format code and avoid some warnings

denoiser.hpp

@@ -80,31 +80,30 @@ struct DiscreteSchedule : SigmaSchedule {
 https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
 */
 struct AYSSchedule : SigmaSchedule {
-    /* interp and linearInterp adapted from dpilger26's NumCpp library:
+    /* interp and linear_interp adapted from dpilger26's NumCpp library:
      * https://github.com/dpilger26/NumCpp/tree/5e40aab74d14e257d65d3dc385c9ff9e2120c60e */
     constexpr double interp(double left, double right, double perc) noexcept {
         return (left * (1. - perc)) + (right * perc);
     }
 
-    /* This will make the assumption that the reference x and y values are 
-     * already sorted in ascending order because they are being generated as 
+    /* This will make the assumption that the reference x and y values are
+     * already sorted in ascending order because they are being generated as
      * such in the calling function */
-    std::vector<double> linearInterp(std::vector<float> new_x, 
-        const std::vector<float> ref_x, const std::vector<float> ref_y)
-    {
+    std::vector<double> linear_interp(std::vector<float> new_x,
+                                      const std::vector<float> ref_x,
+                                      const std::vector<float> ref_y) {
         const size_t len_x = new_x.size();
-        size_t i = 0;
-        size_t j = 0;
+        size_t i           = 0;
+        size_t j           = 0;
         std::vector<double> new_y(len_x);
 
-        if (ref_x.size() != ref_y.size()) { 
+        if (ref_x.size() != ref_y.size()) {
             LOG_ERROR("Linear Interoplation Failed: length mismatch");
-            return new_y;    
+            return new_y;
         }
 
         /* serves as the bounds checking for the below while loop */
-        if ((new_x[0] < ref_x[0]) 
-        || (new_x[new_x.size() - 1] > ref_x[ref_x.size() - 1])) {
+        if ((new_x[0] < ref_x[0]) || (new_x[new_x.size() - 1] > ref_x[ref_x.size() - 1])) {
             LOG_ERROR("Linear Interpolation Failed: bad bounds");
             return new_y;
         }
@@ -115,8 +114,7 @@ struct AYSSchedule : SigmaSchedule {
                 continue;
             }
 
-            const double perc = static_cast<double>(new_x[i] - ref_x[j])
-                / static_cast<double>(ref_x[j + 1] - ref_x[j]);
+            const double perc = static_cast<double>(new_x[i] - ref_x[j]) / static_cast<double>(ref_x[j + 1] - ref_x[j]);
 
             new_y[i] = interp(ref_y[j], ref_y[j + 1], perc);
             i++;
@@ -125,8 +123,7 @@ struct AYSSchedule : SigmaSchedule {
         return new_y;
     }
 
-    std::vector<float> linearSpace(const float start, const float end, 
-        const size_t num_points) {
+    std::vector<float> linear_space(const float start, const float end, const size_t num_points) {
         std::vector<float> result(num_points);
         const float inc = (end - start) / (static_cast<float>(num_points - 1));
 
@@ -141,10 +138,10 @@ struct AYSSchedule : SigmaSchedule {
         return result;
     }
 
-    std::vector<float> logLinearInterpolation(std::vector<float> sigma_in, 
-        const size_t new_len) {
+    std::vector<float> log_linear_interpolation(std::vector<float> sigma_in,
+                                                const size_t new_len) {
         const size_t s_len        = sigma_in.size();
-        std::vector<float> x_vals = linearSpace(0.f, 1.f, s_len);
+        std::vector<float> x_vals = linear_space(0.f, 1.f, s_len);
         std::vector<float> y_vals(s_len);
 
         /* Reverses the input array to be ascending instead of descending,
@@ -153,8 +150,8 @@ struct AYSSchedule : SigmaSchedule {
             y_vals[i] = std::log(sigma_in[s_len - i - 1]);
         }
 
-        std::vector<float>  new_x_vals = linearSpace(0.f, 1.f, new_len);
-        std::vector<double> new_y_vals = linearInterp(new_x_vals, x_vals, y_vals);
+        std::vector<float> new_x_vals  = linear_space(0.f, 1.f, new_len);
+        std::vector<double> new_y_vals = linear_interp(new_x_vals, x_vals, y_vals);
         std::vector<float> results(new_len);
 
         for (size_t i = 0; i < new_len; i++) {
@@ -164,56 +161,55 @@ struct AYSSchedule : SigmaSchedule {
         return results;
     }
 
-
     std::vector<float> get_sigmas(uint32_t len) {
         const std::vector<float> noise_levels[] = {
             /* SD1.5 */
-            {14.6146412293, 6.4745760956,  3.8636745985,  2.6946151520, 
-            1.8841921177,   1.3943805092,  0.9642583904,  0.6523686016, 
-            0.3977456272,   0.1515232662,  0.0291671582},
+            {14.6146412293f, 6.4745760956f, 3.8636745985f, 2.6946151520f,
+             1.8841921177f, 1.3943805092f, 0.9642583904f, 0.6523686016f,
+             0.3977456272f, 0.1515232662f, 0.0291671582f},
             /* SDXL */
-            {14.6146412293, 6.3184485287,  3.7681790315,  2.1811480769, 
-            1.3405244945,   0.8620721141,  0.5550693289,  0.3798540708, 
-            0.2332364134,   0.1114188177,  0.0291671582},
+            {14.6146412293f, 6.3184485287f, 3.7681790315f, 2.1811480769f,
+             1.3405244945f, 0.8620721141f, 0.5550693289f, 0.3798540708f,
+             0.2332364134f, 0.1114188177f, 0.0291671582f},
             /* SVD */
-            {700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 
-            0.173, 0.034, 0.002},
+            {700.00f, 54.5f, 15.886f, 7.977f, 4.248f, 1.789f, 0.981f, 0.403f,
+             0.173f, 0.034f, 0.002f},
         };
-    
+
         std::vector<float> inputs;
         std::vector<float> results(len + 1);
-    
+
         switch (version) {
             case VERSION_2_x: /* fallthrough */
-	        LOG_WARN("AYS not designed for SD2.X models");
-            case VERSION_1_x: 
-	    	LOG_INFO("AYS using SD1.5 noise levels");
+                LOG_WARN("AYS not designed for SD2.X models");
+            case VERSION_1_x:
+                LOG_INFO("AYS using SD1.5 noise levels");
                 inputs = noise_levels[0];
                 break;
             case VERSION_XL:
-	    	LOG_INFO("AYS using SDXL noise levels");
+                LOG_INFO("AYS using SDXL noise levels");
                 inputs = noise_levels[1];
                 break;
             case VERSION_SVD:
-	    	LOG_INFO("AYS using SVD noise levels");
+                LOG_INFO("AYS using SVD noise levels");
                 inputs = noise_levels[2];
                 break;
             default:
                 LOG_ERROR("Version not compatable with AYS scheduler");
                 return results;
         }
-            
+
         /* Stretches those pre-calculated reference levels out to the desired
          * size using log-linear interpolation */
         if ((len + 1) != inputs.size()) {
-            results = logLinearInterpolation(inputs, len + 1);
+            results = log_linear_interpolation(inputs, len + 1);
         } else {
-	    results = inputs; 
-	}
-    
+            results = inputs;
+        }
+
         /* Not sure if this is strictly neccessary */
         results[len] = 0.0f;
-    
+
         return results;
     }
 };
@@ -230,7 +226,7 @@ struct KarrasSchedule : SigmaSchedule {
 
         float min_inv_rho = pow(sigma_min, (1.f / rho));
         float max_inv_rho = pow(sigma_max, (1.f / rho));
-        for (uint32_t i = 0; i < n; i++) { 
+        for (uint32_t i = 0; i < n; i++) {
             // Eq. (5) from Karras et al 2022
             result[i] = pow(max_inv_rho + (float)i / ((float)n - 1.f) * (min_inv_rho - max_inv_rho), rho);
         }

model.cpp

@@ -890,6 +890,7 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
 
         // ggml/src/ggml.c:2745
         if (n_dims < 1 || n_dims > GGML_MAX_DIMS) {
+            LOG_ERROR("skip tensor '%s' with n_dims %d", name.c_str(), n_dims);
             continue;
         }
 

stable-diffusion.cpp

@@ -450,11 +450,11 @@ class StableDiffusionGGML {
                     LOG_INFO("running with Karras schedule");
                     denoiser->schedule = std::make_shared<KarrasSchedule>();
                     break;
-		case AYS:
-		    LOG_INFO("Running with Align-Your-Steps schedule");
-		    denoiser->schedule = std::make_shared<AYSSchedule>();
-		    denoiser->schedule->version = version;
-		    break;
+                case AYS:
+                    LOG_INFO("Running with Align-Your-Steps schedule");
+                    denoiser->schedule          = std::make_shared<AYSSchedule>();
+                    denoiser->schedule->version = version;
+                    break;
                 case DEFAULT:
                     // Don't touch anything.
                     break;