format code

Author: leejet

common.hpp

@@ -183,10 +183,10 @@ class GEGLU : public GGMLBlock {
     int64_t dim_out;
 
     void init_params(struct ggml_context* ctx, std::map<std::string, enum ggml_type>& tensor_types, std::string prefix = "") {
-        enum ggml_type wtype = (tensor_types.find(prefix + "proj.weight") != tensor_types.end()) ? tensor_types[prefix + "proj.weight"] : GGML_TYPE_F32;
-        enum ggml_type bias_wtype = GGML_TYPE_F32;//(tensor_types.find(prefix + "proj.bias") != tensor_types.end()) ? tensor_types[prefix + "proj.bias"] : GGML_TYPE_F32;
-        params["proj.weight"] = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
-        params["proj.bias"]   = ggml_new_tensor_1d(ctx, bias_wtype, dim_out * 2);
+        enum ggml_type wtype      = (tensor_types.find(prefix + "proj.weight") != tensor_types.end()) ? tensor_types[prefix + "proj.weight"] : GGML_TYPE_F32;
+        enum ggml_type bias_wtype = GGML_TYPE_F32;  //(tensor_types.find(prefix + "proj.bias") != tensor_types.end()) ? tensor_types[prefix + "proj.bias"] : GGML_TYPE_F32;
+        params["proj.weight"]     = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
+        params["proj.bias"]       = ggml_new_tensor_1d(ctx, bias_wtype, dim_out * 2);
     }
 
 public:
@@ -442,7 +442,7 @@ class AlphaBlender : public GGMLBlock {
 protected:
     void init_params(struct ggml_context* ctx, std::map<std::string, enum ggml_type>& tensor_types, std::string prefix = "") {
         // Get the type of the "mix_factor" tensor from the input tensors map with the specified prefix
-        enum ggml_type wtype = GGML_TYPE_F32;//(tensor_types.ypes.find(prefix + "mix_factor") != tensor_types.end()) ? tensor_types[prefix + "mix_factor"] : GGML_TYPE_F32;
+        enum ggml_type wtype = GGML_TYPE_F32;  //(tensor_types.ypes.find(prefix + "mix_factor") != tensor_types.end()) ? tensor_types[prefix + "mix_factor"] : GGML_TYPE_F32;
         params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
     }
 

control.hpp

@@ -318,7 +318,7 @@ struct ControlNet : public GGMLRunner {
 
     ControlNet(ggml_backend_t backend,
                std::map<std::string, enum ggml_type>& tensor_types,
-               SDVersion version        = VERSION_SD1)
+               SDVersion version = VERSION_SD1)
         : GGMLRunner(backend), control_net(version) {
         control_net.init(params_ctx, tensor_types, "");
     }

diffusion_model.hpp

@@ -133,7 +133,7 @@ struct FluxModel : public DiffusionModel {
 
     FluxModel(ggml_backend_t backend,
               std::map<std::string, enum ggml_type>& tensor_types,
-              bool flash_attn   = false)
+              bool flash_attn = false)
         : flux(backend, tensor_types, "model.diffusion_model", flash_attn) {
     }
 

esrgan.hpp

@@ -142,12 +142,11 @@ struct ESRGAN : public GGMLRunner {
     int scale     = 4;
     int tile_size = 128;  // avoid cuda OOM for 4gb VRAM
 
-    ESRGAN(ggml_backend_t backend,std::map<std::string, enum ggml_type>& tensor_types)
+    ESRGAN(ggml_backend_t backend, std::map<std::string, enum ggml_type>& tensor_types)
         : GGMLRunner(backend) {
         rrdb_net.init(params_ctx, tensor_types, "");
     }
 
-
     std::string get_desc() {
         return "esrgan";
     }

ggml_extend.hpp

@@ -675,13 +675,13 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention(struct ggml_context* ctx
 #if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL) && !defined(SD_USE_VULKAN) && !defined(SD_USE_SYCL)
     struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false);  // [N * n_head, n_token, d_head]
 #else
-    float d_head = (float)q->ne[0];
+    float d_head           = (float)q->ne[0];
     struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q);  // [N * n_head, n_token, n_k]
     kq                     = ggml_scale_inplace(ctx, kq, 1.0f / sqrt(d_head));
     if (mask) {
         kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
     }
-    kq = ggml_soft_max_inplace(ctx, kq);
+    kq                      = ggml_soft_max_inplace(ctx, kq);
     struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq);  // [N * n_head, n_token, d_head]
 #endif
     return kqv;

lora.hpp

@@ -17,7 +17,7 @@ struct LoraModel : public GGMLRunner {
 
     LoraModel(ggml_backend_t backend,
               const std::string& file_path = "",
-              const std::string prefix    = "")
+              const std::string prefix     = "")
         : file_path(file_path), GGMLRunner(backend) {
         if (!model_loader.init_from_file(file_path, prefix)) {
             load_failed = true;

mmdit.hpp

@@ -678,7 +678,7 @@ struct MMDiT : public GGMLBlock {
                 continue;
             size_t jb = tensor_name.find("joint_blocks.");
             if (jb != std::string::npos) {
-                tensor_name = tensor_name.substr(jb);  // remove prefix
+                tensor_name     = tensor_name.substr(jb);  // remove prefix
                 int block_depth = atoi(tensor_name.substr(13, tensor_name.find(".", 13)).c_str());
                 if (block_depth + 1 > depth) {
                     depth = block_depth + 1;

t5.hpp

@@ -357,7 +357,7 @@ class T5UniGramTokenizer {
 
         BuildTrie(&pieces);
     }
-    ~T5UniGramTokenizer() {};
+    ~T5UniGramTokenizer(){};
 
     std::string Normalize(const std::string& input) const {
         // Ref: https://github.com/huggingface/tokenizers/blob/1ff56c0c70b045f0cd82da1af9ac08cd4c7a6f9f/bindings/python/py_src/tokenizers/implementations/sentencepiece_unigram.py#L29

tae.hpp

@@ -196,7 +196,7 @@ struct TinyAutoEncoder : public GGMLRunner {
           GGMLRunner(backend) {
         taesd.init(params_ctx, tensor_types, prefix);
     }
-    
+
     std::string get_desc() {
         return "taesd";
     }