chore: format code

src/core/ggml_graph_cut.cpp

@@ -454,7 +454,7 @@ namespace sd::ggml_graph_cut {
             return false;
         }
         return starts_with(tensor->name, GGML_RUNNER_CUT_PREFIX) &&
-            ends_with(tensor->name, GGML_RUNNER_CUT_SUFFIX);
+               ends_with(tensor->name, GGML_RUNNER_CUT_SUFFIX);
     }
 
     std::string make_graph_cut_name(const std::string& group, const std::string& output) {

src/model/vae/tae.hpp

@@ -427,9 +427,9 @@ class TinyVideoDecoder : public UnaryBlock {
     int channels[num_layers + 1] = {256, 128, 64, 64};
     int patch_size               = 1;
     bool is_wide                 = false;
-    
+
 public:
-    int t_upscale                = 1;
+    int t_upscale = 1;
     TinyVideoDecoder(int z_channels = 4, int patch_size = 1, std::vector<bool> time_upscale = {false, true, true}, bool is_wide = false)
         : z_channels(z_channels), patch_size(patch_size), is_wide(is_wide) {
         t_upscale = 1;
@@ -663,7 +663,7 @@ public:
         if (sd_version_is_minimax_h3(version)) {
             return encode_h3(ctx, x);
         }
-        
+
         auto encoder = std::dynamic_pointer_cast<TinyVideoEncoder>(blocks["encoder"]);
 
         int64_t num_frames = x->ne[3];

src/runtime/denoiser.hpp

@@ -2586,7 +2586,7 @@ static sd::Tensor<float> sample_lms(denoise_cb_t model,
     // modified with "history shift" value, which seemingly needs less steps
     int divisions = 1000;
     int max_order = 4;
-    int shift = 1;  // 4, 0 - original; 4, 1 - PR #1843; 3, 1 - smoother image
+    int shift     = 1;  // 4, 0 - original; 4, 1 - PR #1843; 3, 1 - smoother image
     for (const auto& [key, value] : extra_sample_args) {
         int parsed = 0;
         if (key == "lms_max_order") {
@@ -2624,7 +2624,7 @@ static sd::Tensor<float> sample_lms(denoise_cb_t model,
     auto linear_multistep_coeff = [=](const int order, const int m, const int j) -> float {
         if (!divisions)
             return sigmas[m + 1] - sigmas[m];  // delta / 0 * 0
-#define LMS_PRECISION float  // when divisions > 30 millions, the double precision fixes noise
+#define LMS_PRECISION float                    // when divisions > 30 millions, the double precision fixes noise
         const LMS_PRECISION a = sigmas[m], dx = (sigmas[m + 1] - a) / divisions, s = sigmas[m - j];
         const LMS_PRECISION b0 = a + 0.5f * dx;  // using Riemann middle integral
         LMS_PRECISION sum      = 0.0f;
@@ -2672,8 +2672,8 @@ static sd::Tensor<float> sample_lms(denoise_cb_t model,
                 int hist_max = hist.size() - 1;
                 for (int c = 2; c <= order; c++)
                     x += hist[std::min(hist_max, hist_size_p1 - c + shift)] * lms_coeff[c - 1];
-                    // max_order == 4  =>  hist[] index = 2, 1, 0
-                    // shift == 1      =>  hist[] index = 2, 2, 1
+                // max_order == 4  =>  hist[] index = 2, 1, 0
+                // shift == 1      =>  hist[] index = 2, 2, 1
             }
             if (hist_size_p1 == max_order) {
                 hist.erase(hist.begin());

src/stable-diffusion.cpp

@@ -2315,7 +2315,7 @@ public:
                     return;
                 }
             } else if (channels == 24) {
-                if(sd_version_is_minimax_h3(version)){
+                if (sd_version_is_minimax_h3(version)) {
                     latent_rgb_proj = minimax_latent_rgb_proj;
                     latent_rgb_bias = minimax_latent_rgb_bias;
                 } else {