diff --git a/doc/ATWEAKING.md b/doc/ATWEAKING.md
index f2c8523..a8d91da 100644
--- a/doc/ATWEAKING.md
+++ b/doc/ATWEAKING.md
@@ -53,7 +53,7 @@ Cosine also has a decay period to define how long it takes to get to zero LR as
 
 ## Gradient accumulation
 
-Gradient accumulation is sort of like a virtual batch size increase, averaging the learning over more than one step (batch) before applying it to the model update.
+Gradient accumulation is sort of like a virtual batch size increase, averaging the learning over more than one step (batch) before applying it to the model as an update to weights.
 
 Example:
 
diff --git a/train.py b/train.py
index 1a31da2..d66e49b 100644
--- a/train.py
+++ b/train.py
@@ -53,6 +53,12 @@ _GRAD_ACCUM_STEPS = 1 # future use...
 _SIGTERM_EXIT_CODE = 130
 _VERY_LARGE_NUMBER = 1e9
 
+def clean_filename(filename):
+    """
+    removes all non-alphanumeric characters from a string so it is safe to use as a filename
+    """
+    return "".join([c for c in filename if c.isalpha() or c.isdigit() or c==' ']).rstrip()
+
 def convert_to_hf(ckpt_path):
     hf_cache = os.path.join("ckpt_cache", os.path.basename(ckpt_path))
 
@@ -97,6 +103,9 @@ def setup_local_logger(args):
     return datetimestamp
 
 def log_optimizer(optimizer: torch.optim.Optimizer, betas, epsilon):
+    """
+    logs the optimizer settings
+    """
     logging.info(f"{Fore.CYAN} * Optimizer: {optimizer.__class__.__name__} *{Style.RESET_ALL}")
     logging.info(f"    betas: {betas}, epsilon: {epsilon} *{Style.RESET_ALL}")
 
@@ -290,14 +299,16 @@ def main(args):
                 result.paste(image, (x_offset, 0))
                 x_offset += image.width
 
-            result.save(f"{log_folder}/samples/gs{gs:05}-{prompt[:100]}.png")
+            clean_prompt = clean_filename(prompt)
+
+            result.save(f"{log_folder}/samples/gs{gs:05}-{clean_prompt[:100]}.png")
 
             tfimage = transforms.ToTensor()(result)
             if random_captions:
                 log_writer.add_image(tag=f"sample_{i}", img_tensor=tfimage, global_step=gs)
                 i += 1
             else:
-                log_writer.add_image(tag=f"sample_{prompt[:150]}", img_tensor=tfimage, global_step=gs)
+                log_writer.add_image(tag=f"sample_{clean_prompt[:150]}", img_tensor=tfimage, global_step=gs)
 
             del result
             del tfimage
@@ -543,6 +554,10 @@ def main(args):
             for step, batch in enumerate(train_dataloader):
                 step_start_time = time.time()
 
+                if global_step > 0 and global_step > args.text_encoder_steps == 0:
+                    text_encoder.requires_grad_(False)
+                    text_encoder.eval()
+
                 with torch.no_grad():
                     with autocast():
                         pixel_values = batch["image"].to(memory_format=torch.contiguous_format).to(unet.device)
@@ -709,6 +724,7 @@ if __name__ == "__main__":
     argparser.add_argument("--cond_dropout", type=float, default=0.04, help="Conditional drop out as decimal 0.0-1.0, see docs for more info (def: 0.04)")
     argparser.add_argument("--logdir", type=str, default="logs", help="folder to save logs to (def: logs)")
     argparser.add_argument("--save_ckpt_dir", type=str, default=None, help="folder to save checkpoints to (def: root training folder)")
+    argparser.add_argument("--text_encoder_steps", type=int, default=0, help="disable text encoder training after N steps (def: disabled)")
     args = argparser.parse_args()
 
     main(args)