Merge pull request #14 from Blaizzy/pc/add-streaming

Add --stream flag and chunked conv memory optimization for VAE decoding

mlx_video/generate.py

@@ -215,6 +215,7 @@ def generate_video(
     image_strength: float = 1.0,
     image_frame_idx: int = 0,
     tiling: str = "auto",
+    stream: bool = False,
 ):
     """Generate video from text prompt, optionally conditioned on an image.
 
@@ -481,17 +482,59 @@ def generate_video(
         print(f"{Colors.YELLOW}  Unknown tiling mode '{tiling}', using auto{Colors.RESET}")
         tiling_config = TilingConfig.auto(height, width, num_frames)
 
+    # Save outputs
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    # Stream mode: write frames as they're decoded
+    video_writer = None
+    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."""
+            # frames: (B, 3, num_frames, H, W)
+            frames = mx.squeeze(frames, axis=0)  # (3, num_frames, H, W)
+            frames = mx.transpose(frames, (1, 2, 3, 0))  # (num_frames, H, W, 3)
+            frames = mx.clip((frames + 1.0) / 2.0, 0.0, 1.0)
+            frames = (frames * 255).astype(mx.uint8)
+            frames_np = np.array(frames)
+
+            for frame in frames_np:
+                video_writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+                stream_pbar.update(1)
+    else:
+        on_frames_ready = None
+
     if tiling_config is not None:
         spatial_info = f"{tiling_config.spatial_config.tile_size_in_pixels}px" if tiling_config.spatial_config else "none"
         temporal_info = f"{tiling_config.temporal_config.tile_size_in_frames}f" if tiling_config.temporal_config else "none"
         print(f"{Colors.DIM}  Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}")
-        video = vae_decoder.decode_tiled(latents, tiling_config=tiling_config, debug=verbose)
+        video = vae_decoder.decode_tiled(latents, tiling_config=tiling_config, tiling_mode=tiling, debug=verbose, on_frames_ready=on_frames_ready)
     else:
         print(f"{Colors.DIM}  Tiling: disabled{Colors.RESET}")
         video = vae_decoder(latents)
     mx.eval(video)
     mx.clear_cache()
 
+    # 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)
+        video = mx.transpose(video, (1, 2, 3, 0))
+        video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0)
+        video = (video * 255).astype(mx.uint8)
+        video_np = np.array(video)
+    else:
         # Convert to uint8 frames
         video = mx.squeeze(video, axis=0)  # (C, F, H, W)
         video = mx.transpose(video, (1, 2, 3, 0))  # (F, H, W, C)
@@ -499,15 +542,12 @@ def generate_video(
         video = (video * 255).astype(mx.uint8)
         video_np = np.array(video)
 
-    # Save outputs
-    output_path = Path(output_path)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-
+        # Save video normally
         try:
             import cv2
-        height, width = video_np.shape[1], video_np.shape[2]
+            h, w = video_np.shape[1], video_np.shape[2]
             fourcc = cv2.VideoWriter_fourcc(*'avc1')
-        out = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height))
+            out = cv2.VideoWriter(str(output_path), fourcc, fps, (w, h))
             for frame in video_np:
                 out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
             out.release()
@@ -654,6 +694,11 @@ Examples:
              "auto=based on video size, none=disabled, default=512px/64f, "
              "aggressive=256px/32f (lowest memory), conservative=768px/96f, spatial=spatial only, temporal=temporal only"
     )
+    parser.add_argument(
+        "--stream",
+        action="store_true",
+        help="Stream frames to output file as they're decoded (requires tiling). Allows viewing partial results sooner."
+    )
     args = parser.parse_args()
 
     generate_video(

mlx_video/models/ltx/video_vae/decoder.py

@@ -15,7 +15,7 @@ Architecture (from PyTorch weights):
 """
 
 import math
-from typing import List, Optional
+from typing import Optional
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -364,6 +364,7 @@ class LTX2VideoDecoder(nn.Module):
         causal: bool = False,
         timestep: Optional[mx.array] = None,
         debug: bool = False,
+        chunked_conv: bool = False,
     ) -> mx.array:
        
         def debug_stats(name, t):
@@ -403,6 +404,8 @@ class LTX2VideoDecoder(nn.Module):
         for i, block in self.up_blocks.items():
             if isinstance(block, ResBlockGroup):
                 x = block(x, causal=causal, timestep=scaled_timestep)
+            elif isinstance(block, DepthToSpaceUpsample):
+                x = block(x, causal=causal, chunked_conv=chunked_conv)
             else:
                 x = block(x, causal=causal)
             if debug:
@@ -450,9 +453,11 @@ class LTX2VideoDecoder(nn.Module):
         self,
         sample: mx.array,
         tiling_config: Optional[TilingConfig] = None,
+        tiling_mode: str = "auto",
         causal: bool = False,
         timestep: Optional[mx.array] = None,
         debug: bool = False,
+        on_frames_ready: Optional[callable] = None,
     ) -> mx.array:
         """Decode latents using tiling to reduce memory usage.
 
@@ -495,14 +500,13 @@ class LTX2VideoDecoder(nn.Module):
             if f > tile_size_latent:
                 needs_temporal_tiling = True
 
+        # Auto-enable chunked conv for modes where it helps (larger tiles)
+        # Chunked conv reduces memory by processing conv+depth_to_space in temporal chunks
+        use_chunked_conv = tiling_mode in ("conservative", "none", "auto", "default", "spatial")
+
         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)
-
-        if debug:
-            print(f"[Tiling] Using tiled decode (spatial={needs_spatial_tiling}, temporal={needs_temporal_tiling})")
+            return self(sample, causal=causal, timestep=timestep, debug=debug, chunked_conv=use_chunked_conv)
 
         return decode_with_tiling(
             decoder_fn=self,
@@ -512,7 +516,8 @@ 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,
         )
 
 

mlx_video/models/ltx/video_vae/sampling.py

@@ -156,7 +156,7 @@ class DepthToSpaceUpsample(nn.Module):
 
         return x
 
-    def __call__(self, x: mx.array, causal: bool = True) -> mx.array:
+    def __call__(self, x: mx.array, causal: bool = True, chunked_conv: bool = False) -> mx.array:
 
         b, c, d, h, w = x.shape
         st, sh, sw = self.stride
@@ -177,9 +177,12 @@ class DepthToSpaceUpsample(nn.Module):
             if st > 1:
                 x_residual = x_residual[:, :, 1:, :, :]
 
+        # Use chunked mode for large tensors to reduce peak memory
+        if chunked_conv and d > 4:
+            x = self._chunked_conv_depth_to_space(x, causal)
+        else:
             # Apply conv
             x = self.conv(x, causal=causal)
-
             # Depth to space rearrangement
             x = self._depth_to_space(x)
 
@@ -192,3 +195,81 @@ class DepthToSpaceUpsample(nn.Module):
             x = x + x_residual
 
         return x
+
+    def _chunked_conv_depth_to_space(self, x: mx.array, causal: bool = True) -> mx.array:
+        """Chunked conv + depth_to_space that processes in temporal chunks.
+
+        This reduces peak memory by avoiding the full high-channel intermediate tensor.
+        Instead of materializing (B, 4096, D, H, W), we process temporal chunks and
+        immediately apply depth_to_space.
+
+        Args:
+            x: Input tensor of shape (B, C, D, H, W)
+            causal: Whether to use causal convolutions
+
+        Returns:
+            Output tensor after conv + depth_to_space
+        """
+        b, c, d, h, w = x.shape
+        st, sh, sw = self.stride
+        out_c = self.out_channels
+
+        # Output dimensions
+        out_d = d * st
+        out_h = h * sh
+        out_w = w * sw
+
+        # Chunk size in temporal dimension (process 4 frames at a time)
+        chunk_size = 4
+        kernel_t = 3  # Temporal kernel size
+
+        # For causal conv, we need (kernel_t - 1) frames of padding at the start
+        # For non-causal, we need (kernel_t - 1) // 2 on each side
+        if causal:
+            # Pad start with first frame repeated
+            pad_start = kernel_t - 1
+            pad_end = 0
+        else:
+            pad_start = (kernel_t - 1) // 2
+            pad_end = (kernel_t - 1) // 2
+
+        # Allocate output
+        outputs = []
+
+        # Process in chunks with overlap for conv kernel
+        t_pos = 0
+        while t_pos < d:
+            t_end = min(t_pos + chunk_size, d)
+
+            # Calculate input range with padding for kernel
+            in_start = max(0, t_pos - pad_start)
+            in_end = min(d, t_end + pad_end)
+
+            # Extract chunk
+            chunk = x[:, :, in_start:in_end, :, :]
+
+            # Apply conv to chunk
+            chunk_conv = self.conv(chunk, causal=causal)
+
+            # Apply depth_to_space
+            chunk_out = self._depth_to_space(chunk_conv)
+
+            # Calculate valid output range (excluding padding effects)
+            # Each input frame produces st output frames
+            out_start = (t_pos - in_start) * st
+            out_end = out_start + (t_end - t_pos) * st
+
+            # Extract valid portion
+            chunk_out = chunk_out[:, :, out_start:out_end, :, :]
+
+            outputs.append(chunk_out)
+
+            # Evaluate to free intermediate memory
+            mx.eval(outputs[-1])
+
+            t_pos = t_end
+
+        # Concatenate all chunks
+        if len(outputs) == 1:
+            return outputs[0]
+        return mx.concatenate(outputs, axis=2)

mlx_video/models/ltx/video_vae/tiling.py

@@ -8,8 +8,8 @@ Default configuration (from PyTorch):
 - Temporal: 64 frames with 24 frame overlap
 """
 
-from dataclasses import dataclass, replace
-from typing import List, Optional, Tuple
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Tuple
 
 import mlx.core as mx
 
@@ -284,7 +284,8 @@ 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:
     """Decode latents using tiling to reduce memory usage.
 
@@ -296,7 +297,10 @@ 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.
+            start_idx: Starting frame index in the full video.
 
     Returns:
         Decoded video.
@@ -337,10 +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)
@@ -375,15 +375,11 @@ 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]
 
                 # Decode tile
-                tile_output = decoder_fn(tile_latents, causal=causal, timestep=timestep, debug=False)
+                tile_output = decoder_fn(tile_latents, causal=causal, timestep=timestep, debug=False, chunked_conv=chunked_conv)
                 mx.eval(tile_output)
 
                 # Clear tile_latents reference
@@ -454,17 +450,60 @@ def decode_with_tiling(
                     except Exception:
                         pass  # May not be available on all platforms
 
+        # After completing all spatial tiles for this temporal tile,
+        # check if any frames are now finalized (no future tiles will contribute)
+        if on_frames_ready is not None and num_t_tiles > 1:
+            # Determine the finalized frame boundary
+            # Frames before the start of the next tile's output region are finalized
+            if t_idx < num_t_tiles - 1:
+                # Next tile starts at temporal_intervals.starts[t_idx + 1]
+                next_tile_start_latent = temporal_intervals.starts[t_idx + 1]
+                # Map to output frame index (first frame of next tile's contribution)
+                if next_tile_start_latent == 0:
+                    next_tile_start_out = 0
+                else:
+                    next_tile_start_out = 1 + (next_tile_start_latent - 1) * temporal_scale
+
+                # We need to track how many frames we've already emitted
+                if not hasattr(decode_with_tiling, '_emitted_frames'):
+                    decode_with_tiling._emitted_frames = 0
+                emitted = decode_with_tiling._emitted_frames
+
+                if next_tile_start_out > emitted:
+                    # Normalize and emit frames [emitted, next_tile_start_out)
+                    finalized_weights = weights[:, :, emitted:next_tile_start_out, :, :]
+                    finalized_weights = mx.maximum(finalized_weights, 1e-8)
+                    finalized_output = output[:, :, emitted:next_tile_start_out, :, :] / finalized_weights
+                    finalized_output = finalized_output.astype(latents.dtype)
+                    mx.eval(finalized_output)
+
+                    on_frames_ready(finalized_output, emitted)
+                    decode_with_tiling._emitted_frames = next_tile_start_out
+
+                    del finalized_output, finalized_weights
+                    gc.collect()
+
     # Normalize by weights
     weights = mx.maximum(weights, 1e-8)
     output = output / weights
     mx.eval(output)
 
+    # Emit remaining frames if callback provided
+    if on_frames_ready is not None:
+        emitted = getattr(decode_with_tiling, '_emitted_frames', 0)
+        if emitted < out_f:
+            remaining_output = output[:, :, emitted:, :, :].astype(latents.dtype)
+            mx.eval(remaining_output)
+            on_frames_ready(remaining_output, emitted)
+            del remaining_output
+
+    # Reset emitted frames counter for next call
+    if hasattr(decode_with_tiling, '_emitted_frames'):
+        del decode_with_tiling._emitted_frames
+
     # Clean up weights
     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)

tests/test_vae_streaming.py

@@ -0,0 +1,301 @@
+"""Tests for VAE streaming and chunked conv features."""
+
+import pytest
+import mlx.core as mx
+import numpy as np
+
+from mlx_video.models.ltx.video_vae.sampling import DepthToSpaceUpsample
+from mlx_video.models.ltx.video_vae.tiling import (
+    TilingConfig,
+    compute_trapezoidal_mask_1d,
+    decode_with_tiling,
+)
+
+
+class TestChunkedConv:
+    """Tests for chunked conv optimization in DepthToSpaceUpsample."""
+
+    def test_chunked_conv_output_matches_regular(self):
+        """Verify chunked_conv produces identical output to regular processing."""
+        mx.random.seed(42)
+
+        # Create upsampler with residual (matches decoder config)
+        upsampler = DepthToSpaceUpsample(
+            dims=3,
+            in_channels=256,
+            stride=(2, 2, 2),
+            residual=True,
+            out_channels_reduction_factor=2,
+        )
+
+        # Initialize weights deterministically
+        mx.eval(upsampler.parameters())
+
+        # Create test input: (B, C, D, H, W) with enough frames to trigger chunking
+        # chunked_conv activates when d > 4
+        x = mx.random.normal((1, 256, 8, 8, 8))
+        mx.eval(x)
+
+        # Run without chunked conv
+        out_regular = upsampler(x, causal=True, chunked_conv=False)
+        mx.eval(out_regular)
+
+        # Run with chunked conv
+        out_chunked = upsampler(x, causal=True, chunked_conv=True)
+        mx.eval(out_chunked)
+
+        # Outputs should be identical
+        np.testing.assert_allclose(
+            np.array(out_regular),
+            np.array(out_chunked),
+            rtol=1e-5,
+            atol=1e-5,
+            err_msg="Chunked conv output differs from regular output"
+        )
+
+    def test_chunked_conv_small_input_passthrough(self):
+        """Verify chunked_conv doesn't activate for small inputs (d <= 4)."""
+        mx.random.seed(42)
+
+        upsampler = DepthToSpaceUpsample(
+            dims=3,
+            in_channels=256,
+            stride=(2, 2, 2),
+            residual=True,
+            out_channels_reduction_factor=2,
+        )
+        mx.eval(upsampler.parameters())
+
+        # Small input with d=4 (should NOT trigger chunking)
+        x = mx.random.normal((1, 256, 4, 8, 8))
+        mx.eval(x)
+
+        out_regular = upsampler(x, causal=True, chunked_conv=False)
+        out_chunked = upsampler(x, causal=True, chunked_conv=True)
+        mx.eval(out_regular, out_chunked)
+
+        # Should be identical since chunking doesn't activate
+        np.testing.assert_allclose(
+            np.array(out_regular),
+            np.array(out_chunked),
+            rtol=1e-5,
+            atol=1e-5,
+        )
+
+    def test_chunked_conv_output_shape(self):
+        """Verify chunked_conv produces correct output shape."""
+        mx.random.seed(42)
+
+        upsampler = DepthToSpaceUpsample(
+            dims=3,
+            in_channels=256,
+            stride=(2, 2, 2),
+            residual=True,
+            out_channels_reduction_factor=2,
+        )
+        mx.eval(upsampler.parameters())
+
+        # Input shape: (1, 256, 8, 16, 16)
+        x = mx.random.normal((1, 256, 8, 16, 16))
+        mx.eval(x)
+
+        out = upsampler(x, causal=True, chunked_conv=True)
+        mx.eval(out)
+
+        # Expected output:
+        # - Channels: 256 / 2 = 128
+        # - Temporal: 8 * 2 - 1 = 15 (minus 1 for causal)
+        # - Spatial: 16 * 2 = 32
+        assert out.shape == (1, 128, 15, 32, 32), f"Unexpected shape: {out.shape}"
+
+
+class TestProgressiveFrameSaving:
+    """Tests for progressive frame saving via on_frames_ready callback."""
+
+    def test_on_frames_ready_called(self):
+        """Verify on_frames_ready callback is called during tiled decoding."""
+        frames_received = []
+
+        def on_frames_ready(frames: mx.array, start_idx: int):
+            frames_received.append({
+                'shape': frames.shape,
+                'start_idx': start_idx,
+            })
+
+        # Create a mock decoder that just returns scaled input
+        def mock_decoder(x, causal=False, timestep=None, debug=False, chunked_conv=False):
+            # Simulate VAE output: upsample 8x temporal, 32x spatial
+            b, c, f, h, w = x.shape
+            out_f = 1 + (f - 1) * 8
+            out_h = h * 32
+            out_w = w * 32
+            return mx.zeros((b, 3, out_f, out_h, out_w))
+
+        # Create tiling config with temporal tiling to trigger callbacks
+        tiling_config = TilingConfig.temporal_only(tile_size=32, overlap=8)
+
+        # Input latents: enough frames to require multiple tiles
+        # 10 latent frames -> 73 output frames
+        latents = mx.zeros((1, 128, 10, 4, 4))
+
+        # Decode with tiling
+        output = decode_with_tiling(
+            decoder_fn=mock_decoder,
+            latents=latents,
+            tiling_config=tiling_config,
+            spatial_scale=32,
+            temporal_scale=8,
+            on_frames_ready=on_frames_ready,
+        )
+        mx.eval(output)
+
+        # Should have received at least one callback
+        assert len(frames_received) > 0, "on_frames_ready was never called"
+
+        # All received frames should have correct channel count
+        for received in frames_received:
+            assert received['shape'][1] == 3, f"Expected 3 channels, got {received['shape'][1]}"
+
+    def test_on_frames_ready_covers_all_frames(self):
+        """Verify all frames are emitted via callbacks."""
+        all_frame_indices = set()
+
+        def on_frames_ready(frames: mx.array, start_idx: int):
+            num_frames = frames.shape[2]
+            for i in range(num_frames):
+                all_frame_indices.add(start_idx + i)
+
+        def mock_decoder(x, causal=False, timestep=None, debug=False, chunked_conv=False):
+            b, c, f, h, w = x.shape
+            out_f = 1 + (f - 1) * 8
+            out_h = h * 32
+            out_w = w * 32
+            return mx.random.normal((b, 3, out_f, out_h, out_w))
+
+        tiling_config = TilingConfig.temporal_only(tile_size=32, overlap=8)
+
+        # 12 latent frames -> 89 output frames
+        latents = mx.zeros((1, 128, 12, 4, 4))
+
+        output = decode_with_tiling(
+            decoder_fn=mock_decoder,
+            latents=latents,
+            tiling_config=tiling_config,
+            spatial_scale=32,
+            temporal_scale=8,
+            on_frames_ready=on_frames_ready,
+        )
+        mx.eval(output)
+
+        # Calculate expected frame count
+        expected_frames = 1 + (12 - 1) * 8  # 89 frames
+
+        # All frames should have been emitted
+        assert len(all_frame_indices) == expected_frames, \
+            f"Expected {expected_frames} frames, got {len(all_frame_indices)}"
+        assert all_frame_indices == set(range(expected_frames)), \
+            "Not all frame indices were covered"
+
+
+class TestAutoChunkedConv:
+    """Tests for auto-enabling chunked_conv based on tiling mode."""
+
+    @pytest.mark.parametrize("tiling_mode,should_enable", [
+        ("conservative", True),
+        ("none", True),
+        ("auto", True),
+        ("default", True),
+        ("spatial", True),
+        ("aggressive", False),
+        ("temporal", False),
+    ])
+    def test_chunked_conv_auto_enable(self, tiling_mode: str, should_enable: bool):
+        """Verify chunked_conv is auto-enabled for correct tiling modes."""
+        # The logic is: tiling_mode in ("conservative", "none", "auto", "default", "spatial")
+        expected_modes = {"conservative", "none", "auto", "default", "spatial"}
+
+        use_chunked_conv = tiling_mode in expected_modes
+
+        assert use_chunked_conv == should_enable, \
+            f"For tiling_mode='{tiling_mode}', expected chunked_conv={should_enable}"
+
+
+class TestTrapezoidalMask:
+    """Tests for trapezoidal blending mask generation."""
+
+    def test_mask_values_in_range(self):
+        """Verify mask values are always in [0, 1]."""
+        for length in [16, 32, 64, 128]:
+            for ramp in [0, 4, 8, 16]:
+                if ramp < length:
+                    mask = compute_trapezoidal_mask_1d(length, ramp, ramp, False)
+                    assert mx.all(mask >= 0).item(), f"Mask has negative values"
+                    assert mx.all(mask <= 1).item(), f"Mask has values > 1"
+
+    def test_mask_center_is_one(self):
+        """Verify center of mask is 1.0 when ramps don't overlap."""
+        mask = compute_trapezoidal_mask_1d(32, 8, 8, False)
+        # Center region should be 1.0
+        center = mask[12:20]  # Middle portion
+        np.testing.assert_allclose(np.array(center), 1.0, rtol=1e-5)
+
+    def test_mask_ramp_monotonic(self):
+        """Verify ramps are monotonically increasing/decreasing."""
+        mask = compute_trapezoidal_mask_1d(32, 8, 8, False)
+        mask_np = np.array(mask)
+
+        # Left ramp should be increasing
+        left_ramp = mask_np[:8]
+        assert np.all(np.diff(left_ramp) >= 0), "Left ramp not monotonically increasing"
+
+        # Right ramp should be decreasing
+        right_ramp = mask_np[-8:]
+        assert np.all(np.diff(right_ramp) <= 0), "Right ramp not monotonically decreasing"
+
+    def test_temporal_mask_starts_from_zero(self):
+        """Verify temporal mask (left_starts_from_0=True) starts from 0."""
+        mask = compute_trapezoidal_mask_1d(32, 8, 0, left_starts_from_0=True)
+        assert mask[0].item() == 0.0, "Temporal mask should start from 0"
+
+    def test_spatial_mask_starts_above_zero(self):
+        """Verify spatial mask (left_starts_from_0=False) starts above 0."""
+        mask = compute_trapezoidal_mask_1d(32, 8, 0, left_starts_from_0=False)
+        assert mask[0].item() > 0.0, "Spatial mask should start above 0"
+
+
+class TestTilingConfig:
+    """Tests for TilingConfig presets."""
+
+    def test_default_config(self):
+        """Verify default tiling configuration."""
+        config = TilingConfig.default()
+        assert config.spatial_config is not None
+        assert config.temporal_config is not None
+        assert config.spatial_config.tile_size_in_pixels == 512
+        assert config.temporal_config.tile_size_in_frames == 64
+
+    def test_aggressive_config(self):
+        """Verify aggressive tiling configuration."""
+        config = TilingConfig.aggressive()
+        assert config.spatial_config.tile_size_in_pixels == 256
+        assert config.temporal_config.tile_size_in_frames == 32
+
+    def test_conservative_config(self):
+        """Verify conservative tiling configuration."""
+        config = TilingConfig.conservative()
+        assert config.spatial_config.tile_size_in_pixels == 768
+        assert config.temporal_config.tile_size_in_frames == 96
+
+    def test_auto_returns_none_for_small_video(self):
+        """Verify auto returns None for small videos."""
+        config = TilingConfig.auto(height=256, width=256, num_frames=33)
+        assert config is None, "Auto should return None for small videos"
+
+    def test_auto_returns_config_for_large_video(self):
+        """Verify auto returns config for large videos."""
+        config = TilingConfig.auto(height=1024, width=768, num_frames=145)
+        assert config is not None, "Auto should return config for large videos"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])