chore: add .clang-format

Author: leejet

.clang-format

@@ -0,0 +1,13 @@
+BasedOnStyle: Chromium
+UseTab: Never
+IndentWidth: 4
+TabWidth: 4
+AllowShortIfStatementsOnASingleLine: false
+IndentCaseLabels: false
+ColumnLimit: 0
+AccessModifierOffset: -4
+NamespaceIndentation: All
+FixNamespaceComments: false 
+AlignAfterOpenBracket: true
+AlignConsecutiveAssignments: true
+IndentCaseLabels: true

.gitignore

@@ -3,3 +3,4 @@ test/
 
 .cache/
 *.swp
+.vscode/

examples/main.cpp

@@ -88,28 +88,27 @@ const char* sample_method_str[] = {
 const char* schedule_str[] = {
     "default",
     "discrete",
-    "karras"
-};
+    "karras"};
 
 struct Option {
-    int n_threads = -1;
+    int n_threads    = -1;
     std::string mode = TXT2IMG;
     std::string model_path;
     std::string lora_model_dir;
     std::string output_path = "output.png";
     std::string init_img;
     std::string prompt;
     std::string negative_prompt;
-    float cfg_scale = 7.0f;
-    int w = 512;
-    int h = 512;
+    float cfg_scale            = 7.0f;
+    int w                      = 512;
+    int h                      = 512;
     SampleMethod sample_method = EULER_A;
-    Schedule schedule = DEFAULT;
-    int sample_steps = 20;
-    float strength = 0.75f;
-    RNGType rng_type = CUDA_RNG;
-    int64_t seed = 42;
-    bool verbose = false;
+    Schedule schedule          = DEFAULT;
+    int sample_steps           = 20;
+    float strength             = 0.75f;
+    RNGType rng_type           = CUDA_RNG;
+    int64_t seed               = 42;
+    bool verbose               = false;
 
     void print() {
         printf("Option: \n");
@@ -129,7 +128,7 @@ struct Option {
         printf("    sample_steps:    %d\n", sample_steps);
         printf("    strength:        %.2f\n", strength);
         printf("    rng:             %s\n", rng_type_to_str[rng_type]);
-        printf("    seed:            %lld\n", seed);
+        printf("    seed:            %ld\n", seed);
     }
 };
 
@@ -266,7 +265,7 @@ void parse_args(int argc, const char* argv[], Option* opt) {
                 break;
             }
             const char* schedule_selected = argv[i];
-            int schedule_found = -1;
+            int schedule_found            = -1;
             for (int d = 0; d < N_SCHEDULES; d++) {
                 if (!strcmp(schedule_selected, schedule_str[d])) {
                     schedule_found = d;
@@ -289,7 +288,7 @@ void parse_args(int argc, const char* argv[], Option* opt) {
                 break;
             }
             const char* sample_method_selected = argv[i];
-            int sample_method_found = -1;
+            int sample_method_found            = -1;
             for (int m = 0; m < N_SAMPLE_METHODS; m++) {
                 if (!strcmp(sample_method_selected, sample_method_str[m])) {
                     sample_method_found = m;
@@ -405,6 +404,7 @@ int main(int argc, const char* argv[]) {
         vae_decode_only = false;
 
         int c = 0;
+
         unsigned char* img_data = stbi_load(opt.init_img.c_str(), &opt.w, &opt.h, &c, 3);
         if (img_data == NULL) {
             fprintf(stderr, "load image from '%s' failed\n", opt.init_img.c_str());

rng.h

@@ -5,23 +5,23 @@
 #include <vector>
 
 class RNG {
-   public:
-    virtual void manual_seed(uint64_t seed) = 0;
+public:
+    virtual void manual_seed(uint64_t seed)      = 0;
     virtual std::vector<float> randn(uint32_t n) = 0;
 };
 
 class STDDefaultRNG : public RNG {
-   private:
+private:
     std::default_random_engine generator;
 
-   public:
+public:
     void manual_seed(uint64_t seed) {
         generator.seed((unsigned int)seed);
     }
 
     std::vector<float> randn(uint32_t n) {
         std::vector<float> result;
-        float mean = 0.0;
+        float mean   = 0.0;
         float stddev = 1.0;
         std::normal_distribution<float> distribution(mean, stddev);
         for (uint32_t i = 0; i < n; i++) {

rng_philox.h

@@ -9,15 +9,15 @@
 // RNG imitiating torch cuda randn on CPU.
 // Port from: https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/5ef669de080814067961f28357256e8fe27544f4/modules/rng_philox.py
 class PhiloxRNG : public RNG {
-   private:
+private:
     uint64_t seed;
     uint32_t offset;
 
-   private:
+private:
     std::vector<uint32_t> philox_m = {0xD2511F53, 0xCD9E8D57};
     std::vector<uint32_t> philox_w = {0x9E3779B9, 0xBB67AE85};
-    float two_pow32_inv = 2.3283064e-10f;
-    float two_pow32_inv_2pi = 2.3283064e-10f * 6.2831855f;
+    float two_pow32_inv            = 2.3283064e-10f;
+    float two_pow32_inv_2pi        = 2.3283064e-10f * 6.2831855f;
 
     std::vector<uint32_t> uint32(uint64_t x) {
         std::vector<uint32_t> result(2);
@@ -87,14 +87,14 @@ class PhiloxRNG : public RNG {
         return r1;
     }
 
-   public:
+public:
     PhiloxRNG(uint64_t seed = 0) {
-        this->seed = seed;
+        this->seed   = seed;
         this->offset = 0;
     }
 
     void manual_seed(uint64_t seed) {
-        this->seed = seed;
+        this->seed   = seed;
         this->offset = 0;
     }