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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
"""The causal continuous video tokenizer with VAE or AE formulation for 3D data.."""
import logging
import torch
from torch import nn
from enum import Enum
import math
from .cosmos_tokenizer.layers3d import (
EncoderFactorized,
DecoderFactorized,
CausalConv3d,
)
class IdentityDistribution(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, parameters):
return parameters, (torch.tensor([0.0]), torch.tensor([0.0]))
class GaussianDistribution(torch.nn.Module):
def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0):
super().__init__()
self.min_logvar = min_logvar
self.max_logvar = max_logvar
def sample(self, mean, logvar):
std = torch.exp(0.5 * logvar)
return mean + std * torch.randn_like(mean)
def forward(self, parameters):
mean, logvar = torch.chunk(parameters, 2, dim=1)
logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar)
return self.sample(mean, logvar), (mean, logvar)
class ContinuousFormulation(Enum):
VAE = GaussianDistribution
AE = IdentityDistribution
class CausalContinuousVideoTokenizer(nn.Module):
def __init__(
self, z_channels: int, z_factor: int, latent_channels: int, **kwargs
) -> None:
super().__init__()
self.name = kwargs.get("name", "CausalContinuousVideoTokenizer")
self.latent_channels = latent_channels
self.sigma_data = 0.5
# encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name)
self.encoder = EncoderFactorized(
z_channels=z_factor * z_channels, **kwargs
)
if kwargs.get("temporal_compression", 4) == 4:
kwargs["channels_mult"] = [2, 4]
# decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name)
self.decoder = DecoderFactorized(
z_channels=z_channels, **kwargs
)
self.quant_conv = CausalConv3d(
z_factor * z_channels,
z_factor * latent_channels,
kernel_size=1,
padding=0,
)
self.post_quant_conv = CausalConv3d(
latent_channels, z_channels, kernel_size=1, padding=0
)
# formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name)
self.distribution = IdentityDistribution() # ContinuousFormulation[formulation_name].value()
num_parameters = sum(param.numel() for param in self.parameters())
logging.debug(f"model={self.name}, num_parameters={num_parameters:,}")
logging.debug(
f"z_channels={z_channels}, latent_channels={self.latent_channels}."
)
latent_temporal_chunk = 16
self.latent_mean = nn.Parameter(torch.zeros([self.latent_channels * latent_temporal_chunk], dtype=torch.float32))
self.latent_std = nn.Parameter(torch.ones([self.latent_channels * latent_temporal_chunk], dtype=torch.float32))
def encode(self, x):
h = self.encoder(x)
moments = self.quant_conv(h)
z, posteriors = self.distribution(moments)
latent_ch = z.shape[1]
latent_t = z.shape[2]
in_dtype = z.dtype
mean = self.latent_mean.view(latent_ch, -1)
std = self.latent_std.view(latent_ch, -1)
mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
return ((z - mean) / std) * self.sigma_data
def decode(self, z):
in_dtype = z.dtype
latent_ch = z.shape[1]
latent_t = z.shape[2]
mean = self.latent_mean.view(latent_ch, -1)
std = self.latent_std.view(latent_ch, -1)
mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
z = z / self.sigma_data
z = z * std + mean
z = self.post_quant_conv(z)
return self.decoder(z)
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