diff --git a/mlx_video/models/ltx/text_encoder.py b/mlx_video/models/ltx/text_encoder.py
index d6461d5..a38bb6d 100644
--- a/mlx_video/models/ltx/text_encoder.py
+++ b/mlx_video/models/ltx/text_encoder.py
@@ -11,6 +11,8 @@ from typing import Dict, List, Optional, Tuple
 import mlx.core as mx
 import mlx.nn as nn
 import numpy as np
+from rich.console import Console
+from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn
 
 from mlx_video.utils import rms_norm, apply_quantization
 from mlx_video.models.ltx.rope import apply_interleaved_rotary_emb
@@ -854,7 +856,6 @@ class LTX2TextEncoder(nn.Module):
         Returns:
             Enhanced prompt string
         """
-        from tqdm import tqdm
         try:
             from mlx_lm import stream_generate
             from mlx_lm.sample_utils import make_logits_processors, make_sampler
@@ -878,7 +879,6 @@ class LTX2TextEncoder(nn.Module):
         # Use mlx-lm generate with temperature sampling
         mx.random.seed(seed)
 
-       
         # Tokenize
         inputs = self.processor(
             formatted,
@@ -893,39 +893,51 @@ class LTX2TextEncoder(nn.Module):
             kwargs.get("repetition_penalty", 1.3),
             kwargs.get("repetition_context_size", 20),
         )
-        
+
         generated_token_count = 0
         generated_tokens = []
-        for i, response in enumerate(
-            tqdm(
-                stream_generate(
-                    self.language_model,
-                    tokenizer=self.processor,
-                    prompt=input_ids.squeeze(0),
-                    max_tokens=max_tokens,
-                    sampler=sampler,
-                    logits_processors=logits_processors,
-                ),
-                total=max_tokens,
-                disable=not verbose,
-            )
-        ):
-            next_token = mx.array([response.token])
-            input_ids = mx.concatenate([input_ids, next_token[None, :]], axis=1)
-            generated_tokens.append(next_token.squeeze())
-            generated_token_count += 1
+        console = Console()
 
-            if i % 50 == 0:
-                mx.clear_cache()
+        generator = stream_generate(
+            self.language_model,
+            tokenizer=self.processor,
+            prompt=input_ids.squeeze(0),
+            max_tokens=max_tokens,
+            sampler=sampler,
+            logits_processors=logits_processors,
+        )
 
-            # Check for EOS
-            if response.token == 1 or response.token == 107:  # EOS tokens
-                break
+        progress = Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            TimeRemainingColumn(),
+            console=console,
+            disable=not verbose,
+        )
+
+        with progress:
+            task = progress.add_task("[cyan]Generating[/]", total=max_tokens)
+
+            for i, response in enumerate(generator):
+                next_token = mx.array([response.token])
+                input_ids = mx.concatenate([input_ids, next_token[None, :]], axis=1)
+                generated_tokens.append(next_token.squeeze())
+                generated_token_count += 1
+                progress.update(task, advance=1)
+
+                if i % 50 == 0:
+                    mx.clear_cache()
+
+                # Check for EOS
+                if response.token == 1 or response.token == 107:  # EOS tokens
+                    progress.update(task, completed=max_tokens)
+                    break
 
         mx.clear_cache()
 
         # Decode only the new tokens
-
         enhanced_prompt = self.processor.decode(generated_tokens, skip_special_tokens=True)
 
         enhanced_prompt = self._clean_response(enhanced_prompt)