conditioner.hpp

#ifndef __CONDITIONER_HPP__
#define __CONDITIONER_HPP__

#include "clip.hpp"
#include "llm.hpp"
#include "t5.hpp"

struct SDCondition {
    struct ggml_tensor* c_crossattn = nullptr;  // aka context
    struct ggml_tensor* c_vector    = nullptr;  // aka y
    struct ggml_tensor* c_concat    = nullptr;

    SDCondition() = default;
    SDCondition(struct ggml_tensor* c_crossattn, struct ggml_tensor* c_vector, struct ggml_tensor* c_concat)
        : c_crossattn(c_crossattn), c_vector(c_vector), c_concat(c_concat) {}
};

struct ConditionerParams {
    std::string text;
    int clip_skip                       = -1;
    int width                           = -1;
    int height                          = -1;
    int adm_in_channels                 = -1;
    bool zero_out_masked                = false;
    int num_input_imgs                  = 0;   // for photomaker
    std::vector<sd_image_t*> ref_images = {};  // for qwen image edit
};

struct Conditioner {
    virtual SDCondition get_learned_condition(ggml_context* work_ctx,
                                              int n_threads,
                                              const ConditionerParams& conditioner_params) = 0;
    virtual void alloc_params_buffer()                                                     = 0;
    virtual void free_params_buffer()                                                      = 0;
    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors)    = 0;
    virtual size_t get_params_buffer_size()                                                = 0;
    virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {}
    virtual std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
                                                                                          int n_threads,
                                                                                          const ConditionerParams& conditioner_params) {
        GGML_ABORT("Not implemented yet!");
    }
    virtual std::string remove_trigger_from_prompt(ggml_context* work_ctx,
                                                   const std::string& prompt) {
        GGML_ABORT("Not implemented yet!");
    }
};

// ldm.modules.encoders.modules.FrozenCLIPEmbedder
// Ref: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/cad87bf4e3e0b0a759afa94e933527c3123d59bc/modules/sd_hijack_clip.py#L283
struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
    SDVersion version    = VERSION_SD1;
    PMVersion pm_version = PM_VERSION_1;
    CLIPTokenizer tokenizer;
    std::shared_ptr<CLIPTextModelRunner> text_model;
    std::shared_ptr<CLIPTextModelRunner> text_model2;

    std::string trigger_word = "img";  // should be user settable
    std::map<std::string, std::string> embedding_map;
    int32_t num_custom_embeddings   = 0;
    int32_t num_custom_embeddings_2 = 0;
    std::vector<uint8_t> token_embed_custom;
    std::map<std::string, std::pair<int, int>> embedding_pos_map;

    FrozenCLIPEmbedderWithCustomWords(ggml_backend_t backend,
                                      bool offload_params_to_cpu,
                                      const String2TensorStorage& tensor_storage_map,
                                      const std::map<std::string, std::string>& orig_embedding_map,
                                      SDVersion version = VERSION_SD1,
                                      PMVersion pv      = PM_VERSION_1)
        : version(version), pm_version(pv), tokenizer(sd_version_is_sd2(version) ? 0 : 49407) {
        for (const auto& kv : orig_embedding_map) {
            std::string name = kv.first;
            std::transform(name.begin(), name.end(), name.begin(), [](unsigned char c) { return std::tolower(c); });
            embedding_map[name] = kv.second;
            tokenizer.add_special_token(name);
        }
        bool force_clip_f32 = !embedding_map.empty();
        if (sd_version_is_sd1(version)) {
            text_model = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, force_clip_f32);
        } else if (sd_version_is_sd2(version)) {
            text_model = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_storage_map, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, true, force_clip_f32);
        } else if (sd_version_is_sdxl(version)) {
            text_model  = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, force_clip_f32);
            text_model2 = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_storage_map, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, force_clip_f32);
        }
    }

    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
        text_model->get_param_tensors(tensors, "cond_stage_model.transformer.text_model");
        if (sd_version_is_sdxl(version)) {
            text_model2->get_param_tensors(tensors, "cond_stage_model.1.transformer.text_model");
        }
    }

    void alloc_params_buffer() override {
        text_model->alloc_params_buffer();
        if (sd_version_is_sdxl(version)) {
            text_model2->alloc_params_buffer();
        }
    }

    void free_params_buffer() override {
        text_model->free_params_buffer();
        if (sd_version_is_sdxl(version)) {
            text_model2->free_params_buffer();
        }
    }

    size_t get_params_buffer_size() override {
        size_t buffer_size = text_model->get_params_buffer_size();
        if (sd_version_is_sdxl(version)) {
            buffer_size += text_model2->get_params_buffer_size();
        }
        return buffer_size;
    }

    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
        text_model->set_weight_adapter(adapter);
        if (sd_version_is_sdxl(version)) {
            text_model2->set_weight_adapter(adapter);
        }
    }

    bool load_embedding(std::string embd_name, std::string embd_path, std::vector<int32_t>& bpe_tokens) {
        ModelLoader model_loader;
        if (!model_loader.init_from_file_and_convert_name(embd_path)) {
            LOG_ERROR("embedding '%s' failed", embd_name.c_str());
            return false;
        }
        auto iter = embedding_pos_map.find(embd_name);
        if (iter != embedding_pos_map.end()) {
            LOG_DEBUG("embedding already read in: %s", embd_name.c_str());
            for (int i = iter->second.first; i < iter->second.second; i++) {
                bpe_tokens.push_back(text_model->model.vocab_size + i);
            }
            return true;
        }
        struct ggml_init_params params;
        params.mem_size               = 100 * 1024 * 1024;  // max for custom embeddings 100 MB
        params.mem_buffer             = nullptr;
        params.no_alloc               = false;
        struct ggml_context* embd_ctx = ggml_init(params);
        struct ggml_tensor* embd      = nullptr;
        struct ggml_tensor* embd2     = nullptr;
        auto on_load                  = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) {
            if (tensor_storage.ne[0] != text_model->model.hidden_size) {
                if (text_model2) {
                    if (tensor_storage.ne[0] == text_model2->model.hidden_size) {
                        embd2       = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model2->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
                        *dst_tensor = embd2;
                    } else {
                        LOG_DEBUG("embedding wrong hidden size, got %i, expected %i or %i", tensor_storage.ne[0], text_model->model.hidden_size, text_model2->model.hidden_size);
                        return false;
                    }
                } else {
                    LOG_DEBUG("embedding wrong hidden size, got %i, expected %i", tensor_storage.ne[0], text_model->model.hidden_size);
                    return false;
                }
            } else {
                embd        = ggml_new_tensor_2d(embd_ctx, tensor_storage.type, text_model->model.hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
                *dst_tensor = embd;
            }
            return true;
        };
        model_loader.load_tensors(on_load, 1);
        int pos_start = num_custom_embeddings;
        if (embd) {
            int64_t hidden_size = text_model->model.hidden_size;
            token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd));
            memcpy((void*)(token_embed_custom.data() + num_custom_embeddings * hidden_size * ggml_type_size(embd->type)),
                   embd->data,
                   ggml_nbytes(embd));
            for (int i = 0; i < embd->ne[1]; i++) {
                bpe_tokens.push_back(text_model->model.vocab_size + num_custom_embeddings);
                // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
                num_custom_embeddings++;
            }
            LOG_DEBUG("embedding '%s' applied, custom embeddings: %i", embd_name.c_str(), num_custom_embeddings);
        }
        if (embd2) {
            int64_t hidden_size = text_model2->model.hidden_size;
            token_embed_custom.resize(token_embed_custom.size() + ggml_nbytes(embd2));
            memcpy((void*)(token_embed_custom.data() + num_custom_embeddings_2 * hidden_size * ggml_type_size(embd2->type)),
                   embd2->data,
                   ggml_nbytes(embd2));
            for (int i = 0; i < embd2->ne[1]; i++) {
                bpe_tokens.push_back(text_model2->model.vocab_size + num_custom_embeddings_2);
                // LOG_DEBUG("new custom token: %i", text_model.vocab_size + num_custom_embeddings);
                num_custom_embeddings_2++;
            }
            LOG_DEBUG("embedding '%s' applied, custom embeddings: %i (text model 2)", embd_name.c_str(), num_custom_embeddings_2);
        }
        int pos_end = num_custom_embeddings;
        if (pos_end == pos_start) {
            return false;
        }
        embedding_pos_map[embd_name] = std::pair{pos_start, pos_end};
        return true;
    }

    std::tuple<std::vector<int>, std::vector<float>, std::vector<bool>>
    tokenize_with_trigger_token(std::string text,
                                int num_input_imgs,
                                int32_t image_token,
                                bool padding = false) {
        return tokenize_with_trigger_token(text, num_input_imgs, image_token,
                                           text_model->model.n_token, padding);
    }

    std::vector<int> convert_token_to_id(std::string text) {
        auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
            auto iter = embedding_map.find(str);
            if (iter == embedding_map.end()) {
                return false;
            }
            std::string embedding_path = iter->second;
            if (load_embedding(str, embedding_path, bpe_tokens)) {
                return true;
            }
            return false;
        };
        std::vector<int> curr_tokens = tokenizer.encode(text, on_new_token_cb);
        return curr_tokens;
    }

    std::string decode(const std::vector<int>& tokens) {
        return tokenizer.decode(tokens);
    }

    std::tuple<std::vector<int>, std::vector<float>, std::vector<bool>>
    tokenize_with_trigger_token(std::string text,
                                int num_input_imgs,
                                int32_t image_token,
                                size_t max_length = 0,
                                bool padding      = false) {
        auto parsed_attention = parse_prompt_attention(text);

        {
            std::stringstream ss;
            ss << "[";
            for (const auto& item : parsed_attention) {
                ss << "['" << item.first << "', " << item.second << "], ";
            }
            ss << "]";
            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
        }

        auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
            auto iter = embedding_map.find(str);
            if (iter == embedding_map.end()) {
                return false;
            }
            std::string embedding_path = iter->second;
            if (load_embedding(str, embedding_path, bpe_tokens)) {
                return true;
            }
            return false;
        };

        std::vector<int> tokens;
        std::vector<float> weights;
        std::vector<bool> class_token_mask;
        int32_t class_idx = -1, tokens_acc = 0;
        for (const auto& item : parsed_attention) {
            std::vector<int> class_token_index;
            std::vector<int> clean_input_ids;
            const std::string& curr_text = item.first;
            float curr_weight            = item.second;
            // printf(" %s: %f \n", curr_text.c_str(), curr_weight);
            int32_t clean_index = 0;
            if (curr_text == "BREAK" && curr_weight == -1.0f) {
                // Pad token array up to chunk size at this point.
                // TODO: This is a hardcoded chunk_len, like in stable-diffusion.cpp, make it a parameter for the future?
                // Also, this is 75 instead of 77 to leave room for BOS and EOS tokens.
                int padding_size = 75 - (tokens_acc % 75);
                for (int j = 0; j < padding_size; j++) {
                    clean_input_ids.push_back(tokenizer.EOS_TOKEN_ID);
                    clean_index++;
                }

                // After padding, continue to the next iteration to process the following text as a new segment
                tokens.insert(tokens.end(), clean_input_ids.begin(), clean_input_ids.end());
                weights.insert(weights.end(), padding_size, curr_weight);
                continue;
            }

            // Regular token, process normally
            std::vector<int> curr_tokens = tokenizer.encode(curr_text, on_new_token_cb);
            for (uint32_t i = 0; i < curr_tokens.size(); i++) {
                int token_id = curr_tokens[i];
                if (token_id == image_token) {
                    class_token_index.push_back(clean_index - 1);
                } else {
                    clean_input_ids.push_back(token_id);
                    clean_index++;
                }
            }
            // GGML_ASSERT(class_token_index.size() == 1); // PhotoMaker currently does not support multiple
            //     trigger words in a single prompt.
            if (class_token_index.size() == 1) {
                // Expand the class word token and corresponding mask
                int class_token = clean_input_ids[class_token_index[0]];
                class_idx       = tokens_acc + class_token_index[0];
                std::vector<int> clean_input_ids_tmp;
                for (int i = 0; i < class_token_index[0]; i++)
                    clean_input_ids_tmp.push_back(clean_input_ids[i]);
                for (int i = 0; i < (pm_version == PM_VERSION_2 ? 2 * num_input_imgs : num_input_imgs); i++)
                    clean_input_ids_tmp.push_back(class_token);
                for (int i = class_token_index[0] + 1; i < clean_input_ids.size(); i++)
                    clean_input_ids_tmp.push_back(clean_input_ids[i]);
                clean_input_ids.clear();
                clean_input_ids = clean_input_ids_tmp;
            }
            tokens_acc += clean_index;
            tokens.insert(tokens.end(), clean_input_ids.begin(), clean_input_ids.end());
            weights.insert(weights.end(), clean_input_ids.size(), curr_weight);
        }
        // BUG!! double couting, pad_tokens will add BOS at the beginning
        // tokens.insert(tokens.begin(), tokenizer.BOS_TOKEN_ID);
        // weights.insert(weights.begin(), 1.0);

        tokenizer.pad_tokens(tokens, weights, max_length, padding);
        int offset = pm_version == PM_VERSION_2 ? 2 * num_input_imgs : num_input_imgs;
        for (int i = 0; i < tokens.size(); i++) {
            // if (class_idx + 1 <= i && i < class_idx + 1 + 2*num_input_imgs) // photomaker V2 has num_tokens(=2)*num_input_imgs
            if (class_idx + 1 <= i && i < class_idx + 1 + offset)  // photomaker V2 has num_tokens(=2)*num_input_imgs
                                                                   // hardcode for now
                class_token_mask.push_back(true);
            else
                class_token_mask.push_back(false);
        }

        // printf("[");
        // for (int i = 0; i < tokens.size(); i++) {
        //     printf("%d, ", class_token_mask[i] ? 1 : 0);
        // }
        // printf("]\n");

        // for (int i = 0; i < tokens.size(); i++) {
        //     std::cout << tokens[i] << ":" << weights[i] << ", ";
        // }
        // std::cout << std::endl;

        return std::make_tuple(tokens, weights, class_token_mask);
    }

    std::pair<std::vector<int>, std::vector<float>> tokenize(std::string text,
                                                             bool padding = false) {
        return tokenize(text, text_model->model.n_token, padding);
    }

    std::pair<std::vector<int>, std::vector<float>> tokenize(std::string text,
                                                             size_t max_length = 0,
                                                             bool padding      = false) {
        auto parsed_attention = parse_prompt_attention(text);

        {
            std::stringstream ss;
            ss << "[";
            for (const auto& item : parsed_attention) {
                ss << "['" << item.first << "', " << item.second << "], ";
            }
            ss << "]";
            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
        }

        auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
            auto iter = embedding_map.find(str);
            if (iter == embedding_map.end()) {
                return false;
            }
            std::string embedding_path = iter->second;
            if (load_embedding(str, embedding_path, bpe_tokens)) {
                return true;
            }
            return false;
        };

        std::vector<int> tokens;
        std::vector<float> weights;
        for (const auto& item : parsed_attention) {
            const std::string& curr_text = item.first;
            float curr_weight            = item.second;

            if (curr_text == "BREAK" && curr_weight == -1.0f) {
                // Pad token array up to chunk size at this point.
                // TODO: This is a hardcoded chunk_len, like in stable-diffusion.cpp, make it a parameter for the future?
                // Also, this is 75 instead of 77 to leave room for BOS and EOS tokens.
                size_t current_size = tokens.size();
                size_t padding_size = (75 - (current_size % 75)) % 75;  // Ensure no negative padding

                if (padding_size > 0) {
                    LOG_DEBUG("BREAK token encountered, padding current chunk by %zu tokens.", padding_size);
                    tokens.insert(tokens.end(), padding_size, tokenizer.EOS_TOKEN_ID);
                    weights.insert(weights.end(), padding_size, 1.0f);
                }
                continue;  // Skip to the next item after handling BREAK
            }

            std::vector<int> curr_tokens = tokenizer.encode(curr_text, on_new_token_cb);
            tokens.insert(tokens.end(), curr_tokens.begin(), curr_tokens.end());
            weights.insert(weights.end(), curr_tokens.size(), curr_weight);
        }

        tokenizer.pad_tokens(tokens, weights, max_length, padding);

        // for (int i = 0; i < tokens.size(); i++) {
        //     std::cout << tokens[i] << ":" << weights[i] << ", ";
        // }
        // std::cout << std::endl;

        return {tokens, weights};
    }

    SDCondition get_learned_condition_common(ggml_context* work_ctx,
                                             int n_threads,
                                             std::vector<int>& tokens,
                                             std::vector<float>& weights,
                                             int clip_skip,
                                             int width,
                                             int height,
                                             int adm_in_channels  = -1,
                                             bool zero_out_masked = false) {
        int64_t t0                               = ggml_time_ms();
        struct ggml_tensor* hidden_states        = nullptr;  // [N, n_token, hidden_size]
        struct ggml_tensor* chunk_hidden_states  = nullptr;  // [n_token, hidden_size] or [n_token, hidden_size + hidden_size2]
        struct ggml_tensor* chunk_hidden_states1 = nullptr;  // [n_token, hidden_size]
        struct ggml_tensor* chunk_hidden_states2 = nullptr;  // [n_token, hidden_size2]
        struct ggml_tensor* pooled               = nullptr;
        std::vector<float> hidden_states_vec;

        if (clip_skip <= 0) {
            clip_skip = (sd_version_is_sd2(version) || sd_version_is_sdxl(version)) ? 2 : 1;
        }

        size_t chunk_len   = 77;
        size_t chunk_count = tokens.size() / chunk_len;
        for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
            std::vector<int> chunk_tokens(tokens.begin() + chunk_idx * chunk_len,
                                          tokens.begin() + (chunk_idx + 1) * chunk_len);
            std::vector<float> chunk_weights(weights.begin() + chunk_idx * chunk_len,
                                             weights.begin() + (chunk_idx + 1) * chunk_len);

            auto input_ids                 = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
            struct ggml_tensor* input_ids2 = nullptr;
            size_t max_token_idx           = 0;
            if (sd_version_is_sdxl(version)) {
                auto it = std::find(chunk_tokens.begin(), chunk_tokens.end(), tokenizer.EOS_TOKEN_ID);
                if (it != chunk_tokens.end()) {
                    std::fill(std::next(it), chunk_tokens.end(), 0);
                }

                max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);

                input_ids2 = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);

                // for (int i = 0; i < chunk_tokens.size(); i++) {
                //     printf("%d ", chunk_tokens[i]);
                // }
                // printf("\n");
            }

            {
                text_model->compute(n_threads,
                                    input_ids,
                                    num_custom_embeddings,
                                    token_embed_custom.data(),
                                    max_token_idx,
                                    false,
                                    clip_skip,
                                    &chunk_hidden_states1,
                                    work_ctx);
                if (sd_version_is_sdxl(version)) {
                    text_model2->compute(n_threads,
                                         input_ids2,
                                         num_custom_embeddings,
                                         token_embed_custom.data(),
                                         max_token_idx,
                                         false,
                                         clip_skip,
                                         &chunk_hidden_states2, work_ctx);
                    // concat
                    chunk_hidden_states = ggml_ext_tensor_concat(work_ctx, chunk_hidden_states1, chunk_hidden_states2, 0);

                    if (chunk_idx == 0) {
                        text_model2->compute(n_threads,
                                             input_ids2,
                                             num_custom_embeddings,
                                             token_embed_custom.data(),
                                             max_token_idx,
                                             true,
                                             clip_skip,
                                             &pooled,
                                             work_ctx);
                    }
                } else {
                    chunk_hidden_states = chunk_hidden_states1;
                }
            }

            int64_t t1 = ggml_time_ms();
            LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
            ggml_tensor* result = ggml_dup_tensor(work_ctx, chunk_hidden_states);
            {
                float original_mean = ggml_ext_tensor_mean(chunk_hidden_states);
                for (int i2 = 0; i2 < chunk_hidden_states->ne[2]; i2++) {
                    for (int i1 = 0; i1 < chunk_hidden_states->ne[1]; i1++) {
                        for (int i0 = 0; i0 < chunk_hidden_states->ne[0]; i0++) {
                            float value = ggml_ext_tensor_get_f32(chunk_hidden_states, i0, i1, i2);
                            value *= chunk_weights[i1];
                            ggml_ext_tensor_set_f32(result, value, i0, i1, i2);
                        }
                    }
                }
                float new_mean = ggml_ext_tensor_mean(result);
                ggml_ext_tensor_scale_inplace(result, (original_mean / new_mean));
            }
            if (zero_out_masked) {
                float* vec = (float*)result->data;
                for (int i = 0; i < ggml_nelements(result); i++) {
                    vec[i] = 0;
                }
            }
            hidden_states_vec.insert(hidden_states_vec.end(), (float*)result->data, ((float*)result->data) + ggml_nelements(result));
        }

        hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
        hidden_states = ggml_reshape_2d(work_ctx,
                                        hidden_states,
                                        chunk_hidden_states->ne[0],
                                        ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);

        ggml_tensor* vec = nullptr;
        if (sd_version_is_sdxl(version)) {
            int out_dim = 256;
            vec         = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, adm_in_channels);
            // [0:1280]
            size_t offset = 0;
            memcpy(vec->data, pooled->data, ggml_nbytes(pooled));
            offset += ggml_nbytes(pooled);

            // original_size_as_tuple
            float orig_width             = (float)width;
            float orig_height            = (float)height;
            std::vector<float> timesteps = {orig_height, orig_width};

            ggml_tensor* embed_view = ggml_view_2d(work_ctx, vec, out_dim, 2, ggml_type_size(GGML_TYPE_F32) * out_dim, offset);
            offset += ggml_nbytes(embed_view);
            set_timestep_embedding(timesteps, embed_view, out_dim);
            // print_ggml_tensor(ggml_reshape_1d(work_ctx, embed_view, out_dim * 2));
            // crop_coords_top_left
            float crop_coord_top  = 0.f;
            float crop_coord_left = 0.f;
            timesteps             = {crop_coord_top, crop_coord_left};
            embed_view            = ggml_view_2d(work_ctx, vec, out_dim, 2, ggml_type_size(GGML_TYPE_F32) * out_dim, offset);
            offset += ggml_nbytes(embed_view);
            set_timestep_embedding(timesteps, embed_view, out_dim);
            // print_ggml_tensor(ggml_reshape_1d(work_ctx, embed_view, out_dim * 2));
            // target_size_as_tuple
            float target_width  = (float)width;
            float target_height = (float)height;
            timesteps           = {target_height, target_width};
            embed_view          = ggml_view_2d(work_ctx, vec, out_dim, 2, ggml_type_size(GGML_TYPE_F32) * out_dim, offset);
            offset += ggml_nbytes(embed_view);
            set_timestep_embedding(timesteps, embed_view, out_dim);
            // print_ggml_tensor(ggml_reshape_1d(work_ctx, embed_view, out_dim * 2));
            GGML_ASSERT(offset == ggml_nbytes(vec));
        }
        // print_ggml_tensor(result);
        return {hidden_states, vec, nullptr};
    }

    std::tuple<SDCondition, std::vector<bool>>
    get_learned_condition_with_trigger(ggml_context* work_ctx,
                                       int n_threads,
                                       const ConditionerParams& conditioner_params) override {
        auto image_tokens = convert_token_to_id(trigger_word);
        // if(image_tokens.size() == 1){
        //     printf(" image token id is: %d \n", image_tokens[0]);
        // }
        GGML_ASSERT(image_tokens.size() == 1);
        auto tokens_and_weights     = tokenize_with_trigger_token(conditioner_params.text,
                                                                  conditioner_params.num_input_imgs,
                                                                  image_tokens[0],
                                                                  true);
        std::vector<int>& tokens    = std::get<0>(tokens_and_weights);
        std::vector<float>& weights = std::get<1>(tokens_and_weights);
        std::vector<bool>& clsm     = std::get<2>(tokens_and_weights);
        // printf("tokens: \n");
        // for(int i = 0; i < tokens.size(); ++i)
        //    printf("%d ", tokens[i]);
        // printf("\n");
        // printf("clsm: \n");
        // for(int i = 0; i < clsm.size(); ++i)
        //    printf("%d ", clsm[i]?1:0);
        // printf("\n");
        auto cond = get_learned_condition_common(work_ctx,
                                                 n_threads,
                                                 tokens,
                                                 weights,
                                                 conditioner_params.clip_skip,
                                                 conditioner_params.width,
                                                 conditioner_params.height,
                                                 conditioner_params.adm_in_channels,
                                                 conditioner_params.zero_out_masked);
        return std::make_tuple(cond, clsm);
    }

    std::string remove_trigger_from_prompt(ggml_context* work_ctx,
                                           const std::string& prompt) override {
        auto image_tokens = convert_token_to_id(trigger_word);
        GGML_ASSERT(image_tokens.size() == 1);
        auto tokens_and_weights  = tokenize(prompt, false);
        std::vector<int>& tokens = tokens_and_weights.first;
        auto it                  = std::find(tokens.begin(), tokens.end(), image_tokens[0]);
        GGML_ASSERT(it != tokens.end());  // prompt must have trigger word
        tokens.erase(it);
        return decode(tokens);
    }

    SDCondition get_learned_condition(ggml_context* work_ctx,
                                      int n_threads,
                                      const ConditionerParams& conditioner_params) override {
        auto tokens_and_weights     = tokenize(conditioner_params.text, true);
        std::vector<int>& tokens    = tokens_and_weights.first;
        std::vector<float>& weights = tokens_and_weights.second;
        return get_learned_condition_common(work_ctx,
                                            n_threads,
                                            tokens,
                                            weights,
                                            conditioner_params.clip_skip,
                                            conditioner_params.width,
                                            conditioner_params.height,
                                            conditioner_params.adm_in_channels,
                                            conditioner_params.zero_out_masked);
    }
};

struct FrozenCLIPVisionEmbedder : public GGMLRunner {
    CLIPVisionModelProjection vision_model;

    FrozenCLIPVisionEmbedder(ggml_backend_t backend,
                             bool offload_params_to_cpu,
                             const String2TensorStorage& tensor_storage_map = {})
        : GGMLRunner(backend, offload_params_to_cpu) {
        std::string prefix = "cond_stage_model.transformer";
        bool proj_in       = false;
        for (const auto& [name, tensor_storage] : tensor_storage_map) {
            if (!starts_with(name, prefix)) {
                continue;
            }
            if (contains(name, "self_attn.in_proj")) {
                proj_in = true;
                break;
            }
        }
        vision_model = CLIPVisionModelProjection(OPEN_CLIP_VIT_H_14, false, proj_in);
        vision_model.init(params_ctx, tensor_storage_map, prefix);
    }

    std::string get_desc() override {
        return "clip_vision";
    }

    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
        vision_model.get_param_tensors(tensors, "cond_stage_model.transformer");
    }

    struct ggml_cgraph* build_graph(struct ggml_tensor* pixel_values, bool return_pooled, int clip_skip) {
        struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);

        pixel_values = to_backend(pixel_values);

        auto runner_ctx = get_context();

        struct ggml_tensor* hidden_states = vision_model.forward(&runner_ctx, pixel_values, return_pooled, clip_skip);

        ggml_build_forward_expand(gf, hidden_states);

        return gf;
    }

    bool compute(const int n_threads,
                 ggml_tensor* pixel_values,
                 bool return_pooled,
                 int clip_skip,
                 ggml_tensor** output,
                 ggml_context* output_ctx) {
        auto get_graph = [&]() -> struct ggml_cgraph* {
            return build_graph(pixel_values, return_pooled, clip_skip);
        };
        return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
    }
};

struct SD3CLIPEmbedder : public Conditioner {
    CLIPTokenizer clip_l_tokenizer;
    CLIPTokenizer clip_g_tokenizer;
    T5UniGramTokenizer t5_tokenizer;
    std::shared_ptr<CLIPTextModelRunner> clip_l;
    std::shared_ptr<CLIPTextModelRunner> clip_g;
    std::shared_ptr<T5Runner> t5;

    SD3CLIPEmbedder(ggml_backend_t backend,
                    bool offload_params_to_cpu,
                    const String2TensorStorage& tensor_storage_map = {})
        : clip_g_tokenizer(0) {
        bool use_clip_l = false;
        bool use_clip_g = false;
        bool use_t5     = false;
        for (auto pair : tensor_storage_map) {
            if (pair.first.find("text_encoders.clip_l") != std::string::npos) {
                use_clip_l = true;
            } else if (pair.first.find("text_encoders.clip_g") != std::string::npos) {
                use_clip_g = true;
            } else if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
                use_t5 = true;
            }
        }
        if (!use_clip_l && !use_clip_g && !use_t5) {
            LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
            return;
        }
        if (use_clip_l) {
            clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false);
        }
        if (use_clip_g) {
            clip_g = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_storage_map, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false);
        }
        if (use_t5) {
            t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_storage_map, "text_encoders.t5xxl.transformer");
        }
    }

    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
        if (clip_l) {
            clip_l->get_param_tensors(tensors, "text_encoders.clip_l.transformer.text_model");
        }
        if (clip_g) {
            clip_g->get_param_tensors(tensors, "text_encoders.clip_g.transformer.text_model");
        }
        if (t5) {
            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
        }
    }

    void alloc_params_buffer() override {
        if (clip_l) {
            clip_l->alloc_params_buffer();
        }
        if (clip_g) {
            clip_g->alloc_params_buffer();
        }
        if (t5) {
            t5->alloc_params_buffer();
        }
    }

    void free_params_buffer() override {
        if (clip_l) {
            clip_l->free_params_buffer();
        }
        if (clip_g) {
            clip_g->free_params_buffer();
        }
        if (t5) {
            t5->free_params_buffer();
        }
    }

    size_t get_params_buffer_size() override {
        size_t buffer_size = 0;
        if (clip_l) {
            buffer_size += clip_l->get_params_buffer_size();
        }
        if (clip_g) {
            buffer_size += clip_g->get_params_buffer_size();
        }
        if (t5) {
            buffer_size += t5->get_params_buffer_size();
        }
        return buffer_size;
    }

    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
        if (clip_l) {
            clip_l->set_weight_adapter(adapter);
        }
        if (clip_g) {
            clip_g->set_weight_adapter(adapter);
        }
        if (t5) {
            t5->set_weight_adapter(adapter);
        }
    }

    std::vector<std::pair<std::vector<int>, std::vector<float>>> tokenize(std::string text,
                                                                          size_t max_length = 0,
                                                                          bool padding      = false) {
        auto parsed_attention = parse_prompt_attention(text);

        {
            std::stringstream ss;
            ss << "[";
            for (const auto& item : parsed_attention) {
                ss << "['" << item.first << "', " << item.second << "], ";
            }
            ss << "]";
            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
        }

        auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
            return false;
        };

        std::vector<int> clip_l_tokens;
        std::vector<float> clip_l_weights;
        std::vector<int> clip_g_tokens;
        std::vector<float> clip_g_weights;
        std::vector<int> t5_tokens;
        std::vector<float> t5_weights;
        for (const auto& item : parsed_attention) {
            const std::string& curr_text = item.first;
            float curr_weight            = item.second;
            if (clip_l) {
                std::vector<int> curr_tokens = clip_l_tokenizer.encode(curr_text, on_new_token_cb);
                clip_l_tokens.insert(clip_l_tokens.end(), curr_tokens.begin(), curr_tokens.end());
                clip_l_weights.insert(clip_l_weights.end(), curr_tokens.size(), curr_weight);
            }
            if (clip_g) {
                std::vector<int> curr_tokens = clip_g_tokenizer.encode(curr_text, on_new_token_cb);
                clip_g_tokens.insert(clip_g_tokens.end(), curr_tokens.begin(), curr_tokens.end());
                clip_g_weights.insert(clip_g_weights.end(), curr_tokens.size(), curr_weight);
            }
            if (t5) {
                std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
                t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
                t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
            }
        }

        if (clip_l) {
            clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, max_length, padding);
        }
        if (clip_g) {
            clip_g_tokenizer.pad_tokens(clip_g_tokens, clip_g_weights, max_length, padding);
        }
        if (t5) {
            t5_tokenizer.pad_tokens(t5_tokens, t5_weights, nullptr, max_length, padding);
        }

        // for (int i = 0; i < clip_l_tokens.size(); i++) {
        //     std::cout << clip_l_tokens[i] << ":" << clip_l_weights[i] << ", ";
        // }
        // std::cout << std::endl;

        // for (int i = 0; i < clip_g_tokens.size(); i++) {
        //     std::cout << clip_g_tokens[i] << ":" << clip_g_weights[i] << ", ";
        // }
        // std::cout << std::endl;

        // for (int i = 0; i < t5_tokens.size(); i++) {
        //     std::cout << t5_tokens[i] << ":" << t5_weights[i] << ", ";
        // }
        // std::cout << std::endl;

        return {{clip_l_tokens, clip_l_weights}, {clip_g_tokens, clip_g_weights}, {t5_tokens, t5_weights}};
    }

    SDCondition get_learned_condition_common(ggml_context* work_ctx,
                                             int n_threads,
                                             std::vector<std::pair<std::vector<int>, std::vector<float>>> token_and_weights,
                                             int clip_skip,
                                             bool zero_out_masked = false) {
        auto& clip_l_tokens  = token_and_weights[0].first;
        auto& clip_l_weights = token_and_weights[0].second;
        auto& clip_g_tokens  = token_and_weights[1].first;
        auto& clip_g_weights = token_and_weights[1].second;
        auto& t5_tokens      = token_and_weights[2].first;
        auto& t5_weights     = token_and_weights[2].second;

        if (clip_skip <= 0) {
            clip_skip = 2;
        }

        int64_t t0                                 = ggml_time_ms();
        struct ggml_tensor* hidden_states          = nullptr;  // [N, n_token*2, 4096]
        struct ggml_tensor* chunk_hidden_states    = nullptr;  // [n_token*2, 4096]
        struct ggml_tensor* chunk_hidden_states_l  = nullptr;  // [n_token, hidden_size_l]
        struct ggml_tensor* chunk_hidden_states_g  = nullptr;  // [n_token, hidden_size_g]
        struct ggml_tensor* chunk_hidden_states_t5 = nullptr;  // [n_token, hidden_size_t5]
        struct ggml_tensor* pooled                 = nullptr;
        struct ggml_tensor* pooled_l               = nullptr;  // [768,]
        struct ggml_tensor* pooled_g               = nullptr;  // [1280,]
        std::vector<float> hidden_states_vec;

        size_t chunk_len   = 77;
        size_t chunk_count = std::max(std::max(clip_l_tokens.size(), clip_g_tokens.size()), t5_tokens.size()) / chunk_len;
        for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
            // clip_l
            if (clip_l) {
                std::vector<int> chunk_tokens(clip_l_tokens.begin() + chunk_idx * chunk_len,
                                              clip_l_tokens.begin() + (chunk_idx + 1) * chunk_len);
                std::vector<float> chunk_weights(clip_l_weights.begin() + chunk_idx * chunk_len,
                                                 clip_l_weights.begin() + (chunk_idx + 1) * chunk_len);

                auto input_ids       = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
                size_t max_token_idx = 0;

                clip_l->compute(n_threads,
                                input_ids,
                                0,
                                nullptr,
                                max_token_idx,
                                false,
                                clip_skip,
                                &chunk_hidden_states_l,
                                work_ctx);
                {
                    auto tensor         = chunk_hidden_states_l;
                    float original_mean = ggml_ext_tensor_mean(tensor);
                    for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
                        for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                            for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                                float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
                                value *= chunk_weights[i1];
                                ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
                            }
                        }
                    }
                    float new_mean = ggml_ext_tensor_mean(tensor);
                    ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
                }

                if (chunk_idx == 0) {
                    auto it       = std::find(chunk_tokens.begin(), chunk_tokens.end(), clip_l_tokenizer.EOS_TOKEN_ID);
                    max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);
                    clip_l->compute(n_threads,
                                    input_ids,
                                    0,
                                    nullptr,
                                    max_token_idx,
                                    true,
                                    clip_skip,
                                    &pooled_l,
                                    work_ctx);
                }
            } else {
                chunk_hidden_states_l = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, chunk_len);
                ggml_set_f32(chunk_hidden_states_l, 0.f);
                if (chunk_idx == 0) {
                    pooled_l = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 768);
                    ggml_set_f32(pooled_l, 0.f);
                }
            }

            // clip_g
            if (clip_g) {
                std::vector<int> chunk_tokens(clip_g_tokens.begin() + chunk_idx * chunk_len,
                                              clip_g_tokens.begin() + (chunk_idx + 1) * chunk_len);
                std::vector<float> chunk_weights(clip_g_weights.begin() + chunk_idx * chunk_len,
                                                 clip_g_weights.begin() + (chunk_idx + 1) * chunk_len);

                auto input_ids       = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
                size_t max_token_idx = 0;

                clip_g->compute(n_threads,
                                input_ids,
                                0,
                                nullptr,
                                max_token_idx,
                                false,
                                clip_skip,
                                &chunk_hidden_states_g,
                                work_ctx);

                {
                    auto tensor         = chunk_hidden_states_g;
                    float original_mean = ggml_ext_tensor_mean(tensor);
                    for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
                        for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                            for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                                float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
                                value *= chunk_weights[i1];
                                ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
                            }
                        }
                    }
                    float new_mean = ggml_ext_tensor_mean(tensor);
                    ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
                }

                if (chunk_idx == 0) {
                    auto it       = std::find(chunk_tokens.begin(), chunk_tokens.end(), clip_g_tokenizer.EOS_TOKEN_ID);
                    max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);
                    clip_g->compute(n_threads,
                                    input_ids,
                                    0,
                                    nullptr,
                                    max_token_idx,
                                    true,
                                    clip_skip,
                                    &pooled_g,
                                    work_ctx);
                }
            } else {
                chunk_hidden_states_g = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 1280, chunk_len);
                ggml_set_f32(chunk_hidden_states_g, 0.f);
                if (chunk_idx == 0) {
                    pooled_g = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 1280);
                    ggml_set_f32(pooled_g, 0.f);
                }
            }

            // t5
            if (t5) {
                std::vector<int> chunk_tokens(t5_tokens.begin() + chunk_idx * chunk_len,
                                              t5_tokens.begin() + (chunk_idx + 1) * chunk_len);
                std::vector<float> chunk_weights(t5_weights.begin() + chunk_idx * chunk_len,
                                                 t5_weights.begin() + (chunk_idx + 1) * chunk_len);

                auto input_ids = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);

                t5->compute(n_threads,
                            input_ids,
                            nullptr,
                            &chunk_hidden_states_t5,
                            work_ctx);
                {
                    auto tensor         = chunk_hidden_states_t5;
                    float original_mean = ggml_ext_tensor_mean(tensor);
                    for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
                        for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                            for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                                float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
                                value *= chunk_weights[i1];
                                ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
                            }
                        }
                    }
                    float new_mean = ggml_ext_tensor_mean(tensor);
                    ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
                }
            } else {
                chunk_hidden_states_t5 = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, chunk_len);
                ggml_set_f32(chunk_hidden_states_t5, 0.f);
            }

            auto chunk_hidden_states_lg_pad = ggml_new_tensor_3d(work_ctx,
                                                                 chunk_hidden_states_l->type,
                                                                 4096,
                                                                 chunk_hidden_states_l->ne[1],
                                                                 chunk_hidden_states_l->ne[2]);  // [n_token, 4096]

            for (int i2 = 0; i2 < chunk_hidden_states_lg_pad->ne[2]; i2++) {
                for (int i1 = 0; i1 < chunk_hidden_states_lg_pad->ne[1]; i1++) {
                    for (int i0 = 0; i0 < chunk_hidden_states_lg_pad->ne[0]; i0++) {
                        float value = 0.f;
                        if (i0 < chunk_hidden_states_l->ne[0]) {
                            value = ggml_ext_tensor_get_f32(chunk_hidden_states_l, i0, i1, i2);
                        } else if (i0 < chunk_hidden_states_l->ne[0] + chunk_hidden_states_g->ne[0]) {
                            value = ggml_ext_tensor_get_f32(chunk_hidden_states_g, i0 - chunk_hidden_states_l->ne[0], i1, i2);
                        }
                        ggml_ext_tensor_set_f32(chunk_hidden_states_lg_pad, value, i0, i1, i2);
                    }
                }
            }

            chunk_hidden_states = ggml_ext_tensor_concat(work_ctx, chunk_hidden_states_lg_pad, chunk_hidden_states_t5, 1);  // [n_token*2, 4096]

            if (chunk_idx == 0) {
                pooled = ggml_ext_tensor_concat(work_ctx, pooled_l, pooled_g, 0);  // [768 + 1280]
            }

            int64_t t1 = ggml_time_ms();
            LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
            if (zero_out_masked) {
                float* vec = (float*)chunk_hidden_states->data;
                for (int i = 0; i < ggml_nelements(chunk_hidden_states); i++) {
                    vec[i] = 0;
                }
            }

            hidden_states_vec.insert(hidden_states_vec.end(),
                                     (float*)chunk_hidden_states->data,
                                     ((float*)chunk_hidden_states->data) + ggml_nelements(chunk_hidden_states));
        }

        if (hidden_states_vec.size() > 0) {
            hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
            hidden_states = ggml_reshape_2d(work_ctx,
                                            hidden_states,
                                            chunk_hidden_states->ne[0],
                                            ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
        } else {
            hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, 256);
            ggml_set_f32(hidden_states, 0.f);
        }
        if (pooled == nullptr) {
            pooled = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 2048);
            ggml_set_f32(pooled, 0.f);
        }
        return {hidden_states, pooled, nullptr};
    }

    SDCondition get_learned_condition(ggml_context* work_ctx,
                                      int n_threads,
                                      const ConditionerParams& conditioner_params) override {
        auto tokens_and_weights = tokenize(conditioner_params.text, 77, true);
        return get_learned_condition_common(work_ctx,
                                            n_threads,
                                            tokens_and_weights,
                                            conditioner_params.clip_skip,
                                            conditioner_params.zero_out_masked);
    }
};

struct FluxCLIPEmbedder : public Conditioner {
    CLIPTokenizer clip_l_tokenizer;
    T5UniGramTokenizer t5_tokenizer;
    std::shared_ptr<CLIPTextModelRunner> clip_l;
    std::shared_ptr<T5Runner> t5;
    size_t chunk_len = 256;

    FluxCLIPEmbedder(ggml_backend_t backend,
                     bool offload_params_to_cpu,
                     const String2TensorStorage& tensor_storage_map = {}) {
        bool use_clip_l = false;
        bool use_t5     = false;
        for (auto pair : tensor_storage_map) {
            if (pair.first.find("text_encoders.clip_l") != std::string::npos) {
                use_clip_l = true;
            } else if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
                use_t5 = true;
            }
        }

        if (!use_clip_l && !use_t5) {
            LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
            return;
        }

        if (use_clip_l) {
            clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true);
        } else {
            LOG_WARN("clip_l text encoder not found! Prompt adherence might be degraded.");
        }
        if (use_t5) {
            t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_storage_map, "text_encoders.t5xxl.transformer");
        } else {
            LOG_WARN("t5xxl text encoder not found! Prompt adherence might be degraded.");
        }
    }

    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
        if (clip_l) {
            clip_l->get_param_tensors(tensors, "text_encoders.clip_l.transformer.text_model");
        }
        if (t5) {
            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
        }
    }

    void alloc_params_buffer() override {
        if (clip_l) {
            clip_l->alloc_params_buffer();
        }
        if (t5) {
            t5->alloc_params_buffer();
        }
    }

    void free_params_buffer() override {
        if (clip_l) {
            clip_l->free_params_buffer();
        }
        if (t5) {
            t5->free_params_buffer();
        }
    }

    size_t get_params_buffer_size() override {
        size_t buffer_size = 0;
        if (clip_l) {
            buffer_size += clip_l->get_params_buffer_size();
        }
        if (t5) {
            buffer_size += t5->get_params_buffer_size();
        }
        return buffer_size;
    }

    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {
        if (clip_l) {
            clip_l->set_weight_adapter(adapter);
        }
        if (t5) {
            t5->set_weight_adapter(adapter);
        }
    }

    std::vector<std::pair<std::vector<int>, std::vector<float>>> tokenize(std::string text,
                                                                          size_t max_length = 0,
                                                                          bool padding      = false) {
        auto parsed_attention = parse_prompt_attention(text);

        {
            std::stringstream ss;
            ss << "[";
            for (const auto& item : parsed_attention) {
                ss << "['" << item.first << "', " << item.second << "], ";
            }
            ss << "]";
            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
        }

        auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
            return false;
        };

        std::vector<int> clip_l_tokens;
        std::vector<float> clip_l_weights;
        std::vector<int> t5_tokens;
        std::vector<float> t5_weights;
        for (const auto& item : parsed_attention) {
            const std::string& curr_text = item.first;
            float curr_weight            = item.second;
            if (clip_l) {
                std::vector<int> curr_tokens = clip_l_tokenizer.encode(curr_text, on_new_token_cb);
                clip_l_tokens.insert(clip_l_tokens.end(), curr_tokens.begin(), curr_tokens.end());
                clip_l_weights.insert(clip_l_weights.end(), curr_tokens.size(), curr_weight);
            }
            if (t5) {
                std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
                t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
                t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
            }
        }

        if (clip_l) {
            clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, 77, padding);
        }
        if (t5) {
            t5_tokenizer.pad_tokens(t5_tokens, t5_weights, nullptr, max_length, padding);
        }

        // for (int i = 0; i < clip_l_tokens.size(); i++) {
        //     std::cout << clip_l_tokens[i] << ":" << clip_l_weights[i] << ", ";
        // }
        // std::cout << std::endl;

        // for (int i = 0; i < t5_tokens.size(); i++) {
        //     std::cout << t5_tokens[i] << ":" << t5_weights[i] << ", ";
        // }
        // std::cout << std::endl;

        return {{clip_l_tokens, clip_l_weights}, {t5_tokens, t5_weights}};
    }

    SDCondition get_learned_condition_common(ggml_context* work_ctx,
                                             int n_threads,
                                             std::vector<std::pair<std::vector<int>, std::vector<float>>> token_and_weights,
                                             int clip_skip,
                                             bool zero_out_masked = false) {
        auto& clip_l_tokens  = token_and_weights[0].first;
        auto& clip_l_weights = token_and_weights[0].second;
        auto& t5_tokens      = token_and_weights[1].first;
        auto& t5_weights     = token_and_weights[1].second;

        if (clip_skip <= 0) {
            clip_skip = 2;
        }

        int64_t t0                              = ggml_time_ms();
        struct ggml_tensor* hidden_states       = nullptr;  // [N, n_token, 4096]
        struct ggml_tensor* chunk_hidden_states = nullptr;  // [n_token, 4096]
        struct ggml_tensor* pooled              = nullptr;  // [768,]
        std::vector<float> hidden_states_vec;

        size_t chunk_count = std::max(clip_l_tokens.size() > 0 ? chunk_len : 0, t5_tokens.size()) / chunk_len;
        for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
            // clip_l
            if (chunk_idx == 0) {
                if (clip_l) {
                    size_t chunk_len_l = 77;
                    std::vector<int> chunk_tokens(clip_l_tokens.begin(),
                                                  clip_l_tokens.begin() + chunk_len_l);
                    std::vector<float> chunk_weights(clip_l_weights.begin(),
                                                     clip_l_weights.begin() + chunk_len_l);

                    auto input_ids       = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
                    size_t max_token_idx = 0;

                    auto it       = std::find(chunk_tokens.begin(), chunk_tokens.end(), clip_l_tokenizer.EOS_TOKEN_ID);
                    max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);

                    clip_l->compute(n_threads,
                                    input_ids,
                                    0,
                                    nullptr,
                                    max_token_idx,
                                    true,
                                    clip_skip,
                                    &pooled,
                                    work_ctx);
                }
            }

            // t5
            if (t5) {
                std::vector<int> chunk_tokens(t5_tokens.begin() + chunk_idx * chunk_len,
                                              t5_tokens.begin() + (chunk_idx + 1) * chunk_len);
                std::vector<float> chunk_weights(t5_weights.begin() + chunk_idx * chunk_len,
                                                 t5_weights.begin() + (chunk_idx + 1) * chunk_len);

                auto input_ids = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);

                t5->compute(n_threads,
                            input_ids,
                            nullptr,
                            &chunk_hidden_states,
                            work_ctx);
                {
                    auto tensor         = chunk_hidden_states;
                    float original_mean = ggml_ext_tensor_mean(tensor);
                    for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
                        for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                            for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                                float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
                                value *= chunk_weights[i1];
                                ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
                            }
                        }
                    }
                    float new_mean = ggml_ext_tensor_mean(tensor);
                    ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
                }
            } else {
                chunk_hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, chunk_len);
                ggml_set_f32(chunk_hidden_states, 0.f);
            }

            int64_t t1 = ggml_time_ms();
            LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
            if (zero_out_masked) {
                float* vec = (float*)chunk_hidden_states->data;
                for (int i = 0; i < ggml_nelements(chunk_hidden_states); i++) {
                    vec[i] = 0;
                }
            }

            hidden_states_vec.insert(hidden_states_vec.end(),
                                     (float*)chunk_hidden_states->data,
                                     ((float*)chunk_hidden_states->data) + ggml_nelements(chunk_hidden_states));
        }

        if (hidden_states_vec.size() > 0) {
            hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
            hidden_states = ggml_reshape_2d(work_ctx,
                                            hidden_states,
                                            chunk_hidden_states->ne[0],
                                            ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
        } else {
            hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, 256);
            ggml_set_f32(hidden_states, 0.f);
        }
        if (pooled == nullptr) {
            pooled = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 768);
            ggml_set_f32(pooled, 0.f);
        }
        return {hidden_states, pooled, nullptr};
    }

    SDCondition get_learned_condition(ggml_context* work_ctx,
                                      int n_threads,
                                      const ConditionerParams& conditioner_params) override {
        auto tokens_and_weights = tokenize(conditioner_params.text, chunk_len, true);
        return get_learned_condition_common(work_ctx,
                                            n_threads,
                                            tokens_and_weights,
                                            conditioner_params.clip_skip,
                                            conditioner_params.zero_out_masked);
    }
};

struct T5CLIPEmbedder : public Conditioner {
    T5UniGramTokenizer t5_tokenizer;
    std::shared_ptr<T5Runner> t5;
    size_t chunk_len = 512;
    bool use_mask    = false;
    int mask_pad     = 1;
    bool is_umt5     = false;

    T5CLIPEmbedder(ggml_backend_t backend,
                   bool offload_params_to_cpu,
                   const String2TensorStorage& tensor_storage_map = {},
                   bool use_mask                                  = false,
                   int mask_pad                                   = 1,
                   bool is_umt5                                   = false)
        : use_mask(use_mask), mask_pad(mask_pad), t5_tokenizer(is_umt5) {
        bool use_t5 = false;
        for (auto pair : tensor_storage_map) {
            if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
                use_t5 = true;
            }
        }

        if (!use_t5) {
            LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
            return;
        } else {
            t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_storage_map, "text_encoders.t5xxl.transformer", is_umt5);
        }
    }

    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
        if (t5) {
            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
        }
    }

    void alloc_params_buffer() override {
        if (t5) {
            t5->alloc_params_buffer();
        }
    }

    void free_params_buffer() override {
        if (t5) {
            t5->free_params_buffer();
        }
    }

    size_t get_params_buffer_size() override {
        size_t buffer_size = 0;
        if (t5) {
            buffer_size += t5->get_params_buffer_size();
        }
        return buffer_size;
    }

    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
        if (t5) {
            t5->set_weight_adapter(adapter);
        }
    }

    std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> tokenize(std::string text,
                                                                                  size_t max_length = 0,
                                                                                  bool padding      = false) {
        auto parsed_attention = parse_prompt_attention(text);

        {
            std::stringstream ss;
            ss << "[";
            for (const auto& item : parsed_attention) {
                ss << "['" << item.first << "', " << item.second << "], ";
            }
            ss << "]";
            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
        }

        auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
            return false;
        };

        std::vector<int> t5_tokens;
        std::vector<float> t5_weights;
        std::vector<float> t5_mask;
        if (t5) {
            for (const auto& item : parsed_attention) {
                const std::string& curr_text = item.first;
                float curr_weight            = item.second;

                std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
                t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
                t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
            }

            t5_tokenizer.pad_tokens(t5_tokens, t5_weights, &t5_mask, max_length, padding);
        }
        return {t5_tokens, t5_weights, t5_mask};
    }

    void modify_mask_to_attend_padding(struct ggml_tensor* mask, int max_seq_length, int num_extra_padding = 8) {
        float* mask_data = (float*)mask->data;
        int num_pad      = 0;
        for (int64_t i = 0; i < max_seq_length; i++) {
            if (num_pad >= num_extra_padding) {
                break;
            }
            if (std::isinf(mask_data[i])) {
                mask_data[i] = 0;
                ++num_pad;
            }
        }
        // LOG_DEBUG("PAD: %d", num_pad);
    }

    SDCondition get_learned_condition_common(ggml_context* work_ctx,
                                             int n_threads,
                                             std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> token_and_weights,
                                             int clip_skip,
                                             bool zero_out_masked = false) {
        if (!t5) {
            auto hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, 256);
            ggml_set_f32(hidden_states, 0.f);
            auto t5_attn_mask = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 256);
            ggml_set_f32(t5_attn_mask, -HUGE_VALF);
            return {hidden_states, t5_attn_mask, nullptr};
        }
        auto& t5_tokens        = std::get<0>(token_and_weights);
        auto& t5_weights       = std::get<1>(token_and_weights);
        auto& t5_attn_mask_vec = std::get<2>(token_and_weights);

        int64_t t0                              = ggml_time_ms();
        struct ggml_tensor* hidden_states       = nullptr;  // [N, n_token, 4096]
        struct ggml_tensor* chunk_hidden_states = nullptr;  // [n_token, 4096]
        struct ggml_tensor* pooled              = nullptr;
        struct ggml_tensor* t5_attn_mask        = vector_to_ggml_tensor(work_ctx, t5_attn_mask_vec);  // [n_token]

        std::vector<float> hidden_states_vec;

        size_t chunk_count = t5_tokens.size() / chunk_len;

        for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
            // t5
            std::vector<int> chunk_tokens(t5_tokens.begin() + chunk_idx * chunk_len,
                                          t5_tokens.begin() + (chunk_idx + 1) * chunk_len);
            std::vector<float> chunk_weights(t5_weights.begin() + chunk_idx * chunk_len,
                                             t5_weights.begin() + (chunk_idx + 1) * chunk_len);
            std::vector<float> chunk_mask(t5_attn_mask_vec.begin() + chunk_idx * chunk_len,
                                          t5_attn_mask_vec.begin() + (chunk_idx + 1) * chunk_len);

            auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
            auto t5_attn_mask_chunk = use_mask ? vector_to_ggml_tensor(work_ctx, chunk_mask) : nullptr;

            t5->compute(n_threads,
                        input_ids,
                        t5_attn_mask_chunk,
                        &chunk_hidden_states,
                        work_ctx);
            {
                auto tensor         = chunk_hidden_states;
                float original_mean = ggml_ext_tensor_mean(tensor);
                for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
                    for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                        for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                            float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
                            value *= chunk_weights[i1];
                            ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
                        }
                    }
                }
                float new_mean = ggml_ext_tensor_mean(tensor);
                ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
            }

            int64_t t1 = ggml_time_ms();
            LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
            if (zero_out_masked) {
                auto tensor = chunk_hidden_states;
                for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
                    for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                        for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                            if (chunk_mask[i1] < 0.f) {
                                ggml_ext_tensor_set_f32(tensor, 0.f, i0, i1, i2);
                            }
                        }
                    }
                }
            }

            hidden_states_vec.insert(hidden_states_vec.end(),
                                     (float*)chunk_hidden_states->data,
                                     ((float*)chunk_hidden_states->data) + ggml_nelements(chunk_hidden_states));
        }

        GGML_ASSERT(hidden_states_vec.size() > 0);
        hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
        hidden_states = ggml_reshape_2d(work_ctx,
                                        hidden_states,
                                        chunk_hidden_states->ne[0],
                                        ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);

        modify_mask_to_attend_padding(t5_attn_mask, static_cast<int>(ggml_nelements(t5_attn_mask)), mask_pad);

        return {hidden_states, t5_attn_mask, nullptr};
    }

    SDCondition get_learned_condition(ggml_context* work_ctx,
                                      int n_threads,
                                      const ConditionerParams& conditioner_params) override {
        auto tokens_and_weights = tokenize(conditioner_params.text, chunk_len, true);
        return get_learned_condition_common(work_ctx,
                                            n_threads,
                                            tokens_and_weights,
                                            conditioner_params.clip_skip,
                                            conditioner_params.zero_out_masked);
    }
};

struct LLMEmbedder : public Conditioner {
    SDVersion version;
    std::shared_ptr<LLM::BPETokenizer> tokenizer;
    std::shared_ptr<LLM::LLMRunner> llm;

    LLMEmbedder(ggml_backend_t backend,
                bool offload_params_to_cpu,
                const String2TensorStorage& tensor_storage_map = {},
                SDVersion version                              = VERSION_QWEN_IMAGE,
                const std::string prefix                       = "",
                bool enable_vision                             = false)
        : version(version) {
        LLM::LLMArch arch = LLM::LLMArch::QWEN2_5_VL;
        if (version == VERSION_FLUX2) {
            arch = LLM::LLMArch::MISTRAL_SMALL_3_2;
        } else if (sd_version_is_z_image(version) || version == VERSION_OVIS_IMAGE || version == VERSION_FLUX2_KLEIN) {
            arch = LLM::LLMArch::QWEN3;
        }
        if (arch == LLM::LLMArch::MISTRAL_SMALL_3_2) {
            tokenizer = std::make_shared<LLM::MistralTokenizer>();
        } else {
            tokenizer = std::make_shared<LLM::Qwen2Tokenizer>();
        }
        llm = std::make_shared<LLM::LLMRunner>(arch,
                                               backend,
                                               offload_params_to_cpu,
                                               tensor_storage_map,
                                               "text_encoders.llm",
                                               enable_vision);
    }

    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
        llm->get_param_tensors(tensors, "text_encoders.llm");
    }

    void alloc_params_buffer() override {
        llm->alloc_params_buffer();
    }

    void free_params_buffer() override {
        llm->free_params_buffer();
    }

    size_t get_params_buffer_size() override {
        size_t buffer_size = 0;
        buffer_size += llm->get_params_buffer_size();
        return buffer_size;
    }

    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
        if (llm) {
            llm->set_weight_adapter(adapter);
        }
    }

    std::tuple<std::vector<int>, std::vector<float>> tokenize(std::string text,
                                                              std::pair<int, int> attn_range,
                                                              size_t max_length = 0,
                                                              bool padding      = false) {
        std::vector<std::pair<std::string, float>> parsed_attention;
        parsed_attention.emplace_back(text.substr(0, attn_range.first), 1.f);
        if (attn_range.second - attn_range.first > 0) {
            auto new_parsed_attention = parse_prompt_attention(text.substr(attn_range.first, attn_range.second - attn_range.first));
            parsed_attention.insert(parsed_attention.end(),
                                    new_parsed_attention.begin(),
                                    new_parsed_attention.end());
        }
        parsed_attention.emplace_back(text.substr(attn_range.second), 1.f);
        {
            std::stringstream ss;
            ss << "[";
            for (const auto& item : parsed_attention) {
                ss << "['" << item.first << "', " << item.second << "], ";
            }
            ss << "]";
            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
        }

        std::vector<int> tokens;
        std::vector<float> weights;
        for (const auto& item : parsed_attention) {
            const std::string& curr_text = item.first;
            float curr_weight            = item.second;
            std::vector<int> curr_tokens = tokenizer->tokenize(curr_text, nullptr);
            tokens.insert(tokens.end(), curr_tokens.begin(), curr_tokens.end());
            weights.insert(weights.end(), curr_tokens.size(), curr_weight);
        }

        tokenizer->pad_tokens(tokens, weights, max_length, padding);

        // for (int i = 0; i < tokens.size(); i++) {
        //     std::cout << tokens[i] << ":" << weights[i] << ", " << i << std::endl;
        // }
        // std::cout << std::endl;

        return {tokens, weights};
    }

    SDCondition get_learned_condition(ggml_context* work_ctx,
                                      int n_threads,
                                      const ConditionerParams& conditioner_params) override {
        std::string prompt;
        std::vector<std::pair<int, ggml_tensor*>> image_embeds;
        std::pair<int, int> prompt_attn_range;
        int prompt_template_encode_start_idx = 34;
        int max_length                       = 0;
        std::set<int> out_layers;
        std::vector<int> tokens;
        std::vector<float> weights;
        std::vector<float> mask;
        if (llm->enable_vision && conditioner_params.ref_images.size() > 0) {
            LOG_INFO("QwenImageEditPlusPipeline");
            prompt_template_encode_start_idx = 64;
            int image_embed_idx              = 64 + 6;

            int min_pixels          = 384 * 384;
            int max_pixels          = 560 * 560;
            std::string placeholder = "<|image_pad|>";
            std::string img_prompt;

            for (int i = 0; i < conditioner_params.ref_images.size(); i++) {
                sd_image_f32_t image = sd_image_t_to_sd_image_f32_t(*conditioner_params.ref_images[i]);
                double factor        = llm->params.vision.patch_size * llm->params.vision.spatial_merge_size;
                int height           = image.height;
                int width            = image.width;
                int h_bar            = static_cast<int>(std::round(height / factor) * factor);
                int w_bar            = static_cast<int>(std::round(width / factor) * factor);

                if (static_cast<double>(h_bar) * w_bar > max_pixels) {
                    double beta = std::sqrt((height * width) / static_cast<double>(max_pixels));
                    h_bar       = std::max(static_cast<int>(factor),
                                           static_cast<int>(std::floor(height / beta / factor)) * static_cast<int>(factor));
                    w_bar       = std::max(static_cast<int>(factor),
                                           static_cast<int>(std::floor(width / beta / factor)) * static_cast<int>(factor));
                } else if (static_cast<double>(h_bar) * w_bar < min_pixels) {
                    double beta = std::sqrt(static_cast<double>(min_pixels) / (height * width));
                    h_bar       = static_cast<int>(std::ceil(height * beta / factor)) * static_cast<int>(factor);
                    w_bar       = static_cast<int>(std::ceil(width * beta / factor)) * static_cast<int>(factor);
                }

                LOG_DEBUG("resize conditioner ref image %d from %dx%d to %dx%d", i, image.height, image.width, h_bar, w_bar);

                sd_image_f32_t resized_image = clip_preprocess(image, w_bar, h_bar);
                free(image.data);
                image.data = nullptr;

                ggml_tensor* image_tensor = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, resized_image.width, resized_image.height, 3, 1);
                sd_image_f32_to_ggml_tensor(resized_image, image_tensor, false);
                free(resized_image.data);
                resized_image.data = nullptr;

                ggml_tensor* image_embed = nullptr;
                llm->encode_image(n_threads, image_tensor, &image_embed, work_ctx);
                image_embeds.emplace_back(image_embed_idx, image_embed);
                image_embed_idx += 1 + static_cast<int>(image_embed->ne[1]) + 6;

                img_prompt += "Picture " + std::to_string(i + 1) + ": <|vision_start|>";  // [24669, 220, index, 25, 220, 151652]
                int64_t num_image_tokens = image_embed->ne[1];
                img_prompt.reserve(num_image_tokens * placeholder.size());
                for (int j = 0; j < num_image_tokens; j++) {
                    img_prompt += placeholder;
                }
                img_prompt += "<|vision_end|>";
            }

            prompt = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n";
            prompt += img_prompt;

            prompt_attn_range.first = static_cast<int>(prompt.size());
            prompt += conditioner_params.text;
            prompt_attn_range.second = static_cast<int>(prompt.size());

            prompt += "<|im_end|>\n<|im_start|>assistant\n";
        } else if (version == VERSION_FLUX2) {
            prompt_template_encode_start_idx = 0;
            out_layers                       = {10, 20, 30};

            prompt = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";

            prompt_attn_range.first = static_cast<int>(prompt.size());
            prompt += conditioner_params.text;
            prompt_attn_range.second = static_cast<int>(prompt.size());

            prompt += "[/INST]";
        } else if (sd_version_is_z_image(version)) {
            prompt_template_encode_start_idx = 0;
            out_layers                       = {35};  // -2

            prompt = "<|im_start|>user\n";

            prompt_attn_range.first = static_cast<int>(prompt.size());
            prompt += conditioner_params.text;
            prompt_attn_range.second = static_cast<int>(prompt.size());

            prompt += "<|im_end|>\n<|im_start|>assistant\n";
        } else if (version == VERSION_FLUX2_KLEIN) {
            prompt_template_encode_start_idx = 0;
            max_length                       = 512;
            out_layers                       = {9, 18, 27};

            prompt = "<|im_start|>user\n";

            prompt_attn_range.first = static_cast<int>(prompt.size());
            prompt += conditioner_params.text;
            prompt_attn_range.second = static_cast<int>(prompt.size());

            prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";

            auto tokens_and_weights = tokenize(prompt, prompt_attn_range, 0, false);
            tokens                  = std::get<0>(tokens_and_weights);
            weights                 = std::get<1>(tokens_and_weights);

            mask.insert(mask.end(), tokens.size(), 1.f);
            if (tokens.size() < max_length) {
                mask.insert(mask.end(), max_length - tokens.size(), 0.f);
                tokenizer->pad_tokens(tokens, weights, max_length, true);
            }
        } else if (version == VERSION_OVIS_IMAGE) {
            prompt_template_encode_start_idx = 28;
            max_length                       = prompt_template_encode_start_idx + 256;

            prompt = "<|im_start|>user\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background:";

            prompt_attn_range.first = static_cast<int>(prompt.size());
            prompt += " " + conditioner_params.text;
            prompt_attn_range.second = static_cast<int>(prompt.size());

            prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
        } else {
            prompt_template_encode_start_idx = 34;

            prompt = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";

            prompt_attn_range.first = static_cast<int>(prompt.size());
            prompt += conditioner_params.text;
            prompt_attn_range.second = static_cast<int>(prompt.size());

            prompt += "<|im_end|>\n<|im_start|>assistant\n";
        }

        if (tokens.empty()) {
            auto tokens_and_weights = tokenize(prompt, prompt_attn_range, max_length, max_length > 0);
            tokens                  = std::get<0>(tokens_and_weights);
            weights                 = std::get<1>(tokens_and_weights);
        }

        int64_t t0                        = ggml_time_ms();
        struct ggml_tensor* hidden_states = nullptr;  // [N, n_token, 3584]

        auto input_ids = vector_to_ggml_tensor_i32(work_ctx, tokens);

        ggml_tensor* attention_mask = nullptr;
        if (!mask.empty()) {
            attention_mask = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, mask.size(), mask.size());
            ggml_ext_tensor_iter(attention_mask, [&](ggml_tensor* attention_mask, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
                float value = 0.f;
                if (mask[i0] == 0.f) {
                    value = -INFINITY;
                } else if (i0 > i1) {
                    value = -INFINITY;
                }
                ggml_ext_tensor_set_f32(attention_mask, value, i0, i1, i2, i3);
            });
        }

        llm->compute(n_threads,
                     input_ids,
                     attention_mask,
                     image_embeds,
                     out_layers,
                     &hidden_states,
                     work_ctx);
        {
            auto tensor         = hidden_states;
            float original_mean = ggml_ext_tensor_mean(tensor);
            for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
                for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
                    for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
                        float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
                        value *= weights[i1];
                        ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
                    }
                }
            }
            float new_mean = ggml_ext_tensor_mean(tensor);
            ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
        }

        GGML_ASSERT(hidden_states->ne[1] > prompt_template_encode_start_idx);

        int64_t min_length = 0;
        if (version == VERSION_FLUX2) {
            min_length = 512;
        }

        int64_t zero_pad_len = 0;
        if (min_length > 0) {
            if (hidden_states->ne[1] - prompt_template_encode_start_idx < min_length) {
                zero_pad_len = min_length - hidden_states->ne[1] + prompt_template_encode_start_idx;
            }
        }

        ggml_tensor* new_hidden_states = ggml_new_tensor_3d(work_ctx,
                                                            GGML_TYPE_F32,
                                                            hidden_states->ne[0],
                                                            hidden_states->ne[1] - prompt_template_encode_start_idx + zero_pad_len,
                                                            hidden_states->ne[2]);

        ggml_ext_tensor_iter(new_hidden_states, [&](ggml_tensor* new_hidden_states, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
            float value = 0.f;
            if (i1 + prompt_template_encode_start_idx < hidden_states->ne[1]) {
                value = ggml_ext_tensor_get_f32(hidden_states, i0, i1 + prompt_template_encode_start_idx, i2, i3);
            }
            ggml_ext_tensor_set_f32(new_hidden_states, value, i0, i1, i2, i3);
        });

        // print_ggml_tensor(new_hidden_states);

        int64_t t1 = ggml_time_ms();
        LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
        return {new_hidden_states, nullptr, nullptr};
    }
};

#endif