set descriptions

modules/hypernetworks/hypernetwork.py

@@ -619,7 +619,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 epoch_num = hypernetwork.step // steps_per_epoch
                 epoch_step = hypernetwork.step % steps_per_epoch
 
-                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+                pbar.set_description(description)
+                shared.state.textinfo = description
                 if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
                     # Before saving, change name to match current checkpoint.
                     hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'

modules/textual_inversion/preprocess.py

@@ -135,7 +135,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
     params.process_caption_deepbooru = process_caption_deepbooru
     params.preprocess_txt_action = preprocess_txt_action
 
-    for index, imagefile in enumerate(tqdm.tqdm(files)):
+    pbar = tqdm.tqdm(files)
+    for index, imagefile in enumerate(pbar):
         params.subindex = 0
         filename = os.path.join(src, imagefile)
         try:
@@ -143,6 +144,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         except Exception:
             continue
 
+        description = f"Preprocessing [Image {index}/{len(files)}]"
+        pbar.set_description(description)
+        shared.state.textinfo = description
+
         params.src = filename
 
         existing_caption = None

modules/textual_inversion/textual_inversion.py

@@ -476,7 +476,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 epoch_num = embedding.step // steps_per_epoch
                 epoch_step = embedding.step % steps_per_epoch
 
-                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+                pbar.set_description(description)
+                shared.state.textinfo = description
                 if embedding_dir is not None and steps_done % save_embedding_every == 0:
                     # Before saving, change name to match current checkpoint.
                     embedding_name_every = f'{embedding_name}-{steps_done}'