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# Copyright 2025 Lightricks and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import torch
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class ResBlock(torch.nn.Module):
def __init__(self, channels: int, mid_channels: Optional[int] = None, dims: int = 3):
super().__init__()
if mid_channels is None:
mid_channels = channels
Conv = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
self.conv1 = Conv(channels, mid_channels, kernel_size=3, padding=1)
self.norm1 = torch.nn.GroupNorm(32, mid_channels)
self.conv2 = Conv(mid_channels, channels, kernel_size=3, padding=1)
self.norm2 = torch.nn.GroupNorm(32, channels)
self.activation = torch.nn.SiLU()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
residual = hidden_states
hidden_states = self.conv1(hidden_states)
hidden_states = self.norm1(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.conv2(hidden_states)
hidden_states = self.norm2(hidden_states)
hidden_states = self.activation(hidden_states + residual)
return hidden_states
class PixelShuffleND(torch.nn.Module):
def __init__(self, dims, upscale_factors=(2, 2, 2)):
super().__init__()
self.dims = dims
self.upscale_factors = upscale_factors
if dims not in [1, 2, 3]:
raise ValueError("dims must be 1, 2, or 3")
def forward(self, x):
if self.dims == 3:
# spatiotemporal: b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)
return (
x.unflatten(1, (-1, *self.upscale_factors[:3]))
.permute(0, 1, 5, 2, 6, 3, 7, 4)
.flatten(6, 7)
.flatten(4, 5)
.flatten(2, 3)
)
elif self.dims == 2:
# spatial: b (c p1 p2) h w -> b c (h p1) (w p2)
return (
x.unflatten(1, (-1, *self.upscale_factors[:2])).permute(0, 1, 4, 2, 5, 3).flatten(4, 5).flatten(2, 3)
)
elif self.dims == 1:
# temporal: b (c p1) f h w -> b c (f p1) h w
return x.unflatten(1, (-1, *self.upscale_factors[:1])).permute(0, 1, 3, 2, 4, 5).flatten(2, 3)
class LTXLatentUpsamplerModel(ModelMixin, ConfigMixin):
"""
Model to spatially upsample VAE latents.
Args:
in_channels (`int`, defaults to `128`):
Number of channels in the input latent
mid_channels (`int`, defaults to `512`):
Number of channels in the middle layers
num_blocks_per_stage (`int`, defaults to `4`):
Number of ResBlocks to use in each stage (pre/post upsampling)
dims (`int`, defaults to `3`):
Number of dimensions for convolutions (2 or 3)
spatial_upsample (`bool`, defaults to `True`):
Whether to spatially upsample the latent
temporal_upsample (`bool`, defaults to `False`):
Whether to temporally upsample the latent
"""
@register_to_config
def __init__(
self,
in_channels: int = 128,
mid_channels: int = 512,
num_blocks_per_stage: int = 4,
dims: int = 3,
spatial_upsample: bool = True,
temporal_upsample: bool = False,
):
super().__init__()
self.in_channels = in_channels
self.mid_channels = mid_channels
self.num_blocks_per_stage = num_blocks_per_stage
self.dims = dims
self.spatial_upsample = spatial_upsample
self.temporal_upsample = temporal_upsample
ConvNd = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
self.initial_conv = ConvNd(in_channels, mid_channels, kernel_size=3, padding=1)
self.initial_norm = torch.nn.GroupNorm(32, mid_channels)
self.initial_activation = torch.nn.SiLU()
self.res_blocks = torch.nn.ModuleList([ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)])
if spatial_upsample and temporal_upsample:
self.upsampler = torch.nn.Sequential(
torch.nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1),
PixelShuffleND(3),
)
elif spatial_upsample:
self.upsampler = torch.nn.Sequential(
torch.nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1),
PixelShuffleND(2),
)
elif temporal_upsample:
self.upsampler = torch.nn.Sequential(
torch.nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1),
PixelShuffleND(1),
)
else:
raise ValueError("Either spatial_upsample or temporal_upsample must be True")
self.post_upsample_res_blocks = torch.nn.ModuleList(
[ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
)
self.final_conv = ConvNd(mid_channels, in_channels, kernel_size=3, padding=1)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
batch_size, num_channels, num_frames, height, width = hidden_states.shape
if self.dims == 2:
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
hidden_states = self.initial_conv(hidden_states)
hidden_states = self.initial_norm(hidden_states)
hidden_states = self.initial_activation(hidden_states)
for block in self.res_blocks:
hidden_states = block(hidden_states)
hidden_states = self.upsampler(hidden_states)
for block in self.post_upsample_res_blocks:
hidden_states = block(hidden_states)
hidden_states = self.final_conv(hidden_states)
hidden_states = hidden_states.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
else:
hidden_states = self.initial_conv(hidden_states)
hidden_states = self.initial_norm(hidden_states)
hidden_states = self.initial_activation(hidden_states)
for block in self.res_blocks:
hidden_states = block(hidden_states)
if self.temporal_upsample:
hidden_states = self.upsampler(hidden_states)
hidden_states = hidden_states[:, :, 1:, :, :]
else:
hidden_states = hidden_states.permute(0, 2, 1, 3, 4).flatten(0, 1)
hidden_states = self.upsampler(hidden_states)
hidden_states = hidden_states.unflatten(0, (batch_size, -1)).permute(0, 2, 1, 3, 4)
for block in self.post_upsample_res_blocks:
hidden_states = block(hidden_states)
hidden_states = self.final_conv(hidden_states)
return hidden_states
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