Rework progress bar and ui speed string so it gets the same value at a more accurate and snappier wall clock time

extensions_built_in/sd_trainer/DiffusionTrainer.py

@@ -337,6 +337,17 @@ class DiffusionTrainer(SDTrainer):
                 self.thread_pool.shutdown(wait=True)
 
     def handle_timing_print_hook(self, timing_dict):
+        # pull the rate from the progress bar's EMA so the UI matches it exactly
+        rate = None  # iter/sec
+        if self.progress_bar is not None:
+            rate = self.progress_bar.format_dict.get("rate")
+        if rate:
+            if rate >= 1:
+                self.update_db_key("speed_string", f"{rate:.2f} iter/sec")
+            else:
+                self.update_db_key("speed_string", f"{1 / rate:.2f} sec/iter")
+            return
+        # fallback: bar not available yet (no rate until its first refresh)
         if "train_loop" not in timing_dict:
             print("train_loop not found in timing_dict", timing_dict)
             return

jobs/process/BaseSDTrainProcess.py

@@ -2573,15 +2573,11 @@ class BaseSDTrainProcess(BaseTrainProcess):
                         except StopIteration:
                             with self.timer('reset_batch:reg'):
                                 # hit the end of an epoch, reset
-                                if self.progress_bar is not None:
-                                    self.progress_bar.pause()
                                 dataloader_iterator_reg = iter(dataloader_reg)
                                 trigger_dataloader_setup_epoch(dataloader_reg)
 
                             with self.timer('get_batch:reg'):
                                 batch = next(dataloader_iterator_reg)
-                            if self.progress_bar is not None:
-                                self.progress_bar.unpause()
                         is_reg_step = True
                     elif dataloader is not None:
                         try:
@@ -2590,8 +2586,6 @@ class BaseSDTrainProcess(BaseTrainProcess):
                         except StopIteration:
                             with self.timer('reset_batch'):
                                 # hit the end of an epoch, reset
-                                if self.progress_bar is not None:
-                                    self.progress_bar.pause()
                                 dataloader_iterator = iter(dataloader)
                                 trigger_dataloader_setup_epoch(dataloader)
                                 self.epoch_num += 1
@@ -2601,8 +2595,6 @@ class BaseSDTrainProcess(BaseTrainProcess):
                                     self.grad_accumulation_step = 0
                             with self.timer('get_batch'):
                                 batch = next(dataloader_iterator)
-                            if self.progress_bar is not None:
-                                self.progress_bar.unpause()
                     else:
                         batch = None
                     batch_list.append(batch)
@@ -2748,8 +2740,6 @@ class BaseSDTrainProcess(BaseTrainProcess):
                             self.progress_bar.unpause()
 
                     if self.logging_config.log_every and self.step_num % self.logging_config.log_every == 0:
-                        if self.progress_bar is not None:
-                            self.progress_bar.pause()
                         with self.timer('log_to_tensorboard'):
                             # log to tensorboard
                             if self.accelerator.is_main_process:
@@ -2758,9 +2748,7 @@ class BaseSDTrainProcess(BaseTrainProcess):
                                         for key, value in loss_dict.items():
                                             self.writer.add_scalar(f"{key}", value, self.step_num)
                                         self.writer.add_scalar(f"lr", learning_rate, self.step_num)
-                                if self.progress_bar is not None:
+
-                                    self.progress_bar.unpause()
-                        
                         if self.accelerator.is_main_process:
                             # log to logger
                             self.logger.log({
@@ -2796,22 +2784,18 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
 
                     if self.performance_log_every > 0 and self.step_num % self.performance_log_every == 0:
-                        if self.progress_bar is not None:
-                            self.progress_bar.pause()
                         # print the timers and clear them
                         self.timer.print()
                         self.timer.reset()
-                        if self.progress_bar is not None:
-                            self.progress_bar.unpause()
                 
                 # commit log
                 if self.accelerator.is_main_process:
                     with self.timer('commit_logger'):
                         self.logger.commit(step=self.step_num)
 
-                # sets progress bar to match out step
+                # sets progress bar to match our step (step is complete, so completed count is step + 1)
                 if self.progress_bar is not None:
-                    self.progress_bar.update(step - self.progress_bar.n)
+                    self.progress_bar.update(step + 1 - self.progress_bar.n)
 
                 #############################
                 # End of step

toolkit/progress_bar.py

@@ -4,6 +4,9 @@ import time
 
 class ToolkitProgressBar(tqdm):
     def __init__(self, *args, **kwargs):
+        # high EMA alpha so the rate/ETA responds within a few steps
+        # (tqdm default of 0.3 takes ~10 steps to wash out old samples)
+        kwargs.setdefault('smoothing', 0.7)
         super().__init__(*args, **kwargs)
         self.paused = False
         self.last_time = self._time()