do not spam pretty progress when using tiled vae/tae as preview

Author: stduhpf

stable-diffusion.cpp

@@ -71,6 +71,14 @@ void calculate_alphas_cumprod(float* alphas_cumprod,
     }
 }
 
+void suppress_pp(int step, int steps, float time, void* data) {
+    (void)step;
+    (void)steps;
+    (void)time;
+    (void)data;
+    return;
+}
+
 /*=============================================== StableDiffusionGGML ================================================*/
 
 class StableDiffusionGGML {
@@ -837,7 +845,16 @@ class StableDiffusionGGML {
         LOG_DEBUG("computing svd condition graph completed, taking %" PRId64 " ms", t1 - t0);
         return {c_crossattn, y, c_concat};
     }
-void preview_image(ggml_context* work_ctx,
+
+    void silent_tiling(ggml_tensor* input, ggml_tensor* output, const int scale, const int tile_size, const float tile_overlap_factor, on_tile_process on_processing) {
+        sd_progress_cb_t cb = sd_get_progress_callback();
+        void* cbd           = sd_get_progress_callback_data();
+        sd_set_progress_callback((sd_progress_cb_t)suppress_pp, NULL);
+        sd_tiling(input, output, scale, tile_size, tile_overlap_factor, on_processing);
+        sd_set_progress_callback(cb, cbd);
+    }
+
+    void preview_image(ggml_context* work_ctx,
                        int step,
                        struct ggml_tensor* latents,
                        enum SDVersion version,
@@ -898,7 +915,8 @@ void preview_image(ggml_context* work_ctx,
                     auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
                         first_stage_model->compute(n_threads, in, true, &out);
                     };
-                    sd_tiling(latents, result, 8, 32, 0.5f, on_tiling);
+                    silent_tiling(latents, result, 8, 32, 0.5f, on_tiling);
+
                 } else {
                     first_stage_model->compute(n_threads, latents, true, &result);
                 }
@@ -916,7 +934,7 @@ void preview_image(ggml_context* work_ctx,
                     auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
                         tae_first_stage->compute(n_threads, in, true, &out);
                     };
-                    sd_tiling(latents, result, 8, 64, 0.5f, on_tiling);
+                    silent_tiling(latents, result, 8, 64, 0.5f, on_tiling);
                 } else {
                     tae_first_stage->compute(n_threads, latents, true, &result);
                 }
@@ -1194,6 +1212,10 @@ void preview_image(ggml_context* work_ctx,
                     }
                 }
             }
+            if (step > 0) {
+                pretty_progress(step, (int)steps, (t1 - t0) / 1000000.f);
+                // LOG_INFO("step %d sampling completed taking %.2fs", step, (t1 - t0) * 1.0f / 1000000);
+            }
 
             if (step_callback != nullptr) {
                 if (step % preview_interval == 0) {

stable-diffusion.h

@@ -217,6 +217,8 @@ typedef void (*sd_progress_cb_t)(int step, int steps, float time, void* data);
 
 SD_API void sd_set_log_callback(sd_log_cb_t sd_log_cb, void* data);
 SD_API void sd_set_progress_callback(sd_progress_cb_t cb, void* data);
+SD_API sd_progress_cb_t sd_get_progress_callback();
+SD_API void* sd_get_progress_callback_data();
 SD_API int32_t get_num_physical_cores();
 SD_API const char* sd_get_system_info();
 

util.cpp

@@ -420,6 +420,12 @@ void sd_set_progress_callback(sd_progress_cb_t cb, void* data) {
     sd_progress_cb      = cb;
     sd_progress_cb_data = data;
 }
+sd_progress_cb_t sd_get_progress_callback(){
+    return sd_progress_cb;
+}
+void* sd_get_progress_callback_data(){
+    return sd_progress_cb_data;
+}
 const char* sd_get_system_info() {
     static char buffer[1024];
     std::stringstream ss;