format code

Author: leejet

stable-diffusion.cpp

@@ -115,8 +115,8 @@ class StableDiffusionGGML {
     std::shared_ptr<FrozenCLIPVisionEmbedder> clip_vision;  // for svd or wan2.1 i2v
     std::shared_ptr<DiffusionModel> diffusion_model;
     std::shared_ptr<DiffusionModel> high_noise_diffusion_model;
-    std::shared_ptr<VAE> first_stage_model = nullptr;
-    std::shared_ptr<TinyAutoEncoder> tae_first_stage = nullptr;
+    std::shared_ptr<VAE> first_stage_model;
+    std::shared_ptr<TinyAutoEncoder> tae_first_stage;
     std::shared_ptr<ControlNet> control_net;
     std::shared_ptr<PhotoMakerIDEncoder> pmid_model;
     std::shared_ptr<LoraModel> pmid_lora;
@@ -647,8 +647,8 @@ class StableDiffusionGGML {
                                                                              version);
                     if (version == VERSION_SDXS) {
                         tae_first_stage->alloc_params_buffer();
-                        tae_first_stage->get_param_tensors(tensors,"first_stage_model");
-                    } 
+                        tae_first_stage->get_param_tensors(tensors, "first_stage_model");
+                    }
                 }
                 if (sd_ctx_params->vae_conv_direct) {
                     LOG_INFO("Using Conv2d direct in the tae model");
@@ -793,8 +793,8 @@ class StableDiffusionGGML {
                 if (use_tiny_autoencoder && !tae_first_stage->load_from_file(taesd_path, n_threads)) {
                     return false;
                 }
-                use_tiny_autoencoder = true; // now the processing is identical for VERSION_SDXS
-                vae_params_mem_size = tae_first_stage->get_params_buffer_size();
+                use_tiny_autoencoder = true;  // now the processing is identical for VERSION_SDXS
+                vae_params_mem_size  = tae_first_stage->get_params_buffer_size();
             }
             size_t control_net_params_mem_size = 0;
             if (control_net) {

tae.hpp

@@ -505,7 +505,7 @@ struct TinyAutoEncoder : public GGMLRunner {
                          struct ggml_tensor** output,
                          struct ggml_context* output_ctx = nullptr) = 0;
 
-    virtual bool load_from_file(const std::string& file_path, int n_threads) = 0;
+    virtual bool load_from_file(const std::string& file_path, int n_threads)                                      = 0;
     virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
 };
 
@@ -557,7 +557,7 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
-        taesd.get_param_tensors(tensors,prefix);
+        taesd.get_param_tensors(tensors, prefix);
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
@@ -630,7 +630,7 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
-        taehv.get_param_tensors(tensors,prefix);
+        taehv.get_param_tensors(tensors, prefix);
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {