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refactor: move pmid condition logic into get_pmid_condition #1148

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Merged
leejet merged 1 commit into from
Dec 27, 2025
+92 −109
Merged

refactor: move pmid condition logic into get_pmid_condition #1148

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201 changes: 92 additions & 109 deletions stable-diffusion.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -129,7 +129,7 @@ class StableDiffusionGGML {
bool use_tiny_autoencoder = false;
sd_tiling_params_t vae_tiling_params = {false, 0, 0, 0.5f, 0, 0};
bool offload_params_to_cpu = false;
bool stacked_id = false;
bool use_pmid = false;

bool is_using_v_parameterization = false;
bool is_using_edm_v_parameterization = false;
Expand Down Expand Up @@ -701,10 +701,10 @@ class StableDiffusionGGML {
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 {
stacked_id = true;
use_pmid = true;
}
}
if (stacked_id) {
if (use_pmid) {
if (!pmid_model->alloc_params_buffer()) {
LOG_ERROR(" pmid model params buffer allocation failed");
return false;
Expand Down Expand Up @@ -745,7 +745,7 @@ class StableDiffusionGGML {
if (use_tiny_autoencoder) {
ignore_tensors.insert("first_stage_model.");
}
if (stacked_id) {
if (use_pmid) {
ignore_tensors.insert("pmid.unet.");
}
ignore_tensors.insert("model.diffusion_model.__x0__");
Expand Down Expand Up @@ -799,7 +799,7 @@ class StableDiffusionGGML {
control_net_params_mem_size = control_net->get_params_buffer_size();
}
size_t pmid_params_mem_size = 0;
if (stacked_id) {
if (use_pmid) {
pmid_params_mem_size = pmid_model->get_params_buffer_size();
}

Expand Down Expand Up @@ -1211,14 +1211,89 @@ class StableDiffusionGGML {
}
}

ggml_tensor* id_encoder(ggml_context* work_ctx,
ggml_tensor* init_img,
ggml_tensor* prompts_embeds,
ggml_tensor* id_embeds,
std::vector<bool>& class_tokens_mask) {
ggml_tensor* res = nullptr;
pmid_model->compute(n_threads, init_img, prompts_embeds, id_embeds, class_tokens_mask, &res, work_ctx);
return res;
SDCondition get_pmid_conditon(ggml_context* work_ctx,
sd_pm_params_t pm_params,
ConditionerParams& condition_params) {
SDCondition id_cond;
if (use_pmid) {
if (!pmid_lora->applied) {
int64_t t0 = ggml_time_ms();
pmid_lora->apply(tensors, version, n_threads);
int64_t t1 = ggml_time_ms();
pmid_lora->applied = true;
LOG_INFO("pmid_lora apply completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
if (free_params_immediately) {
pmid_lora->free_params_buffer();
}
}
// preprocess input id images
bool pmv2 = pmid_model->get_version() == PM_VERSION_2;
if (pm_params.id_images_count > 0) {
int clip_image_size = 224;
pmid_model->style_strength = pm_params.style_strength;

auto id_image_tensor = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, clip_image_size, clip_image_size, 3, pm_params.id_images_count);

std::vector<sd_image_f32_t> processed_id_images;
for (int i = 0; i < pm_params.id_images_count; i++) {
sd_image_f32_t id_image = sd_image_t_to_sd_image_f32_t(pm_params.id_images[i]);
sd_image_f32_t processed_id_image = clip_preprocess(id_image, clip_image_size, clip_image_size);
free(id_image.data);
id_image.data = nullptr;
processed_id_images.push_back(processed_id_image);
}

ggml_ext_tensor_iter(id_image_tensor, [&](ggml_tensor* id_image_tensor, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
float value = sd_image_get_f32(processed_id_images[i3], i0, i1, i2, false);
ggml_ext_tensor_set_f32(id_image_tensor, value, i0, i1, i2, i3);
});

for (auto& image : processed_id_images) {
free(image.data);
image.data = nullptr;
}
processed_id_images.clear();

int64_t t0 = ggml_time_ms();
condition_params.num_input_imgs = pm_params.id_images_count;
auto cond_tup = cond_stage_model->get_learned_condition_with_trigger(work_ctx,
n_threads,
condition_params);
id_cond = std::get<0>(cond_tup);
auto class_tokens_mask = std::get<1>(cond_tup);
struct ggml_tensor* id_embeds = nullptr;
if (pmv2 && pm_params.id_embed_path != nullptr) {
id_embeds = load_tensor_from_file(work_ctx, pm_params.id_embed_path);
}
if (pmv2 && id_embeds == nullptr) {
LOG_WARN("Provided PhotoMaker images, but NO valid ID embeds file for PM v2");
LOG_WARN("Turn off PhotoMaker");
use_pmid = false;
} else {
if (pmv2 && pm_params.id_images_count != id_embeds->ne[1]) {
LOG_WARN("PhotoMaker image count (%d) does NOT match ID embeds (%d). You should run face_detect.py again.", pm_params.id_images_count, id_embeds->ne[1]);
LOG_WARN("Turn off PhotoMaker");
use_pmid = false;
} else {
ggml_tensor* res = nullptr;
pmid_model->compute(n_threads, id_image_tensor, id_cond.c_crossattn, id_embeds, class_tokens_mask, &res, work_ctx);
id_cond.c_crossattn = res;
int64_t t1 = ggml_time_ms();
LOG_INFO("Photomaker ID Stacking, taking %" PRId64 " ms", t1 - t0);
if (free_params_immediately) {
pmid_model->free_params_buffer();
}
// Encode input prompt without the trigger word for delayed conditioning
condition_params.text = cond_stage_model->remove_trigger_from_prompt(work_ctx, condition_params.text);
}
}
} else {
LOG_WARN("Provided PhotoMaker model file, but NO input ID images");
LOG_WARN("Turn off PhotoMaker");
use_pmid = false;
}
}
return id_cond;
}

ggml_tensor* get_clip_vision_output(ggml_context* work_ctx,
Expand Down Expand Up @@ -3117,114 +3192,22 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
guidance.img_cfg = guidance.txt_cfg;
}

// for (auto v : sigmas) {
// std::cout << v << " ";
// }
// std::cout << std::endl;

int sample_steps = sigmas.size() - 1;

int64_t t0 = ggml_time_ms();

// Photo Maker
std::string prompt_text_only;
ggml_tensor* init_img = nullptr;
SDCondition id_cond;
std::vector<bool> class_tokens_mask;

ConditionerParams condition_params;
condition_params.text = prompt;
condition_params.clip_skip = clip_skip;
condition_params.width = width;
condition_params.height = height;
condition_params.ref_images = ref_images;
condition_params.adm_in_channels = sd_ctx->sd->diffusion_model->get_adm_in_channels();

if (sd_ctx->sd->stacked_id) {
if (!sd_ctx->sd->pmid_lora->applied) {
int64_t t0 = ggml_time_ms();
sd_ctx->sd->pmid_lora->apply(sd_ctx->sd->tensors, sd_ctx->sd->version, sd_ctx->sd->n_threads);
int64_t t1 = ggml_time_ms();
sd_ctx->sd->pmid_lora->applied = true;
LOG_INFO("pmid_lora apply completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
if (sd_ctx->sd->free_params_immediately) {
sd_ctx->sd->pmid_lora->free_params_buffer();
}
}
// preprocess input id images
bool pmv2 = sd_ctx->sd->pmid_model->get_version() == PM_VERSION_2;
if (pm_params.id_images_count > 0) {
int clip_image_size = 224;
sd_ctx->sd->pmid_model->style_strength = pm_params.style_strength;

init_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, clip_image_size, clip_image_size, 3, pm_params.id_images_count);

std::vector<sd_image_f32_t> processed_id_images;
for (int i = 0; i < pm_params.id_images_count; i++) {
sd_image_f32_t id_image = sd_image_t_to_sd_image_f32_t(pm_params.id_images[i]);
sd_image_f32_t processed_id_image = clip_preprocess(id_image, clip_image_size, clip_image_size);
free(id_image.data);
id_image.data = nullptr;
processed_id_images.push_back(processed_id_image);
}

ggml_ext_tensor_iter(init_img, [&](ggml_tensor* init_img, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
float value = sd_image_get_f32(processed_id_images[i3], i0, i1, i2, false);
ggml_ext_tensor_set_f32(init_img, value, i0, i1, i2, i3);
});

for (auto& image : processed_id_images) {
free(image.data);
image.data = nullptr;
}
processed_id_images.clear();

int64_t t0 = ggml_time_ms();
condition_params.text = prompt;
condition_params.num_input_imgs = pm_params.id_images_count;
auto cond_tup = sd_ctx->sd->cond_stage_model->get_learned_condition_with_trigger(work_ctx,
sd_ctx->sd->n_threads,
condition_params);
id_cond = std::get<0>(cond_tup);
class_tokens_mask = std::get<1>(cond_tup); //
struct ggml_tensor* id_embeds = nullptr;
if (pmv2 && pm_params.id_embed_path != nullptr) {
id_embeds = load_tensor_from_file(work_ctx, pm_params.id_embed_path);
// print_ggml_tensor(id_embeds, true, "id_embeds:");
}
if (pmv2 && id_embeds == nullptr) {
LOG_WARN("Provided PhotoMaker images, but NO valid ID embeds file for PM v2");
LOG_WARN("Turn off PhotoMaker");
sd_ctx->sd->stacked_id = false;
} else {
if (pmv2 && pm_params.id_images_count != id_embeds->ne[1]) {
LOG_WARN("PhotoMaker image count (%d) does NOT match ID embeds (%d). You should run face_detect.py again.", pm_params.id_images_count, id_embeds->ne[1]);
LOG_WARN("Turn off PhotoMaker");
sd_ctx->sd->stacked_id = false;
} else {
id_cond.c_crossattn = sd_ctx->sd->id_encoder(work_ctx, init_img, id_cond.c_crossattn, id_embeds, class_tokens_mask);
int64_t t1 = ggml_time_ms();
LOG_INFO("Photomaker ID Stacking, taking %" PRId64 " ms", t1 - t0);
if (sd_ctx->sd->free_params_immediately) {
sd_ctx->sd->pmid_model->free_params_buffer();
}
// Encode input prompt without the trigger word for delayed conditioning
prompt_text_only = sd_ctx->sd->cond_stage_model->remove_trigger_from_prompt(work_ctx, prompt);
// printf("%s || %s \n", prompt.c_str(), prompt_text_only.c_str());
prompt = prompt_text_only; //
if (sample_steps < 50) {
LOG_WARN("It's recommended to use >= 50 steps for photo maker!");
}
}
}
} else {
LOG_WARN("Provided PhotoMaker model file, but NO input ID images");
LOG_WARN("Turn off PhotoMaker");
sd_ctx->sd->stacked_id = false;
}
}
// Photo Maker
SDCondition id_cond = sd_ctx->sd->get_pmid_conditon(work_ctx, pm_params, condition_params);

// Get learned condition
condition_params.text = prompt;
condition_params.zero_out_masked = false;
SDCondition cond = sd_ctx->sd->cond_stage_model->get_learned_condition(work_ctx,
sd_ctx->sd->n_threads,
Expand Down Expand Up @@ -3364,7 +3347,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
ggml_ext_im_set_randn_f32(noise, sd_ctx->sd->rng);

int start_merge_step = -1;
if (sd_ctx->sd->stacked_id) {
if (sd_ctx->sd->use_pmid) {
start_merge_step = int(sd_ctx->sd->pmid_model->style_strength / 100.f * sample_steps);
// if (start_merge_step > 30)
// start_merge_step = 30;
Expand Down
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