diff --git a/mlx_video/generate.py b/mlx_video/generate.py
index c400081..9a72fe9 100644
--- a/mlx_video/generate.py
+++ b/mlx_video/generate.py
@@ -488,12 +488,13 @@ def generate_video(
 
     # Stream mode: write frames as they're decoded
     video_writer = None
-    frames_written = [0]  # Use list to allow mutation in closure
+    stream_pbar = None
 
     if stream and tiling_config is not None:
         import cv2
         fourcc = cv2.VideoWriter_fourcc(*'avc1')
         video_writer = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height))
+        stream_pbar = tqdm(total=num_frames, desc="Streaming", unit="frame")
 
         def on_frames_ready(frames: mx.array, start_idx: int):
             """Callback to write frames as they're finalized."""
@@ -506,11 +507,7 @@ def generate_video(
 
             for frame in frames_np:
                 video_writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
-                frames_written[0] += 1
-
-            print(f"{Colors.DIM}  Progressive: wrote frames {start_idx}-{start_idx + len(frames_np) - 1} ({frames_written[0]} total){Colors.RESET}")
-
-        print(f"{Colors.MAGENTA}📹 Streaming enabled - frames will be written as decoded{Colors.RESET}")
+                stream_pbar.update(1)
     else:
         on_frames_ready = None
 
@@ -528,6 +525,8 @@ def generate_video(
     # Close progressive video writer if used
     if video_writer is not None:
         video_writer.release()
+        if stream_pbar is not None:
+            stream_pbar.close()
         print(f"{Colors.GREEN}✅ Streamed video to{Colors.RESET} {output_path}")
         # Still need video_np for save_frames option
         video = mx.squeeze(video, axis=0)
diff --git a/mlx_video/models/ltx/video_vae/decoder.py b/mlx_video/models/ltx/video_vae/decoder.py
index 464b873..9a6cbb3 100644
--- a/mlx_video/models/ltx/video_vae/decoder.py
+++ b/mlx_video/models/ltx/video_vae/decoder.py
@@ -506,13 +506,8 @@ class LTX2VideoDecoder(nn.Module):
 
         if not needs_spatial_tiling and not needs_temporal_tiling:
             # No tiling needed, use regular decode
-            if debug:
-                print("[Tiling] Input fits within tile size, using regular decode")
             return self(sample, causal=causal, timestep=timestep, debug=debug, chunked_conv=use_chunked_conv)
 
-        if debug:
-            print(f"[Tiling] Using tiled decode (spatial={needs_spatial_tiling}, temporal={needs_temporal_tiling})")
-
         return decode_with_tiling(
             decoder_fn=self,
             latents=sample,
@@ -521,7 +516,6 @@ class LTX2VideoDecoder(nn.Module):
             temporal_scale=8,  # VAE temporal upsampling factor
             causal=causal,
             timestep=timestep,
-            debug=debug,
             chunked_conv=use_chunked_conv,
             on_frames_ready=on_frames_ready,
         )
diff --git a/mlx_video/models/ltx/video_vae/tiling.py b/mlx_video/models/ltx/video_vae/tiling.py
index ee55026..72d32e4 100644
--- a/mlx_video/models/ltx/video_vae/tiling.py
+++ b/mlx_video/models/ltx/video_vae/tiling.py
@@ -284,7 +284,6 @@ def decode_with_tiling(
     temporal_scale: int = 8,
     causal: bool = False,
     timestep: Optional[mx.array] = None,
-    debug: bool = False,
     chunked_conv: bool = False,
     on_frames_ready: Optional[Callable[[mx.array, int], None]] = None,
 ) -> mx.array:
@@ -298,7 +297,6 @@ def decode_with_tiling(
         temporal_scale: Temporal scale factor (8 for LTX VAE).
         causal: Whether to use causal convolutions.
         timestep: Optional timestep for conditioning.
-        debug: Whether to print debug info.
         chunked_conv: Whether to use chunked conv mode for upsampling (reduces memory).
         on_frames_ready: Optional callback called with (frames, start_idx) when frames are finalized.
             frames: Tensor of shape (B, 3, num_frames, H, W) with finalized RGB frames.
@@ -343,10 +341,6 @@ def decode_with_tiling(
     num_w_tiles = len(width_intervals.starts)
     total_tiles = num_t_tiles * num_h_tiles * num_w_tiles
 
-    if debug:
-        print(f"[Tiling] Latent shape: {latents.shape}, Output shape: ({b}, 3, {out_f}, {out_h}, {out_w})")
-        print(f"[Tiling] Tiles: {num_t_tiles} temporal x {num_h_tiles} height x {num_w_tiles} width = {total_tiles}")
-
     # Initialize output and weight accumulator
     # Use float32 for accumulation to avoid precision issues
     output = mx.zeros((b, 3, out_f, out_h, out_w), dtype=mx.float32)
@@ -381,10 +375,6 @@ def decode_with_tiling(
                 # Map width coordinates
                 out_w_slice, w_mask = map_spatial_slice(w_start, w_end, w_left, w_right, spatial_scale)
 
-                if debug:
-                    print(f"[Tiling] Tile {tile_idx + 1}/{total_tiles}: "
-                          f"latent t=[{t_start},{t_end}) h=[{h_start},{h_end}) w=[{w_start},{w_end})")
-
                 # Extract tile latents (small slice)
                 tile_latents = latents[:, :, t_start:t_end, h_start:h_end, w_start:w_end]
 
@@ -487,9 +477,6 @@ def decode_with_tiling(
                     finalized_output = finalized_output.astype(latents.dtype)
                     mx.eval(finalized_output)
 
-                    if debug:
-                        print(f"[Tiling] Emitting finalized frames {emitted}-{next_tile_start_out - 1}")
-
                     on_frames_ready(finalized_output, emitted)
                     decode_with_tiling._emitted_frames = next_tile_start_out
 
@@ -507,8 +494,6 @@ def decode_with_tiling(
         if emitted < out_f:
             remaining_output = output[:, :, emitted:, :, :].astype(latents.dtype)
             mx.eval(remaining_output)
-            if debug:
-                print(f"[Tiling] Emitting remaining frames {emitted}-{out_f - 1}")
             on_frames_ready(remaining_output, emitted)
             del remaining_output
 
@@ -520,8 +505,5 @@ def decode_with_tiling(
     del weights
     gc.collect()
 
-    if debug:
-        print(f"[Tiling] Done. Final shape: {output.shape}")
-
     # Convert back to original dtype if needed
     return output.astype(latents.dtype)