146f5d2981
- Introduced `load_image`, `prepare_image_for_encoding`, and `apply_conditioning` functions for handling image inputs and conditioning during video generation. - Enhanced `generate_video` and `denoise_av` functions to accept optional image inputs for I2V conditioning. - Updated command-line interface to include parameters for image conditioning, such as `--image`, `--image-strength`, and `--image-frame-idx`. - Added new `VideoConditionByLatentIndex` and `LatentState` classes for managing latent states with conditioning. - Implemented VAE encoder loading and image encoding for conditioning in the video generation process.d
195 lines
6.3 KiB
Python
195 lines
6.3 KiB
Python
"""Sampling operations for Video VAE (upsampling/downsampling)."""
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from typing import Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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from mlx_video.models.ltx.video_vae.convolution import CausalConv3d, PaddingModeType
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class SpaceToDepthDownsample(nn.Module):
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"""Space-to-depth downsampling with 3x3 conv and skip connection.
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PyTorch-compatible implementation:
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1. Apply 3x3 conv: in_channels -> out_channels // prod(stride)
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2. Space-to-depth on conv output: channels * prod(stride)
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3. Space-to-depth on input with group averaging for skip connection
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4. Add skip connection
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"""
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def __init__(
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self,
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dims: int,
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in_channels: int,
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out_channels: int,
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stride: Union[int, Tuple[int, int, int]],
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spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS,
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):
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super().__init__()
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if isinstance(stride, int):
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stride = (stride, stride, stride)
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self.stride = stride
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self.dims = dims
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self.out_channels = out_channels
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# Calculate channels
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multiplier = stride[0] * stride[1] * stride[2]
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self.group_size = in_channels * multiplier // out_channels
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conv_out_channels = out_channels // multiplier
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# 3x3 convolution (not 1x1)
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self.conv = CausalConv3d(
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in_channels=in_channels,
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out_channels=conv_out_channels,
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kernel_size=3,
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stride=1,
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padding=1,
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spatial_padding_mode=spatial_padding_mode,
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)
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def _space_to_depth(self, x: mx.array) -> mx.array:
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"""Rearrange: b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w"""
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b, c, d, h, w = x.shape
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st, sh, sw = self.stride
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# Reshape to group spatial elements
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x = mx.reshape(x, (b, c, d // st, st, h // sh, sh, w // sw, sw))
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# Permute: (B, C, D', st, H', sh, W', sw) -> (B, C, st, sh, sw, D', H', W')
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x = mx.transpose(x, (0, 1, 3, 5, 7, 2, 4, 6))
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# Reshape to combine channels
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new_c = c * st * sh * sw
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new_d = d // st
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new_h = h // sh
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new_w = w // sw
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x = mx.reshape(x, (b, new_c, new_d, new_h, new_w))
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return x
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def __call__(self, x: mx.array, causal: bool = True) -> mx.array:
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b, c, d, h, w = x.shape
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st, sh, sw = self.stride
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# Temporal padding for causal mode
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if st == 2:
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# Duplicate first frame for padding
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x = mx.concatenate([x[:, :, :1, :, :], x], axis=2)
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d = d + 1
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# Pad if necessary to make dimensions divisible by stride
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pad_d = (st - d % st) % st
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pad_h = (sh - h % sh) % sh
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pad_w = (sw - w % sw) % sw
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if pad_d > 0 or pad_h > 0 or pad_w > 0:
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x = mx.pad(x, [(0, 0), (0, 0), (0, pad_d), (0, pad_h), (0, pad_w)])
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# Skip connection: space-to-depth on input, then group mean
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x_in = self._space_to_depth(x)
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# Reshape for group mean: (b, c*prod(stride), d, h, w) -> (b, out_channels, group_size, d, h, w)
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b2, c2, d2, h2, w2 = x_in.shape
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x_in = mx.reshape(x_in, (b2, self.out_channels, self.group_size, d2, h2, w2))
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x_in = mx.mean(x_in, axis=2) # (b, out_channels, d, h, w)
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# Conv branch: apply conv then space-to-depth
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x_conv = self.conv(x, causal=causal)
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x_conv = self._space_to_depth(x_conv)
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# Add skip connection
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return x_conv + x_in
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class DepthToSpaceUpsample(nn.Module):
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def __init__(
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self,
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dims: int,
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in_channels: int,
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stride: Union[int, Tuple[int, int, int]],
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residual: bool = False,
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out_channels_reduction_factor: int = 1,
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spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS,
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):
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super().__init__()
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if isinstance(stride, int):
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stride = (stride, stride, stride)
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self.stride = stride
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self.dims = dims
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self.residual = residual
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self.out_channels_reduction_factor = out_channels_reduction_factor
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# Calculate output channels
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multiplier = stride[0] * stride[1] * stride[2]
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out_channels = in_channels // out_channels_reduction_factor
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self.out_channels = out_channels
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# 3x3x3 convolution to prepare channels for unpacking (matches PyTorch)
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self.conv = CausalConv3d(
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in_channels=in_channels,
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out_channels=out_channels * multiplier,
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kernel_size=3,
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stride=1,
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padding=1,
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spatial_padding_mode=spatial_padding_mode,
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)
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def _depth_to_space(self, x: mx.array) -> mx.array:
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b, c_packed, d, h, w = x.shape
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st, sh, sw = self.stride
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c = c_packed // (st * sh * sw)
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# (B, C*st*sh*sw, D, H, W) -> (B, C, st, sh, sw, D, H, W)
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x = mx.reshape(x, (b, c, st, sh, sw, d, h, w))
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# (B, C, st, sh, sw, D, H, W) -> (B, C, D, st, H, sh, W, sw)
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x = mx.transpose(x, (0, 1, 5, 2, 6, 3, 7, 4))
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# (B, C, D, st, H, sh, W, sw) -> (B, C, D*st, H*sh, W*sw)
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x = mx.reshape(x, (b, c, d * st, h * sh, w * sw))
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return x
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def __call__(self, x: mx.array, causal: bool = True) -> mx.array:
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b, c, d, h, w = x.shape
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st, sh, sw = self.stride
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# Compute residual path if enabled
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x_residual = None
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if self.residual:
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# Reshape input: treat channels as spatial factors
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# "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)"
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x_residual = self._depth_to_space(x)
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# Tile channels to match output (PyTorch .repeat() tiles, not element-repeat!)
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# num_repeat = prod(stride) / out_channels_reduction_factor
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num_repeat = (st * sh * sw) // self.out_channels_reduction_factor
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x_residual = mx.tile(x_residual, (1, num_repeat, 1, 1, 1))
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# Remove first temporal frame if temporal upsampling
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if st > 1:
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x_residual = x_residual[:, :, 1:, :, :]
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# Apply conv
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x = self.conv(x, causal=causal)
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# Depth to space rearrangement
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x = self._depth_to_space(x)
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# Remove first frame for causal temporal upsampling
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if st > 1:
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x = x[:, :, 1:, :, :]
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# Add residual
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if self.residual and x_residual is not None:
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x = x + x_residual
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return x
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