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LTX-2 Patchifier Module (WAN-style)
Patchify / unpatchify helpers for video and audio latents.
"""
import math
from dataclasses import dataclass
from typing import NamedTuple, Optional, Tuple
import einops
import torch
# =============================================================================
# =============================================================================
class SpatioTemporalScaleFactors(NamedTuple):
"""Spatio-temporal downsampling factors."""
time: int
width: int
height: int
@classmethod
def default(cls) -> "SpatioTemporalScaleFactors":
return cls(time=8, width=32, height=32)
VIDEO_SCALE_FACTORS = SpatioTemporalScaleFactors.default()
class VideoLatentShape(NamedTuple):
"""Video latent shape: (batch, channels, frames, height, width)."""
batch: int
channels: int
frames: int
height: int
width: int
def to_torch_shape(self) -> torch.Size:
return torch.Size([self.batch, self.channels, self.frames, self.height, self.width])
@staticmethod
def from_torch_shape(shape: torch.Size) -> "VideoLatentShape":
return VideoLatentShape(
batch=shape[0],
channels=shape[1],
frames=shape[2],
height=shape[3],
width=shape[4],
)
def mask_shape(self) -> "VideoLatentShape":
return self._replace(channels=1)
class AudioLatentShape(NamedTuple):
"""Audio latent shape: (batch, channels, frames, mel_bins)."""
batch: int
channels: int
frames: int
mel_bins: int
def to_torch_shape(self) -> torch.Size:
return torch.Size([self.batch, self.channels, self.frames, self.mel_bins])
def mask_shape(self) -> "AudioLatentShape":
return self._replace(channels=1, mel_bins=1)
@staticmethod
def from_torch_shape(shape: torch.Size) -> "AudioLatentShape":
return AudioLatentShape(
batch=shape[0],
channels=shape[1],
frames=shape[2],
mel_bins=shape[3],
)
@staticmethod
def from_duration(
batch: int,
duration: float,
channels: int = 8,
mel_bins: int = 16,
sample_rate: int = 16000,
hop_length: int = 160,
audio_latent_downsample_factor: int = 4,
) -> "AudioLatentShape":
latents_per_second = float(sample_rate) / float(hop_length) / float(audio_latent_downsample_factor)
return AudioLatentShape(
batch=batch,
channels=channels,
frames=round(duration * latents_per_second),
mel_bins=mel_bins,
)
@dataclass(frozen=True)
class LatentState:
"""Latent state during diffusion."""
latent: torch.Tensor
denoise_mask: torch.Tensor
positions: torch.Tensor
clean_latent: torch.Tensor
def clone(self) -> "LatentState":
return LatentState(
latent=self.latent.clone(),
denoise_mask=self.denoise_mask.clone(),
positions=self.positions.clone(),
clean_latent=self.clean_latent.clone(),
)
# =============================================================================
# Video Patchifier
# =============================================================================
class VideoLatentPatchifier:
"""Video latent patchifier."""
def __init__(self, patch_size: int = 1):
self._patch_size = (1, patch_size, patch_size)
@property
def patch_size(self) -> Tuple[int, int, int]:
return self._patch_size
def get_token_count(self, tgt_shape: VideoLatentShape) -> int:
return math.prod(tgt_shape.to_torch_shape()[2:]) // math.prod(self._patch_size)
def patchify(self, latents: torch.Tensor) -> torch.Tensor:
"""Convert [B, C, F, H, W] into [B, F*H*W, C*p1*p2*p3]."""
latents = einops.rearrange(
latents,
"b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)",
p1=self._patch_size[0],
p2=self._patch_size[1],
p3=self._patch_size[2],
)
return latents
def unpatchify(self, latents: torch.Tensor, output_shape: VideoLatentShape) -> torch.Tensor:
"""Convert [B, F*H*W, C*p*q] back into [B, C, F, H, W]."""
assert self._patch_size[0] == 1, "Temporal patch size must be 1"
patch_grid_frames = output_shape.frames // self._patch_size[0]
patch_grid_height = output_shape.height // self._patch_size[1]
patch_grid_width = output_shape.width // self._patch_size[2]
latents = einops.rearrange(
latents,
"b (f h w) (c p q) -> b c f (h p) (w q)",
f=patch_grid_frames,
h=patch_grid_height,
w=patch_grid_width,
p=self._patch_size[1],
q=self._patch_size[2],
)
return latents
def get_patch_grid_bounds(
self,
output_shape: VideoLatentShape,
device: Optional[torch.device] = None,
) -> torch.Tensor:
"""Return the boundary coordinates of every patch: [batch, 3, num_patches, 2]."""
frames = output_shape.frames
height = output_shape.height
width = output_shape.width
batch_size = output_shape.batch
grid_coords = torch.meshgrid(
torch.arange(start=0, end=frames, step=self._patch_size[0], device=device),
torch.arange(start=0, end=height, step=self._patch_size[1], device=device),
torch.arange(start=0, end=width, step=self._patch_size[2], device=device),
indexing="ij",
)
patch_starts = torch.stack(grid_coords, dim=0)
patch_size_delta = torch.tensor(
self._patch_size,
device=patch_starts.device,
dtype=patch_starts.dtype,
).view(3, 1, 1, 1)
patch_ends = patch_starts + patch_size_delta
latent_coords = torch.stack((patch_starts, patch_ends), dim=-1)
latent_coords = einops.repeat(
latent_coords,
"c f h w bounds -> b c (f h w) bounds",
b=batch_size,
bounds=2,
)
return latent_coords
# =============================================================================
# Audio Patchifier
# =============================================================================
class AudioPatchifier:
"""Audio latent patchifier."""
def __init__(
self,
patch_size: int = 16,
sample_rate: int = 16000,
hop_length: int = 160,
audio_latent_downsample_factor: int = 4,
is_causal: bool = True,
shift: int = 0,
):
self.hop_length = hop_length
self.sample_rate = sample_rate
self.audio_latent_downsample_factor = audio_latent_downsample_factor
self.is_causal = is_causal
self.shift = shift
self._patch_size = (1, patch_size, patch_size)
@property
def patch_size(self) -> Tuple[int, int, int]:
return self._patch_size
def get_token_count(self, tgt_shape: AudioLatentShape) -> int:
return tgt_shape.frames
def _get_audio_latent_time_in_sec(
self,
start_latent: int,
end_latent: int,
dtype: torch.dtype,
device: Optional[torch.device] = None,
) -> torch.Tensor:
if device is None:
device = torch.device("cpu")
audio_latent_frame = torch.arange(start_latent, end_latent, dtype=dtype, device=device)
audio_mel_frame = audio_latent_frame * self.audio_latent_downsample_factor
if self.is_causal:
causal_offset = 1
audio_mel_frame = (audio_mel_frame + causal_offset - self.audio_latent_downsample_factor).clip(min=0)
return audio_mel_frame * self.hop_length / self.sample_rate
def _compute_audio_timings(
self,
batch_size: int,
num_steps: int,
device: Optional[torch.device] = None,
) -> torch.Tensor:
resolved_device = device or torch.device("cpu")
start_timings = self._get_audio_latent_time_in_sec(
self.shift, num_steps + self.shift, torch.float32, resolved_device
)
start_timings = start_timings.unsqueeze(0).expand(batch_size, -1).unsqueeze(1)
end_timings = self._get_audio_latent_time_in_sec(
self.shift + 1, num_steps + self.shift + 1, torch.float32, resolved_device
)
end_timings = end_timings.unsqueeze(0).expand(batch_size, -1).unsqueeze(1)
return torch.stack([start_timings, end_timings], dim=-1)
def patchify(self, audio_latents: torch.Tensor) -> torch.Tensor:
"""Convert [B, C, T, F] into [B, T, C*F]."""
audio_latents = einops.rearrange(audio_latents, "b c t f -> b t (c f)")
return audio_latents
def unpatchify(self, audio_latents: torch.Tensor, output_shape: AudioLatentShape) -> torch.Tensor:
"""Convert [B, T, C*F] back into [B, C, T, F]."""
audio_latents = einops.rearrange(
audio_latents,
"b t (c f) -> b c t f",
c=output_shape.channels,
f=output_shape.mel_bins,
)
return audio_latents
def get_patch_grid_bounds(
self,
output_shape: AudioLatentShape,
device: Optional[torch.device] = None,
) -> torch.Tensor:
"""Return the temporal boundaries of the audio patches: [batch, 1, time_steps, 2]."""
return self._compute_audio_timings(output_shape.batch, output_shape.frames, device)
# =============================================================================
# =============================================================================
def get_pixel_coords(
latent_coords: torch.Tensor,
scale_factors: SpatioTemporalScaleFactors,
causal_fix: bool = False,
) -> torch.Tensor:
"""
Convert latent coordinates into pixel coordinates.
Args:
latent_coords: [batch, 3, num_patches, 2] latent boundaries
scale_factors: spatio-temporal scale factors
causal_fix: correct for causal encoding
Returns:
[batch, 3, num_patches, 2] pixel coordinates
"""
broadcast_shape = [1] * latent_coords.ndim
broadcast_shape[1] = -1
scale_tensor = torch.tensor(scale_factors, device=latent_coords.device).view(*broadcast_shape)
pixel_coords = latent_coords * scale_tensor
if causal_fix:
pixel_coords[:, 0, ...] = (pixel_coords[:, 0, ...] + 1 - scale_factors[0]).clamp(min=0)
return pixel_coords
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