feat: set tensor names on block params (#1622)

CMakeLists.txt

@@ -210,6 +210,21 @@ file(GLOB SD_LIB_SOURCES CONFIGURE_DEPENDS
     "src/*.h"
     "src/*.cpp"
     "src/*.hpp"
+    "src/conditioning/*.h"
+    "src/conditioning/*.cpp"
+    "src/conditioning/*.hpp"
+    "src/core/*.h"
+    "src/core/*.cpp"
+    "src/core/*.hpp"
+    "src/extensions/*.h"
+    "src/extensions/*.cpp"
+    "src/extensions/*.hpp"
+    "src/model/*/*.h"
+    "src/model/*/*.cpp"
+    "src/model/*/*.hpp"
+    "src/runtime/*.h"
+    "src/runtime/*.cpp"
+    "src/runtime/*.hpp"
     "src/model_io/*.h"
     "src/model_io/*.cpp"
     "src/tokenizers/*.h"
@@ -312,6 +327,7 @@ add_subdirectory(thirdparty)
 
 target_link_libraries(${SD_LIB} PUBLIC ggml zip)
 target_include_directories(${SD_LIB} PUBLIC . src include)
+target_include_directories(${SD_LIB} PRIVATE src/core)
 target_include_directories(${SD_LIB} PUBLIC . thirdparty)
 target_compile_features(${SD_LIB} PUBLIC c_std_11 cxx_std_17)
 

format-code.sh

@@ -1,10 +1,17 @@
-for f in src/*.cpp src/*.h src/*.hpp src/tokenizers/*.h src/tokenizers/*.cpp src/tokenizers/vocab/*.h src/tokenizers/vocab/*.cpp \
+for f in src/*.cpp src/*.h src/*.hpp \
+         src/conditioning/*.cpp src/conditioning/*.h src/conditioning/*.hpp \
+         src/core/*.cpp src/core/*.h src/core/*.hpp \
+         src/extensions/*.cpp src/extensions/*.h src/extensions/*.hpp \
+         src/runtime/*.cpp src/runtime/*.h src/runtime/*.hpp \
+         src/model/*/*.cpp src/model/*/*.h src/model/*/*.hpp \
+         src/tokenizers/*.h src/tokenizers/*.cpp src/tokenizers/vocab/*.h src/tokenizers/vocab/*.cpp \
          src/model_io/*.h src/model_io/*.cpp examples/cli/*.cpp examples/cli/*.h examples/server/*.cpp \
          examples/common/*.hpp examples/common/*.h examples/common/*.cpp; do
+  [[ -e "$f" ]] || continue
   [[ "$f" == vocab* ]] && continue
   echo "formatting '$f'"
   # if [ "$f" != "stable-diffusion.h" ]; then
   #   clang-tidy -fix -p build_linux/ "$f"
   # fi
   clang-format -style=file -i "$f"
-done
+done

src/conditioning/conditioner.hpp

@@ -1,14 +1,15 @@
-#ifndef __CONDITIONER_HPP__
-#define __CONDITIONER_HPP__
+#ifndef __SD_CONDITIONING_CONDITIONER_HPP__
+#define __SD_CONDITIONING_CONDITIONER_HPP__
 
 #include <cmath>
 #include <limits>
 #include <optional>
 
-#include "clip.hpp"
-#include "llm.hpp"
-#include "t5.hpp"
-#include "tensor_ggml.hpp"
+#include "core/tensor_ggml.hpp"
+#include "model/te/clip.hpp"
+#include "model/te/llm.hpp"
+#include "model/te/t5.hpp"
+#include "model_loader.h"
 
 struct SDCondition {
     sd::Tensor<float> c_crossattn;
@@ -103,7 +104,6 @@ struct ConditionerParams {
     int width                                        = -1;
     int height                                       = -1;
     bool zero_out_masked                             = false;
-    int num_input_imgs                               = 0;        // for photomaker
     const std::vector<sd::Tensor<float>>* ref_images = nullptr;  // for qwen image edit
 };
 
@@ -121,25 +121,16 @@ public:
     virtual void set_stream_layers_enabled(bool enabled) {}
     virtual void set_flash_attention_enabled(bool enabled) = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {}
-    virtual std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(int n_threads,
-                                                                                          const ConditionerParams& conditioner_params) {
-        GGML_ABORT("Not implemented yet!");
-    }
-    virtual std::string remove_trigger_from_prompt(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;
+    SDVersion version = VERSION_SD1;
     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;
@@ -150,9 +141,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                       ggml_backend_t params_backend,
                                       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) {
+                                      SDVersion version = VERSION_SD1)
+        : version(version), 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); });
@@ -329,121 +319,6 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         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) {
-        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, nullptr, text_model->model.n_token, text_model->model.n_token, true);
-        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,
                                                              size_t min_length          = 0,
                                                              size_t max_length          = 0,
@@ -631,49 +506,6 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         return result;
     }
 
-    std::tuple<SDCondition, std::vector<bool>>
-    get_learned_condition_with_trigger(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]);
-        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(n_threads,
-                                                 tokens,
-                                                 weights,
-                                                 conditioner_params.clip_skip,
-                                                 conditioner_params.width,
-                                                 conditioner_params.height,
-                                                 conditioner_params.zero_out_masked);
-        return std::make_tuple(cond, clsm);
-    }
-
-    std::string remove_trigger_from_prompt(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);
-        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(int n_threads,
                                       const ConditionerParams& conditioner_params) override {
         auto tokens_and_weights     = tokenize(conditioner_params.text, text_model->model.n_token, text_model->model.n_token, true);
@@ -2554,4 +2386,4 @@ struct LTXAVEmbedder : public Conditioner {
     }
 };
 
-#endif
+#endif  // __SD_CONDITIONING_CONDITIONER_HPP__

src/convert.cpp

@@ -3,9 +3,9 @@
 #include <regex>
 #include <vector>
 
-#include "model.h"
 #include "model_io/gguf_io.h"
 #include "model_io/safetensors_io.h"
+#include "model_loader.h"
 #include "util.h"
 
 #include "ggml_extend_backend.h"

src/core/ggml_extend.hpp

@@ -1,5 +1,5 @@
-#ifndef __GGML_EXTEND_HPP__
-#define __GGML_EXTEND_HPP__
+#ifndef __SD_CORE_GGML_EXTEND_HPP__
+#define __SD_CORE_GGML_EXTEND_HPP__
 
 #include <assert.h>
 #include <inttypes.h>
@@ -23,19 +23,19 @@
 #include <unordered_map>
 #include <vector>
 
+#include "core/ggml_extend_backend.h"
+#include "core/ggml_graph_cut.h"
+#include "core/layer_registry.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "ggml.h"
-#include "ggml_extend_backend.h"
-#include "ggml_graph_cut.h"
-#include "layer_registry.h"
 
+#include "core/tensor.hpp"
 #include "model.h"
-#include "tensor.hpp"
 
-#include "rng.hpp"
-#include "tensor_ggml.hpp"
-#include "util.h"
+#include "core/rng.hpp"
+#include "core/tensor_ggml.hpp"
+#include "core/util.h"
 
 #define EPS 1e-05f
 
@@ -2470,10 +2470,26 @@ protected:
             *effective_budget_out = effective_budget;
         }
 
+        // When streaming and the model dwarfs the budget, cap the planner at
+        // a quarter so it builds smaller merged segments and chunk-K can fit
+        // alongside. Without streaming the cap only adds dispatch overhead.
+        size_t planner_budget = effective_budget;
+        if (stream_layers_enabled) {
+            size_t total_params_bytes = 0;
+            for (const ggml_tensor* t : params_tensor_set_) {
+                if (t != nullptr) {
+                    total_params_bytes += ggml_nbytes(t);
+                }
+            }
+            if (total_params_bytes * 4 > effective_budget * 3) {
+                planner_budget = effective_budget / 4;
+            }
+        }
+
         *plan_out = sd::ggml_graph_cut::resolve_plan(runtime_backend,
                                                      gf,
                                                      &graph_cut_plan_cache_,
-                                                     effective_budget,
+                                                     planner_budget,
                                                      params_tensor_set_,
                                                      get_desc().c_str());
         if (stream_layers_enabled) {
@@ -3311,6 +3327,7 @@ public:
         for (auto& pair : params) {
             ggml_tensor* param           = pair.second;
             tensors[prefix + pair.first] = pair.second;
+            ggml_set_name(param, (prefix + pair.first).c_str());
         }
     }
 
@@ -4145,4 +4162,4 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_lokr_forward(
     }
 }
 
-#endif  // __GGML_EXTEND__HPP__
+#endif  // __SD_CORE_GGML_EXTEND_HPP__

src/core/ggml_extend_backend.cpp

@@ -1,4 +1,4 @@
-#include "ggml_extend_backend.h"
+#include "core/ggml_extend_backend.h"
 
 #include <algorithm>
 #include <cctype>
@@ -8,8 +8,8 @@
 #include <stdexcept>
 #include <vector>
 
+#include "core/util.h"
 #include "stable-diffusion.h"
-#include "util.h"
 
 static std::string trim_copy(const std::string& value) {
     size_t begin = 0;

src/core/ggml_extend_backend.h

@@ -1,5 +1,5 @@
-#ifndef __SD_GGML_EXTEND_BACKEND_H__
-#define __SD_GGML_EXTEND_BACKEND_H__
+#ifndef __SD_CORE_GGML_EXTEND_BACKEND_H__
+#define __SD_CORE_GGML_EXTEND_BACKEND_H__
 
 #include <cstdint>
 #include <cstring>
@@ -76,4 +76,4 @@ ggml_backend_t sd_backend_cpu_init();
 bool sd_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
 const char* sd_backend_module_name(SDBackendModule module);
 void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value);
-#endif
+#endif  // __SD_CORE_GGML_EXTEND_BACKEND_H__

src/core/ggml_graph_cut.cpp

@@ -1,4 +1,4 @@
-#include "ggml_graph_cut.h"
+#include "core/ggml_graph_cut.h"
 
 #include <algorithm>
 #include <cstring>
@@ -8,11 +8,11 @@
 #include <stack>
 #include <unordered_map>
 
+#include "core/util.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
-#include "util.h"
 
-#include "../ggml/src/ggml-impl.h"
+#include "ggml/src/ggml-impl.h"
 
 namespace sd::ggml_graph_cut {
 

src/core/ggml_graph_cut.h

@@ -1,5 +1,5 @@
-#ifndef __SD_GGML_GRAPH_CUT_H__
-#define __SD_GGML_GRAPH_CUT_H__
+#ifndef __SD_CORE_GGML_GRAPH_CUT_H__
+#define __SD_CORE_GGML_GRAPH_CUT_H__
 
 #include <array>
 #include <cstdint>
@@ -114,4 +114,4 @@ namespace sd::ggml_graph_cut {
     void annotate_residency(Plan& plan, size_t max_graph_vram_bytes);
 }  // namespace sd::ggml_graph_cut
 
-#endif
+#endif  // __SD_CORE_GGML_GRAPH_CUT_H__

src/core/layer_registry.cpp

@@ -1,8 +1,8 @@
-#include "layer_registry.h"
+#include "core/layer_registry.h"
 
 #include <utility>
 
-#include "util.h"
+#include "core/util.h"
 
 namespace sd::layer_registry {
 

src/core/layer_registry.h

@@ -1,5 +1,5 @@
-#ifndef __LAYER_REGISTRY_H__
-#define __LAYER_REGISTRY_H__
+#ifndef __SD_CORE_LAYER_REGISTRY_H__
+#define __SD_CORE_LAYER_REGISTRY_H__
 
 #include <map>
 #include <set>
@@ -47,4 +47,4 @@ namespace sd::layer_registry {
 
 }  // namespace sd::layer_registry
 
-#endif
+#endif  // __SD_CORE_LAYER_REGISTRY_H__

src/core/ordered_map.hpp

@@ -1,5 +1,5 @@
-#ifndef __ORDERED_MAP_HPP__
-#define __ORDERED_MAP_HPP__
+#ifndef __SD_CORE_ORDERED_MAP_HPP__
+#define __SD_CORE_ORDERED_MAP_HPP__
 
 #include <iostream>
 #include <list>
@@ -174,4 +174,4 @@ public:
     }
 };
 
-#endif  // __ORDERED_MAP_HPP__
+#endif  // __SD_CORE_ORDERED_MAP_HPP__

src/core/rng.hpp

@@ -1,5 +1,5 @@
-#ifndef __RNG_H__
-#define __RNG_H__
+#ifndef __SD_CORE_RNG_HPP__
+#define __SD_CORE_RNG_HPP__
 
 #include <random>
 #include <vector>
@@ -32,4 +32,4 @@ public:
     }
 };
 
-#endif  // __RNG_H__
+#endif  // __SD_CORE_RNG_HPP__

src/core/rng_mt19937.hpp

@@ -1,10 +1,10 @@
-#ifndef __RNG_MT19937_HPP__
-#define __RNG_MT19937_HPP__
+#ifndef __SD_CORE_RNG_MT19937_HPP__
+#define __SD_CORE_RNG_MT19937_HPP__
 
 #include <cmath>
 #include <vector>
 
-#include "rng.hpp"
+#include "core/rng.hpp"
 
 // RNG imitiating torch cpu randn on CPU.
 // Port from pytorch, original license: https://github.com/pytorch/pytorch/blob/d01a7b0241ed1c4cded7e7ca097249feb343f072/LICENSE
@@ -144,4 +144,4 @@ public:
     }
 };
 
-#endif  // __RNG_MT19937_HPP__
+#endif  // __SD_CORE_RNG_MT19937_HPP__

src/core/rng_philox.hpp

@@ -1,10 +1,10 @@
-#ifndef __RNG_PHILOX_H__
-#define __RNG_PHILOX_H__
+#ifndef __SD_CORE_RNG_PHILOX_HPP__
+#define __SD_CORE_RNG_PHILOX_HPP__
 
 #include <cmath>
 #include <vector>
 
-#include "rng.hpp"
+#include "core/rng.hpp"
 
 // RNG imitiating torch cuda randn on CPU.
 // Port from: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/5ef669de080814067961f28357256e8fe27544f4/modules/rng_philox.py
@@ -122,4 +122,4 @@ public:
     }
 };
 
-#endif  // __RNG_PHILOX_H__
+#endif  // __SD_CORE_RNG_PHILOX_HPP__

src/core/tensor.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_TENSOR_HPP__
-#define __SD_TENSOR_HPP__
+#ifndef __SD_CORE_TENSOR_HPP__
+#define __SD_CORE_TENSOR_HPP__
 
 #include <algorithm>
 #include <cmath>
@@ -16,7 +16,7 @@
 #include <utility>
 #include <vector>
 
-#include "rng.hpp"
+#include "core/rng.hpp"
 
 namespace sd {
 
@@ -1661,4 +1661,4 @@ namespace sd {
 
 }  // namespace sd
 
-#endif
+#endif  // __SD_CORE_TENSOR_HPP__

src/core/tensor_ggml.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_TENSOR_GGML_HPP__
-#define __SD_TENSOR_GGML_HPP__
+#ifndef __SD_CORE_TENSOR_GGML_HPP__
+#define __SD_CORE_TENSOR_GGML_HPP__
 
 #include <array>
 #include <cstring>
@@ -8,8 +8,8 @@
 #include <string>
 #include <type_traits>
 
+#include "core/tensor.hpp"
 #include "ggml.h"
-#include "tensor.hpp"
 
 namespace sd {
 
@@ -124,4 +124,4 @@ namespace sd {
 
 }  // namespace sd
 
-#endif
+#endif  // __SD_CORE_TENSOR_GGML_HPP__

src/core/util.cpp

@@ -1,4 +1,4 @@
-#include "util.h"
+#include "core/util.h"
 #include <algorithm>
 #include <cctype>
 #include <cmath>
@@ -13,7 +13,7 @@
 #include <thread>
 #include <unordered_set>
 #include <vector>
-#include "preprocessing.hpp"
+#include "runtime/preprocessing.hpp"
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/sysctl.h>

src/core/util.h

@@ -1,5 +1,5 @@
-#ifndef __UTIL_H__
-#define __UTIL_H__
+#ifndef __SD_CORE_UTIL_H__
+#define __SD_CORE_UTIL_H__
 
 #include <cstdint>
 #include <memory>
@@ -7,9 +7,9 @@
 #include <utility>
 #include <vector>
 
+#include "core/tensor.hpp"
 #include "ggml-backend.h"
 #include "stable-diffusion.h"
-#include "tensor.hpp"
 
 #define SAFE_STR(s) ((s) ? (s) : "")
 #define BOOL_STR(b) ((b) ? "true" : "false")
@@ -103,4 +103,4 @@ bool sd_backend_is(ggml_backend_t backend, const std::string& name);
 #define LOG_INFO(format, ...) log_printf(SD_LOG_INFO, __FILE__, __LINE__, format, ##__VA_ARGS__)
 #define LOG_WARN(format, ...) log_printf(SD_LOG_WARN, __FILE__, __LINE__, format, ##__VA_ARGS__)
 #define LOG_ERROR(format, ...) log_printf(SD_LOG_ERROR, __FILE__, __LINE__, format, ##__VA_ARGS__)
-#endif  // __UTIL_H__
+#endif  // __SD_CORE_UTIL_H__

src/extensions/generation_extension.h

@@ -0,0 +1,73 @@
+#ifndef __SD_EXTENSIONS_GENERATION_EXTENSION_H__
+#define __SD_EXTENSIONS_GENERATION_EXTENSION_H__
+
+#include <functional>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+
+#include "conditioning/conditioner.hpp"
+#include "core/ggml_extend_backend.h"
+#include "model_loader.h"
+#include "stable-diffusion.h"
+
+struct GenerationExtensionInitContext {
+    const sd_ctx_params_t* params;
+    SDVersion version;
+    const String2TensorStorage& tensor_storage_map;
+    ModelLoader& model_loader;
+    int n_threads;
+    std::function<bool(SDBackendModule)> ensure_backend_pair;
+    std::function<ggml_backend_t(SDBackendModule)> backend_for;
+    std::function<ggml_backend_t(SDBackendModule)> params_backend_for;
+};
+
+struct GenerationExtensionTensorContext {
+    std::map<std::string, ggml_tensor*>& tensors;
+    std::map<std::string, ggml_tensor*>& mmap_able_tensors;
+    std::function<bool(SDBackendModule)> module_can_mmap;
+};
+
+struct GenerationExtensionConditionContext {
+    Conditioner* conditioner;
+    ConditionerParams& condition_params;
+    const sd_pm_params_t& pm_params;
+    std::map<std::string, ggml_tensor*>& tensors;
+    SDVersion version;
+    int n_threads;
+    int total_steps;
+    bool free_params_immediately;
+};
+
+struct GenerationExtension {
+    virtual ~GenerationExtension() = default;
+
+    virtual const char* name() const = 0;
+    virtual bool is_enabled() const {
+        return false;
+    }
+    virtual bool init(const GenerationExtensionInitContext&) {
+        return true;
+    }
+    virtual void collect_param_tensors(GenerationExtensionTensorContext&) {}
+    virtual void add_ignore_tensors(std::set<std::string>&) const {}
+    virtual bool alloc_params_buffer() {
+        return true;
+    }
+    virtual size_t get_params_buffer_size() const {
+        return 0;
+    }
+    virtual void reset_runtime_condition() {}
+    virtual bool prepare_condition(GenerationExtensionConditionContext&) {
+        return false;
+    }
+    virtual const SDCondition& before_condition(int step,
+                                                const SDCondition& condition) const {
+        return condition;
+    }
+};
+
+std::shared_ptr<GenerationExtension> create_photomaker_extension();
+
+#endif

src/extensions/photomaker_extension.cpp

@@ -0,0 +1,325 @@
+#include "extensions/generation_extension.h"
+
+#include <algorithm>
+#include <cstring>
+#include <tuple>
+#include <utility>
+
+#include "core/tensor_ggml.hpp"
+#include "core/util.h"
+#include "model/adapter/lora.hpp"
+#include "model/adapter/pmid.hpp"
+
+static std::tuple<std::vector<int>, std::vector<float>, std::vector<bool>>
+tokenize_photomaker_trigger(FrozenCLIPEmbedderWithCustomWords& clip_conditioner,
+                            const std::string& text,
+                            int trigger_token_count,
+                            int32_t image_token) {
+    auto tokens_and_weights           = clip_conditioner.tokenize(text);
+    std::vector<int> source_tokens    = std::move(tokens_and_weights.first);
+    std::vector<float> source_weights = std::move(tokens_and_weights.second);
+
+    if (!source_tokens.empty() && source_tokens.front() == clip_conditioner.tokenizer.BOS_TOKEN_ID) {
+        source_tokens.erase(source_tokens.begin());
+        source_weights.erase(source_weights.begin());
+    }
+    if (!source_tokens.empty() && source_tokens.back() == clip_conditioner.tokenizer.EOS_TOKEN_ID) {
+        source_tokens.pop_back();
+        source_weights.pop_back();
+    }
+
+    std::vector<int> tokens;
+    std::vector<float> weights;
+    int32_t class_idx = -1;
+    for (size_t i = 0; i < source_tokens.size(); i++) {
+        int token = source_tokens[i];
+        if (token == image_token) {
+            if (!tokens.empty()) {
+                class_idx          = static_cast<int32_t>(tokens.size()) - 1;
+                int class_token    = tokens.back();
+                float class_weight = weights.back();
+                for (int j = 1; j < trigger_token_count; j++) {
+                    tokens.push_back(class_token);
+                    weights.push_back(class_weight);
+                }
+            }
+            continue;
+        }
+        tokens.push_back(token);
+        weights.push_back(source_weights[i]);
+    }
+
+    clip_conditioner.tokenizer.pad_tokens(tokens,
+                                          &weights,
+                                          nullptr,
+                                          clip_conditioner.text_model->model.n_token,
+                                          clip_conditioner.text_model->model.n_token,
+                                          true);
+    std::vector<bool> class_token_mask;
+    for (int i = 0; i < tokens.size(); i++) {
+        class_token_mask.push_back(class_idx + 1 <= i && i < class_idx + 1 + trigger_token_count);
+    }
+
+    return std::make_tuple(tokens, weights, class_token_mask);
+}
+
+static std::tuple<SDCondition, std::vector<bool>>
+get_photomaker_condition_with_trigger(FrozenCLIPEmbedderWithCustomWords& clip_conditioner,
+                                      int n_threads,
+                                      const ConditionerParams& conditioner_params,
+                                      const std::string& trigger_word,
+                                      int trigger_token_count) {
+    auto image_tokens = clip_conditioner.convert_token_to_id(trigger_word);
+    GGML_ASSERT(image_tokens.size() == 1);
+    auto tokens_and_weights         = tokenize_photomaker_trigger(clip_conditioner,
+                                                                  conditioner_params.text,
+                                                                  trigger_token_count,
+                                                                  image_tokens[0]);
+    std::vector<int>& tokens        = std::get<0>(tokens_and_weights);
+    std::vector<float>& weights     = std::get<1>(tokens_and_weights);
+    std::vector<bool>& trigger_mask = std::get<2>(tokens_and_weights);
+    auto cond                       = clip_conditioner.get_learned_condition_common(n_threads,
+                                                                                    tokens,
+                                                                                    weights,
+                                                                                    conditioner_params.clip_skip,
+                                                                                    conditioner_params.width,
+                                                                                    conditioner_params.height,
+                                                                                    conditioner_params.zero_out_masked);
+    return std::make_tuple(std::move(cond), trigger_mask);
+}
+
+static std::string remove_photomaker_trigger_from_prompt(FrozenCLIPEmbedderWithCustomWords& clip_conditioner,
+                                                         const std::string& prompt,
+                                                         const std::string& trigger_word) {
+    auto image_tokens = clip_conditioner.convert_token_to_id(trigger_word);
+    GGML_ASSERT(image_tokens.size() == 1);
+    auto tokens_and_weights  = clip_conditioner.tokenize(prompt);
+    std::vector<int>& tokens = tokens_and_weights.first;
+    auto it                  = std::find(tokens.begin(), tokens.end(), image_tokens[0]);
+    GGML_ASSERT(it != tokens.end());
+    tokens.erase(it);
+    return clip_conditioner.decode(tokens);
+}
+
+struct PhotoMakerExtension : public GenerationExtension {
+    std::shared_ptr<PhotoMakerIDEncoder> pmid_model;
+    std::shared_ptr<LoraModel> pmid_lora;
+    bool enabled = false;
+    std::string model_path;
+    std::string trigger_word = "img";
+    SDCondition id_condition;
+    int start_merge_step = -1;
+
+    const char* name() const override {
+        return "photomaker";
+    }
+
+    bool is_enabled() const override {
+        return enabled;
+    }
+
+    bool init(const GenerationExtensionInitContext& ctx) override {
+        model_path = SAFE_STR(ctx.params->photo_maker_path);
+        if (model_path.empty()) {
+            return true;
+        }
+
+        if (!ctx.ensure_backend_pair(SDBackendModule::PHOTOMAKER)) {
+            return false;
+        }
+
+        PMVersion pm_version = std::strstr(model_path.c_str(), "v2") != nullptr ? PM_VERSION_2 : PM_VERSION_1;
+        pmid_model           = std::make_shared<PhotoMakerIDEncoder>(ctx.backend_for(SDBackendModule::PHOTOMAKER),
+                                                           ctx.params_backend_for(SDBackendModule::PHOTOMAKER),
+                                                           ctx.tensor_storage_map,
+                                                           "pmid",
+                                                           ctx.version,
+                                                           pm_version);
+        if (pm_version == PM_VERSION_2) {
+            LOG_INFO("using PhotoMaker Version 2");
+        }
+
+        pmid_lora               = std::make_shared<LoraModel>("pmid",
+                                                ctx.backend_for(SDBackendModule::PHOTOMAKER),
+                                                ctx.params_backend_for(SDBackendModule::PHOTOMAKER),
+                                                model_path,
+                                                "",
+                                                ctx.version);
+        auto lora_tensor_filter = [&](const std::string& tensor_name) {
+            return starts_with(tensor_name, "lora.model");
+        };
+        if (!pmid_lora->load_from_file(ctx.n_threads, lora_tensor_filter)) {
+            LOG_WARN("load photomaker lora tensors from %s failed", model_path.c_str());
+            return false;
+        }
+
+        LOG_INFO("loading stacked ID embedding (PHOTOMAKER) model file from '%s'", model_path.c_str());
+        if (!ctx.model_loader.init_from_file_and_convert_name(model_path, "pmid.")) {
+            LOG_WARN("loading stacked ID embedding from '%s' failed", model_path.c_str());
+            return true;
+        }
+
+        enabled = true;
+        return true;
+    }
+
+    void collect_param_tensors(GenerationExtensionTensorContext& ctx) override {
+        if (!enabled || pmid_model == nullptr) {
+            return;
+        }
+
+        std::map<std::string, ggml_tensor*> temp;
+        pmid_model->get_param_tensors(temp, "pmid");
+        bool do_mmap = ctx.module_can_mmap(SDBackendModule::PHOTOMAKER);
+        for (const auto& [key, tensor] : temp) {
+            ctx.tensors[key] = tensor;
+            if (do_mmap) {
+                ctx.mmap_able_tensors[key] = tensor;
+            }
+        }
+    }
+
+    void add_ignore_tensors(std::set<std::string>& ignore_tensors) const override {
+        if (!enabled) {
+            return;
+        }
+        ignore_tensors.insert("pmid.unet.");
+    }
+
+    bool alloc_params_buffer() override {
+        if (!enabled || pmid_model == nullptr) {
+            return true;
+        }
+        return pmid_model->alloc_params_buffer();
+    }
+
+    size_t get_params_buffer_size() const override {
+        if (!enabled || pmid_model == nullptr) {
+            return 0;
+        }
+        return pmid_model->get_params_buffer_size();
+    }
+
+    void reset_runtime_condition() override {
+        id_condition     = {};
+        start_merge_step = -1;
+    }
+
+    bool prepare_condition(GenerationExtensionConditionContext& ctx) override {
+        reset_runtime_condition();
+        if (!enabled || pmid_model == nullptr || pmid_lora == nullptr) {
+            return false;
+        }
+
+        if (!pmid_lora->applied) {
+            int64_t t0 = ggml_time_ms();
+            pmid_lora->apply(ctx.tensors, ctx.version, ctx.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 (ctx.free_params_immediately) {
+                pmid_lora->free_params_buffer();
+            }
+        }
+
+        bool pmv2 = pmid_model->get_version() == PM_VERSION_2;
+        if (ctx.pm_params.id_images_count <= 0 || ctx.pm_params.id_images == nullptr) {
+            LOG_WARN("Provided PhotoMaker model file, but NO input ID images");
+            LOG_WARN("Turn off PhotoMaker for this request");
+            return false;
+        }
+        auto* clip_conditioner = dynamic_cast<FrozenCLIPEmbedderWithCustomWords*>(ctx.conditioner);
+        if (clip_conditioner == nullptr) {
+            LOG_WARN("PhotoMaker requires FrozenCLIPEmbedderWithCustomWords conditioner");
+            LOG_WARN("Turn off PhotoMaker for this request");
+            return false;
+        }
+
+        int clip_image_size        = 224;
+        pmid_model->style_strength = ctx.pm_params.style_strength;
+        sd::Tensor<float> id_image_tensor;
+        for (int i = 0; i < ctx.pm_params.id_images_count; i++) {
+            auto id_image           = sd_image_to_tensor(ctx.pm_params.id_images[i]);
+            auto processed_id_image = clip_preprocess(id_image, clip_image_size, clip_image_size);
+            if (id_image_tensor.empty()) {
+                id_image_tensor = processed_id_image;
+            } else {
+                id_image_tensor = sd::ops::concat(id_image_tensor, processed_id_image, 3);
+            }
+        }
+
+        int64_t t0                        = ggml_time_ms();
+        int trigger_token_count           = pmv2 ? 2 * ctx.pm_params.id_images_count : ctx.pm_params.id_images_count;
+        auto cond_tup                     = get_photomaker_condition_with_trigger(*clip_conditioner,
+                                                                                  ctx.n_threads,
+                                                                                  ctx.condition_params,
+                                                                                  trigger_word,
+                                                                                  trigger_token_count);
+        SDCondition prepared_id_condition = std::get<0>(cond_tup);
+        auto class_tokens_mask            = std::get<1>(cond_tup);
+        if (std::find(class_tokens_mask.begin(), class_tokens_mask.end(), true) == class_tokens_mask.end()) {
+            LOG_WARN("PhotoMaker trigger word '%s' was not found in prompt", trigger_word.c_str());
+            LOG_WARN("Turn off PhotoMaker for this request");
+            return false;
+        }
+
+        sd::Tensor<float> id_embeds;
+        if (pmv2 && ctx.pm_params.id_embed_path != nullptr) {
+            try {
+                id_embeds = sd::load_tensor_from_file_as_tensor<float>(ctx.pm_params.id_embed_path);
+            } catch (const std::exception&) {
+                id_embeds = {};
+            }
+        }
+        if (pmv2 && id_embeds.empty()) {
+            LOG_WARN("Provided PhotoMaker images, but NO valid ID embeds file for PM v2");
+            LOG_WARN("Turn off PhotoMaker for this request");
+            return false;
+        }
+        if (pmv2 && ctx.pm_params.id_images_count != id_embeds.shape()[1]) {
+            LOG_WARN("PhotoMaker image count (%d) does NOT match ID embeds (%d). You should run face_detect.py again.",
+                     ctx.pm_params.id_images_count,
+                     static_cast<int>(id_embeds.shape()[1]));
+            LOG_WARN("Turn off PhotoMaker for this request");
+            return false;
+        }
+
+        auto res = pmid_model->compute(ctx.n_threads,
+                                       id_image_tensor,
+                                       prepared_id_condition.c_crossattn,
+                                       id_embeds,
+                                       class_tokens_mask);
+        if (res.empty()) {
+            LOG_ERROR("Photomaker ID Stacking failed");
+            LOG_WARN("Turn off PhotoMaker for this request");
+            return false;
+        }
+
+        prepared_id_condition.c_crossattn = std::move(res);
+        int64_t t1                        = ggml_time_ms();
+        id_condition                      = std::move(prepared_id_condition);
+        start_merge_step                  = int(ctx.pm_params.style_strength / 100.f * ctx.total_steps);
+        ctx.condition_params.text         = remove_photomaker_trigger_from_prompt(*clip_conditioner,
+                                                                                  ctx.condition_params.text,
+                                                                                  trigger_word);
+        LOG_INFO("Photomaker ID Stacking, taking %" PRId64 " ms", t1 - t0);
+        LOG_INFO("PHOTOMAKER: start_merge_step: %d", start_merge_step);
+
+        if (ctx.free_params_immediately) {
+            pmid_model->free_params_buffer();
+        }
+        return true;
+    }
+
+    const SDCondition& before_condition(int step,
+                                        const SDCondition& condition) const override {
+        if (!id_condition.empty() && start_merge_step != -1 && step > start_merge_step) {
+            return id_condition;
+        }
+        return condition;
+    }
+};
+
+std::shared_ptr<GenerationExtension> create_photomaker_extension() {
+    return std::make_shared<PhotoMakerExtension>();
+}

src/gits_noise.inl

@@ -1,349 +0,0 @@
-#ifndef GITS_NOISE_INL
-#define GITS_NOISE_INL
-
-const std::vector<std::vector<float>> GITS_NOISE_0_80 = {
-    { 14.61464119f, 7.49001646f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 6.77309084f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 3.07277966f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 2.05039096f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 2.05039096f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.19567990f, 1.98035145f, 0.86115354f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.19567990f, 1.98035145f, 0.86115354f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.88507891f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.07277966f, 1.84880662f, 0.83188516f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_0_85 = {
-    { 14.61464119f, 7.49001646f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 1.84880662f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 6.77309084f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.11996698f, 3.07277966f, 1.24153244f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.09240818f, 2.84484982f, 0.95350921f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.09240818f, 2.84484982f, 0.95350921f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.58536053f, 3.19567990f, 1.84880662f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 8.75849152f, 7.49001646f, 5.58536053f, 3.19567990f, 1.84880662f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 8.75849152f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 8.75849152f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.60512662f, 2.63833880f, 1.56271636f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.88507891f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_0_90 = {
-    { 14.61464119f, 6.77309084f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 3.07277966f, 0.95350921f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.54230714f, 0.89115214f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 2.54230714f, 0.89115214f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.09240818f, 3.07277966f, 1.61558151f, 0.69515091f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.11996698f, 4.86714602f, 3.07277966f, 1.61558151f, 0.69515091f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 2.95596409f, 1.61558151f, 0.69515091f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.24153244f, 0.57119018f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.24153244f, 0.57119018f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.24153244f, 0.57119018f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.75677586f, 2.84484982f, 1.84880662f, 1.08895338f, 0.52423614f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.75677586f, 2.84484982f, 1.84880662f, 1.08895338f, 0.52423614f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.45427561f, 3.32507086f, 2.45070267f, 1.61558151f, 0.95350921f, 0.45573691f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.45427561f, 3.32507086f, 2.45070267f, 1.61558151f, 0.95350921f, 0.45573691f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.86714602f, 3.91689563f, 3.07277966f, 2.27973175f, 1.56271636f, 0.95350921f, 0.45573691f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.86714602f, 3.91689563f, 3.07277966f, 2.27973175f, 1.56271636f, 0.95350921f, 0.45573691f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.86714602f, 3.91689563f, 3.07277966f, 2.27973175f, 1.56271636f, 0.95350921f, 0.45573691f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.60512662f, 2.95596409f, 2.19988537f, 1.51179266f, 0.89115214f, 0.43325692f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_0_95 = {
-    { 14.61464119f, 6.77309084f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 2.84484982f, 0.89115214f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.36326075f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.95596409f, 1.56271636f, 0.64427125f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 2.95596409f, 1.56271636f, 0.64427125f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 3.07277966f, 1.91321158f, 1.08895338f, 0.50118381f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.07277966f, 1.91321158f, 1.08895338f, 0.50118381f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.07277966f, 1.91321158f, 1.08895338f, 0.50118381f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.41535246f, 0.803307f, 0.38853383f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.46139455f, 2.63833880f, 1.84880662f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.46139455f, 2.63833880f, 1.84880662f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 6.14220476f, 4.86714602f, 3.75677586f, 2.95596409f, 2.19988537f, 1.56271636f, 1.05362725f, 0.64427125f, 0.32104823f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 6.44769001f, 5.58536053f, 4.65472794f, 3.60512662f, 2.95596409f, 2.19988537f, 1.56271636f, 1.05362725f, 0.64427125f, 0.32104823f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.65472794f, 3.60512662f, 2.95596409f, 2.19988537f, 1.56271636f, 1.05362725f, 0.64427125f, 0.32104823f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.65472794f, 3.75677586f, 3.07277966f, 2.45070267f, 1.78698075f, 1.24153244f, 0.83188516f, 0.50118381f, 0.22545385f, 0.02916753f },
-    { 14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.60512662f, 2.95596409f, 2.36326075f, 1.72759056f, 1.24153244f, 0.83188516f, 0.50118381f, 0.22545385f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.60512662f, 2.95596409f, 2.36326075f, 1.72759056f, 1.24153244f, 0.83188516f, 0.50118381f, 0.22545385f, 0.02916753f },
-    { 14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.75677586f, 3.07277966f, 2.45070267f, 1.91321158f, 1.46270394f, 1.05362725f, 0.72133851f, 0.43325692f, 0.19894916f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_00 = {
-    { 14.61464119f, 1.56271636f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 0.95350921f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 2.36326075f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 7.11996698f, 3.07277966f, 1.56271636f, 0.59516323f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.41535246f, 0.57119018f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.86115354f, 0.38853383f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.86115354f, 0.38853383f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 3.07277966f, 1.98035145f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.07277966f, 1.98035145f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.27973175f, 1.51179266f, 0.95350921f, 0.54755926f, 0.25053367f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.36326075f, 1.61558151f, 1.08895338f, 0.72133851f, 0.41087446f, 0.17026083f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.36326075f, 1.61558151f, 1.08895338f, 0.72133851f, 0.41087446f, 0.17026083f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.12350607f, 1.56271636f, 1.08895338f, 0.72133851f, 0.41087446f, 0.17026083f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.19988537f, 1.61558151f, 1.162866f, 0.803307f, 0.50118381f, 0.27464288f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.75677586f, 3.07277966f, 2.45070267f, 1.84880662f, 1.36964464f, 1.01931262f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 6.14220476f, 5.09240818f, 4.26497746f, 3.46139455f, 2.84484982f, 2.19988537f, 1.67050016f, 1.24153244f, 0.92192322f, 0.64427125f, 0.43325692f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 6.14220476f, 5.09240818f, 4.26497746f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.12534678f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 5.09240818f, 4.26497746f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.12534678f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 12.23089790f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.26497746f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.12534678f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_05 = {
-    { 14.61464119f, 0.95350921f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 0.89115214f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 2.05039096f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 2.84484982f, 1.28281462f, 0.52423614f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 3.07277966f, 1.61558151f, 0.803307f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.56271636f, 0.803307f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.95350921f, 0.52423614f, 0.22545385f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 1.98035145f, 1.24153244f, 0.74807048f, 0.41087446f, 0.17026083f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.27973175f, 1.51179266f, 0.95350921f, 0.59516323f, 0.34370604f, 0.13792117f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 5.09240818f, 3.46139455f, 2.45070267f, 1.61558151f, 1.08895338f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.09240818f, 3.46139455f, 2.45070267f, 1.61558151f, 1.08895338f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.36326075f, 1.61558151f, 1.08895338f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.45070267f, 1.72759056f, 1.24153244f, 0.86115354f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.19988537f, 1.61558151f, 1.162866f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.19988537f, 1.67050016f, 1.28281462f, 0.95350921f, 0.72133851f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.36326075f, 1.84880662f, 1.41535246f, 1.08895338f, 0.83188516f, 0.61951244f, 0.45573691f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.20157266f, 0.95350921f, 0.74807048f, 0.57119018f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.30717278f, 7.11996698f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.20157266f, 0.95350921f, 0.74807048f, 0.57119018f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 8.30717278f, 7.11996698f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.45070267f, 1.98035145f, 1.61558151f, 1.32549286f, 1.08895338f, 0.86115354f, 0.69515091f, 0.54755926f, 0.41087446f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_10 = {
-    { 14.61464119f, 0.89115214f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 1.61558151f, 0.57119018f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 2.45070267f, 1.08895338f, 0.45573691f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 2.95596409f, 1.56271636f, 0.803307f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 3.07277966f, 1.61558151f, 0.89115214f, 0.4783645f, 0.19894916f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 3.07277966f, 1.84880662f, 1.08895338f, 0.64427125f, 0.34370604f, 0.13792117f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.95350921f, 0.54755926f, 0.27464288f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.95596409f, 1.91321158f, 1.24153244f, 0.803307f, 0.4783645f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.05039096f, 1.41535246f, 0.95350921f, 0.64427125f, 0.41087446f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.27973175f, 1.61558151f, 1.12534678f, 0.803307f, 0.54755926f, 0.36617002f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.32507086f, 2.45070267f, 1.72759056f, 1.24153244f, 0.89115214f, 0.64427125f, 0.45573691f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 5.09240818f, 3.60512662f, 2.84484982f, 2.05039096f, 1.51179266f, 1.08895338f, 0.803307f, 0.59516323f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 5.09240818f, 3.60512662f, 2.84484982f, 2.12350607f, 1.61558151f, 1.24153244f, 0.95350921f, 0.72133851f, 0.54755926f, 0.41087446f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.45070267f, 1.84880662f, 1.41535246f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.45070267f, 1.91321158f, 1.51179266f, 1.20157266f, 0.95350921f, 0.74807048f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 5.85520077f, 4.45427561f, 3.46139455f, 2.84484982f, 2.19988537f, 1.72759056f, 1.36964464f, 1.08895338f, 0.86115354f, 0.69515091f, 0.54755926f, 0.43325692f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.46139455f, 2.84484982f, 2.19988537f, 1.72759056f, 1.36964464f, 1.08895338f, 0.86115354f, 0.69515091f, 0.54755926f, 0.43325692f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.46139455f, 2.84484982f, 2.19988537f, 1.72759056f, 1.36964464f, 1.08895338f, 0.89115214f, 0.72133851f, 0.59516323f, 0.4783645f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_15 = {
-    { 14.61464119f, 0.83188516f, 0.02916753f },
-    { 14.61464119f, 1.84880662f, 0.59516323f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 1.56271636f, 0.52423614f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 1.91321158f, 0.83188516f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.45070267f, 1.24153244f, 0.59516323f, 0.25053367f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.51179266f, 0.803307f, 0.41087446f, 0.17026083f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.56271636f, 0.89115214f, 0.50118381f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 3.07277966f, 1.84880662f, 1.12534678f, 0.72133851f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 3.07277966f, 1.91321158f, 1.24153244f, 0.803307f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 2.95596409f, 1.91321158f, 1.24153244f, 0.803307f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.05039096f, 1.36964464f, 0.95350921f, 0.69515091f, 0.4783645f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.803307f, 0.59516323f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.803307f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.19988537f, 1.61558151f, 1.24153244f, 0.95350921f, 0.74807048f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.78698075f, 1.32549286f, 1.01931262f, 0.803307f, 0.64427125f, 0.50118381f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.78698075f, 1.32549286f, 1.01931262f, 0.803307f, 0.64427125f, 0.52423614f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.84880662f, 1.41535246f, 1.12534678f, 0.89115214f, 0.72133851f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.84880662f, 1.41535246f, 1.12534678f, 0.89115214f, 0.72133851f, 0.59516323f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_20 = {
-    { 14.61464119f, 0.803307f, 0.02916753f },
-    { 14.61464119f, 1.56271636f, 0.52423614f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 0.92192322f, 0.36617002f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.24153244f, 0.59516323f, 0.25053367f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.05039096f, 0.95350921f, 0.45573691f, 0.17026083f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.45070267f, 1.24153244f, 0.64427125f, 0.29807833f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.45070267f, 1.36964464f, 0.803307f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 0.95350921f, 0.59516323f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.67050016f, 1.08895338f, 0.74807048f, 0.50118381f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.95596409f, 1.84880662f, 1.24153244f, 0.83188516f, 0.59516323f, 0.41087446f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 3.07277966f, 1.98035145f, 1.36964464f, 0.95350921f, 0.69515091f, 0.50118381f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 3.46139455f, 2.36326075f, 1.56271636f, 1.08895338f, 0.803307f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 6.77309084f, 3.46139455f, 2.45070267f, 1.61558151f, 1.162866f, 0.86115354f, 0.64427125f, 0.50118381f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.41087446f, 0.34370604f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.19988537f, 1.61558151f, 1.20157266f, 0.92192322f, 0.72133851f, 0.57119018f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.19988537f, 1.61558151f, 1.24153244f, 0.95350921f, 0.74807048f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.19988537f, 1.61558151f, 1.24153244f, 0.95350921f, 0.74807048f, 0.59516323f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_25 = {
-    { 14.61464119f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 1.56271636f, 0.50118381f, 0.02916753f },
-    { 14.61464119f, 2.05039096f, 0.803307f, 0.32104823f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 0.95350921f, 0.43325692f, 0.17026083f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.24153244f, 0.59516323f, 0.27464288f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.51179266f, 0.803307f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.36326075f, 1.24153244f, 0.72133851f, 0.41087446f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.45070267f, 1.36964464f, 0.83188516f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 0.98595673f, 0.64427125f, 0.43325692f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.67050016f, 1.08895338f, 0.74807048f, 0.52423614f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.72759056f, 1.162866f, 0.803307f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.95596409f, 1.84880662f, 1.24153244f, 0.86115354f, 0.64427125f, 0.4783645f, 0.36617002f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.95596409f, 1.84880662f, 1.28281462f, 0.92192322f, 0.69515091f, 0.52423614f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.95596409f, 1.91321158f, 1.32549286f, 0.95350921f, 0.72133851f, 0.54755926f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.95596409f, 1.91321158f, 1.32549286f, 0.95350921f, 0.72133851f, 0.57119018f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.95596409f, 1.91321158f, 1.32549286f, 0.95350921f, 0.74807048f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 3.07277966f, 2.05039096f, 1.41535246f, 1.05362725f, 0.803307f, 0.61951244f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 3.07277966f, 2.05039096f, 1.41535246f, 1.05362725f, 0.803307f, 0.64427125f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 3.07277966f, 2.05039096f, 1.46270394f, 1.08895338f, 0.83188516f, 0.66947293f, 0.54755926f, 0.45573691f, 0.38853383f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_30 = {
-    { 14.61464119f, 0.72133851f, 0.02916753f },
-    { 14.61464119f, 1.24153244f, 0.43325692f, 0.02916753f },
-    { 14.61464119f, 1.56271636f, 0.59516323f, 0.22545385f, 0.02916753f },
-    { 14.61464119f, 1.84880662f, 0.803307f, 0.36617002f, 0.13792117f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 1.01931262f, 0.52423614f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.36964464f, 0.74807048f, 0.41087446f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.56271636f, 0.89115214f, 0.54755926f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.61558151f, 0.95350921f, 0.61951244f, 0.41087446f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.45070267f, 1.36964464f, 0.83188516f, 0.54755926f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.45070267f, 1.41535246f, 0.92192322f, 0.64427125f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.6383388f, 1.56271636f, 1.01931262f, 0.72133851f, 0.50118381f, 0.36617002f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.05362725f, 0.74807048f, 0.54755926f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.08895338f, 0.77538133f, 0.57119018f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.59516323f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.72759056f, 1.162866f, 0.83188516f, 0.64427125f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.72759056f, 1.162866f, 0.83188516f, 0.64427125f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.78698075f, 1.24153244f, 0.92192322f, 0.72133851f, 0.57119018f, 0.45573691f, 0.38853383f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.84484982f, 1.78698075f, 1.24153244f, 0.92192322f, 0.72133851f, 0.57119018f, 0.4783645f, 0.41087446f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_35 = {
-    { 14.61464119f, 0.69515091f, 0.02916753f },
-    { 14.61464119f, 0.95350921f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 1.56271636f, 0.57119018f, 0.19894916f, 0.02916753f },
-    { 14.61464119f, 1.61558151f, 0.69515091f, 0.29807833f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.84880662f, 0.83188516f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.162866f, 0.64427125f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.36964464f, 0.803307f, 0.50118381f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.41535246f, 0.83188516f, 0.54755926f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.56271636f, 0.95350921f, 0.64427125f, 0.45573691f, 0.32104823f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.56271636f, 0.95350921f, 0.64427125f, 0.45573691f, 0.34370604f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.61558151f, 1.01931262f, 0.72133851f, 0.52423614f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.61558151f, 1.01931262f, 0.72133851f, 0.52423614f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.61558151f, 1.05362725f, 0.74807048f, 0.54755926f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.72759056f, 1.12534678f, 0.803307f, 0.59516323f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 3.07277966f, 1.72759056f, 1.12534678f, 0.803307f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.45070267f, 1.51179266f, 1.01931262f, 0.74807048f, 0.57119018f, 0.45573691f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.6383388f, 1.61558151f, 1.08895338f, 0.803307f, 0.61951244f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.6383388f, 1.61558151f, 1.08895338f, 0.803307f, 0.64427125f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 5.85520077f, 2.6383388f, 1.61558151f, 1.08895338f, 0.803307f, 0.64427125f, 0.52423614f, 0.45573691f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_40 = {
-    { 14.61464119f, 0.59516323f, 0.02916753f },
-    { 14.61464119f, 0.95350921f, 0.34370604f, 0.02916753f },
-    { 14.61464119f, 1.08895338f, 0.43325692f, 0.13792117f, 0.02916753f },
-    { 14.61464119f, 1.56271636f, 0.64427125f, 0.27464288f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.61558151f, 0.803307f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.05039096f, 0.95350921f, 0.54755926f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.24153244f, 0.72133851f, 0.43325692f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.24153244f, 0.74807048f, 0.50118381f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.52423614f, 0.36617002f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.54755926f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.41535246f, 0.86115354f, 0.59516323f, 0.43325692f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.64427125f, 0.45573691f, 0.34370604f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.64427125f, 0.4783645f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.56271636f, 0.98595673f, 0.69515091f, 0.52423614f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.56271636f, 1.01931262f, 0.72133851f, 0.54755926f, 0.43325692f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.61558151f, 1.05362725f, 0.74807048f, 0.57119018f, 0.45573691f, 0.38853383f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.61951244f, 0.50118381f, 0.41087446f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.61951244f, 0.50118381f, 0.43325692f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.64427125f, 0.52423614f, 0.45573691f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_45 = {
-    { 14.61464119f, 0.59516323f, 0.02916753f },
-    { 14.61464119f, 0.803307f, 0.25053367f, 0.02916753f },
-    { 14.61464119f, 0.95350921f, 0.34370604f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.24153244f, 0.54755926f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.56271636f, 0.72133851f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.61558151f, 0.803307f, 0.45573691f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.91321158f, 0.95350921f, 0.57119018f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.19988537f, 1.08895338f, 0.64427125f, 0.41087446f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.24153244f, 0.74807048f, 0.50118381f, 0.34370604f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.24153244f, 0.74807048f, 0.50118381f, 0.36617002f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.54755926f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.57119018f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.28281462f, 0.83188516f, 0.59516323f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.28281462f, 0.83188516f, 0.59516323f, 0.45573691f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.69515091f, 0.52423614f, 0.41087446f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.69515091f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.56271636f, 0.98595673f, 0.72133851f, 0.54755926f, 0.45573691f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.56271636f, 1.01931262f, 0.74807048f, 0.57119018f, 0.4783645f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.84484982f, 1.56271636f, 1.01931262f, 0.74807048f, 0.59516323f, 0.50118381f, 0.43325692f, 0.38853383f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<std::vector<float>> GITS_NOISE_1_50 = {
-    { 14.61464119f, 0.54755926f, 0.02916753f },
-    { 14.61464119f, 0.803307f, 0.25053367f, 0.02916753f },
-    { 14.61464119f, 0.86115354f, 0.32104823f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.24153244f, 0.54755926f, 0.25053367f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.56271636f, 0.72133851f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.61558151f, 0.803307f, 0.45573691f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.61558151f, 0.83188516f, 0.52423614f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.84880662f, 0.95350921f, 0.59516323f, 0.38853383f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.84880662f, 0.95350921f, 0.59516323f, 0.41087446f, 0.29807833f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 1.84880662f, 0.95350921f, 0.61951244f, 0.43325692f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.19988537f, 1.12534678f, 0.72133851f, 0.50118381f, 0.36617002f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.19988537f, 1.12534678f, 0.72133851f, 0.50118381f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.57119018f, 0.43325692f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.57119018f, 0.43325692f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.59516323f, 0.45573691f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.59516323f, 0.45573691f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.32549286f, 0.86115354f, 0.64427125f, 0.50118381f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.36964464f, 0.92192322f, 0.69515091f, 0.54755926f, 0.45573691f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f },
-    { 14.61464119f, 2.45070267f, 1.41535246f, 0.95350921f, 0.72133851f, 0.57119018f, 0.4783645f, 0.43325692f, 0.38853383f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f }
-};
-
-const std::vector<const std::vector<std::vector<float>>*> GITS_NOISE = {
-    &GITS_NOISE_0_80,
-    &GITS_NOISE_0_85,
-    &GITS_NOISE_0_90,
-    &GITS_NOISE_0_95,
-    &GITS_NOISE_1_00,
-    &GITS_NOISE_1_05,
-    &GITS_NOISE_1_10,
-    &GITS_NOISE_1_15,
-    &GITS_NOISE_1_20,
-    &GITS_NOISE_1_25,
-    &GITS_NOISE_1_30,
-    &GITS_NOISE_1_35,
-    &GITS_NOISE_1_40,
-    &GITS_NOISE_1_45,
-    &GITS_NOISE_1_50
-};
-
-#endif // GITS_NOISE_INL

src/model.h

@@ -1,17 +1,14 @@
 #ifndef __MODEL_H__
 #define __MODEL_H__
 
-#include <functional>
-#include <map>
-#include <memory>
-#include <set>
 #include <string>
+#include <utility>
 #include <vector>
 
+#include "core/ordered_map.hpp"
 #include "ggml-backend.h"
 #include "ggml.h"
 #include "model_io/tensor_storage.h"
-#include "ordered_map.hpp"
 
 enum SDVersion {
     VERSION_SD1,
@@ -238,73 +235,4 @@ enum PMVersion {
 typedef OrderedMap<std::string, TensorStorage> String2TensorStorage;
 using TensorTypeRules = std::vector<std::pair<std::string, ggml_type>>;
 
-TensorTypeRules parse_tensor_type_rules(const std::string& tensor_type_rules);
-
-class MmapWrapper;
-
-struct ModelFileData {
-    std::string path;
-    std::vector<TensorStorage> tensors;
-    std::shared_ptr<MmapWrapper> mmapped;
-    std::shared_ptr<struct ggml_backend_buffer> mmbuffer;
-    bool is_zip;
-};
-
-struct MmapTensorStore {
-    std::shared_ptr<MmapWrapper> mmapped;
-    std::shared_ptr<struct ggml_backend_buffer> mmbuffer;
-};
-
-class ModelLoader {
-protected:
-    SDVersion version_ = VERSION_COUNT;
-    std::vector<std::string> file_paths_;
-    std::vector<ModelFileData> file_data;
-    bool model_files_processed = false;
-    String2TensorStorage tensor_storage_map;
-
-    void add_tensor_storage(const TensorStorage& tensor_storage);
-
-    bool init_from_gguf_file(const std::string& file_path, const std::string& prefix = "");
-    bool init_from_safetensors_file(const std::string& file_path, const std::string& prefix = "");
-    bool init_from_torch_zip_file(const std::string& file_path, const std::string& prefix = "");
-    bool init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix = "");
-    bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");
-
-public:
-    bool init_from_file(const std::string& file_path, const std::string& prefix = "");
-    void convert_tensors_name();
-    bool init_from_file_and_convert_name(const std::string& file_path,
-                                         const std::string& prefix = "",
-                                         SDVersion version         = VERSION_COUNT);
-    SDVersion get_sd_version();
-    std::map<ggml_type, uint32_t> get_wtype_stat();
-    std::map<ggml_type, uint32_t> get_conditioner_wtype_stat();
-    std::map<ggml_type, uint32_t> get_diffusion_model_wtype_stat();
-    std::map<ggml_type, uint32_t> get_vae_wtype_stat();
-    String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
-    void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
-    void process_model_files(bool enable_mmap = false, bool writable_mmap = true);
-    std::vector<MmapTensorStore> mmap_tensors(std::map<std::string, ggml_tensor*>& tensors,
-                                              std::set<std::string> ignore_tensors = {},
-                                              bool writable                        = true);
-    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
-    bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
-                      std::set<std::string> ignore_tensors = {},
-                      int n_threads                        = 0,
-                      bool use_mmap                        = false);
-
-    std::vector<std::string> get_tensor_names() const {
-        std::vector<std::string> names;
-        for (const auto& [name, tensor_storage] : tensor_storage_map) {
-            names.push_back(name);
-        }
-        return names;
-    }
-
-    bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
-    int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
-    ~ModelLoader() = default;
-};
-
 #endif  // __MODEL_H__

src/model/adapter/lora.hpp

@@ -1,8 +1,9 @@
-#ifndef __LORA_HPP__
-#define __LORA_HPP__
+#ifndef __SD_MODEL_ADAPTER_LORA_HPP__
+#define __SD_MODEL_ADAPTER_LORA_HPP__
 
 #include <mutex>
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
+#include "model_loader.h"
 
 #define LORA_GRAPH_BASE_SIZE 10240
 
@@ -914,4 +915,4 @@ public:
     }
 };
 
-#endif  // __LORA_HPP__
+#endif  // __SD_MODEL_ADAPTER_LORA_HPP__

src/model/adapter/pmid.hpp

@@ -1,10 +1,12 @@
-#ifndef __PMI_HPP__
-#define __PMI_HPP__
+#ifndef __SD_MODEL_ADAPTER_PMID_HPP__
+#define __SD_MODEL_ADAPTER_PMID_HPP__
 
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
 
-#include "clip.hpp"
-#include "lora.hpp"
+#include "model/adapter/lora.hpp"
+#include "model/common/block.hpp"
+#include "model/te/clip.hpp"
+#include "model_loader.h"
 
 struct FuseBlock : public GGMLBlock {
     // network hparams
@@ -636,4 +638,4 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
     }
 };
 
-#endif  // __PMI_HPP__
+#endif  // __SD_MODEL_ADAPTER_PMID_HPP__

src/model/common/block.hpp

@@ -1,9 +1,9 @@
-#ifndef __COMMON_BLOCK_HPP__
-#define __COMMON_BLOCK_HPP__
+#ifndef __SD_MODEL_COMMON_BLOCK_HPP__
+#define __SD_MODEL_COMMON_BLOCK_HPP__
 
+#include "core/ggml_extend.hpp"
+#include "core/util.h"
 #include "ggml-backend.h"
-#include "ggml_extend.hpp"
-#include "util.h"
 
 class DownSampleBlock : public GGMLBlock {
 protected:
@@ -227,6 +227,37 @@ public:
     }
 };
 
+struct Mlp : public GGMLBlock {
+public:
+    Mlp(int64_t in_features,
+        int64_t hidden_features = -1,
+        int64_t out_features    = -1,
+        bool bias               = true) {
+        // act_layer is always lambda: nn.GELU(approximate="tanh")
+        // norm_layer is always None
+        // use_conv is always False
+        if (hidden_features == -1) {
+            hidden_features = in_features;
+        }
+        if (out_features == -1) {
+            out_features = in_features;
+        }
+        blocks["fc1"] = std::shared_ptr<GGMLBlock>(new Linear(in_features, hidden_features, bias));
+        blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_features, out_features, bias));
+    }
+
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+        // x: [N, n_token, in_features]
+        auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
+        auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
+
+        x = fc1->forward(ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
+        x = fc2->forward(ctx, x);
+        return x;
+    }
+};
+
 class FeedForward : public GGMLBlock {
 public:
     enum class Activation {
@@ -603,4 +634,4 @@ public:
     }
 };
 
-#endif  // __COMMON_BLOCK_HPP__
+#endif  // __SD_MODEL_COMMON_BLOCK_HPP__

src/model/common/rope.hpp

@@ -1,10 +1,10 @@
-#ifndef __ROPE_HPP__
-#define __ROPE_HPP__
+#ifndef __SD_MODEL_COMMON_ROPE_HPP__
+#define __SD_MODEL_COMMON_ROPE_HPP__
 
 #include <algorithm>
 #include <cmath>
 #include <vector>
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
 
 namespace Rope {
     enum class EmbedNDLayout {
@@ -902,4 +902,4 @@ namespace Rope {
     }
 };  // namespace Rope
 
-#endif  // __ROPE_HPP__
+#endif  // __SD_MODEL_COMMON_ROPE_HPP__

src/model/diffusion/anima.hpp

@@ -1,5 +1,5 @@
-#ifndef __ANIMA_HPP__
-#define __ANIMA_HPP__
+#ifndef __SD_MODEL_DIFFUSION_ANIMA_HPP__
+#define __SD_MODEL_DIFFUSION_ANIMA_HPP__
 
 #include <algorithm>
 #include <cmath>
@@ -7,10 +7,10 @@
 #include <utility>
 #include <vector>
 
-#include "common_block.hpp"
-#include "diffusion_model.hpp"
-#include "flux.hpp"
-#include "rope.hpp"
+#include "model/common/block.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/model.hpp"
 
 namespace Anima {
     constexpr int ANIMA_GRAPH_SIZE = 65536;
@@ -715,4 +715,4 @@ namespace Anima {
     };
 }  // namespace Anima
 
-#endif  // __ANIMA_HPP__
+#endif  // __SD_MODEL_DIFFUSION_ANIMA_HPP__

src/model/diffusion/control.hpp

@@ -1,8 +1,8 @@
-#ifndef __CONTROL_HPP__
-#define __CONTROL_HPP__
+#ifndef __SD_MODEL_DIFFUSION_CONTROL_HPP__
+#define __SD_MODEL_DIFFUSION_CONTROL_HPP__
 
-#include "common_block.hpp"
-#include "model.h"
+#include "model/common/block.hpp"
+#include "model_loader.h"
 
 #define CONTROL_NET_GRAPH_SIZE 1536
 
@@ -484,4 +484,4 @@ struct ControlNet : public GGMLRunner {
     }
 };
 
-#endif  // __CONTROL_HPP__
+#endif  // __SD_MODEL_DIFFUSION_CONTROL_HPP__

src/model/diffusion/dit.hpp

@@ -1,7 +1,7 @@
-#ifndef __COMMON_DIT_HPP__
-#define __COMMON_DIT_HPP__
+#ifndef __SD_MODEL_DIFFUSION_DIT_HPP__
+#define __SD_MODEL_DIFFUSION_DIT_HPP__
 
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
 
 namespace DiT {
     inline ggml_tensor* patchify(ggml_context* ctx,
@@ -163,4 +163,4 @@ namespace DiT {
     }
 }  // namespace DiT
 
-#endif  // __COMMON_DIT_HPP__
+#endif  // __SD_MODEL_DIFFUSION_DIT_HPP__

src/model/diffusion/ernie_image.hpp

@@ -1,14 +1,14 @@
-#ifndef __SD_ERNIE_IMAGE_HPP__
-#define __SD_ERNIE_IMAGE_HPP__
+#ifndef __SD_MODEL_DIFFUSION_ERNIE_IMAGE_HPP__
+#define __SD_MODEL_DIFFUSION_ERNIE_IMAGE_HPP__
 
 #include <memory>
 #include <vector>
 
-#include "common_dit.hpp"
-#include "diffusion_model.hpp"
-#include "flux.hpp"
-#include "qwen_image.hpp"
-#include "rope.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/dit.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/model.hpp"
+#include "model/diffusion/qwen_image.hpp"
 
 namespace ErnieImage {
     constexpr int ERNIE_IMAGE_GRAPH_SIZE = 40960;
@@ -455,4 +455,4 @@ namespace ErnieImage {
     };
 }  // namespace ErnieImage
 
-#endif  // __SD_ERNIE_IMAGE_HPP__
+#endif  // __SD_MODEL_DIFFUSION_ERNIE_IMAGE_HPP__

src/model/diffusion/flux.hpp

@@ -1,13 +1,13 @@
-#ifndef __FLUX_HPP__
-#define __FLUX_HPP__
+#ifndef __SD_MODEL_DIFFUSION_FLUX_HPP__
+#define __SD_MODEL_DIFFUSION_FLUX_HPP__
 
 #include <memory>
 #include <vector>
 
-#include "common_dit.hpp"
-#include "diffusion_model.hpp"
-#include "model.h"
-#include "rope.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/dit.hpp"
+#include "model/diffusion/model.hpp"
+#include "model_loader.h"
 
 #define FLUX_GRAPH_SIZE 10240
 
@@ -1627,4 +1627,4 @@ namespace Flux {
 
 }  // namespace Flux
 
-#endif  // __FLUX_HPP__
+#endif  // __SD_MODEL_DIFFUSION_FLUX_HPP__

src/model/diffusion/hidream_o1.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_HIDREAM_O1_H__
-#define __SD_HIDREAM_O1_H__
+#ifndef __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
+#define __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
 
 #include <algorithm>
 #include <array>
@@ -10,11 +10,11 @@
 #include <utility>
 #include <vector>
 
-#include "common_dit.hpp"
-#include "conditioner.hpp"
-#include "diffusion_model.hpp"
-#include "llm.hpp"
-#include "util.h"
+#include "conditioning/conditioner.hpp"
+#include "core/util.h"
+#include "model/diffusion/dit.hpp"
+#include "model/diffusion/model.hpp"
+#include "model/te/llm.hpp"
 
 namespace HiDreamO1 {
     constexpr int HIDREAM_O1_GRAPH_SIZE = 32768;
@@ -678,4 +678,4 @@ namespace HiDreamO1 {
     };
 }  // namespace HiDreamO1
 
-#endif  // __SD_HIDREAM_O1_H__
+#endif  // __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__

src/model/diffusion/ideogram4.hpp

@@ -1,5 +1,5 @@
-#ifndef __IDEOGRAM4_HPP__
-#define __IDEOGRAM4_HPP__
+#ifndef __SD_MODEL_DIFFUSION_IDEOGRAM4_HPP__
+#define __SD_MODEL_DIFFUSION_IDEOGRAM4_HPP__
 
 #include <algorithm>
 #include <cmath>
@@ -8,10 +8,10 @@
 #include <string>
 #include <vector>
 
-#include "diffusion_model.hpp"
-#include "ggml_extend.hpp"
-#include "ggml_graph_cut.h"
-#include "rope.hpp"
+#include "core/ggml_extend.hpp"
+#include "core/ggml_graph_cut.h"
+#include "model/common/rope.hpp"
+#include "model/diffusion/model.hpp"
 
 namespace Ideogram4 {
     constexpr int IDEOGRAM4_GRAPH_SIZE    = 65536;
@@ -528,4 +528,4 @@ namespace Ideogram4 {
     };
 }  // namespace Ideogram4
 
-#endif  // __IDEOGRAM4_HPP__
+#endif  // __SD_MODEL_DIFFUSION_IDEOGRAM4_HPP__

src/model/diffusion/lens.hpp

@@ -1,14 +1,14 @@
-#ifndef __SD_LENS_HPP__
-#define __SD_LENS_HPP__
+#ifndef __SD_MODEL_DIFFUSION_LENS_HPP__
+#define __SD_MODEL_DIFFUSION_LENS_HPP__
 
 #include <memory>
 #include <vector>
 
-#include "common_block.hpp"
-#include "diffusion_model.hpp"
-#include "flux.hpp"
-#include "qwen_image.hpp"
-#include "rope.hpp"
+#include "model/common/block.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/model.hpp"
+#include "model/diffusion/qwen_image.hpp"
 
 namespace Lens {
     constexpr int LENS_GRAPH_SIZE = 40960;
@@ -423,4 +423,4 @@ namespace Lens {
     };
 }  // namespace Lens
 
-#endif  // __SD_LENS_HPP__
+#endif  // __SD_MODEL_DIFFUSION_LENS_HPP__

src/model/diffusion/ltxv.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_LTXV_HPP__
-#define __SD_LTXV_HPP__
+#ifndef __SD_MODEL_DIFFUSION_LTXV_HPP__
+#define __SD_MODEL_DIFFUSION_LTXV_HPP__
 
 #include <algorithm>
 #include <cmath>
@@ -9,10 +9,11 @@
 #include <utility>
 #include <vector>
 
-#include "common_block.hpp"
-#include "diffusion_model.hpp"
-#include "flux.hpp"
-#include "rope.hpp"
+#include "model/common/block.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/model.hpp"
+#include "model_loader.h"
 
 namespace LTXV {
 
@@ -2062,4 +2063,4 @@ namespace LTXV {
 
 };  // namespace LTXV
 
-#endif
+#endif  // __SD_MODEL_DIFFUSION_LTXV_HPP__

src/model/diffusion/mmdit.hpp

@@ -1,14 +1,15 @@
-#ifndef __MMDIT_HPP__
-#define __MMDIT_HPP__
+#ifndef __SD_MODEL_DIFFUSION_MMDIT_HPP__
+#define __SD_MODEL_DIFFUSION_MMDIT_HPP__
 
 #include <algorithm>
 #include <memory>
 #include <string>
 #include <vector>
 
-#include "diffusion_model.hpp"
-#include "ggml_extend.hpp"
-#include "model.h"
+#include "core/ggml_extend.hpp"
+#include "model/common/block.hpp"
+#include "model/diffusion/model.hpp"
+#include "model_loader.h"
 
 #define MMDIT_GRAPH_SIZE 10240
 
@@ -134,37 +135,6 @@ struct MMDiTConfig {
     }
 };
 
-struct Mlp : public GGMLBlock {
-public:
-    Mlp(int64_t in_features,
-        int64_t hidden_features = -1,
-        int64_t out_features    = -1,
-        bool bias               = true) {
-        // act_layer is always lambda: nn.GELU(approximate="tanh")
-        // norm_layer is always None
-        // use_conv is always False
-        if (hidden_features == -1) {
-            hidden_features = in_features;
-        }
-        if (out_features == -1) {
-            out_features = in_features;
-        }
-        blocks["fc1"] = std::shared_ptr<GGMLBlock>(new Linear(in_features, hidden_features, bias));
-        blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_features, out_features, bias));
-    }
-
-    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
-        // x: [N, n_token, in_features]
-        auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
-        auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
-
-        x = fc1->forward(ctx, x);
-        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
-        x = fc2->forward(ctx, x);
-        return x;
-    }
-};
-
 struct PatchEmbed : public GGMLBlock {
     // 2D Image to Patch Embedding
 protected:
@@ -1062,4 +1032,4 @@ struct MMDiTRunner : public DiffusionModelRunner {
     }
 };
 
-#endif
+#endif  // __SD_MODEL_DIFFUSION_MMDIT_HPP__

src/model/diffusion/model.hpp

@@ -1,12 +1,12 @@
-#ifndef __DIFFUSION_MODEL_H__
-#define __DIFFUSION_MODEL_H__
+#ifndef __SD_MODEL_DIFFUSION_MODEL_HPP__
+#define __SD_MODEL_DIFFUSION_MODEL_HPP__
 
 #include <string>
 #include <utility>
 #include <variant>
 
-#include "ggml_extend.hpp"
-#include "tensor_ggml.hpp"
+#include "core/ggml_extend.hpp"
+#include "core/tensor_ggml.hpp"
 
 struct UNetDiffusionExtra {
     int num_video_frames                           = -1;
@@ -104,4 +104,4 @@ public:
                                    const std::string& prefix) = 0;
 };
 
-#endif
+#endif  // __SD_MODEL_DIFFUSION_MODEL_HPP__

src/model/diffusion/pid.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_PID_HPP__
-#define __SD_PID_HPP__
+#ifndef __SD_MODEL_DIFFUSION_PID_HPP__
+#define __SD_MODEL_DIFFUSION_PID_HPP__
 
 #include <cmath>
 #include <cstdlib>
@@ -7,10 +7,10 @@
 #include <string>
 #include <vector>
 
-#include "common_dit.hpp"
-#include "ggml_extend.hpp"
-#include "mmdit.hpp"
-#include "rope.hpp"
+#include "core/ggml_extend.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/dit.hpp"
+#include "model/diffusion/mmdit.hpp"
 
 namespace Pid {
     constexpr int PID_GRAPH_SIZE = 196608;
@@ -844,4 +844,4 @@ namespace Pid {
     };
 }  // namespace Pid
 
-#endif  // __SD_PID_HPP__
+#endif  // __SD_MODEL_DIFFUSION_PID_HPP__

src/model/diffusion/qwen_image.hpp

@@ -1,11 +1,12 @@
-#ifndef __QWEN_IMAGE_HPP__
-#define __QWEN_IMAGE_HPP__
+#ifndef __SD_MODEL_DIFFUSION_QWEN_IMAGE_HPP__
+#define __SD_MODEL_DIFFUSION_QWEN_IMAGE_HPP__
 
 #include <memory>
 
-#include "common_block.hpp"
-#include "diffusion_model.hpp"
-#include "flux.hpp"
+#include "model/common/block.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/model.hpp"
+#include "model_loader.h"
 
 namespace Qwen {
     constexpr int QWEN_IMAGE_GRAPH_SIZE = 20480;
@@ -731,4 +732,4 @@ namespace Qwen {
 
 }  // namespace name
 
-#endif  // __QWEN_IMAGE_HPP__
+#endif  // __SD_MODEL_DIFFUSION_QWEN_IMAGE_HPP__

src/model/diffusion/unet.hpp

@@ -1,12 +1,12 @@
-#ifndef __UNET_HPP__
-#define __UNET_HPP__
+#ifndef __SD_MODEL_DIFFUSION_UNET_HPP__
+#define __SD_MODEL_DIFFUSION_UNET_HPP__
 
 #include <algorithm>
 #include <vector>
 
-#include "common_block.hpp"
-#include "diffusion_model.hpp"
 #include "model.h"
+#include "model/common/block.hpp"
+#include "model/diffusion/model.hpp"
 
 /*==================================================== UnetModel =====================================================*/
 
@@ -844,4 +844,4 @@ struct UNetModelRunner : public DiffusionModelRunner {
     }
 };
 
-#endif  // __UNET_HPP__
+#endif  // __SD_MODEL_DIFFUSION_UNET_HPP__

src/model/diffusion/z_image.hpp

@@ -1,14 +1,15 @@
-#ifndef __Z_IMAGE_HPP__
-#define __Z_IMAGE_HPP__
+#ifndef __SD_MODEL_DIFFUSION_Z_IMAGE_HPP__
+#define __SD_MODEL_DIFFUSION_Z_IMAGE_HPP__
 
 #include <algorithm>
 
-#include "diffusion_model.hpp"
-#include "flux.hpp"
-#include "ggml_extend.hpp"
-#include "mmdit.hpp"
+#include "core/ggml_extend.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/mmdit.hpp"
+#include "model/diffusion/model.hpp"
+#include "model_loader.h"
 
-// Ref: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py
+// Ref: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/model/model.py
 // Ref: https://github.com/huggingface/diffusers/pull/12703
 
 #ifndef MIN
@@ -739,4 +740,4 @@ namespace ZImage {
 
 }  // namespace ZImage
 
-#endif  // __Z_IMAGE_HPP__
+#endif  // __SD_MODEL_DIFFUSION_Z_IMAGE_HPP__

src/model/te/clip.hpp

@@ -1,13 +1,13 @@
-#ifndef __CLIP_HPP__
-#define __CLIP_HPP__
+#ifndef __SD_MODEL_TE_CLIP_HPP__
+#define __SD_MODEL_TE_CLIP_HPP__
 
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
 #include "model.h"
 #include "tokenizers/clip_tokenizer.h"
 
 /*================================================ FrozenCLIPEmbedder ================================================*/
 
-// Ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/modeling_clip.py
+// Ref: https://github.com/huggingface/transformers/blob/main/src/transformers/model/clip/modeling_clip.py
 
 struct CLIPMLP : public GGMLBlock {
 protected:
@@ -579,4 +579,4 @@ struct CLIPTextModelRunner : public GGMLRunner {
     }
 };
 
-#endif  // __CLIP_HPP__
+#endif  // __SD_MODEL_TE_CLIP_HPP__

src/model/te/llm.hpp

@@ -1,5 +1,5 @@
-#ifndef __LLM_HPP__
-#define __LLM_HPP__
+#ifndef __SD_MODEL_TE_LLM_HPP__
+#define __SD_MODEL_TE_LLM_HPP__
 
 #include <algorithm>
 #include <array>
@@ -18,9 +18,10 @@
 #include <utility>
 #include <vector>
 
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
 #include "json.hpp"
-#include "rope.hpp"
+#include "model/common/rope.hpp"
+#include "model_loader.h"
 #include "tokenizers/bpe_tokenizer.h"
 #include "tokenizers/gemma_tokenizer.h"
 #include "tokenizers/gpt_oss_tokenizer.h"
@@ -2100,4 +2101,4 @@ namespace LLM {
     };
 };  // LLM
 
-#endif  // __LLM_HPP__
+#endif  // __SD_MODEL_TE_LLM_HPP__

src/model/te/t5.hpp

@@ -1,5 +1,5 @@
-#ifndef __T5_HPP__
-#define __T5_HPP__
+#ifndef __SD_MODEL_TE_T5_HPP__
+#define __SD_MODEL_TE_T5_HPP__
 
 #include <cfloat>
 #include <limits>
@@ -10,8 +10,8 @@
 #include <string>
 #include <unordered_map>
 
-#include "ggml_extend.hpp"
-#include "model.h"
+#include "core/ggml_extend.hpp"
+#include "model_loader.h"
 #include "tokenizers/t5_unigram_tokenizer.h"
 
 struct T5Config {
@@ -610,4 +610,4 @@ struct T5Embedder {
     }
 };
 
-#endif  // __T5_HPP__
+#endif  // __SD_MODEL_TE_T5_HPP__

src/model/upscaler/esrgan.hpp

@@ -1,8 +1,8 @@
-#ifndef __ESRGAN_HPP__
-#define __ESRGAN_HPP__
+#ifndef __SD_MODEL_UPSCALER_ESRGAN_HPP__
+#define __SD_MODEL_UPSCALER_ESRGAN_HPP__
 
-#include "ggml_extend.hpp"
-#include "model.h"
+#include "core/ggml_extend.hpp"
+#include "model_loader.h"
 
 /*
     ===================================    ESRGAN  ===================================
@@ -372,4 +372,4 @@ struct ESRGAN : public GGMLRunner {
     }
 };
 
-#endif  // __ESRGAN_HPP__
+#endif  // __SD_MODEL_UPSCALER_ESRGAN_HPP__

src/model/upscaler/ltx_latent_upscaler.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_LTX_LATENT_UPSCALER_HPP__
-#define __SD_LTX_LATENT_UPSCALER_HPP__
+#ifndef __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
+#define __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
 
 #include <cinttypes>
 #include <cmath>
@@ -11,11 +11,11 @@
 #include <utility>
 #include <vector>
 
-#include "common_dit.hpp"
-#include "ggml_extend.hpp"
-#include "ggml_graph_cut.h"
-#include "model.h"
-#include "util.h"
+#include "core/ggml_extend.hpp"
+#include "core/ggml_graph_cut.h"
+#include "core/util.h"
+#include "model/diffusion/dit.hpp"
+#include "model_loader.h"
 
 namespace LTXVUpsampler {
     constexpr int LTX_UPSAMPLER_GRAPH_SIZE = 10240;
@@ -548,4 +548,4 @@ namespace LTXVUpsampler {
 
 }  // namespace LTXVUpsampler
 
-#endif  // __SD_LTX_LATENT_UPSCALER_HPP__
+#endif  // __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__

src/model/vae/auto_encoder_kl.hpp

@@ -1,7 +1,7 @@
-#ifndef __AUTO_ENCODER_KL_HPP__
-#define __AUTO_ENCODER_KL_HPP__
+#ifndef __SD_MODEL_VAE_AUTO_ENCODER_KL_HPP__
+#define __SD_MODEL_VAE_AUTO_ENCODER_KL_HPP__
 
-#include "vae.hpp"
+#include "model/vae/vae.hpp"
 
 /*================================================== AutoEncoderKL ===================================================*/
 
@@ -886,4 +886,4 @@ struct AutoEncoderKL : public VAE {
     };
 };
 
-#endif  // __AUTO_ENCODER_KL_HPP__
+#endif  // __SD_MODEL_VAE_AUTO_ENCODER_KL_HPP__

src/model/vae/ltx_audio_vae.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_LTX_AUDIO_VAE_H__
-#define __SD_LTX_AUDIO_VAE_H__
+#ifndef __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
+#define __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
 
 #include <cmath>
 #include <limits>
@@ -7,7 +7,8 @@
 #include <string>
 #include <vector>
 
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
+#include "model_loader.h"
 
 namespace LTXV {
 
@@ -1095,4 +1096,4 @@ namespace LTXV {
 
 }  // namespace LTXV
 
-#endif  // __SD_LTX_AUDIO_VAE_H__
+#endif  // __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__

src/model/vae/ltx_vae.hpp

@@ -1,5 +1,5 @@
-#ifndef __SD_LTX_VAE_HPP__
-#define __SD_LTX_VAE_HPP__
+#ifndef __SD_MODEL_VAE_LTX_VAE_HPP__
+#define __SD_MODEL_VAE_LTX_VAE_HPP__
 
 #include <algorithm>
 #include <fstream>
@@ -9,9 +9,10 @@
 #include <utility>
 #include <vector>
 
-#include "ltxv.hpp"
-#include "vae.hpp"
-#include "wan.hpp"
+#include "model/diffusion/ltxv.hpp"
+#include "model/vae/vae.hpp"
+#include "model/vae/wan_vae.hpp"
+#include "model_loader.h"
 
 namespace LTXVAE {
 
@@ -1552,4 +1553,4 @@ struct LTXVideoVAE : public VAE {
     }
 };
 
-#endif  // __SD_LTX_VAE_HPP__
+#endif  // __SD_MODEL_VAE_LTX_VAE_HPP__

src/model/vae/tae.hpp

@@ -1,13 +1,13 @@
-#ifndef __TAE_HPP__
-#define __TAE_HPP__
+#ifndef __SD_MODEL_VAE_TAE_HPP__
+#define __SD_MODEL_VAE_TAE_HPP__
 
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
 #include "model.h"
 
 /*
     ===================================    TinyAutoEncoder  ===================================
     References:
-    https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/vae.py
+    https://github.com/huggingface/diffusers/blob/main/src/diffusers/model/autoencoders/vae.py
     https://github.com/madebyollin/taesd/blob/main/taesd.py
 
 */
@@ -750,4 +750,4 @@ struct TinyVideoAutoEncoder : public VAE {
     }
 };
 
-#endif  // __TAE_HPP__
+#endif  // __SD_MODEL_VAE_TAE_HPP__

src/model/vae/vae.hpp

@@ -1,8 +1,8 @@
-#ifndef __VAE_HPP__
-#define __VAE_HPP__
+#ifndef __SD_MODEL_VAE_VAE_HPP__
+#define __SD_MODEL_VAE_VAE_HPP__
 
-#include "common_block.hpp"
-#include "tensor_ggml.hpp"
+#include "core/tensor_ggml.hpp"
+#include "model/common/block.hpp"
 
 struct VAE : public GGMLRunner {
 protected:
@@ -258,4 +258,4 @@ struct FakeVAE : public VAE {
     }
 };
 
-#endif  // __VAE_HPP__
+#endif  // __SD_MODEL_VAE_VAE_HPP__

src/model_io/gguf_io.cpp

@@ -5,9 +5,9 @@
 #include <string>
 #include <vector>
 
+#include "core/util.h"
 #include "gguf.h"
 #include "gguf_reader_ext.h"
-#include "util.h"
 
 static void set_error(std::string* error, const std::string& message) {
     if (error != nullptr) {

src/model_io/gguf_reader_ext.h

@@ -6,8 +6,8 @@
 #include <string>
 #include <vector>
 
+#include "core/util.h"
 #include "ggml.h"
-#include "util.h"
 
 struct GGUFTensorInfo {
     std::string name;

src/model_io/pickle_io.cpp

@@ -8,7 +8,7 @@
 #include <vector>
 
 #include "binary_io.h"
-#include "util.h"
+#include "core/util.h"
 
 // $ python -m pickletools sd-v1-4/archive/data.pkl | head -n 100
 //     0: \x80 PROTO      2

src/model_io/safetensors_io.cpp

@@ -7,8 +7,8 @@
 #include <vector>
 
 #include "binary_io.h"
+#include "core/util.h"
 #include "json.hpp"
-#include "util.h"
 
 static constexpr size_t ST_HEADER_SIZE_LEN = 8;
 

src/model_io/torch_legacy_io.cpp

@@ -7,8 +7,8 @@
 #include <unordered_map>
 #include <vector>
 
+#include "core/util.h"
 #include "pickle_io.h"
-#include "util.h"
 
 // torch.save format background:
 //

src/model_loader.cpp

@@ -13,18 +13,18 @@
 #include <unordered_map>
 #include <vector>
 
-#include "model.h"
+#include "core/util.h"
 #include "model_io/gguf_io.h"
 #include "model_io/safetensors_io.h"
 #include "model_io/torch_legacy_io.h"
 #include "model_io/torch_zip_io.h"
+#include "model_loader.h"
 #include "stable-diffusion.h"
-#include "util.h"
 
+#include "core/ggml_extend_backend.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "ggml.h"
-#include "ggml_extend_backend.h"
 #include "zip.h"
 
 #include "name_conversion.h"

src/model_loader.h

@@ -0,0 +1,82 @@
+#ifndef __MODEL_LOADER_H__
+#define __MODEL_LOADER_H__
+
+#include <cstdint>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+#include <vector>
+
+#include "model.h"
+
+TensorTypeRules parse_tensor_type_rules(const std::string& tensor_type_rules);
+
+class MmapWrapper;
+
+struct ModelFileData {
+    std::string path;
+    std::vector<TensorStorage> tensors;
+    std::shared_ptr<MmapWrapper> mmapped;
+    std::shared_ptr<struct ggml_backend_buffer> mmbuffer;
+    bool is_zip;
+};
+
+struct MmapTensorStore {
+    std::shared_ptr<MmapWrapper> mmapped;
+    std::shared_ptr<struct ggml_backend_buffer> mmbuffer;
+};
+
+class ModelLoader {
+protected:
+    SDVersion version_ = VERSION_COUNT;
+    std::vector<std::string> file_paths_;
+    std::vector<ModelFileData> file_data;
+    bool model_files_processed = false;
+    String2TensorStorage tensor_storage_map;
+
+    void add_tensor_storage(const TensorStorage& tensor_storage);
+
+    bool init_from_gguf_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_safetensors_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_torch_zip_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");
+
+public:
+    bool init_from_file(const std::string& file_path, const std::string& prefix = "");
+    void convert_tensors_name();
+    bool init_from_file_and_convert_name(const std::string& file_path,
+                                         const std::string& prefix = "",
+                                         SDVersion version         = VERSION_COUNT);
+    SDVersion get_sd_version();
+    std::map<ggml_type, uint32_t> get_wtype_stat();
+    std::map<ggml_type, uint32_t> get_conditioner_wtype_stat();
+    std::map<ggml_type, uint32_t> get_diffusion_model_wtype_stat();
+    std::map<ggml_type, uint32_t> get_vae_wtype_stat();
+    String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
+    void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
+    void process_model_files(bool enable_mmap = false, bool writable_mmap = true);
+    std::vector<MmapTensorStore> mmap_tensors(std::map<std::string, ggml_tensor*>& tensors,
+                                              std::set<std::string> ignore_tensors = {},
+                                              bool writable                        = true);
+    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
+    bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
+                      std::set<std::string> ignore_tensors = {},
+                      int n_threads                        = 0,
+                      bool use_mmap                        = false);
+
+    std::vector<std::string> get_tensor_names() const {
+        std::vector<std::string> names;
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            names.push_back(name);
+        }
+        return names;
+    }
+
+    bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
+    int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
+    ~ModelLoader() = default;
+};
+
+#endif  // __MODEL_LOADER_H__

src/name_conversion.cpp

@@ -1,8 +1,9 @@
+#include <map>
 #include <unordered_map>
 #include <unordered_set>
 
+#include "core/util.h"
 #include "name_conversion.h"
-#include "util.h"
 
 void replace_with_name_map(std::string& name, const std::vector<std::pair<std::string, std::string>>& name_map) {
     for (auto kv : name_map) {

src/runtime/cache_dit.hpp

@@ -1,5 +1,5 @@
-#ifndef __CACHE_DIT_HPP__
-#define __CACHE_DIT_HPP__
+#ifndef __SD_RUNTIME_CACHE_DIT_HPP__
+#define __SD_RUNTIME_CACHE_DIT_HPP__
 
 #include <algorithm>
 #include <cmath>
@@ -8,9 +8,9 @@
 #include <unordered_map>
 #include <vector>
 
-#include "condition_cache_utils.hpp"
-#include "ggml_extend.hpp"
-#include "tensor.hpp"
+#include "core/ggml_extend.hpp"
+#include "core/tensor.hpp"
+#include "runtime/condition_cache_utils.hpp"
 
 struct DBCacheConfig {
     bool enabled                        = false;
@@ -893,4 +893,4 @@ struct CacheDitConditionState {
     }
 };
 
-#endif
+#endif  // __SD_RUNTIME_CACHE_DIT_HPP__

src/runtime/condition_cache_utils.hpp

@@ -1,9 +1,9 @@
-#ifndef __CONDITION_CACHE_UTILS_HPP__
-#define __CONDITION_CACHE_UTILS_HPP__
+#ifndef __SD_RUNTIME_CONDITION_CACHE_UTILS_HPP__
+#define __SD_RUNTIME_CONDITION_CACHE_UTILS_HPP__
 
 #include <vector>
 
-#include "tensor.hpp"
+#include "core/tensor.hpp"
 
 namespace sd {
 
@@ -61,4 +61,4 @@ namespace sd {
 
 }  // namespace sd
 
-#endif  // __CONDITION_CACHE_UTILS_HPP__
+#endif  // __SD_RUNTIME_CONDITION_CACHE_UTILS_HPP__

src/runtime/denoiser.hpp

@@ -1,5 +1,5 @@
-#ifndef __DENOISER_HPP__
-#define __DENOISER_HPP__
+#ifndef __SD_RUNTIME_DENOISER_HPP__
+#define __SD_RUNTIME_DENOISER_HPP__
 
 #include <algorithm>
 #include <cctype>
@@ -8,10 +8,10 @@
 #include <string>
 #include <utility>
 
-#include "ggml_extend.hpp"
-#include "gits_noise.inl"
-#include "guidance.h"
-#include "tensor.hpp"
+#include "core/ggml_extend.hpp"
+#include "core/tensor.hpp"
+#include "runtime/gits_noise.h"
+#include "runtime/guidance.h"
 
 /*================================================= CompVisDenoiser ==================================================*/
 
@@ -1902,4 +1902,4 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
     }
 }
 
-#endif  // __DENOISER_HPP__
+#endif  // __SD_RUNTIME_DENOISER_HPP__

src/runtime/easycache.hpp

@@ -1,15 +1,15 @@
-#ifndef __EASYCACHE_HPP__
-#define __EASYCACHE_HPP__
+#ifndef __SD_RUNTIME_EASYCACHE_HPP__
+#define __SD_RUNTIME_EASYCACHE_HPP__
 
 #include <cmath>
 #include <limits>
 #include <unordered_map>
 #include <vector>
 
-#include "condition_cache_utils.hpp"
-#include "denoiser.hpp"
-#include "ggml_extend.hpp"
-#include "tensor.hpp"
+#include "core/ggml_extend.hpp"
+#include "core/tensor.hpp"
+#include "runtime/condition_cache_utils.hpp"
+#include "runtime/denoiser.hpp"
 
 struct EasyCacheConfig {
     bool enabled          = false;
@@ -258,4 +258,4 @@ struct EasyCacheState {
     }
 };
 
-#endif
+#endif  // __SD_RUNTIME_EASYCACHE_HPP__

src/runtime/gits_noise.h

@@ -0,0 +1,333 @@
+#ifndef __SD_RUNTIME_GITS_NOISE_H__
+#define __SD_RUNTIME_GITS_NOISE_H__
+
+const std::vector<std::vector<float>> GITS_NOISE_0_80 = {
+    {14.61464119f, 7.49001646f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 6.77309084f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 3.07277966f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 2.05039096f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 2.05039096f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 5.85520077f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.07277966f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.19567990f, 1.98035145f, 0.86115354f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.19567990f, 1.98035145f, 0.86115354f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.88507891f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.07277966f, 1.84880662f, 0.83188516f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_0_85 = {
+    {14.61464119f, 7.49001646f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 1.84880662f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 6.77309084f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.11996698f, 3.07277966f, 1.24153244f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.09240818f, 2.84484982f, 0.95350921f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.09240818f, 2.84484982f, 0.95350921f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.58536053f, 3.19567990f, 1.84880662f, 0.803307f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 8.75849152f, 7.49001646f, 5.58536053f, 3.19567990f, 1.84880662f, 0.803307f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 8.75849152f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 8.75849152f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.65472794f, 3.07277966f, 1.84880662f, 0.803307f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.60512662f, 2.63833880f, 1.56271636f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.90732002f, 10.31284904f, 9.75859547f, 9.24142551f, 8.75849152f, 8.30717278f, 7.88507891f, 7.49001646f, 6.77309084f, 5.85520077f, 4.65472794f, 3.46139455f, 2.45070267f, 1.56271636f, 0.72133851f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_0_90 = {
+    {14.61464119f, 6.77309084f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 3.07277966f, 0.95350921f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.54230714f, 0.89115214f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 2.54230714f, 0.89115214f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.09240818f, 3.07277966f, 1.61558151f, 0.69515091f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.11996698f, 4.86714602f, 3.07277966f, 1.61558151f, 0.69515091f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 2.95596409f, 1.61558151f, 0.69515091f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.24153244f, 0.57119018f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.24153244f, 0.57119018f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.24153244f, 0.57119018f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.75677586f, 2.84484982f, 1.84880662f, 1.08895338f, 0.52423614f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 4.86714602f, 3.75677586f, 2.84484982f, 1.84880662f, 1.08895338f, 0.52423614f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.45427561f, 3.32507086f, 2.45070267f, 1.61558151f, 0.95350921f, 0.45573691f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.45427561f, 3.32507086f, 2.45070267f, 1.61558151f, 0.95350921f, 0.45573691f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.86714602f, 3.91689563f, 3.07277966f, 2.27973175f, 1.56271636f, 0.95350921f, 0.45573691f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.86714602f, 3.91689563f, 3.07277966f, 2.27973175f, 1.56271636f, 0.95350921f, 0.45573691f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 4.86714602f, 3.91689563f, 3.07277966f, 2.27973175f, 1.56271636f, 0.95350921f, 0.45573691f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.96784878f, 12.23089790f, 11.54541874f, 10.31284904f, 9.24142551f, 8.75849152f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.60512662f, 2.95596409f, 2.19988537f, 1.51179266f, 0.89115214f, 0.43325692f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_0_95 = {
+    {14.61464119f, 6.77309084f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 2.84484982f, 0.89115214f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.36326075f, 0.803307f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.95596409f, 1.56271636f, 0.64427125f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 2.95596409f, 1.56271636f, 0.64427125f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 3.07277966f, 1.91321158f, 1.08895338f, 0.50118381f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.07277966f, 1.91321158f, 1.08895338f, 0.50118381f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.07277966f, 1.91321158f, 1.08895338f, 0.50118381f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.19988537f, 1.41535246f, 0.803307f, 0.38853383f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.46139455f, 2.63833880f, 1.84880662f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.46139455f, 2.63833880f, 1.84880662f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 6.14220476f, 4.86714602f, 3.75677586f, 2.95596409f, 2.19988537f, 1.56271636f, 1.05362725f, 0.64427125f, 0.32104823f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 10.90732002f, 8.75849152f, 7.49001646f, 6.44769001f, 5.58536053f, 4.65472794f, 3.60512662f, 2.95596409f, 2.19988537f, 1.56271636f, 1.05362725f, 0.64427125f, 0.32104823f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.65472794f, 3.60512662f, 2.95596409f, 2.19988537f, 1.56271636f, 1.05362725f, 0.64427125f, 0.32104823f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.44769001f, 5.58536053f, 4.65472794f, 3.75677586f, 3.07277966f, 2.45070267f, 1.78698075f, 1.24153244f, 0.83188516f, 0.50118381f, 0.22545385f, 0.02916753f},
+    {14.61464119f, 12.96784878f, 11.54541874f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.60512662f, 2.95596409f, 2.36326075f, 1.72759056f, 1.24153244f, 0.83188516f, 0.50118381f, 0.22545385f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.60512662f, 2.95596409f, 2.36326075f, 1.72759056f, 1.24153244f, 0.83188516f, 0.50118381f, 0.22545385f, 0.02916753f},
+    {14.61464119f, 13.76078796f, 12.23089790f, 10.90732002f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.45427561f, 3.75677586f, 3.07277966f, 2.45070267f, 1.91321158f, 1.46270394f, 1.05362725f, 0.72133851f, 0.43325692f, 0.19894916f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_00 = {
+    {14.61464119f, 1.56271636f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 0.95350921f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 2.36326075f, 0.803307f, 0.02916753f},
+    {14.61464119f, 7.11996698f, 3.07277966f, 1.56271636f, 0.59516323f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.41535246f, 0.57119018f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.86115354f, 0.38853383f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.86115354f, 0.38853383f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 4.86714602f, 3.07277966f, 1.98035145f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.07277966f, 1.98035145f, 1.24153244f, 0.72133851f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.27973175f, 1.51179266f, 0.95350921f, 0.54755926f, 0.25053367f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.36326075f, 1.61558151f, 1.08895338f, 0.72133851f, 0.41087446f, 0.17026083f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.36326075f, 1.61558151f, 1.08895338f, 0.72133851f, 0.41087446f, 0.17026083f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.12350607f, 1.56271636f, 1.08895338f, 0.72133851f, 0.41087446f, 0.17026083f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.19988537f, 1.61558151f, 1.162866f, 0.803307f, 0.50118381f, 0.27464288f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 5.85520077f, 4.65472794f, 3.75677586f, 3.07277966f, 2.45070267f, 1.84880662f, 1.36964464f, 1.01931262f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 6.14220476f, 5.09240818f, 4.26497746f, 3.46139455f, 2.84484982f, 2.19988537f, 1.67050016f, 1.24153244f, 0.92192322f, 0.64427125f, 0.43325692f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.75849152f, 7.49001646f, 6.14220476f, 5.09240818f, 4.26497746f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.12534678f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 9.24142551f, 8.30717278f, 7.49001646f, 6.14220476f, 5.09240818f, 4.26497746f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.12534678f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 12.23089790f, 9.24142551f, 8.30717278f, 7.49001646f, 6.77309084f, 5.85520077f, 5.09240818f, 4.26497746f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.12534678f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_05 = {
+    {14.61464119f, 0.95350921f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 0.89115214f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 2.05039096f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 2.84484982f, 1.28281462f, 0.52423614f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 3.07277966f, 1.61558151f, 0.803307f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.56271636f, 0.803307f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.95350921f, 0.52423614f, 0.22545385f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 1.98035145f, 1.24153244f, 0.74807048f, 0.41087446f, 0.17026083f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.27973175f, 1.51179266f, 0.95350921f, 0.59516323f, 0.34370604f, 0.13792117f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 5.09240818f, 3.46139455f, 2.45070267f, 1.61558151f, 1.08895338f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.09240818f, 3.46139455f, 2.45070267f, 1.61558151f, 1.08895338f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.36326075f, 1.61558151f, 1.08895338f, 0.72133851f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.45070267f, 1.72759056f, 1.24153244f, 0.86115354f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.19988537f, 1.61558151f, 1.162866f, 0.83188516f, 0.59516323f, 0.38853383f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.84484982f, 2.19988537f, 1.67050016f, 1.28281462f, 0.95350921f, 0.72133851f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.36326075f, 1.84880662f, 1.41535246f, 1.08895338f, 0.83188516f, 0.61951244f, 0.45573691f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.20157266f, 0.95350921f, 0.74807048f, 0.57119018f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.30717278f, 7.11996698f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.45070267f, 1.91321158f, 1.51179266f, 1.20157266f, 0.95350921f, 0.74807048f, 0.57119018f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 8.30717278f, 7.11996698f, 5.85520077f, 4.65472794f, 3.60512662f, 2.95596409f, 2.45070267f, 1.98035145f, 1.61558151f, 1.32549286f, 1.08895338f, 0.86115354f, 0.69515091f, 0.54755926f, 0.41087446f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_10 = {
+    {14.61464119f, 0.89115214f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 1.61558151f, 0.57119018f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 2.45070267f, 1.08895338f, 0.45573691f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 2.95596409f, 1.56271636f, 0.803307f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 3.07277966f, 1.61558151f, 0.89115214f, 0.4783645f, 0.19894916f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 3.07277966f, 1.84880662f, 1.08895338f, 0.64427125f, 0.34370604f, 0.13792117f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.84484982f, 1.61558151f, 0.95350921f, 0.54755926f, 0.27464288f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.95596409f, 1.91321158f, 1.24153244f, 0.803307f, 0.4783645f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.05039096f, 1.41535246f, 0.95350921f, 0.64427125f, 0.41087446f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.27973175f, 1.61558151f, 1.12534678f, 0.803307f, 0.54755926f, 0.36617002f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.32507086f, 2.45070267f, 1.72759056f, 1.24153244f, 0.89115214f, 0.64427125f, 0.45573691f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 5.09240818f, 3.60512662f, 2.84484982f, 2.05039096f, 1.51179266f, 1.08895338f, 0.803307f, 0.59516323f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 5.09240818f, 3.60512662f, 2.84484982f, 2.12350607f, 1.61558151f, 1.24153244f, 0.95350921f, 0.72133851f, 0.54755926f, 0.41087446f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.45070267f, 1.84880662f, 1.41535246f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 5.85520077f, 4.45427561f, 3.19567990f, 2.45070267f, 1.91321158f, 1.51179266f, 1.20157266f, 0.95350921f, 0.74807048f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 5.85520077f, 4.45427561f, 3.46139455f, 2.84484982f, 2.19988537f, 1.72759056f, 1.36964464f, 1.08895338f, 0.86115354f, 0.69515091f, 0.54755926f, 0.43325692f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.46139455f, 2.84484982f, 2.19988537f, 1.72759056f, 1.36964464f, 1.08895338f, 0.86115354f, 0.69515091f, 0.54755926f, 0.43325692f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 11.54541874f, 7.49001646f, 5.85520077f, 4.45427561f, 3.46139455f, 2.84484982f, 2.19988537f, 1.72759056f, 1.36964464f, 1.08895338f, 0.89115214f, 0.72133851f, 0.59516323f, 0.4783645f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_15 = {
+    {14.61464119f, 0.83188516f, 0.02916753f},
+    {14.61464119f, 1.84880662f, 0.59516323f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 1.56271636f, 0.52423614f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 1.91321158f, 0.83188516f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.45070267f, 1.24153244f, 0.59516323f, 0.25053367f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.51179266f, 0.803307f, 0.41087446f, 0.17026083f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.56271636f, 0.89115214f, 0.50118381f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 3.07277966f, 1.84880662f, 1.12534678f, 0.72133851f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 3.07277966f, 1.91321158f, 1.24153244f, 0.803307f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 2.95596409f, 1.91321158f, 1.24153244f, 0.803307f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.05039096f, 1.36964464f, 0.95350921f, 0.69515091f, 0.4783645f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.803307f, 0.59516323f, 0.43325692f, 0.29807833f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.803307f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.07277966f, 2.19988537f, 1.61558151f, 1.24153244f, 0.95350921f, 0.74807048f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.78698075f, 1.32549286f, 1.01931262f, 0.803307f, 0.64427125f, 0.50118381f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.78698075f, 1.32549286f, 1.01931262f, 0.803307f, 0.64427125f, 0.52423614f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.84880662f, 1.41535246f, 1.12534678f, 0.89115214f, 0.72133851f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.86714602f, 3.19567990f, 2.45070267f, 1.84880662f, 1.41535246f, 1.12534678f, 0.89115214f, 0.72133851f, 0.59516323f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_20 = {
+    {14.61464119f, 0.803307f, 0.02916753f},
+    {14.61464119f, 1.56271636f, 0.52423614f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 0.92192322f, 0.36617002f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.24153244f, 0.59516323f, 0.25053367f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.05039096f, 0.95350921f, 0.45573691f, 0.17026083f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.45070267f, 1.24153244f, 0.64427125f, 0.29807833f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.45070267f, 1.36964464f, 0.803307f, 0.45573691f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 0.95350921f, 0.59516323f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.67050016f, 1.08895338f, 0.74807048f, 0.50118381f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.95596409f, 1.84880662f, 1.24153244f, 0.83188516f, 0.59516323f, 0.41087446f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 3.07277966f, 1.98035145f, 1.36964464f, 0.95350921f, 0.69515091f, 0.50118381f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 3.46139455f, 2.36326075f, 1.56271636f, 1.08895338f, 0.803307f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 6.77309084f, 3.46139455f, 2.45070267f, 1.61558151f, 1.162866f, 0.86115354f, 0.64427125f, 0.50118381f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.12350607f, 1.51179266f, 1.08895338f, 0.83188516f, 0.64427125f, 0.50118381f, 0.41087446f, 0.34370604f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.19988537f, 1.61558151f, 1.20157266f, 0.92192322f, 0.72133851f, 0.57119018f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.19988537f, 1.61558151f, 1.24153244f, 0.95350921f, 0.74807048f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 7.49001646f, 4.65472794f, 3.07277966f, 2.19988537f, 1.61558151f, 1.24153244f, 0.95350921f, 0.74807048f, 0.59516323f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_25 = {
+    {14.61464119f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 1.56271636f, 0.50118381f, 0.02916753f},
+    {14.61464119f, 2.05039096f, 0.803307f, 0.32104823f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 0.95350921f, 0.43325692f, 0.17026083f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.24153244f, 0.59516323f, 0.27464288f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.51179266f, 0.803307f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.36326075f, 1.24153244f, 0.72133851f, 0.41087446f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.45070267f, 1.36964464f, 0.83188516f, 0.52423614f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 0.98595673f, 0.64427125f, 0.43325692f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.67050016f, 1.08895338f, 0.74807048f, 0.52423614f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.72759056f, 1.162866f, 0.803307f, 0.59516323f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.95596409f, 1.84880662f, 1.24153244f, 0.86115354f, 0.64427125f, 0.4783645f, 0.36617002f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.95596409f, 1.84880662f, 1.28281462f, 0.92192322f, 0.69515091f, 0.52423614f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.95596409f, 1.91321158f, 1.32549286f, 0.95350921f, 0.72133851f, 0.54755926f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.95596409f, 1.91321158f, 1.32549286f, 0.95350921f, 0.72133851f, 0.57119018f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.95596409f, 1.91321158f, 1.32549286f, 0.95350921f, 0.74807048f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 3.07277966f, 2.05039096f, 1.41535246f, 1.05362725f, 0.803307f, 0.61951244f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 3.07277966f, 2.05039096f, 1.41535246f, 1.05362725f, 0.803307f, 0.64427125f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 3.07277966f, 2.05039096f, 1.46270394f, 1.08895338f, 0.83188516f, 0.66947293f, 0.54755926f, 0.45573691f, 0.38853383f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_30 = {
+    {14.61464119f, 0.72133851f, 0.02916753f},
+    {14.61464119f, 1.24153244f, 0.43325692f, 0.02916753f},
+    {14.61464119f, 1.56271636f, 0.59516323f, 0.22545385f, 0.02916753f},
+    {14.61464119f, 1.84880662f, 0.803307f, 0.36617002f, 0.13792117f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 1.01931262f, 0.52423614f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.36964464f, 0.74807048f, 0.41087446f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.56271636f, 0.89115214f, 0.54755926f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.61558151f, 0.95350921f, 0.61951244f, 0.41087446f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.45070267f, 1.36964464f, 0.83188516f, 0.54755926f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.45070267f, 1.41535246f, 0.92192322f, 0.64427125f, 0.45573691f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.6383388f, 1.56271636f, 1.01931262f, 0.72133851f, 0.50118381f, 0.36617002f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.05362725f, 0.74807048f, 0.54755926f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.08895338f, 0.77538133f, 0.57119018f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.59516323f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.72759056f, 1.162866f, 0.83188516f, 0.64427125f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.72759056f, 1.162866f, 0.83188516f, 0.64427125f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.78698075f, 1.24153244f, 0.92192322f, 0.72133851f, 0.57119018f, 0.45573691f, 0.38853383f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.84484982f, 1.78698075f, 1.24153244f, 0.92192322f, 0.72133851f, 0.57119018f, 0.4783645f, 0.41087446f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_35 = {
+    {14.61464119f, 0.69515091f, 0.02916753f},
+    {14.61464119f, 0.95350921f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 1.56271636f, 0.57119018f, 0.19894916f, 0.02916753f},
+    {14.61464119f, 1.61558151f, 0.69515091f, 0.29807833f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.84880662f, 0.83188516f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.162866f, 0.64427125f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.36964464f, 0.803307f, 0.50118381f, 0.32104823f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.41535246f, 0.83188516f, 0.54755926f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.56271636f, 0.95350921f, 0.64427125f, 0.45573691f, 0.32104823f, 0.22545385f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.56271636f, 0.95350921f, 0.64427125f, 0.45573691f, 0.34370604f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.61558151f, 1.01931262f, 0.72133851f, 0.52423614f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.61558151f, 1.01931262f, 0.72133851f, 0.52423614f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.61558151f, 1.05362725f, 0.74807048f, 0.54755926f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.72759056f, 1.12534678f, 0.803307f, 0.59516323f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 3.07277966f, 1.72759056f, 1.12534678f, 0.803307f, 0.59516323f, 0.4783645f, 0.38853383f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.45070267f, 1.51179266f, 1.01931262f, 0.74807048f, 0.57119018f, 0.45573691f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.6383388f, 1.61558151f, 1.08895338f, 0.803307f, 0.61951244f, 0.50118381f, 0.41087446f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.6383388f, 1.61558151f, 1.08895338f, 0.803307f, 0.64427125f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 5.85520077f, 2.6383388f, 1.61558151f, 1.08895338f, 0.803307f, 0.64427125f, 0.52423614f, 0.45573691f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_40 = {
+    {14.61464119f, 0.59516323f, 0.02916753f},
+    {14.61464119f, 0.95350921f, 0.34370604f, 0.02916753f},
+    {14.61464119f, 1.08895338f, 0.43325692f, 0.13792117f, 0.02916753f},
+    {14.61464119f, 1.56271636f, 0.64427125f, 0.27464288f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.61558151f, 0.803307f, 0.43325692f, 0.22545385f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.05039096f, 0.95350921f, 0.54755926f, 0.34370604f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.24153244f, 0.72133851f, 0.43325692f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.24153244f, 0.74807048f, 0.50118381f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.52423614f, 0.36617002f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.54755926f, 0.38853383f, 0.29807833f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.41535246f, 0.86115354f, 0.59516323f, 0.43325692f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.64427125f, 0.45573691f, 0.34370604f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.64427125f, 0.4783645f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.56271636f, 0.98595673f, 0.69515091f, 0.52423614f, 0.41087446f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.56271636f, 1.01931262f, 0.72133851f, 0.54755926f, 0.43325692f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.61558151f, 1.05362725f, 0.74807048f, 0.57119018f, 0.45573691f, 0.38853383f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.61951244f, 0.50118381f, 0.41087446f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.61951244f, 0.50118381f, 0.43325692f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.61558151f, 1.08895338f, 0.803307f, 0.64427125f, 0.52423614f, 0.45573691f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_45 = {
+    {14.61464119f, 0.59516323f, 0.02916753f},
+    {14.61464119f, 0.803307f, 0.25053367f, 0.02916753f},
+    {14.61464119f, 0.95350921f, 0.34370604f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.24153244f, 0.54755926f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.56271636f, 0.72133851f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.61558151f, 0.803307f, 0.45573691f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.91321158f, 0.95350921f, 0.57119018f, 0.36617002f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.19988537f, 1.08895338f, 0.64427125f, 0.41087446f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.24153244f, 0.74807048f, 0.50118381f, 0.34370604f, 0.25053367f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.24153244f, 0.74807048f, 0.50118381f, 0.36617002f, 0.27464288f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.54755926f, 0.41087446f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.28281462f, 0.803307f, 0.57119018f, 0.43325692f, 0.34370604f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.28281462f, 0.83188516f, 0.59516323f, 0.45573691f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.28281462f, 0.83188516f, 0.59516323f, 0.45573691f, 0.36617002f, 0.32104823f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.69515091f, 0.52423614f, 0.41087446f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.51179266f, 0.95350921f, 0.69515091f, 0.52423614f, 0.43325692f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.56271636f, 0.98595673f, 0.72133851f, 0.54755926f, 0.45573691f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.56271636f, 1.01931262f, 0.74807048f, 0.57119018f, 0.4783645f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.84484982f, 1.56271636f, 1.01931262f, 0.74807048f, 0.59516323f, 0.50118381f, 0.43325692f, 0.38853383f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<std::vector<float>> GITS_NOISE_1_50 = {
+    {14.61464119f, 0.54755926f, 0.02916753f},
+    {14.61464119f, 0.803307f, 0.25053367f, 0.02916753f},
+    {14.61464119f, 0.86115354f, 0.32104823f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.24153244f, 0.54755926f, 0.25053367f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.56271636f, 0.72133851f, 0.36617002f, 0.19894916f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.61558151f, 0.803307f, 0.45573691f, 0.27464288f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.61558151f, 0.83188516f, 0.52423614f, 0.34370604f, 0.25053367f, 0.17026083f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.84880662f, 0.95350921f, 0.59516323f, 0.38853383f, 0.27464288f, 0.19894916f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.84880662f, 0.95350921f, 0.59516323f, 0.41087446f, 0.29807833f, 0.22545385f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 1.84880662f, 0.95350921f, 0.61951244f, 0.43325692f, 0.32104823f, 0.25053367f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.19988537f, 1.12534678f, 0.72133851f, 0.50118381f, 0.36617002f, 0.27464288f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.19988537f, 1.12534678f, 0.72133851f, 0.50118381f, 0.36617002f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.57119018f, 0.43325692f, 0.34370604f, 0.29807833f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.57119018f, 0.43325692f, 0.34370604f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.59516323f, 0.45573691f, 0.36617002f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.36326075f, 1.24153244f, 0.803307f, 0.59516323f, 0.45573691f, 0.38853383f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.32549286f, 0.86115354f, 0.64427125f, 0.50118381f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.36964464f, 0.92192322f, 0.69515091f, 0.54755926f, 0.45573691f, 0.41087446f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f},
+    {14.61464119f, 2.45070267f, 1.41535246f, 0.95350921f, 0.72133851f, 0.57119018f, 0.4783645f, 0.43325692f, 0.38853383f, 0.36617002f, 0.34370604f, 0.32104823f, 0.29807833f, 0.27464288f, 0.25053367f, 0.22545385f, 0.19894916f, 0.17026083f, 0.13792117f, 0.09824532f, 0.02916753f}};
+
+const std::vector<const std::vector<std::vector<float>>*> GITS_NOISE = {
+    &GITS_NOISE_0_80,
+    &GITS_NOISE_0_85,
+    &GITS_NOISE_0_90,
+    &GITS_NOISE_0_95,
+    &GITS_NOISE_1_00,
+    &GITS_NOISE_1_05,
+    &GITS_NOISE_1_10,
+    &GITS_NOISE_1_15,
+    &GITS_NOISE_1_20,
+    &GITS_NOISE_1_25,
+    &GITS_NOISE_1_30,
+    &GITS_NOISE_1_35,
+    &GITS_NOISE_1_40,
+    &GITS_NOISE_1_45,
+    &GITS_NOISE_1_50};
+
+#endif  // __SD_RUNTIME_GITS_NOISE_H__

src/runtime/guidance.cpp

@@ -1,4 +1,4 @@
-#include "guidance.h"
+#include "runtime/guidance.h"
 
 #include <algorithm>
 #include <cmath>
@@ -6,7 +6,7 @@
 #include <string>
 #include <utility>
 
-#include "util.h"
+#include "core/util.h"
 
 namespace sd::guidance {
 
@@ -173,8 +173,9 @@ namespace sd::guidance {
         }
 
         float diff_norm = 0.0f;
+        const int standard_res = 2 * 1024 / 8; // Use SDXL as the standard resolution (1024x1024, 8x8 patches, 4=2x2 channels)
         if (params_.norm_threshold > 0.0f) {
-            diff_norm = std::sqrt((deltas * deltas).sum());
+            diff_norm = std::sqrt((deltas * deltas).sum()) * standard_res / std::sqrt(static_cast<float>(deltas.numel()));
         }
 
         float apg_scale_factor = 1.0f;

src/runtime/guidance.h

@@ -1,11 +1,11 @@
-#ifndef __SD_GUIDANCE_H__
-#define __SD_GUIDANCE_H__
+#ifndef __SD_RUNTIME_GUIDANCE_H__
+#define __SD_RUNTIME_GUIDANCE_H__
 
 #include <cstddef>
 #include <functional>
 #include <vector>
 
-#include "tensor.hpp"
+#include "core/tensor.hpp"
 
 namespace sd::guidance {
 
@@ -93,4 +93,4 @@ namespace sd::guidance {
 
 }  // namespace sd::guidance
 
-#endif  // __SD_GUIDANCE_H__
+#endif  // __SD_RUNTIME_GUIDANCE_H__

src/runtime/latent-preview.h

@@ -1,8 +1,8 @@
 #include <algorithm>
 #include <cstddef>
 #include <cstdint>
+#include "core/tensor.hpp"
 #include "ggml.h"
-#include "tensor.hpp"
 
 const float ltxav_latent_rgb_proj[128][3] = {
     {-0.0293802f, -0.0362516f, -0.0291386f},

src/runtime/preprocessing.hpp

@@ -1,10 +1,10 @@
-#ifndef __PREPROCESSING_HPP__
-#define __PREPROCESSING_HPP__
+#ifndef __SD_RUNTIME_PREPROCESSING_HPP__
+#define __SD_RUNTIME_PREPROCESSING_HPP__
 
 #include <cmath>
 #include <limits>
 
-#include "ggml_extend.hpp"
+#include "core/ggml_extend.hpp"
 
 #define M_PI_ 3.14159265358979323846f
 
@@ -331,4 +331,4 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
     return true;
 }
 
-#endif  // __PREPROCESSING_HPP__
+#endif  // __SD_RUNTIME_PREPROCESSING_HPP__

src/runtime/sample-cache.cpp

@@ -1,4 +1,4 @@
-#include "sample-cache.h"
+#include "runtime/sample-cache.h"
 
 namespace sd_sample {
 

src/runtime/sample-cache.h

@@ -1,16 +1,16 @@
-#ifndef __SAMPLE_CACHE_H__
-#define __SAMPLE_CACHE_H__
+#ifndef __SD_RUNTIME_SAMPLE_CACHE_H__
+#define __SD_RUNTIME_SAMPLE_CACHE_H__
 
 #include <vector>
 
-#include "cache_dit.hpp"
-#include "denoiser.hpp"
-#include "easycache.hpp"
+#include "core/tensor.hpp"
+#include "core/util.h"
 #include "model.h"
-#include "spectrum.hpp"
-#include "tensor.hpp"
-#include "ucache.hpp"
-#include "util.h"
+#include "runtime/cache_dit.hpp"
+#include "runtime/denoiser.hpp"
+#include "runtime/easycache.hpp"
+#include "runtime/spectrum.hpp"
+#include "runtime/ucache.hpp"
 
 namespace sd_sample {
 
@@ -58,4 +58,4 @@ namespace sd_sample {
 
 }  // namespace sd_sample
 
-#endif  // __SAMPLE_CACHE_H__
+#endif  // __SD_RUNTIME_SAMPLE_CACHE_H__

src/runtime/spectrum.hpp

@@ -1,12 +1,12 @@
-#ifndef __SPECTRUM_HPP__
-#define __SPECTRUM_HPP__
+#ifndef __SD_RUNTIME_SPECTRUM_HPP__
+#define __SD_RUNTIME_SPECTRUM_HPP__
 
 #include <cmath>
 #include <cstring>
 #include <vector>
 
-#include "ggml_extend.hpp"
-#include "tensor.hpp"
+#include "core/ggml_extend.hpp"
+#include "core/tensor.hpp"
 
 struct SpectrumConfig {
     float w            = 0.40f;
@@ -184,4 +184,4 @@ private:
     }
 };
 
-#endif  // __SPECTRUM_HPP__
+#endif  // __SD_RUNTIME_SPECTRUM_HPP__

src/runtime/ucache.hpp

@@ -1,15 +1,15 @@
-#ifndef __UCACHE_HPP__
-#define __UCACHE_HPP__
+#ifndef __SD_RUNTIME_UCACHE_HPP__
+#define __SD_RUNTIME_UCACHE_HPP__
 
 #include <cmath>
 #include <limits>
 #include <unordered_map>
 #include <vector>
 
-#include "condition_cache_utils.hpp"
-#include "denoiser.hpp"
-#include "ggml_extend.hpp"
-#include "tensor.hpp"
+#include "core/ggml_extend.hpp"
+#include "core/tensor.hpp"
+#include "runtime/condition_cache_utils.hpp"
+#include "runtime/denoiser.hpp"
 
 struct UCacheConfig {
     bool enabled                = false;
@@ -420,4 +420,4 @@ struct UCacheState {
     }
 };
 
-#endif  // __UCACHE_HPP__
+#endif  // __SD_RUNTIME_UCACHE_HPP__

src/stable-diffusion.cpp

@@ -1,49 +1,51 @@
 #include <algorithm>
 #include <cmath>
 #include <cstdlib>
+#include <set>
 
-#include "ggml_extend.hpp"
-#include "ggml_graph_cut.h"
+#include "core/ggml_extend.hpp"
+#include "core/ggml_graph_cut.h"
 
-#include "model.h"
-#include "rng.hpp"
-#include "rng_mt19937.hpp"
-#include "rng_philox.hpp"
+#include "core/rng.hpp"
+#include "core/rng_mt19937.hpp"
+#include "core/rng_philox.hpp"
+#include "core/util.h"
+#include "model_loader.h"
 #include "stable-diffusion.h"
-#include "util.h"
 
-#include "anima.hpp"
-#include "auto_encoder_kl.hpp"
-#include "conditioner.hpp"
-#include "control.hpp"
-#include "denoiser.hpp"
-#include "diffusion_model.hpp"
-#include "ernie_image.hpp"
-#include "esrgan.hpp"
-#include "flux.hpp"
-#include "guidance.h"
-#include "hidream_o1.hpp"
-#include "ideogram4.hpp"
-#include "lens.hpp"
-#include "lora.hpp"
-#include "ltx_audio_vae.h"
-#include "ltx_latent_upscaler.hpp"
-#include "ltx_vae.hpp"
-#include "ltxv.hpp"
-#include "mmdit.hpp"
-#include "pid.hpp"
-#include "pmid.hpp"
-#include "qwen_image.hpp"
-#include "sample-cache.h"
-#include "tae.hpp"
-#include "unet.hpp"
+#include "conditioning/conditioner.hpp"
+#include "extensions/generation_extension.h"
+#include "model/adapter/lora.hpp"
+#include "model/diffusion/anima.hpp"
+#include "model/diffusion/control.hpp"
+#include "model/diffusion/ernie_image.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/hidream_o1.hpp"
+#include "model/diffusion/ideogram4.hpp"
+#include "model/diffusion/lens.hpp"
+#include "model/diffusion/ltxv.hpp"
+#include "model/diffusion/mmdit.hpp"
+#include "model/diffusion/model.hpp"
+#include "model/diffusion/pid.hpp"
+#include "model/diffusion/qwen_image.hpp"
+#include "model/diffusion/unet.hpp"
+#include "model/diffusion/wan.hpp"
+#include "model/diffusion/z_image.hpp"
+#include "model/upscaler/esrgan.hpp"
+#include "model/upscaler/ltx_latent_upscaler.hpp"
+#include "model/vae/auto_encoder_kl.hpp"
+#include "model/vae/ltx_audio_vae.hpp"
+#include "model/vae/ltx_vae.hpp"
+#include "model/vae/tae.hpp"
+#include "model/vae/vae.hpp"
+#include "model/vae/wan_vae.hpp"
+#include "runtime/denoiser.hpp"
+#include "runtime/guidance.h"
+#include "runtime/sample-cache.h"
 #include "upscaler.h"
-#include "vae.hpp"
-#include "wan.hpp"
-#include "z_image.hpp"
 
-#include "latent-preview.h"
 #include "name_conversion.h"
+#include "runtime/latent-preview.h"
 
 const char* sd_vae_format_name(enum sd_vae_format_t format);
 static SDVersion sd_vae_format_to_version(enum sd_vae_format_t format, SDVersion fallback);
@@ -179,9 +181,7 @@ public:
     std::shared_ptr<VAE> preview_vae;
     std::shared_ptr<LTXV::LTXAudioVAERunner> audio_vae_model;
     std::shared_ptr<ControlNet> control_net;
-    std::shared_ptr<PhotoMakerIDEncoder> pmid_model;
-    std::shared_ptr<LoraModel> pmid_lora;
-    std::shared_ptr<PhotoMakerIDEmbed> pmid_id_embeds;
+    std::vector<std::shared_ptr<GenerationExtension>> generation_extensions;
     std::vector<std::shared_ptr<LoraModel>> cond_stage_lora_models;
     std::vector<std::shared_ptr<LoraModel>> diffusion_lora_models;
     std::vector<std::shared_ptr<LoraModel>> first_stage_lora_models;
@@ -192,7 +192,6 @@ public:
     bool offload_params_to_cpu           = false;
     float max_vram                       = 0.f;
     bool stream_layers                   = false;
-    bool use_pmid                        = false;
     std::string backend_spec;
     std::string params_backend_spec;
 
@@ -742,21 +741,12 @@ public:
                 for (uint32_t i = 0; i < sd_ctx_params->embedding_count; i++) {
                     embbeding_map.emplace(SAFE_STR(sd_ctx_params->embeddings[i].name), SAFE_STR(sd_ctx_params->embeddings[i].path));
                 }
-                if (strstr(SAFE_STR(sd_ctx_params->photo_maker_path), "v2")) {
-                    cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(backend_for(SDBackendModule::TE),
-                                                                                           params_backend_for(SDBackendModule::TE),
-                                                                                           tensor_storage_map,
-                                                                                           embbeding_map,
-                                                                                           version,
-                                                                                           PM_VERSION_2);
-                } else {
-                    cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(backend_for(SDBackendModule::TE),
-                                                                                           params_backend_for(SDBackendModule::TE),
-                                                                                           tensor_storage_map,
-                                                                                           embbeding_map,
-                                                                                           version);
-                }
-                diffusion_model = std::make_shared<UNetModelRunner>(backend_for(SDBackendModule::DIFFUSION),
+                cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(backend_for(SDBackendModule::TE),
+                                                                                       params_backend_for(SDBackendModule::TE),
+                                                                                       tensor_storage_map,
+                                                                                       embbeding_map,
+                                                                                       version);
+                diffusion_model  = std::make_shared<UNetModelRunner>(backend_for(SDBackendModule::DIFFUSION),
                                                                     params_backend_for(SDBackendModule::DIFFUSION),
                                                                     tensor_storage_map,
                                                                     "model.diffusion_model",
@@ -913,50 +903,35 @@ public:
                 }
             }
 
-            if (strlen(SAFE_STR(sd_ctx_params->photo_maker_path)) > 0) {
-                if (!ensure_backend_pair(SDBackendModule::PHOTOMAKER)) {
-                    return false;
-                }
-                if (strstr(SAFE_STR(sd_ctx_params->photo_maker_path), "v2")) {
-                    pmid_model = std::make_shared<PhotoMakerIDEncoder>(backend_for(SDBackendModule::PHOTOMAKER),
-                                                                       params_backend_for(SDBackendModule::PHOTOMAKER),
-                                                                       tensor_storage_map,
-                                                                       "pmid",
-                                                                       version,
-                                                                       PM_VERSION_2);
-                    LOG_INFO("using PhotoMaker Version 2");
-                } else {
-                    pmid_model = std::make_shared<PhotoMakerIDEncoder>(backend_for(SDBackendModule::PHOTOMAKER),
-                                                                       params_backend_for(SDBackendModule::PHOTOMAKER),
-                                                                       tensor_storage_map,
-                                                                       "pmid",
-                                                                       version);
-                }
-                pmid_lora               = std::make_shared<LoraModel>("pmid",
-                                                        backend_for(SDBackendModule::PHOTOMAKER),
-                                                        params_backend_for(SDBackendModule::PHOTOMAKER),
-                                                        sd_ctx_params->photo_maker_path,
-                                                        "",
-                                                        version);
-                auto lora_tensor_filter = [&](const std::string& tensor_name) {
-                    if (starts_with(tensor_name, "lora.model")) {
-                        return true;
-                    }
-                    return false;
+            {
+                generation_extensions.clear();
+                auto photomaker_extension = create_photomaker_extension();
+                GenerationExtensionInitContext extension_ctx{
+                    sd_ctx_params,
+                    version,
+                    tensor_storage_map,
+                    model_loader,
+                    n_threads,
+                    [this](SDBackendModule module) { return ensure_backend_pair(module); },
+                    [this](SDBackendModule module) { return backend_for(module); },
+                    [this](SDBackendModule module) { return params_backend_for(module); },
                 };
-                if (!pmid_lora->load_from_file(n_threads, lora_tensor_filter)) {
-                    LOG_WARN("load photomaker lora tensors from %s failed", sd_ctx_params->photo_maker_path);
+                if (!photomaker_extension->init(extension_ctx)) {
                     return false;
                 }
-                LOG_INFO("loading stacked ID embedding (PHOTOMAKER) model file from '%s'", sd_ctx_params->photo_maker_path);
-                if (!model_loader.init_from_file_and_convert_name(sd_ctx_params->photo_maker_path, "pmid.")) {
-                    LOG_WARN("loading stacked ID embedding from '%s' failed", sd_ctx_params->photo_maker_path);
-                } else {
-                    use_pmid = true;
+                if (photomaker_extension->is_enabled()) {
+                    generation_extensions.push_back(photomaker_extension);
                 }
             }
-            if (use_pmid) {
-                get_param_tensors_p(pmid_model, module_can_mmap(SDBackendModule::PHOTOMAKER), "pmid");
+            {
+                GenerationExtensionTensorContext extension_tensor_ctx{
+                    tensors,
+                    mmap_able_tensors,
+                    module_can_mmap,
+                };
+                for (auto& extension : generation_extensions) {
+                    extension->collect_param_tensors(extension_tensor_ctx);
+                }
             }
 
             if (sd_ctx_params->flash_attn) {
@@ -1010,8 +985,8 @@ public:
         if (use_tae && !tae_preview_only) {
             ignore_tensors.insert("first_stage_model.");
         }
-        if (use_pmid) {
-            ignore_tensors.insert("pmid.unet.");
+        for (auto& extension : generation_extensions) {
+            extension->add_ignore_tensors(ignore_tensors);
         }
         ignore_tensors.insert("model.diffusion_model.__x0__");
         ignore_tensors.insert("model.diffusion_model.__32x32__");
@@ -1098,10 +1073,12 @@ public:
             ggml_free(ctx);
             return false;
         }
-        if (use_pmid && pmid_model && !pmid_model->alloc_params_buffer()) {
-            LOG_ERROR("PhotoMaker params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
+        for (auto& extension : generation_extensions) {
+            if (!extension->alloc_params_buffer()) {
+                LOG_ERROR("%s params buffer allocation failed", extension->name());
+                ggml_free(ctx);
+                return false;
+            }
         }
 
         bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads, sd_ctx_params->enable_mmap);
@@ -1135,9 +1112,9 @@ public:
                 }
                 control_net_params_mem_size = control_net->get_params_buffer_size();
             }
-            size_t pmid_params_mem_size = 0;
-            if (use_pmid) {
-                pmid_params_mem_size = pmid_model->get_params_buffer_size();
+            size_t extension_params_mem_size = 0;
+            for (auto& extension : generation_extensions) {
+                extension_params_mem_size += extension->get_params_buffer_size();
             }
 
             size_t total_params_ram_size  = 0;
@@ -1169,7 +1146,7 @@ public:
             };
 
             if (!add_params_memory(clip_params_mem_size, SDBackendModule::TE) ||
-                !add_params_memory(pmid_params_mem_size, SDBackendModule::PHOTOMAKER) ||
+                !add_params_memory(extension_params_mem_size, SDBackendModule::PHOTOMAKER) ||
                 !add_params_memory(unet_params_mem_size, SDBackendModule::DIFFUSION) ||
                 !add_params_memory(vae_params_mem_size, SDBackendModule::VAE) ||
                 !add_params_memory(control_net_params_mem_size, SDBackendModule::CONTROL_NET)) {
@@ -1180,7 +1157,7 @@ public:
             size_t total_params_size = total_params_ram_size + total_params_vram_size;
             LOG_INFO(
                 "total params memory size = %.2fMB (VRAM %.2fMB, RAM %.2fMB): "
-                "text_encoders %.2fMB(%s), diffusion_model %.2fMB(%s), vae %.2fMB(%s), controlnet %.2fMB(%s), pmid %.2fMB(%s)",
+                "text_encoders %.2fMB(%s), diffusion_model %.2fMB(%s), vae %.2fMB(%s), controlnet %.2fMB(%s), extensions %.2fMB(%s)",
                 total_params_size / 1024.0 / 1024.0,
                 total_params_vram_size / 1024.0 / 1024.0,
                 total_params_ram_size / 1024.0 / 1024.0,
@@ -1192,8 +1169,8 @@ public:
                 params_memory_location(vae_params_mem_size, SDBackendModule::VAE),
                 control_net_params_mem_size / 1024.0 / 1024.0,
                 params_memory_location(control_net_params_mem_size, SDBackendModule::CONTROL_NET),
-                pmid_params_mem_size / 1024.0 / 1024.0,
-                params_memory_location(pmid_params_mem_size, SDBackendModule::PHOTOMAKER));
+                extension_params_mem_size / 1024.0 / 1024.0,
+                params_memory_location(extension_params_mem_size, SDBackendModule::PHOTOMAKER));
         }
 
         // init denoiser
@@ -1598,88 +1575,30 @@ public:
         }
     }
 
-    SDCondition get_pmid_conditon(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;
-                sd::Tensor<float> id_image_tensor;
-                for (int i = 0; i < pm_params.id_images_count; i++) {
-                    auto id_image           = sd_image_to_tensor(pm_params.id_images[i]);
-                    auto processed_id_image = clip_preprocess(id_image, clip_image_size, clip_image_size);
-                    if (id_image_tensor.empty()) {
-                        id_image_tensor = processed_id_image;
-                    } else {
-                        id_image_tensor = sd::ops::concat(id_image_tensor, processed_id_image, 3);
-                    }
-                }
+    void reset_generation_extensions() {
+        for (auto& extension : generation_extensions) {
+            extension->reset_runtime_condition();
+        }
+    }
 
-                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(n_threads,
-                                                                                                       condition_params);
-                id_cond                         = std::get<0>(cond_tup);
-                auto class_tokens_mask          = std::get<1>(cond_tup);
-                sd::Tensor<float> id_embeds;
-                if (pmv2 && pm_params.id_embed_path != nullptr) {
-                    try {
-                        id_embeds = sd::load_tensor_from_file_as_tensor<float>(pm_params.id_embed_path);
-                    } catch (const std::exception&) {
-                        id_embeds = {};
-                    }
-                }
-                if (pmv2 && id_embeds.empty()) {
-                    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.shape()[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, static_cast<int>(id_embeds.shape()[1]));
-                        LOG_WARN("Turn off PhotoMaker");
-                        use_pmid = false;
-                    } else {
-                        auto res = pmid_model->compute(n_threads,
-                                                       id_image_tensor,
-                                                       id_cond.c_crossattn,
-                                                       id_embeds,
-                                                       class_tokens_mask);
-                        if (res.empty()) {
-                            LOG_ERROR("Photomaker ID Stacking failed");
-                            LOG_WARN("Turn off PhotoMaker");
-                            use_pmid = false;
-                        } else {
-                            id_cond.c_crossattn = std::move(res);
-                            int64_t t1          = ggml_time_ms();
-                            LOG_INFO("Photomaker ID Stacking, taking %" PRId64 " ms", t1 - t0);
-                            // Encode input prompt without the trigger word for delayed conditioning
-                            condition_params.text = cond_stage_model->remove_trigger_from_prompt(condition_params.text);
-                        }
-                        if (free_params_immediately) {
-                            pmid_model->free_params_buffer();
-                        }
-                    }
-                }
-            } else {
-                LOG_WARN("Provided PhotoMaker model file, but NO input ID images");
-                LOG_WARN("Turn off PhotoMaker");
-                use_pmid = false;
-            }
+    void prepare_generation_extensions(const sd_pm_params_t& pm_params,
+                                       ConditionerParams& condition_params,
+                                       int total_steps) {
+        reset_generation_extensions();
+        GenerationExtensionConditionContext ctx{
+            cond_stage_model.get(),
+            condition_params,
+            pm_params,
+            tensors,
+            version,
+            n_threads,
+            total_steps,
+            free_params_immediately,
+        };
+
+        for (auto& extension : generation_extensions) {
+            extension->prepare_condition(ctx);
         }
-        return id_cond;
     }
 
     sd::Tensor<float> get_clip_vision_output(const sd::Tensor<float>& image,
@@ -1978,7 +1897,6 @@ public:
                              const SDCondition& cond,
                              const SDCondition& uncond,
                              const SDCondition& img_uncond,
-                             const SDCondition& id_cond,
                              const sd::Tensor<float>& control_image,
                              float control_strength,
                              const sd_guidance_params_t& guidance,
@@ -1988,7 +1906,6 @@ public:
                              bool is_flow_denoiser,
                              const char* extra_sample_args,
                              const std::vector<float>& sigmas,
-                             int start_merge_step,
                              const std::vector<sd::Tensor<float>>& ref_latents,
                              bool increase_ref_index,
                              const sd::Tensor<float>& denoise_mask,
@@ -2180,20 +2097,24 @@ public:
                 return output_opt;
             };
 
-            if (start_merge_step == -1 || step <= start_merge_step) {
-                cond_out = run_condition(cond);
-                if (cond_out.empty()) {
-                    return {};
-                }
-            } else {
-                GGML_ASSERT(!id_cond.empty());
-                cond_out = run_condition(id_cond,
-                                         cond.c_concat.empty() ? nullptr : &cond.c_concat);
-                if (cond_out.empty()) {
-                    return {};
+            const SDCondition* positive_condition      = &cond;
+            const sd::Tensor<float>* c_concat_override = nullptr;
+            for (const auto& extension : generation_extensions) {
+                const SDCondition& next_condition = extension->before_condition(step, *positive_condition);
+                if (&next_condition != positive_condition) {
+                    positive_condition = &next_condition;
+                    if (positive_condition != &cond) {
+                        c_concat_override = cond.c_concat.empty() ? nullptr : &cond.c_concat;
+                    }
+                    break;
                 }
             }
 
+            cond_out = run_condition(*positive_condition, c_concat_override);
+            if (cond_out.empty()) {
+                return {};
+            }
+
             if (!uncond.empty()) {
                 if (!step_cache.is_step_skipped()) {
                     compute_sample_controls(control_image,
@@ -3469,7 +3390,6 @@ struct SamplePlan {
     int high_noise_sample_steps                   = 0;
     int total_steps                               = 0;
     float moe_boundary                            = 0.f;
-    int start_merge_step                          = -1;
     std::vector<float> sigmas;
 
     SamplePlan(sd_ctx_t* sd_ctx,
@@ -3554,11 +3474,6 @@ struct SamplePlan {
             high_noise_eta           = resolve_eta(sd_ctx, high_noise_eta, high_noise_sample_method);
             LOG_INFO("sampling(high noise) using %s method", sampling_methods_str[high_noise_sample_method]);
         }
-
-        if (sd_ctx->sd->use_pmid) {
-            start_merge_step = int(sd_ctx->sd->pmid_model->style_strength / 100.f * total_steps);
-            LOG_INFO("PHOTOMAKER: start_merge_step: %d", start_merge_step);
-        }
     }
 };
 
@@ -3889,7 +3804,6 @@ struct ImageGenerationEmbeds {
     SDCondition cond;
     SDCondition uncond;
     SDCondition img_uncond;
-    SDCondition id_cond;
 };
 
 struct CircularAxesState {
@@ -4194,7 +4108,9 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
     condition_params.height     = request->height;
     condition_params.ref_images = &latents->ref_images;
 
-    auto id_cond                     = sd_ctx->sd->get_pmid_conditon(request->pm_params, condition_params);
+    sd_ctx->sd->prepare_generation_extensions(request->pm_params,
+                                              condition_params,
+                                              plan->total_steps);
     int64_t prepare_start_ms         = ggml_time_ms();
     condition_params.zero_out_masked = false;
     auto cond                        = sd_ctx->sd->cond_stage_model->get_learned_condition(sd_ctx->sd->n_threads,
@@ -4264,7 +4180,6 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
     embeds.img_uncond = std::move(img_uncond);
     embeds.cond       = std::move(cond);
     embeds.uncond     = std::move(uncond);
-    embeds.id_cond    = std::move(id_cond);
 
     return embeds;
 }
@@ -4545,7 +4460,6 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                    embeds.cond,
                                                    embeds.uncond,
                                                    embeds.img_uncond,
-                                                   embeds.id_cond,
                                                    latents.control_image,
                                                    request.control_strength,
                                                    request.guidance,
@@ -4555,7 +4469,6 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                    sd_ctx->sd->is_flow_denoiser(),
                                                    plan.extra_sample_args,
                                                    plan.sigmas,
-                                                   plan.start_merge_step,
                                                    latents.ref_latents,
                                                    request.increase_ref_index,
                                                    latents.denoise_mask,
@@ -4665,7 +4578,6 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                             embeds.cond,
                                                             embeds.uncond,
                                                             embeds.img_uncond,
-                                                            embeds.id_cond,
                                                             latents.control_image,
                                                             request.control_strength,
                                                             request.guidance,
@@ -4675,7 +4587,6 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                             sd_ctx->sd->is_flow_denoiser(),
                                                             plan.extra_sample_args,
                                                             hires_sigma_sched,
-                                                            plan.start_merge_step,
                                                             latents.ref_latents,
                                                             request.increase_ref_index,
                                                             hires_denoise_mask,
@@ -5334,6 +5245,7 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
     sd_ctx->sd->sampler_rng->manual_seed(request.seed);
     sd_ctx->sd->set_flow_shift(sd_vid_gen_params->sample_params.flow_shift);
     sd_ctx->sd->apply_loras(sd_vid_gen_params->loras, sd_vid_gen_params->lora_count);
+    sd_ctx->sd->reset_generation_extensions();
 
     SamplePlan plan(sd_ctx, sd_vid_gen_params, request);
     auto latent_inputs_opt = prepare_video_generation_latents(sd_ctx, sd_vid_gen_params, &request);
@@ -5380,7 +5292,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                                            embeds.cond,
                                                            request.use_high_noise_uncond ? embeds.uncond : SDCondition(),
                                                            embeds.img_uncond,
-                                                           embeds.id_cond,
                                                            sd::Tensor<float>(),
                                                            0.f,
                                                            request.high_noise_guidance,
@@ -5390,7 +5301,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                                            sd_ctx->sd->is_flow_denoiser(),
                                                            plan.high_noise_extra_sample_args,
                                                            high_noise_sigmas,
-                                                           -1,
                                                            std::vector<sd::Tensor<float>>{},
                                                            false,
                                                            latents.denoise_mask,
@@ -5426,7 +5336,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                                         embeds.cond,
                                                         request.use_uncond ? embeds.uncond : SDCondition(),
                                                         embeds.img_uncond,
-                                                        embeds.id_cond,
                                                         sd::Tensor<float>(),
                                                         0.f,
                                                         sd_vid_gen_params->sample_params.guidance,
@@ -5436,7 +5345,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                                         sd_ctx->sd->is_flow_denoiser(),
                                                         plan.extra_sample_args,
                                                         plan.sigmas,
-                                                        -1,
                                                         std::vector<sd::Tensor<float>>{},
                                                         false,
                                                         latents.denoise_mask,
@@ -5570,7 +5478,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                             embeds.cond,
                                           hires_request.use_uncond ? embeds.uncond : SDCondition(),
                                             embeds.img_uncond,
-                                            embeds.id_cond,
                                             sd::Tensor<float>(),
                                             0.f,
                                             sd_vid_gen_params->sample_params.guidance,
@@ -5580,7 +5487,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                             sd_ctx->sd->is_flow_denoiser(),
                                             plan.extra_sample_args,
                                             hires_sigma_sched,
-                                            -1,
                                             std::vector<sd::Tensor<float>>{},
                                             false,
                                             hires_denoise_mask,

src/tokenizers/bpe_tokenizer.cpp

@@ -3,8 +3,8 @@
 #include <algorithm>
 #include <sstream>
 
+#include "core/util.h"
 #include "tokenize_util.h"
-#include "util.h"
 
 std::vector<std::pair<int, std::u32string>> BPETokenizer::bytes_to_unicode() {
     std::vector<std::pair<int, std::u32string>> byte_unicode_pairs;

src/tokenizers/clip_tokenizer.cpp

@@ -6,9 +6,9 @@
 #include <regex>
 #include <set>
 
+#include "core/util.h"
 #include "ggml.h"
 #include "tokenize_util.h"
-#include "util.h"
 #include "vocab/vocab.h"
 
 CLIPTokenizer::CLIPTokenizer(int pad_token_id, const std::string& merges_utf8_str) {

src/tokenizers/gemma_tokenizer.cpp

@@ -1,8 +1,8 @@
 #include "gemma_tokenizer.h"
 
+#include "core/util.h"
 #include "ggml.h"
 #include "json.hpp"
-#include "util.h"
 #include "vocab/vocab.h"
 
 std::string GemmaTokenizer::normalize(const std::string& text) const {

src/tokenizers/gpt_oss_tokenizer.cpp

@@ -1,7 +1,7 @@
 #include "gpt_oss_tokenizer.h"
 
+#include "core/util.h"
 #include "json.hpp"
-#include "util.h"
 #include "vocab/vocab.h"
 
 void GPTOSSTokenizer::load_from_merges(const std::string& merges_utf8_str, const std::string& vocab_utf8_str) {

src/tokenizers/mistral_tokenizer.cpp

@@ -1,8 +1,8 @@
 #include "mistral_tokenizer.h"
 
+#include "core/util.h"
 #include "ggml.h"
 #include "json.hpp"
-#include "util.h"
 #include "vocab/vocab.h"
 
 void MistralTokenizer::load_from_merges(const std::string& merges_utf8_str, const std::string& vocab_utf8_str) {

src/tokenizers/qwen2_tokenizer.cpp

@@ -1,6 +1,6 @@
 #include "qwen2_tokenizer.h"
 
-#include "util.h"
+#include "core/util.h"
 #include "vocab/vocab.h"
 
 void Qwen2Tokenizer::load_from_merges(const std::string& merges_utf8_str) {

src/tokenizers/t5_unigram_tokenizer.cpp

@@ -6,9 +6,9 @@
 #include <regex>
 #include <sstream>
 
+#include "core/util.h"
 #include "json.hpp"
 #include "tokenize_util.h"
-#include "util.h"
 #include "vocab/vocab.h"
 
 // Port from: https://github.com/google/sentencepiece/blob/master/src/unigram_model.h

src/tokenizers/tokenizer.cpp

@@ -4,7 +4,7 @@
 #include <cmath>
 #include <regex>
 
-#include "util.h"
+#include "core/util.h"
 
 void Tokenizer::add_special_token(const std::string& token) {
     special_tokens.push_back(token);

src/upscaler.cpp

@@ -1,8 +1,8 @@
 #include "upscaler.h"
-#include "ggml_extend.hpp"
-#include "model.h"
+#include "core/ggml_extend.hpp"
+#include "core/util.h"
+#include "model_loader.h"
 #include "stable-diffusion.h"
-#include "util.h"
 
 #include <utility>
 

src/upscaler.h

@@ -1,10 +1,10 @@
 #ifndef __SD_UPSCALER_H__
 #define __SD_UPSCALER_H__
 
-#include "esrgan.hpp"
-#include "ggml_extend_backend.h"
+#include "core/ggml_extend_backend.h"
+#include "core/tensor.hpp"
+#include "model/upscaler/esrgan.hpp"
 #include "stable-diffusion.h"
-#include "tensor.hpp"
 
 #include <memory>
 #include <string>