float linear_start = 0.00085f,

float linear_end = 0.0120f,

int timesteps = TIMESTEPS) {

float ls_sqrt = sqrtf(linear_start);

float le_sqrt = sqrtf(linear_end);

float amount = le_sqrt - ls_sqrt;

float product = 1.0f;

for (int i = 0; i < timesteps; i++) {

float beta = ls_sqrt + amount * ((float)i / (timesteps - 1));

product *= 1.0f - powf(beta, 2.0f);

alphas_cumprod[i] = product;

}

float reuse_threshold = params.reuse_threshold;

if (reuse_threshold == INFINITY) {

if (params.mode == SD_CACHE_EASYCACHE) {

reuse_threshold = 0.2f;

} else if (params.mode == SD_CACHE_UCACHE) {

reuse_threshold = 1.0f;

}

}

return std::max(0.0f, reuse_threshold);

ggml_backend_t backend = nullptr; // general backend

ggml_backend_t clip_backend = nullptr;

ggml_backend_t control_net_backend = nullptr;

ggml_backend_t vae_backend = nullptr;

SDVersion version;

bool vae_decode_only = false;

bool external_vae_is_invalid = false;

bool free_params_immediately = false;

bool circular_x = false;

bool circular_y = false;

std::shared_ptr<RNG> rng = std::make_shared<PhiloxRNG>();

std::shared_ptr<RNG> sampler_rng = nullptr;

int n_threads = -1;

float default_flow_shift = INFINITY;

std::shared_ptr<Conditioner> cond_stage_model;

std::shared_ptr<FrozenCLIPVisionEmbedder> clip_vision; // for svd or wan2.1 i2v

std::shared_ptr<DiffusionModel> diffusion_model;

std::shared_ptr<DiffusionModel> high_noise_diffusion_model;

std::shared_ptr<VAE> first_stage_model;

std::shared_ptr<VAE> preview_vae;

std::shared_ptr<ControlNet> control_net;

std::shared_ptr<PhotoMakerIDEncoder> pmid_model;

std::shared_ptr<LoraModel> pmid_lora;

std::shared_ptr<PhotoMakerIDEmbed> pmid_id_embeds;

std::vector<std::shared_ptr<LoraModel>> cond_stage_lora_models;

std::vector<std::shared_ptr<LoraModel>> diffusion_lora_models;

std::vector<std::shared_ptr<LoraModel>> first_stage_lora_models;

bool apply_lora_immediately = false;

std::string taesd_path;

sd_tiling_params_t vae_tiling_params = {false, 0, 0, 0.5f, 0, 0};

bool offload_params_to_cpu = false;

float max_vram = 0.f;

bool use_pmid = false;

bool is_using_v_parameterization = false;

bool is_using_edm_v_parameterization = false;

std::map<std::string, ggml_tensor*> tensors;

// lora_name => multiplier

std::unordered_map<std::string, float> curr_lora_state;

std::shared_ptr<Denoiser> denoiser = std::make_shared<CompVisDenoiser>();

StableDiffusionGGML() = default;

~StableDiffusionGGML() {

if (clip_backend != backend) {

ggml_backend_free(clip_backend);

}

if (control_net_backend != backend) {

ggml_backend_free(control_net_backend);

}

if (vae_backend != backend) {

ggml_backend_free(vae_backend);

}

ggml_backend_free(backend);

}

void init_backend() {

backend = sd_get_default_backend();

}

std::shared_ptr<RNG> get_rng(rng_type_t rng_type) {

if (rng_type == STD_DEFAULT_RNG) {

return std::make_shared<STDDefaultRNG>();

} else if (rng_type == CPU_RNG) {

return std::make_shared<MT19937RNG>();

} else { // default: CUDA_RNG

return std::make_shared<PhiloxRNG>();

}

}

bool init(const sd_ctx_params_t* sd_ctx_params) {

n_threads = sd_ctx_params->n_threads;

vae_decode_only = sd_ctx_params->vae_decode_only;

free_params_immediately = sd_ctx_params->free_params_immediately;

offload_params_to_cpu = sd_ctx_params->offload_params_to_cpu;

max_vram = sd_ctx_params->max_vram;

bool use_tae = false;

rng = get_rng(sd_ctx_params->rng_type);

if (sd_ctx_params->sampler_rng_type != RNG_TYPE_COUNT && sd_ctx_params->sampler_rng_type != sd_ctx_params->rng_type) {

sampler_rng = get_rng(sd_ctx_params->sampler_rng_type);

} else {

sampler_rng = rng;

}

ggml_log_set(ggml_log_callback_default, nullptr);

init_backend();

ModelLoader model_loader;

if (strlen(SAFE_STR(sd_ctx_params->model_path)) > 0) {

LOG_INFO("loading model from '%s'", sd_ctx_params->model_path);

if (!model_loader.init_from_file(sd_ctx_params->model_path)) {

LOG_ERROR("init model loader from file failed: '%s'", sd_ctx_params->model_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->diffusion_model_path)) > 0) {

LOG_INFO("loading diffusion model from '%s'", sd_ctx_params->diffusion_model_path);

if (!model_loader.init_from_file(sd_ctx_params->diffusion_model_path, "model.diffusion_model.")) {

LOG_WARN("loading diffusion model from '%s' failed", sd_ctx_params->diffusion_model_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->high_noise_diffusion_model_path)) > 0) {

LOG_INFO("loading high noise diffusion model from '%s'", sd_ctx_params->high_noise_diffusion_model_path);

if (!model_loader.init_from_file(sd_ctx_params->high_noise_diffusion_model_path, "model.high_noise_diffusion_model.")) {

LOG_WARN("loading diffusion model from '%s' failed", sd_ctx_params->high_noise_diffusion_model_path);

}

}

bool is_unet = sd_version_is_unet(model_loader.get_sd_version());

if (strlen(SAFE_STR(sd_ctx_params->clip_l_path)) > 0) {

LOG_INFO("loading clip_l from '%s'", sd_ctx_params->clip_l_path);

std::string prefix = is_unet ? "cond_stage_model.transformer." : "text_encoders.clip_l.transformer.";

if (!model_loader.init_from_file(sd_ctx_params->clip_l_path, prefix)) {

LOG_WARN("loading clip_l from '%s' failed", sd_ctx_params->clip_l_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->clip_g_path)) > 0) {

LOG_INFO("loading clip_g from '%s'", sd_ctx_params->clip_g_path);

std::string prefix = is_unet ? "cond_stage_model.1.transformer." : "text_encoders.clip_g.transformer.";

if (!model_loader.init_from_file(sd_ctx_params->clip_g_path, prefix)) {

LOG_WARN("loading clip_g from '%s' failed", sd_ctx_params->clip_g_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->clip_vision_path)) > 0) {

LOG_INFO("loading clip_vision from '%s'", sd_ctx_params->clip_vision_path);

std::string prefix = "cond_stage_model.transformer.";

if (!model_loader.init_from_file(sd_ctx_params->clip_vision_path, prefix)) {

LOG_WARN("loading clip_vision from '%s' failed", sd_ctx_params->clip_vision_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->t5xxl_path)) > 0) {

LOG_INFO("loading t5xxl from '%s'", sd_ctx_params->t5xxl_path);

if (!model_loader.init_from_file(sd_ctx_params->t5xxl_path, "text_encoders.t5xxl.transformer.")) {

LOG_WARN("loading t5xxl from '%s' failed", sd_ctx_params->t5xxl_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->llm_path)) > 0) {

LOG_INFO("loading llm from '%s'", sd_ctx_params->llm_path);

if (!model_loader.init_from_file(sd_ctx_params->llm_path, "text_encoders.llm.")) {

LOG_WARN("loading llm from '%s' failed", sd_ctx_params->llm_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->llm_vision_path)) > 0) {

LOG_INFO("loading llm vision from '%s'", sd_ctx_params->llm_vision_path);

if (!model_loader.init_from_file(sd_ctx_params->llm_vision_path, "text_encoders.llm.visual.")) {

LOG_WARN("loading llm vision from '%s' failed", sd_ctx_params->llm_vision_path);

}

}

if (strlen(SAFE_STR(sd_ctx_params->vae_path)) > 0) {

LOG_INFO("loading vae from '%s'", sd_ctx_params->vae_path);

if (!model_loader.init_from_file(sd_ctx_params->vae_path, "vae.")) {

LOG_WARN("loading vae from '%s' failed", sd_ctx_params->vae_path);

external_vae_is_invalid = true;

}

}

if (strlen(SAFE_STR(sd_ctx_params->taesd_path)) > 0) {

LOG_INFO("loading tae from '%s'", sd_ctx_params->taesd_path);

if (!model_loader.init_from_file(sd_ctx_params->taesd_path, "tae.")) {

LOG_WARN("loading tae from '%s' failed", sd_ctx_params->taesd_path);

}

use_tae = true;

}

model_loader.convert_tensors_name();

version = model_loader.get_sd_version();

if (version == VERSION_COUNT) {

LOG_ERROR("get sd version from file failed: '%s'", SAFE_STR(sd_ctx_params->model_path));

return false;

}

auto& tensor_storage_map = model_loader.get_tensor_storage_map();

LOG_INFO("Version: %s ", model_version_to_str[version]);

ggml_type wtype = (int)sd_ctx_params->wtype < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)

? (ggml_type)sd_ctx_params->wtype

: GGML_TYPE_COUNT;

std::string tensor_type_rules = SAFE_STR(sd_ctx_params->tensor_type_rules);

if (wtype != GGML_TYPE_COUNT || tensor_type_rules.size() > 0) {

model_loader.set_wtype_override(wtype, tensor_type_rules);

}

std::map<ggml_type, uint32_t> wtype_stat = model_loader.get_wtype_stat();

std::map<ggml_type, uint32_t> conditioner_wtype_stat = model_loader.get_conditioner_wtype_stat();

std::map<ggml_type, uint32_t> diffusion_model_wtype_stat = model_loader.get_diffusion_model_wtype_stat();

std::map<ggml_type, uint32_t> vae_wtype_stat = model_loader.get_vae_wtype_stat();

auto wtype_stat_to_str = [](const std::map<ggml_type, uint32_t>& m, int key_width = 8, int value_width = 5) -> std::string {

std::ostringstream oss;

bool first = true;

for (const auto& [type, count] : m) {

if (!first)

oss << "|";

first = false;

oss << std::right << std::setw(key_width) << ggml_type_name(type)

<< ": "

<< std::left << std::setw(value_width) << count;

}

return oss.str();

};

LOG_INFO("Weight type stat: %s", wtype_stat_to_str(wtype_stat).c_str());

LOG_INFO("Conditioner weight type stat: %s", wtype_stat_to_str(conditioner_wtype_stat).c_str());

LOG_INFO("Diffusion model weight type stat: %s", wtype_stat_to_str(diffusion_model_wtype_stat).c_str());

LOG_INFO("VAE weight type stat: %s", wtype_stat_to_str(vae_wtype_stat).c_str());

LOG_DEBUG("ggml tensor size = %d bytes", (int)sizeof(ggml_tensor));

if (sd_ctx_params->lora_apply_mode == LORA_APPLY_AUTO) {

bool have_quantized_weight = false;

if (wtype != GGML_TYPE_COUNT && ggml_is_quantized(wtype)) {

have_quantized_weight = true;

} else {

for (const auto& [type, _] : wtype_stat) {

if (ggml_is_quantized(type)) {

have_quantized_weight = true;

break;

}

}

}

if (have_quantized_weight) {

apply_lora_immediately = false;

} else {

apply_lora_immediately = true;

}

} else if (sd_ctx_params->lora_apply_mode == LORA_APPLY_IMMEDIATELY) {

apply_lora_immediately = true;

} else {

apply_lora_immediately = false;

}

if (sd_version_is_control(version)) {

// Might need vae encode for control cond

vae_decode_only = false;

}

bool tae_preview_only = sd_ctx_params->tae_preview_only;

if (version == VERSION_SDXS_512_DS || version == VERSION_SDXS_09) {

tae_preview_only = false;

use_tae = true;

}

if (sd_ctx_params->circular_x || sd_ctx_params->circular_y) {

LOG_INFO("Using circular padding for convolutions");

}

bool clip_on_cpu = sd_ctx_params->keep_clip_on_cpu;

const size_t max_graph_vram_bytes = max_vram <= 0.f

? 0

: static_cast<size_t>(static_cast<double>(max_vram) * 1024.0 * 1024.0 * 1024.0);

{

clip_backend = backend;

if (clip_on_cpu && !ggml_backend_is_cpu(backend)) {

LOG_INFO("CLIP: Using CPU backend");

clip_backend = ggml_backend_cpu_init();

}

if (sd_version_is_sd3(version)) {

cond_stage_model = std::make_shared<SD3CLIPEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map);

diffusion_model = std::make_shared<MMDiTModel>(backend,

offload_params_to_cpu,

tensor_storage_map);

} else if (sd_version_is_flux(version)) {

bool is_chroma = false;

for (auto pair : tensor_storage_map) {

if (pair.first.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {

is_chroma = true;

break;

}

}

if (is_chroma) {

if ((sd_ctx_params->flash_attn || sd_ctx_params->diffusion_flash_attn) && sd_ctx_params->chroma_use_dit_mask) {

LOG_WARN(

"!!!It looks like you are using Chroma with flash attention. "

"This is currently unsupported. "

"If you find that the generated images are broken, "

"try either disabling flash attention or specifying "

"--chroma-disable-dit-mask as a workaround.");

}

cond_stage_model = std::make_shared<T5CLIPEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

sd_ctx_params->chroma_use_t5_mask,

sd_ctx_params->chroma_t5_mask_pad);

} else if (version == VERSION_OVIS_IMAGE) {

cond_stage_model = std::make_shared<LLMEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

version,

"",

false);

} else {

cond_stage_model = std::make_shared<FluxCLIPEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map);

}

diffusion_model = std::make_shared<FluxModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

version,

sd_ctx_params->chroma_use_dit_mask);

} else if (sd_version_is_flux2(version)) {

bool is_chroma = false;

cond_stage_model = std::make_shared<LLMEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

version);

diffusion_model = std::make_shared<FluxModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

version,

sd_ctx_params->chroma_use_dit_mask);

} else if (sd_version_is_wan(version)) {

cond_stage_model = std::make_shared<T5CLIPEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

true,

0,

true);

diffusion_model = std::make_shared<WanModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

"model.diffusion_model",

version);

if (strlen(SAFE_STR(sd_ctx_params->high_noise_diffusion_model_path)) > 0) {

high_noise_diffusion_model = std::make_shared<WanModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

"model.high_noise_diffusion_model",

version);

}

if (diffusion_model->get_desc() == "Wan2.1-I2V-14B" ||

diffusion_model->get_desc() == "Wan2.1-FLF2V-14B" ||

diffusion_model->get_desc() == "Wan2.1-I2V-1.3B") {

clip_vision = std::make_shared<FrozenCLIPVisionEmbedder>(backend,

offload_params_to_cpu,

tensor_storage_map);

clip_vision->set_max_graph_vram_bytes(max_graph_vram_bytes);

clip_vision->alloc_params_buffer();

clip_vision->get_param_tensors(tensors);

}

} else if (sd_version_is_qwen_image(version)) {

bool enable_vision = false;

if (!vae_decode_only) {

enable_vision = true;

}

cond_stage_model = std::make_shared<LLMEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

version,

"",

enable_vision);

diffusion_model = std::make_shared<QwenImageModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

"model.diffusion_model",

version,

sd_ctx_params->qwen_image_zero_cond_t);

} else if (sd_version_is_anima(version)) {

cond_stage_model = std::make_shared<AnimaConditioner>(clip_backend,

offload_params_to_cpu,

tensor_storage_map);

diffusion_model = std::make_shared<AnimaModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

"model.diffusion_model");

} else if (sd_version_is_z_image(version)) {

cond_stage_model = std::make_shared<LLMEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

version);

diffusion_model = std::make_shared<ZImageModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

"model.diffusion_model",

version);

} else if (sd_version_is_ernie_image(version)) {

cond_stage_model = std::make_shared<LLMEmbedder>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

version);

diffusion_model = std::make_shared<ErnieImageModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

"model.diffusion_model");

} else { // SD1.x SD2.x SDXL

std::map<std::string, std::string> embbeding_map;

for (uint32_t i = 0; i < sd_ctx_params->embedding_count; i++) {

embbeding_map.emplace(SAFE_STR(sd_ctx_params->embeddings[i].name), SAFE_STR(sd_ctx_params->embeddings[i].path));

}

if (strstr(SAFE_STR(sd_ctx_params->photo_maker_path), "v2")) {

cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

embbeding_map,

version,

PM_VERSION_2);

} else {

cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(clip_backend,

offload_params_to_cpu,

tensor_storage_map,

embbeding_map,

version);

}

diffusion_model = std::make_shared<UNetModel>(backend,

offload_params_to_cpu,

tensor_storage_map,

version);

if (sd_ctx_params->diffusion_conv_direct) {

LOG_INFO("Using Conv2d direct in the diffusion model");

std::dynamic_pointer_cast<UNetModel>(diffusion_model)->unet.set_conv2d_direct_enabled(true);

}

}

cond_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes);

cond_stage_model->alloc_params_buffer();

cond_stage_model->get_param_tensors(tensors);

diffusion_model->set_max_graph_vram_bytes(max_graph_vram_bytes);

diffusion_model->alloc_params_buffer();

diffusion_model->get_param_tensors(tensors);

if (sd_version_is_unet_edit(version)) {

vae_decode_only = false;

}

if (high_noise_diffusion_model) {

high_noise_diffusion_model->set_max_graph_vram_bytes(max_graph_vram_bytes);

high_noise_diffusion_model->alloc_params_buffer();

high_noise_diffusion_model->get_param_tensors(tensors);

}

if (sd_ctx_params->keep_vae_on_cpu && !ggml_backend_is_cpu(backend)) {

LOG_INFO("VAE Autoencoder: Using CPU backend");

vae_backend = ggml_backend_cpu_init();

} else {

vae_backend = backend;

}

auto create_tae = [&]() -> std::shared_ptr<VAE> {

if (sd_version_is_wan(version) ||

sd_version_is_qwen_image(version) ||

sd_version_is_anima(version)) {

return std::make_shared<TinyVideoAutoEncoder>(vae_backend,

offload_params_to_cpu,

tensor_storage_map,

"decoder",

vae_decode_only,

version);

} else {

auto model = std::make_shared<TinyImageAutoEncoder>(vae_backend,

offload_params_to_cpu,

tensor_storage_map,

"decoder.layers",

vae_decode_only,

version);

return model;

}

};

auto create_vae = [&]() -> std::shared_ptr<VAE> {

if (sd_version_is_wan(version) ||

sd_version_is_qwen_image(version) ||

sd_version_is_anima(version)) {

return std::make_shared<WAN::WanVAERunner>(vae_backend,

offload_params_to_cpu,

tensor_storage_map,

"first_stage_model",

vae_decode_only,

version);

} else {

auto model = std::make_shared<AutoEncoderKL>(vae_backend,

offload_params_to_cpu,

tensor_storage_map,

"first_stage_model",

vae_decode_only,

false,

version);

if (sd_version_is_sdxl(version) &&

(strlen(SAFE_STR(sd_ctx_params->vae_path)) == 0 || sd_ctx_params->force_sdxl_vae_conv_scale || external_vae_is_invalid)) {

float vae_conv_2d_scale = 1.f / 32.f;

LOG_WARN(

"No valid VAE specified with --vae or --force-sdxl-vae-conv-scale flag set, "

"using Conv2D scale %.3f",

vae_conv_2d_scale);

model->set_conv2d_scale(vae_conv_2d_scale);

}

return model;

}

};

if (version == VERSION_CHROMA_RADIANCE) {

LOG_INFO("using FakeVAE");

first_stage_model = std::make_shared<FakeVAE>(version,

vae_backend,

offload_params_to_cpu);

} else if (use_tae && !tae_preview_only) {

LOG_INFO("using TAE for encoding / decoding");

first_stage_model = create_tae();

first_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes);

first_stage_model->alloc_params_buffer();

first_stage_model->get_param_tensors(tensors, "tae");

} else {

LOG_INFO("using VAE for encoding / decoding");

first_stage_model = create_vae();

first_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes);

first_stage_model->alloc_params_buffer();

first_stage_model->get_param_tensors(tensors, "first_stage_model");

if (use_tae && tae_preview_only) {

LOG_INFO("using TAE for preview");

preview_vae = create_tae();

preview_vae->set_max_graph_vram_bytes(max_graph_vram_bytes);

preview_vae->alloc_params_buffer();

preview_vae->get_param_tensors(tensors, "tae");

}

}

if (sd_ctx_params->vae_conv_direct) {

LOG_INFO("Using Conv2d direct in the vae model");

first_stage_model->set_conv2d_direct_enabled(true);

if (preview_vae) {

preview_vae->set_conv2d_direct_enabled(true);

}

}

if (strlen(SAFE_STR(sd_ctx_params->control_net_path)) > 0) {

ggml_backend_t controlnet_backend = nullptr;

if (sd_ctx_params->keep_control_net_on_cpu && !ggml_backend_is_cpu(backend)) {

LOG_DEBUG("ControlNet: Using CPU backend");

controlnet_backend = ggml_backend_cpu_init();

} else {

controlnet_backend = backend;

}

control_net = std::make_shared<ControlNet>(controlnet_backend,

offload_params_to_cpu,

tensor_storage_map,

version);

if (sd_ctx_params->diffusion_conv_direct) {

LOG_INFO("Using Conv2d direct in the control net");

control_net->set_conv2d_direct_enabled(true);

}

}

if (strstr(SAFE_STR(sd_ctx_params->photo_maker_path), "v2")) {

pmid_model = std::make_shared<PhotoMakerIDEncoder>(backend,

offload_params_to_cpu,

tensor_storage_map,

"pmid",

version,

PM_VERSION_2);

LOG_INFO("using PhotoMaker Version 2");

} else {

pmid_model = std::make_shared<PhotoMakerIDEncoder>(backend,

offload_params_to_cpu,

tensor_storage_map,

"pmid",

version);

}

if (strlen(SAFE_STR(sd_ctx_params->photo_maker_path)) > 0) {

pmid_lora = std::make_shared<LoraModel>("pmid", backend, sd_ctx_params->photo_maker_path, "", version);

auto lora_tensor_filter = [&](const std::string& tensor_name) {

if (starts_with(tensor_name, "lora.model")) {

return true;

}

return false;

};

if (!pmid_lora->load_from_file(n_threads, lora_tensor_filter)) {

LOG_WARN("load photomaker lora tensors from %s failed", sd_ctx_params->photo_maker_path);

return false;

}

LOG_INFO("loading stacked ID embedding (PHOTOMAKER) model file from '%s'", sd_ctx_params->photo_maker_path);

if (!model_loader.init_from_file_and_convert_name(sd_ctx_params->photo_maker_path, "pmid.")) {

LOG_WARN("loading stacked ID embedding from '%s' failed", sd_ctx_params->photo_maker_path);

} else {

use_pmid = true;

}

}

if (use_pmid) {

if (!pmid_model->alloc_params_buffer()) {

LOG_ERROR(" pmid model params buffer allocation failed");

return false;

}

pmid_model->get_param_tensors(tensors, "pmid");

}

if (sd_ctx_params->flash_attn) {

LOG_INFO("Using flash attention");

cond_stage_model->set_flash_attention_enabled(true);

if (clip_vision) {

clip_vision->set_flash_attention_enabled(true);

}

if (first_stage_model) {

first_stage_model->set_flash_attention_enabled(true);

}

if (preview_vae) {

preview_vae->set_flash_attention_enabled(true);

}

}

if (sd_ctx_params->flash_attn || sd_ctx_params->diffusion_flash_attn) {

LOG_INFO("Using flash attention in the diffusion model");

diffusion_model->set_flash_attention_enabled(true);

if (high_noise_diffusion_model) {

high_noise_diffusion_model->set_flash_attention_enabled(true);

}

}

diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);

if (high_noise_diffusion_model) {

high_noise_diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);

}

if (control_net) {

control_net->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);

}

circular_x = sd_ctx_params->circular_x;

circular_y = sd_ctx_params->circular_y;

}

ggml_init_params params;

params.mem_size = static_cast<size_t>(10 * 1024) * 1024; // 10M

params.mem_buffer = nullptr;

params.no_alloc = false;

// LOG_DEBUG("mem_size %u ", params.mem_size);

ggml_context* ctx = ggml_init(params); // for alphas_cumprod and is_using_v_parameterization check

GGML_ASSERT(ctx != nullptr);

ggml_tensor* alphas_cumprod_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, TIMESTEPS);

calculate_alphas_cumprod((float*)alphas_cumprod_tensor->data);

// load weights

LOG_DEBUG("loading weights");

std::set<std::string> ignore_tensors;

tensors["alphas_cumprod"] = alphas_cumprod_tensor;

if (use_tae && !tae_preview_only) {

ignore_tensors.insert("first_stage_model.");

}

if (use_pmid) {

ignore_tensors.insert("pmid.unet.");

}

ignore_tensors.insert("model.diffusion_model.__x0__");

ignore_tensors.insert("model.diffusion_model.__32x32__");

ignore_tensors.insert("model.diffusion_model.__index_timestep_zero__");

if (vae_decode_only) {

ignore_tensors.insert("first_stage_model.encoder");

ignore_tensors.insert("first_stage_model.conv1");

ignore_tensors.insert("first_stage_model.quant");

ignore_tensors.insert("tae.encoder");

ignore_tensors.insert("text_encoders.llm.visual.");

}

if (version == VERSION_OVIS_IMAGE) {

ignore_tensors.insert("text_encoders.llm.vision_model.");

ignore_tensors.insert("text_encoders.llm.visual_tokenizer.");

ignore_tensors.insert("text_encoders.llm.vte.");

}

if (version == VERSION_SVD) {

ignore_tensors.insert("conditioner.embedders.3");

}

if (sd_version_is_ernie_image(version)) {

ignore_tensors.insert("text_encoders.llm.vision_tower.");

ignore_tensors.insert("text_encoders.llm.multi_modal_projector.");

}

bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads, sd_ctx_params->enable_mmap);

if (!success) {

LOG_ERROR("load tensors from model loader failed");

ggml_free(ctx);

return false;

}

LOG_DEBUG("finished loaded file");

{

size_t clip_params_mem_size = cond_stage_model->get_params_buffer_size();

size_t unet_params_mem_size = diffusion_model->get_params_buffer_size();

if (high_noise_diffusion_model) {

unet_params_mem_size += high_noise_diffusion_model->get_params_buffer_size();

}

size_t vae_params_mem_size = 0;

vae_params_mem_size = first_stage_model->get_params_buffer_size();

if (preview_vae) {

vae_params_mem_size += preview_vae->get_params_buffer_size();

}

size_t control_net_params_mem_size = 0;

if (control_net) {

if (!control_net->load_from_file(SAFE_STR(sd_ctx_params->control_net_path), n_threads)) {

return false;

}

control_net_params_mem_size = control_net->get_params_buffer_size();

}

size_t pmid_params_mem_size = 0;

if (use_pmid) {

pmid_params_mem_size = pmid_model->get_params_buffer_size();

}

size_t total_params_ram_size = 0;

size_t total_params_vram_size = 0;

if (ggml_backend_is_cpu(clip_backend)) {

total_params_ram_size += clip_params_mem_size + pmid_params_mem_size;

} else {

total_params_vram_size += clip_params_mem_size + pmid_params_mem_size;

}

if (ggml_backend_is_cpu(backend)) {

total_params_ram_size += unet_params_mem_size;

} else {

total_params_vram_size += unet_params_mem_size;

}

if (ggml_backend_is_cpu(vae_backend)) {

total_params_ram_size += vae_params_mem_size;

} else {

total_params_vram_size += vae_params_mem_size;

}

if (ggml_backend_is_cpu(control_net_backend)) {

total_params_ram_size += control_net_params_mem_size;

} else {

total_params_vram_size += control_net_params_mem_size;

}

size_t total_params_size = total_params_ram_size + total_params_vram_size;

LOG_INFO(

"total params memory size = %.2fMB (VRAM %.2fMB, RAM %.2fMB): "

"text_encoders %.2fMB(%s), diffusion_model %.2fMB(%s), vae %.2fMB(%s), controlnet %.2fMB(%s), pmid %.2fMB(%s)",

total_params_size / 1024.0 / 1024.0,

total_params_vram_size / 1024.0 / 1024.0,

total_params_ram_size / 1024.0 / 1024.0,

clip_params_mem_size / 1024.0 / 1024.0,

ggml_backend_is_cpu(clip_backend) ? "RAM" : "VRAM",

unet_params_mem_size / 1024.0 / 1024.0,

ggml_backend_is_cpu(backend) ? "RAM" : "VRAM",

vae_params_mem_size / 1024.0 / 1024.0,

ggml_backend_is_cpu(vae_backend) ? "RAM" : "VRAM",

control_net_params_mem_size / 1024.0 / 1024.0,

ggml_backend_is_cpu(control_net_backend) ? "RAM" : "VRAM",

pmid_params_mem_size / 1024.0 / 1024.0,

ggml_backend_is_cpu(clip_backend) ? "RAM" : "VRAM");

}

// init denoiser

{

prediction_t pred_type = sd_ctx_params->prediction;

if (pred_type == PREDICTION_COUNT) {

if (sd_version_is_sd2(version)) {

// check is_using_v_parameterization_for_sd2

if (is_using_v_parameterization_for_sd2(sd_version_is_inpaint(version))) {

pred_type = V_PRED;

} else {

pred_type = EPS_PRED;

}

} else if (sd_version_is_sdxl(version)) {

if (tensor_storage_map.find("edm_vpred.sigma_max") != tensor_storage_map.end()) {

// CosXL models

// TODO: get sigma_min and sigma_max values from file

pred_type = EDM_V_PRED;

} else if (tensor_storage_map.find("v_pred") != tensor_storage_map.end()) {

pred_type = V_PRED;

} else {

pred_type = EPS_PRED;

}

} else if (sd_version_is_sd3(version) ||

sd_version_is_wan(version) ||

sd_version_is_qwen_image(version) ||

sd_version_is_anima(version) ||

sd_version_is_ernie_image(version) ||

sd_version_is_z_image(version)) {

pred_type = FLOW_PRED;

if (sd_version_is_wan(version)) {

default_flow_shift = 5.f;

} else if (sd_version_is_ernie_image(version)) {

default_flow_shift = 4.f;

} else {

default_flow_shift = 3.f;

}

} else if (sd_version_is_flux(version)) {

pred_type = FLUX_FLOW_PRED;

default_flow_shift = 1.0f; // TODO: validate

for (const auto& [name, tensor_storage] : tensor_storage_map) {

if (starts_with(name, "model.diffusion_model.guidance_in.in_layer.weight")) {

default_flow_shift = 1.15f;

break;

}

}

} else if (sd_version_is_flux2(version)) {

pred_type = FLUX2_FLOW_PRED;

} else {

pred_type = EPS_PRED;

}

}

switch (pred_type) {

case EPS_PRED:

LOG_INFO("running in eps-prediction mode");

break;

case V_PRED:

LOG_INFO("running in v-prediction mode");

denoiser = std::make_shared<CompVisVDenoiser>();

break;

case EDM_V_PRED:

LOG_INFO("running in v-prediction EDM mode");

denoiser = std::make_shared<EDMVDenoiser>();

break;

case FLOW_PRED: {

LOG_INFO("running in FLOW mode");

denoiser = std::make_shared<DiscreteFlowDenoiser>();

break;

}

case FLUX_FLOW_PRED: {

LOG_INFO("running in Flux FLOW mode");

denoiser = std::make_shared<FluxFlowDenoiser>();

break;

}

case FLUX2_FLOW_PRED: {

LOG_INFO("running in Flux2 FLOW mode");

denoiser = std::make_shared<Flux2FlowDenoiser>();

break;

}

default: {

LOG_ERROR("Unknown predition type %i", pred_type);

ggml_free(ctx);

return false;

}

}

auto comp_vis_denoiser = std::dynamic_pointer_cast<CompVisDenoiser>(denoiser);

if (comp_vis_denoiser) {

for (int i = 0; i < TIMESTEPS; i++) {

comp_vis_denoiser->sigmas[i] = std::sqrt((1 - ((float*)alphas_cumprod_tensor->data)[i]) / ((float*)alphas_cumprod_tensor->data)[i]);

comp_vis_denoiser->log_sigmas[i] = std::log(comp_vis_denoiser->sigmas[i]);

}

}

}

ggml_free(ctx);

return true;

}

bool is_using_v_parameterization_for_sd2(bool is_inpaint = false) {

sd::Tensor<float> x_t = sd::full<float>({8, 8, 4, 1}, 0.5f);

sd::Tensor<float> c = sd::full<float>({1024, 2, 1, 1}, 0.5f);

sd::Tensor<float> steps = sd::full<float>({1}, 999.0f);

sd::Tensor<float> concat;

if (is_inpaint) {

concat = sd::zeros<float>({8, 8, 5, 1});

}

int64_t t0 = ggml_time_ms();

sd::Tensor<float> out;

DiffusionParams diffusion_params;

diffusion_params.x = &x_t;

diffusion_params.timesteps = &steps;

diffusion_params.context = &c;

if (!concat.empty()) {

diffusion_params.c_concat = &concat;

}

auto out_opt = diffusion_model->compute(n_threads, diffusion_params);

GGML_ASSERT(!out_opt.empty());

out = std::move(out_opt);

diffusion_model->free_compute_buffer();

double result = static_cast<double>((out - x_t).mean());

int64_t t1 = ggml_time_ms();