initial commit (LTX-2)
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Prince Canuma
294
mlx_video/models/ltx/video_vae/convolution.py
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294
mlx_video/models/ltx/video_vae/convolution.py
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from enum import Enum
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from typing import List, Optional, Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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class PaddingModeType(Enum):
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ZEROS = "zeros"
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REFLECT = "reflect"
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def reflect_pad_2d(x: mx.array, pad_h: int, pad_w: int) -> mx.array:
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"""Apply reflect padding to spatial dimensions of a 5D tensor.
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Args:
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x: Input tensor of shape (B, D, H, W, C) - channels last
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pad_h: Padding for height dimension
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pad_w: Padding for width dimension
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Returns:
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Padded tensor
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"""
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if pad_h == 0 and pad_w == 0:
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return x
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# Height padding (axis 2)
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if pad_h > 0:
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# Get reflection indices - exclude boundary
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top_pad = x[:, :, 1:pad_h+1, :, :][:, :, ::-1, :, :] # Flip top portion
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bottom_pad = x[:, :, -pad_h-1:-1, :, :][:, :, ::-1, :, :] # Flip bottom portion
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x = mx.concatenate([top_pad, x, bottom_pad], axis=2)
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# Width padding (axis 3)
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if pad_w > 0:
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left_pad = x[:, :, :, 1:pad_w+1, :][:, :, :, ::-1, :] # Flip left portion
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right_pad = x[:, :, :, -pad_w-1:-1, :][:, :, :, ::-1, :] # Flip right portion
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x = mx.concatenate([left_pad, x, right_pad], axis=3)
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return x
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def make_conv_nd(
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dims: int,
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in_channels: int,
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out_channels: int,
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kernel_size: Union[int, Tuple[int, ...]],
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stride: Union[int, Tuple[int, ...]] = 1,
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padding: Union[int, Tuple[int, ...], str] = 0,
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causal: bool = False,
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spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS,
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) -> nn.Module:
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if dims == 2:
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return CausalConv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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causal=causal,
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spatial_padding_mode=spatial_padding_mode,
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)
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elif dims == 3:
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return CausalConv3d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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causal=causal,
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spatial_padding_mode=spatial_padding_mode,
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)
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else:
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raise ValueError(f"Unsupported number of dimensions: {dims}")
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class CausalConv3d(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Union[int, Tuple[int, int, int]],
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stride: Union[int, Tuple[int, int, int]] = 1,
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padding: Union[int, Tuple[int, int, int], str] = 0,
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causal: bool = False,
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spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS,
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):
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super().__init__()
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self.causal = causal
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self.spatial_padding_mode = spatial_padding_mode
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# Normalize kernel_size and stride to tuples
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if isinstance(kernel_size, int):
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kernel_size = (kernel_size, kernel_size, kernel_size)
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if isinstance(stride, int):
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stride = (stride, stride, stride)
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self.kernel_size = kernel_size
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self.stride = stride
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self.time_kernel_size = kernel_size[0]
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# Calculate spatial padding (temporal is handled separately via frame replication)
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height_pad = kernel_size[1] // 2
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width_pad = kernel_size[2] // 2
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self.spatial_padding = (height_pad, width_pad)
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# Create the base convolution (without padding, we'll handle it manually)
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self.conv = nn.Conv3d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=0, # We handle padding manually
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bias=True,
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)
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def __call__(self, x: mx.array, causal: Optional[bool] = None) -> mx.array:
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use_causal = causal if causal is not None else self.causal
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# Apply temporal padding via frame replication
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# Only apply if kernel_size > 1
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if self.time_kernel_size > 1:
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if use_causal:
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# Causal: replicate first frame kernel_size-1 times at the beginning
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first_frame_pad = mx.repeat(x[:, :, :1, :, :], self.time_kernel_size - 1, axis=2)
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x = mx.concatenate([first_frame_pad, x], axis=2)
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else:
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# Non-causal: replicate first frame at start, last frame at end
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pad_size = (self.time_kernel_size - 1) // 2
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if pad_size > 0:
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first_frame_pad = mx.repeat(x[:, :, :1, :, :], pad_size, axis=2)
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last_frame_pad = mx.repeat(x[:, :, -1:, :, :], pad_size, axis=2)
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x = mx.concatenate([first_frame_pad, x, last_frame_pad], axis=2)
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# Transpose to channels last: (B, C, D, H, W) -> (B, D, H, W, C)
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x = mx.transpose(x, (0, 2, 3, 4, 1))
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# Apply spatial padding
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pad_h, pad_w = self.spatial_padding
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if pad_h > 0 or pad_w > 0:
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if self.spatial_padding_mode == PaddingModeType.REFLECT:
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# Use reflect padding for spatial dimensions
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x = reflect_pad_2d(x, pad_h, pad_w)
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else:
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# Use zero padding for spatial dimensions
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pad_width = [
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(0, 0), # Batch
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(0, 0), # D (temporal - already padded)
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(pad_h, pad_h), # H
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(pad_w, pad_w), # W
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(0, 0), # C
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]
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x = mx.pad(x, pad_width)
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# Apply convolution with chunking for large tensors
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# Note: We choose to use chunking because MLX conv3d fails around 33 frames with 192x192 spatial
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x = self._chunked_conv3d(x)
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# Transpose back to channels first: (B, D, H, W, C) -> (B, C, D, H, W)
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x = mx.transpose(x, (0, 4, 1, 2, 3))
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return x
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def _chunked_conv3d(self, x: mx.array) -> mx.array:
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"""Apply conv3d in temporal chunks to work around MLX bug with large tensors.
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Args:
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x: Input tensor of shape (B, D, H, W, C) in channels-last format
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Returns:
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Output tensor after conv3d
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"""
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b, d, h, w, c = x.shape
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total_elements = d * h * w * c
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max_safe_elements = 30 * 192 * 192 * 128 # ~140M elements per chunk
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if total_elements <= max_safe_elements:
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return self.conv(x)
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elements_per_frame = h * w * c
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max_frames_per_chunk = max(1, max_safe_elements // elements_per_frame)
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chunk_size = min(max_frames_per_chunk, 24) # Cap at 24 frames per chunk
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kernel_t = self.time_kernel_size
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overlap = kernel_t - 1
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expected_output_frames = d - overlap
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outputs = []
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out_idx = 0
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# Process chunks
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in_start = 0
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while out_idx < expected_output_frames:
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remaining = expected_output_frames - out_idx
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out_frames_this_chunk = min(chunk_size, remaining)
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in_frames_needed = out_frames_this_chunk + overlap
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in_end = min(in_start + in_frames_needed, d)
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chunk = x[:, in_start:in_end, :, :, :]
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chunk_out = self.conv(chunk)
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mx.eval(chunk_out)
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outputs.append(chunk_out)
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out_idx += chunk_out.shape[1]
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in_start += chunk_out.shape[1]
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# Concatenate all chunks
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if len(outputs) == 1:
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return outputs[0]
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return mx.concatenate(outputs, axis=1)
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class CausalConv2d(nn.Module):
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"""2D convolution with optional causal padding."""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Union[int, Tuple[int, int]],
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stride: Union[int, Tuple[int, int]] = 1,
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padding: Union[int, Tuple[int, int], str] = 0,
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causal: bool = False,
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spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS,
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):
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"""Initialize CausalConv2d."""
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super().__init__()
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self.causal = causal
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self.spatial_padding_mode = spatial_padding_mode
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# Normalize kernel_size and stride
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if isinstance(kernel_size, int):
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kernel_size = (kernel_size, kernel_size)
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if isinstance(stride, int):
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stride = (stride, stride)
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self.kernel_size = kernel_size
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self.stride = stride
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# Calculate padding
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if isinstance(padding, str) and padding == "same":
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self.padding = (
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(kernel_size[0] - 1) // 2,
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(kernel_size[1] - 1) // 2,
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)
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elif isinstance(padding, int):
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self.padding = (padding, padding)
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else:
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self.padding = padding
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self.conv = nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=0,
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bias=True,
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)
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def __call__(self, x: mx.array, causal: Optional[bool] = None) -> mx.array:
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"""Forward pass."""
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# Transpose to channels last: (B, C, H, W) -> (B, H, W, C)
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x = mx.transpose(x, (0, 2, 3, 1))
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# Apply padding
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pad_h, pad_w = self.padding
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if pad_h != 0 or pad_w != 0:
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pad_width = [
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(0, 0), # Batch
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(pad_h, pad_h), # H
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(pad_w, pad_w), # W
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(0, 0), # C
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]
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x = mx.pad(x, pad_width)
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x = self.conv(x)
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# Transpose back: (B, H, W, C) -> (B, C, H, W)
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x = mx.transpose(x, (0, 3, 1, 2))
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return x
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