Refactor LTX-2 model structure

mlx_video/models/ltx_2/video_vae/sampling.py

@@ -0,0 +1,275 @@
+"""Sampling operations for Video VAE (upsampling/downsampling)."""
+
+from typing import Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from mlx_video.models.ltx_2.video_vae.convolution import CausalConv3d, PaddingModeType
+
+
+class SpaceToDepthDownsample(nn.Module):
+    """Space-to-depth downsampling with 3x3 conv and skip connection.
+
+    PyTorch-compatible implementation:
+    1. Apply 3x3 conv: in_channels -> out_channels // prod(stride)
+    2. Space-to-depth on conv output: channels * prod(stride)
+    3. Space-to-depth on input with group averaging for skip connection
+    4. Add skip connection
+    """
+
+    def __init__(
+        self,
+        dims: int,
+        in_channels: int,
+        out_channels: int,
+        stride: Union[int, Tuple[int, int, int]],
+        spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS,
+    ):
+        super().__init__()
+
+        if isinstance(stride, int):
+            stride = (stride, stride, stride)
+
+        self.stride = stride
+        self.dims = dims
+        self.out_channels = out_channels
+
+        # Calculate channels
+        multiplier = stride[0] * stride[1] * stride[2]
+        self.group_size = in_channels * multiplier // out_channels
+        conv_out_channels = out_channels // multiplier
+
+        # 3x3 convolution (not 1x1)
+        self.conv = CausalConv3d(
+            in_channels=in_channels,
+            out_channels=conv_out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+    def _space_to_depth(self, x: mx.array) -> mx.array:
+        """Rearrange: b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w"""
+        b, c, d, h, w = x.shape
+        st, sh, sw = self.stride
+
+        # Reshape to group spatial elements
+        x = mx.reshape(x, (b, c, d // st, st, h // sh, sh, w // sw, sw))
+
+        # Permute: (B, C, D', st, H', sh, W', sw) -> (B, C, st, sh, sw, D', H', W')
+        x = mx.transpose(x, (0, 1, 3, 5, 7, 2, 4, 6))
+
+        # Reshape to combine channels
+        new_c = c * st * sh * sw
+        new_d = d // st
+        new_h = h // sh
+        new_w = w // sw
+        x = mx.reshape(x, (b, new_c, new_d, new_h, new_w))
+
+        return x
+
+    def __call__(self, x: mx.array, causal: bool = True) -> mx.array:
+        b, c, d, h, w = x.shape
+        st, sh, sw = self.stride
+
+        # Temporal padding for causal mode
+        if st == 2:
+            # Duplicate first frame for padding
+            x = mx.concatenate([x[:, :, :1, :, :], x], axis=2)
+            d = d + 1
+
+        # Pad if necessary to make dimensions divisible by stride
+        pad_d = (st - d % st) % st
+        pad_h = (sh - h % sh) % sh
+        pad_w = (sw - w % sw) % sw
+
+        if pad_d > 0 or pad_h > 0 or pad_w > 0:
+            x = mx.pad(x, [(0, 0), (0, 0), (0, pad_d), (0, pad_h), (0, pad_w)])
+
+        # Skip connection: space-to-depth on input, then group mean
+        x_in = self._space_to_depth(x)
+        # Reshape for group mean: (b, c*prod(stride), d, h, w) -> (b, out_channels, group_size, d, h, w)
+        b2, c2, d2, h2, w2 = x_in.shape
+        x_in = mx.reshape(x_in, (b2, self.out_channels, self.group_size, d2, h2, w2))
+        x_in = mx.mean(x_in, axis=2)  # (b, out_channels, d, h, w)
+
+        # Conv branch: apply conv then space-to-depth
+        x_conv = self.conv(x, causal=causal)
+        x_conv = self._space_to_depth(x_conv)
+
+        # Add skip connection
+        return x_conv + x_in
+
+
+class DepthToSpaceUpsample(nn.Module):
+    
+    def __init__(
+        self,
+        dims: int,
+        in_channels: int,
+        stride: Union[int, Tuple[int, int, int]],
+        residual: bool = False,
+        out_channels_reduction_factor: int = 1,
+        spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS,
+    ):
+       
+        super().__init__()
+
+        if isinstance(stride, int):
+            stride = (stride, stride, stride)
+
+        self.stride = stride
+        self.dims = dims
+        self.residual = residual
+        self.out_channels_reduction_factor = out_channels_reduction_factor
+
+        # Calculate output channels
+        multiplier = stride[0] * stride[1] * stride[2]
+        out_channels = in_channels // out_channels_reduction_factor
+        self.out_channels = out_channels
+
+        # 3x3x3 convolution to prepare channels for unpacking (matches PyTorch)
+        self.conv = CausalConv3d(
+            in_channels=in_channels,
+            out_channels=out_channels * multiplier,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+    def _depth_to_space(self, x: mx.array) -> mx.array:
+        b, c_packed, d, h, w = x.shape
+        st, sh, sw = self.stride
+        c = c_packed // (st * sh * sw)
+
+        # (B, C*st*sh*sw, D, H, W) -> (B, C, st, sh, sw, D, H, W)
+        x = mx.reshape(x, (b, c, st, sh, sw, d, h, w))
+
+        # (B, C, st, sh, sw, D, H, W) -> (B, C, D, st, H, sh, W, sw)
+        x = mx.transpose(x, (0, 1, 5, 2, 6, 3, 7, 4))
+
+        # (B, C, D, st, H, sh, W, sw) -> (B, C, D*st, H*sh, W*sw)
+        x = mx.reshape(x, (b, c, d * st, h * sh, w * sw))
+
+        return x
+
+    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
+
+        # Compute residual path if enabled
+        x_residual = None
+        if self.residual:
+            # Reshape input: treat channels as spatial factors
+            # "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)"
+            x_residual = self._depth_to_space(x)
+
+            # Tile channels to match output (PyTorch .repeat() tiles, not element-repeat!)
+            # num_repeat = prod(stride) / out_channels_reduction_factor
+            num_repeat = (st * sh * sw) // self.out_channels_reduction_factor
+            x_residual = mx.tile(x_residual, (1, num_repeat, 1, 1, 1))
+
+            # Remove first temporal frame if temporal upsampling
+            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)
+
+        # Remove first frame for causal temporal upsampling
+        if st > 1:
+            x = x[:, :, 1:, :, :]
+
+        # Add residual
+        if self.residual and x_residual is not None:
+            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)