42;

    // Photo Maker
    std::string pm_id_images_dir;
    std::string pm_id_embed_path;
    float pm_style_strength = 20.f;

    int upscale_repeats = 1;

    SDGenerationParams() {
        sd_sample_params_init(&sample_params);
        sd_sample_params_init(&high_noise_sample_params);
    }

    ArgOptions get_options() {
        ArgOptions options;
        options.string_options = {
            {"-p",
             "--prompt",
             "the prompt to render",
             &prompt},
            {"-n",
             "--negative-prompt",
             "the negative prompt (default: \"\")",
             &negative_prompt},
            {"-i",
             "--init-img",
             "path to the init image",
             &init_image_path},
            {"",
             "--end-img",
             "path to the end image, required by flf2v",
             &end_image_path},
            {"",
             "--mask",
             "path to the mask image",
             &mask_image_path},
            {"",
             "--control-image",
             "path to control image, control net",
             &control_image_path},
            {"",
             "--control-video",
             "path to control video frames, It must be a directory path. The video frames inside should be stored as images in "
             "lexicographical (character) order. For example, if the control video path is `frames`, the directory contain images "
             "such as 00.png, 01.png, ... etc.",
             &control_video_path},
            {"",
             "--pm-id-images-dir",
             "path to PHOTOMAKER input id images dir",
             &pm_id_images_dir},
            {"",
             "--pm-id-embed-path",
             "path to PHOTOMAKER v2 id embed",
             &pm_id_embed_path},
        };

        options.int_options = {
            {"-H",
             "--height",
             "image height, in pixel space (default: 512)",
             &height},
            {"-W",
             "--width",
             "image width, in pixel space (default: 512)",
             &width},
            {"",
             "--steps",
             "number of sample steps (default: 20)",
             &sample_params.sample_steps},
            {"",
             "--high-noise-steps",
             "(high noise) number of sample steps (default: -1 = auto)",
             &high_noise_sample_params.sample_steps},
            {"",
             "--clip-skip",
             "ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). "
             "<= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x",
             &clip_skip},
            {"-b",
             "--batch-count",
             "batch count",
             &batch_count},
            {"",
             "--video-frames",
             "video frames (default: 1)",
             &video_frames},
            {"",
             "--fps",
             "fps (default: 24)",
             &fps},
            {"",
             "--timestep-shift",
             "shift timestep for NitroFusion models (default: 0). "
             "recommended N for NitroSD-Realism around 250 and 500 for NitroSD-Vibrant",
             &sample_params.shifted_timestep},
            {"",
             "--upscale-repeats",
             "Run the ESRGAN upscaler this many times (default: 1)",
             &upscale_repeats},
        };

        options.float_options = {
            {"",
             "--cfg-scale",
             "unconditional guidance scale: (default: 7.0)",
             &sample_params.guidance.txt_cfg},
            {"",
             "--img-cfg-scale",
             "image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)",
             &sample_params.guidance.img_cfg},
            {"",
             "--guidance",
             "distilled guidance scale for models with guidance input (default: 3.5)",
             &sample_params.guidance.distilled_guidance},
            {"",
             "--slg-scale",
             "skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5 medium",
             &sample_params.guidance.slg.scale},
            {"",
             "--skip-layer-start",
             "SLG enabling point (default: 0.01)",
             &sample_params.guidance.slg.layer_start},
            {"",
             "--skip-layer-end",
             "SLG disabling point (default: 0.2)",
             &sample_params.guidance.slg.layer_end},
            {"",
             "--eta",
             "eta in DDIM, only for DDIM and TCD (default: 0)",
             &sample_params.eta},
            {"",
             "--high-noise-cfg-scale",
             "(high noise) unconditional guidance scale: (default: 7.0)",
             &high_noise_sample_params.guidance.txt_cfg},
            {"",
             "--high-noise-img-cfg-scale",
             "(high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)",
             &high_noise_sample_params.guidance.img_cfg},
            {"",
             "--high-noise-guidance",
             "(high noise) distilled guidance scale for models with guidance input (default: 3.5)",
             &high_noise_sample_params.guidance.distilled_guidance},
            {"",
             "--high-noise-slg-scale",
             "(high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)",
             &high_noise_sample_params.guidance.slg.scale},
            {"",
             "--high-noise-skip-layer-start",
             "(high noise) SLG enabling point (default: 0.01)",
             &high_noise_sample_params.guidance.slg.layer_start},
            {"",
             "--high-noise-skip-layer-end",
             "(high noise) SLG disabling point (default: 0.2)",
             &high_noise_sample_params.guidance.slg.layer_end},
            {"",
             "--high-noise-eta",
             "(high noise) eta in DDIM, only for DDIM and TCD (default: 0)",
             &high_noise_sample_params.eta},
            {"",
             "--strength",
             "strength for noising/unnoising (default: 0.75)",
             &strength},
            {"",
             "--pm-style-strength",
             "",
             &pm_style_strength},
            {"",
             "--control-strength",
             "strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image",
             &control_strength},
            {"",
             "--moe-boundary",
             "timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if `--high-noise-steps` is set to -1",
             &moe_boundary},
            {"",
             "--vace-strength",
             "wan vace strength",
             &vace_strength},
        };

        options.bool_options = {
            {"",
             "--increase-ref-index",
             "automatically increase the indices of references images based on the order they are listed (starting with 1).",
             true,
             &increase_ref_index},
            {"",
             "--disable-auto-resize-ref-image",
             "disable auto resize of ref images",
             false,
             &auto_resize_ref_image},
        };

        auto on_seed_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            seed = std::stoll(argv[index]);
            return 1;
        };

        auto on_sample_method_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg             = argv[index];
            sample_params.sample_method = str_to_sample_method(arg);
            if (sample_params.sample_method == SAMPLE_METHOD_COUNT) {
                fprintf(stderr, "error: invalid sample method %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_high_noise_sample_method_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg                        = argv[index];
            high_noise_sample_params.sample_method = str_to_sample_method(arg);
            if (high_noise_sample_params.sample_method == SAMPLE_METHOD_COUNT) {
                fprintf(stderr, "error: invalid high noise sample method %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_scheduler_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            const char* arg         = argv[index];
            sample_params.scheduler = str_to_scheduler(arg);
            if (sample_params.scheduler == SCHEDULER_COUNT) {
                fprintf(stderr, "error: invalid scheduler %s\n",
                        arg);
                return -1;
            }
            return 1;
        };

        auto on_skip_layers_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            std::string layers_str = argv[index];
            if (layers_str[0] != '[' || layers_str[layers_str.size() - 1] != ']') {
                return -1;
            }

            layers_str = layers_str.substr(1, layers_str.size() - 2);

            std::regex regex("[, ]+");
            std::sregex_token_iterator iter(layers_str.begin(), layers_str.end(), regex, -1);
            std::sregex_token_iterator end;
            std::vector<std::string> tokens(iter, end);
            std::vector<int> layers;
            for (const auto& token : tokens) {
                try {
                    layers.push_back(std::stoi(token));
                } catch (const std::invalid_argument&) {
                    return -1;
                }
            }
            skip_layers = layers;
            return 1;
        };

        auto on_high_noise_skip_layers_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            std::string layers_str = argv[index];
            if (layers_str[0] != '[' || layers_str[layers_str.size() - 1] != ']') {
                return -1;
            }

            layers_str = layers_str.substr(1, layers_str.size() - 2);

            std::regex regex("[, ]+");
            std::sregex_token_iterator iter(layers_str.begin(), layers_str.end(), regex, -1);
            std::sregex_token_iterator end;
            std::vector<std::string> tokens(iter, end);
            std::vector<int> layers;
            for (const auto& token : tokens) {
                try {
                    layers.push_back(std::stoi(token));
                } catch (const std::invalid_argument&) {
                    return -1;
                }
            }
            high_noise_skip_layers = layers;
            return 1;
        };

        auto on_ref_image_arg = [&](int argc, const char** argv, int index) {
            if (++index >= argc) {
                return -1;
            }
            ref_image_paths.push_back(argv[index]);
            return 1;
        };

        auto on_easycache_arg = [&](int argc, const char** argv, int index) {
            const std::string default_values = "0.2,0.15,0.95";
            auto looks_like_value            = [](const std::string& token) {
                if (token.empty()) {
                    return false;
                }
                if (token[0] != '-') {
                    return true;
                }
                if (token.size() == 1) {
                    return false;
                }
                unsigned char next = static_cast<unsigned char>(token[1]);
                return std::isdigit(next) || token[1] == '.';
            };

            std::string option_value;
            int consumed = 0;
            if (index + 1 < argc) {
                std::string next_arg = argv[index + 1];
                if (looks_like_value(next_arg)) {
                    option_value = argv_to_utf8(index + 1, argv);
                    consumed     = 1;
                }
            }
            if (option_value.empty()) {
                option_value = default_values;
            }
            easycache_option = option_value;
            return consumed;
        };

        options.manual_options = {
            {"-s",
             "--seed",
             "RNG seed (default: 42, use random seed for < 0)",
             on_seed_arg},
            {"",
             "--sampling-method",
             "sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd] "
             "(default: euler for Flux/SD3/Wan, euler_a otherwise)",
             on_sample_method_arg},
            {"",
             "--high-noise-sampling-method",
             "(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd]"
             " default: euler for Flux/SD3/Wan, euler_a otherwise",
             on_high_noise_sample_method_arg},
            {"",
             "--scheduler",
             "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm], default: discrete",
             on_scheduler_arg},
            {"",
             "--skip-layers",
             "layers to skip for SLG steps (default: [7,8,9])",
             on_skip_layers_arg},
            {"",
             "--high-noise-skip-layers",
             "(high noise) layers to skip for SLG steps (default: [7,8,9])",
             on_high_noise_skip_layers_arg},
            {"-r",
             "--ref-image",
             "reference image for Flux Kontext models (can be used multiple times)",
             on_ref_image_arg},
            {"",
             "--easycache",
             "enable EasyCache for DiT models with optional \"threshold,start_percent,end_percent\" (default: 0.2,0.15,0.95)",
             on_easycache_arg},

        };

        return options;
    }

    bool from_json_str(const std::string& json_str) {
        json j;
        try {
            j = json::parse(json_str);
        } catch (...) {
            fprintf(stderr, "json parse failed %s\n", json_str.c_str());
            return false;
        }

        auto load_if_exists = [&](const char* key, auto& out) {
            if (j.contains(key)) {
                using T = std::decay_t<decltype(out)>;
                if constexpr (std::is_same_v<T, std::string>) {
                    if (j[key].is_string())
                        out = j[key];
                } else if constexpr (std::is_same_v<T, int> || std::is_same_v<T, int64_t>) {
                    if (j[key].is_number_integer())
                        out = j[key];
                } else if constexpr (std::is_same_v<T, float>) {
                    if (j[key].is_number())
                        out = j[key];
                } else if constexpr (std::is_same_v<T, bool>) {
                    if (j[key].is_boolean())
                        out = j[key];
                } else if constexpr (std::is_same_v<T, std::vector<int>>) {
                    if (j[key].is_array())
                        out = j[key].get<std::vector<int>>();
                } else if constexpr (std::is_same_v<T, std::vector<std::string>>) {
                    if (j[key].is_array())
                        out = j[key].get<std::vector<std::string>>();
                }
            }
        };

        load_if_exists("prompt", prompt);
        load_if_exists("negative_prompt", negative_prompt);
        load_if_exists("easycache_option", easycache_option);

        load_if_exists("clip_skip", clip_skip);
        load_if_exists("width", width);
        load_if_exists("height", height);
        load_if_exists("batch_count", batch_count);
        load_if_exists("video_frames", video_frames);
        load_if_exists("fps", fps);
        load_if_exists("upscale_repeats", upscale_repeats);
        load_if_exists("seed", seed);

        load_if_exists("strength", strength);
        load_if_exists("control_strength", control_strength);
        load_if_exists("pm_style_strength", pm_style_strength);
        load_if_exists("moe_boundary", moe_boundary);
        load_if_exists("vace_strength", vace_strength);

        load_if_exists("auto_resize_ref_image", auto_resize_ref_image);
        load_if_exists("increase_ref_index", increase_ref_index);

        load_if_exists("skip_layers", skip_layers);
        load_if_exists("high_noise_skip_layers", high_noise_skip_layers);

        return true;
    }

    bool process_and_check(SDMode mode) {
        if (width <= 0) {
            fprintf(stderr, "error: the width must be greater than 0\n");
            return false;
        }

        if (height <= 0) {
            fprintf(stderr, "error: the height must be greater than 0\n");
            return false;
        }

        if (sample_params.sample_steps <= 0) {
            fprintf(stderr, "error: the sample_steps must be greater than 0\n");
            return false;
        }

        if (high_noise_sample_params.sample_steps <= 0) {
            high_noise_sample_params.sample_steps = -1;
        }

        if (strength < 0.f || strength > 1.f) {
            fprintf(stderr, "error: can only work with strength in [0.0, 1.0]\n");
            return false;
        }

        if (!easycache_option.empty()) {
            float values[3] = {0.0f, 0.0f, 0.0f};
            std::stringstream ss(easycache_option);
            std::string token;
            int idx = 0;
            while (std::getline(ss, token, ',')) {
                auto trim = [](std::string& s) {
                    const char* whitespace = " \t\r\n";
                    auto start             = s.find_first_not_of(whitespace);
                    if (start == std::string::npos) {
                        s.clear();
                        return;
                    }
                    auto end = s.find_last_not_of(whitespace);
                    s        = s.substr(start, end - start + 1);
                };
                trim(token);
                if (token.empty()) {
                    fprintf(stderr, "error: invalid easycache option '%s'\n", easycache_option.c_str());
                    return false;
                }
                if (idx >= 3) {
                    fprintf(stderr, "error: easycache expects exactly 3 comma-separated values (threshold,start,end)\n");
                    return false;
                }
                try {
                    values[idx] = std::stof(token);
                } catch (const std::exception&) {
                    fprintf(stderr, "error: invalid easycache value '%s'\n", token.c_str());
                    return false;
                }
                idx++;
            }
            if (idx != 3) {
                fprintf(stderr, "error: easycache expects exactly 3 comma-separated values (threshold,start,end)\n");
                return false;
            }
            if (values[0] < 0.0f) {
                fprintf(stderr, "error: easycache threshold must be non-negative\n");
                return false;
            }
            if (values[1] < 0.0f || values[1] >= 1.0f || values[2] <= 0.0f || values[2] > 1.0f || values[1] >= values[2]) {
                fprintf(stderr, "error: easycache start/end percents must satisfy 0.0 <= start < end <= 1.0\n");
                return false;
            }
            easycache_params.enabled         = true;
            easycache_params.reuse_threshold = values[0];
            easycache_params.start_percent   = values[1];
            easycache_params.end_percent     = values[2];
        } else {
            easycache_params.enabled = false;
        }

        sample_params.guidance.slg.layers                 = skip_layers.data();
        sample_params.guidance.slg.layer_count            = skip_layers.size();
        high_noise_sample_params.guidance.slg.layers      = high_noise_skip_layers.data();
        high_noise_sample_params.guidance.slg.layer_count = high_noise_skip_layers.size();

        if (mode == VID_GEN && video_frames <= 0) {
            return false;
        }

        if (mode == VID_GEN && fps <= 0) {
            return false;
        }

        if (sample_params.shifted_timestep < 0 || sample_params.shifted_timestep > 1000) {
            return false;
        }

        if (upscale_repeats < 1) {
            return false;
        }

        if (mode == UPSCALE) {
            if (init_image_path.length() == 0) {
                fprintf(stderr, "error: upscale mode needs an init image (--init-img)\n");
                return false;
            }
        }

        if (seed < 0) {
            srand((int)time(nullptr));
            seed = rand();
        }

        return true;
    }

    std::string to_string() const {
        char* sample_params_str            = sd_sample_params_to_str(&sample_params);
        char* high_noise_sample_params_str = sd_sample_params_to_str(&high_noise_sample_params);
        std::ostringstream oss;
        oss << "SDGenerationParams {\n"
            << "  prompt: \"" << prompt << "\",\n"
            << "  negative_prompt: \"" << negative_prompt << "\",\n"
            << "  clip_skip: " << clip_skip << ",\n"
            << "  width: " << width << ",\n"
            << "  height: " << height << ",\n"
            << "  batch_count: " << batch_count << ",\n"
            << "  init_image_path: \"" << init_image_path << "\",\n"
            << "  end_image_path: \"" << end_image_path << "\",\n"
            << "  mask_image_path: \"" << mask_image_path << "\",\n"
            << "  control_image_path: \"" << control_image_path << "\",\n"
            << "  ref_image_paths: " << vec_str_to_string(ref_image_paths) << ",\n"
            << "  control_video_path: \"" << control_video_path << "\",\n"
            << "  auto_resize_ref_image: " << (auto_resize_ref_image ? "true" : "false") << ",\n"
            << "  increase_ref_index: " << (increase_ref_index ? "true" : "false") << ",\n"
            << "  pm_id_images_dir: \"" << pm_id_images_dir << "\",\n"
            << "  pm_id_embed_path: \"" << pm_id_embed_path << "\",\n"
            << "  pm_style_strength: " << pm_style_strength << ",\n"
            << "  skip_layers: " << vec_to_string(skip_layers) << ",\n"
            << "  sample_params: " << sample_params_str << ",\n"
            << "  high_noise_skip_layers: " << vec_to_string(high_noise_skip_layers) << ",\n"
            << "  high_noise_sample_params: " << high_noise_sample_params_str << ",\n"
            << "  easycache_option: \"" << easycache_option << "\",\n"
            << "  easycache: "
            << (easycache_params.enabled ? "enabled" : "disabled")
            << " (threshold=" << easycache_params.reuse_threshold
            << ", start=" << easycache_params.start_percent
            << ", end=" << easycache_params.end_percent << "),\n"
            << "  moe_boundary: " << moe_boundary << ",\n"
            << "  video_frames: " << video_frames << ",\n"
            << "  fps: " << fps << ",\n"
            << "  vace_strength: " << vace_strength << ",\n"
            << "  strength: " << strength << ",\n"
            << "  control_strength: " << control_strength << ",\n"
            << "  seed: " << seed << ",\n"
            << "  upscale_repeats: " << upscale_repeats << ",\n"
            << "}";
        free(sample_params_str);
        free(high_noise_sample_params_str);
        return oss.str();
    }
};