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# SPDX-FileCopyrightText: Copyright (c) 2025 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 network definition for discrete image tokenization with VQ, LFQ, FSQ or ResidualFSQ."""
from collections import OrderedDict, namedtuple
import torch
from loguru import logger as logging
from torch import nn
from cosmos_predict1.tokenizer.modules import DecoderType, DiscreteQuantizer, EncoderType
from cosmos_predict1.tokenizer.modules.quantizers import InvQuantizerJit
NetworkEval = namedtuple("NetworkEval", ["reconstructions", "quant_loss", "quant_info"])
class DiscreteImageTokenizer(nn.Module):
def __init__(self, z_channels: int, embedding_dim: int, **kwargs) -> None:
super().__init__()
self.name = kwargs.get("name", "DiscreteImageTokenizer")
self.embedding_dim = embedding_dim
encoder_name = kwargs.get("encoder", EncoderType.Default.name)
self.encoder = EncoderType[encoder_name].value(z_channels=z_channels, **kwargs)
decoder_name = kwargs.get("decoder", DecoderType.Default.name)
self.decoder = DecoderType[decoder_name].value(z_channels=z_channels, **kwargs)
self.quant_conv = nn.Conv2d(z_channels, embedding_dim, 1)
self.post_quant_conv = nn.Conv2d(embedding_dim, z_channels, 1)
quantizer_name = kwargs.get("quantizer", DiscreteQuantizer.RESFSQ.name)
if quantizer_name == DiscreteQuantizer.VQ.name:
assert "num_embeddings" in kwargs, f"`num_embeddings` must be provided for {quantizer_name}."
kwargs.update(dict(embedding_dim=embedding_dim))
elif quantizer_name == DiscreteQuantizer.LFQ.name:
assert "codebook_size" in kwargs, f"`codebook_size` must be provided for {quantizer_name}."
assert "codebook_dim" in kwargs, f"`codebook_dim` must be provided for {quantizer_name}."
elif quantizer_name == DiscreteQuantizer.FSQ.name:
assert "levels" in kwargs, f"`levels` must be provided for {quantizer_name}."
elif quantizer_name == DiscreteQuantizer.RESFSQ.name:
assert "levels" in kwargs, f"`levels` must be provided for {quantizer_name}.name."
assert "num_quantizers" in kwargs, f"`num_quantizers` must be provided for {quantizer_name}."
self.quantizer = DiscreteQuantizer[quantizer_name].value(**kwargs)
logging.info(f"{self.name} based on {quantizer_name}-VAE, with {kwargs}.")
num_parameters = sum(param.numel() for param in self.parameters())
logging.info(f"model={self.name}, num_parameters={num_parameters:,}")
logging.info(f"z_channels={z_channels}, embedding_dim={self.embedding_dim}.")
def to(self, *args, **kwargs):
setattr(self.quantizer, "dtype", kwargs.get("dtype", torch.bfloat16))
return super(DiscreteImageTokenizer, self).to(*args, **kwargs)
def encoder_jit(self):
return nn.Sequential(
OrderedDict(
[
("encoder", self.encoder),
("quant_conv", self.quant_conv),
("quantizer", self.quantizer),
]
)
)
def decoder_jit(self):
return nn.Sequential(
OrderedDict(
[
("inv_quant", InvQuantizerJit(self.quantizer)),
("post_quant_conv", self.post_quant_conv),
("decoder", self.decoder),
]
)
)
def last_decoder_layer(self):
return self.decoder.conv_out
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return self.quantizer(h)
def decode(self, quant):
quant = self.post_quant_conv(quant)
return self.decoder(quant)
def decode_code(self, code_b):
quant_b = self.quantizer.indices_to_codes(code_b)
quant_b = self.post_quant_conv(quant_b)
return self.decoder(quant_b)
def forward(self, input):
quant_info, quant_codes, quant_loss = self.encode(input)
reconstructions = self.decode(quant_codes)
if self.training:
return dict(
reconstructions=reconstructions,
quant_loss=quant_loss,
quant_info=quant_info,
)
return NetworkEval(
reconstructions=reconstructions,
quant_loss=quant_loss,
quant_info=quant_info,
)
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