diff --git a/data/every_dream_validation.py b/data/every_dream_validation.py
index 95f5afd..2793ae5 100644
--- a/data/every_dream_validation.py
+++ b/data/every_dream_validation.py
@@ -105,7 +105,7 @@ class EveryDreamValidator:
         [Any, Any], tuple[torch.Tensor, torch.Tensor]]):
         with torch.no_grad(), isolate_rng():
             loss_validation_epoch = []
-            steps_pbar = tqdm(range(len(dataloader)), position=1)
+            steps_pbar = tqdm(range(len(dataloader)), position=1, leave=False)
             steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Validate ({tag}){Style.RESET_ALL}")
 
             for step, batch in enumerate(dataloader):
diff --git a/train.py b/train.py
index 6c5d6b6..d5eeff0 100644
--- a/train.py
+++ b/train.py
@@ -691,7 +691,7 @@ def main(args):
     )
     logging.info(f" Grad scaler enabled: {scaler.is_enabled()} (amp mode)")
 
-    epoch_pbar = tqdm(range(args.max_epochs), position=0, leave=True)
+    epoch_pbar = tqdm(range(args.max_epochs), position=0, leave=True, dynamic_ncols=True)
     epoch_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Epochs{Style.RESET_ALL}")
     epoch_times = []
 
@@ -754,7 +754,12 @@ def main(args):
 
     def generate_samples(global_step: int, batch):
         with isolate_rng():
+            prev_sample_steps = sample_generator.sample_steps
             sample_generator.reload_config()
+            if prev_sample_steps != sample_generator.sample_steps:
+                next_sample_step = math.ceil((global_step + 1) / sample_generator.sample_steps) * sample_generator.sample_steps
+                print(f" * SampleGenerator config changed, now generating images samples every " +
+                      f"{sample_generator.sample_steps} training steps (next={next_sample_step})")
             sample_generator.update_random_captions(batch["captions"])
             inference_pipe = sample_generator.create_inference_pipe(unet=unet,
                                                                     text_encoder=text_encoder,
@@ -787,7 +792,7 @@ def main(args):
             images_per_sec_log_step = []
 
             epoch_len = math.ceil(len(train_batch) / args.batch_size)
-            steps_pbar = tqdm(range(epoch_len), position=1)
+            steps_pbar = tqdm(range(epoch_len), position=1, leave=False, dynamic_ncols=True)
             steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Steps{Style.RESET_ALL}")
 
             for step, batch in enumerate(train_dataloader):
diff --git a/utils/sample_generator.py b/utils/sample_generator.py
index 049a628..c9c6ee6 100644
--- a/utils/sample_generator.py
+++ b/utils/sample_generator.py
@@ -12,6 +12,7 @@ from diffusers import StableDiffusionPipeline, DDIMScheduler, DPMSolverMultistep
 from torch.cuda.amp import autocast
 from torch.utils.tensorboard import SummaryWriter
 from torchvision import transforms
+from tqdm.auto import tqdm
 
 
 def clean_filename(filename):
@@ -89,7 +90,7 @@ class SampleGenerator:
 
         self.sample_requests = None
         self.reload_config()
-        print(f" * SampleGenerator initialized with {len(self.sample_requests)} prompts, using scheduler '{self.scheduler}', {self.num_inference_steps} steps")
+        print(f" * SampleGenerator initialized with {len(self.sample_requests)} prompts, generating samples every {self.sample_steps} training steps, using scheduler '{self.scheduler}' with {self.num_inference_steps} inference steps")
         if not os.path.exists(f"{log_folder}/samples/"):
             os.makedirs(f"{log_folder}/samples/")
 
@@ -169,9 +170,7 @@ class SampleGenerator:
         """
         generates samples at different cfg scales and saves them to disk
         """
-        logging.info(f"Generating samples gs:{global_step}, for {[p.prompt for p in self.sample_requests]}")
-
-        pipe.set_progress_bar_config(disable=(not self.show_progress_bars))
+        disable_progress_bars = not self.show_progress_bars
 
         try:
             font = ImageFont.truetype(font="arial.ttf", size=20)
@@ -183,10 +182,13 @@ class SampleGenerator:
             batch: list[SampleRequest]
             def sample_compatibility_test(a: SampleRequest, b: SampleRequest) -> bool:
                 return a.size == b.size
-            for batch in chunk_list(self.sample_requests, self.batch_size,
-                                    compatibility_test=sample_compatibility_test):
-                #print("batch: ", batch)
+            batches = list(chunk_list(self.sample_requests, self.batch_size,
+                                    compatibility_test=sample_compatibility_test))
+            pbar = tqdm(total=len(batches), disable=disable_progress_bars, position=1, leave=False,
+                              desc=f"{Fore.YELLOW}Image samples (batches of {self.batch_size}){Style.RESET_ALL}")
+            for batch in batches:
                 prompts = [p.prompt for p in batch]
+                pbar.set_postfix(postfix={'prompts': prompts})
                 negative_prompts = [p.negative_prompt for p in batch]
                 seeds = [(p.seed if p.seed != -1 else random.randint(0, 2 ** 30))
                          for p in batch]
@@ -196,6 +198,8 @@ class SampleGenerator:
 
                 batch_images = []
                 for cfg in self.cfgs:
+                    pipe.set_progress_bar_config(disable=disable_progress_bars, position=2, leave=False,
+                                                 desc=f"{Fore.LIGHTYELLOW_EX}CFG scale {cfg}{Style.RESET_ALL}")
                     images = pipe(prompt=prompts,
                                   negative_prompt=negative_prompts,
                                   num_inference_steps=self.num_inference_steps,
@@ -257,6 +261,7 @@ class SampleGenerator:
                     del tfimage
                 del batch_images
 
+                pbar.update(1)
 
     @torch.no_grad()
     def create_inference_pipe(self, unet, text_encoder, tokenizer, vae, diffusers_scheduler_config: dict):