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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.
from typing import Optional
import attrs
from .discrete_video import DiscreteVideoFSQStateDictTokenizer
from .ar_networks import CausalDiscreteVideoTokenizer
from .lazy_config_init import LazyCall as L
from .lazy_config_init import LazyDict
def create_discrete_video_fsq_tokenizer_state_dict_config(
ckpt_path, pixel_chunk_duration=33, compression_ratio=[8, 16, 16]
) -> LazyDict:
CausalDiscreteFactorizedVideoTokenizerConfig: LazyDict = L(CausalDiscreteVideoTokenizer)(
# The new causal discrete tokenizer, that is at least 2x more efficient in memory and runtime.
# - It relies on fully 3D discrete wavelet transform
# - Uses a layer norm instead of a group norm
# - Factorizes full convolutions into spatial and temporal convolutions
# - Factorizes full attention into spatial and temporal attention
# - Strictly causal, with flexible temporal length at inference.
attn_resolutions=[32],
channels=128,
channels_mult=[2, 4, 4],
dropout=0.0,
in_channels=3,
num_res_blocks=2,
out_channels=3,
resolution=1024,
patch_size=4,
patch_method="haar",
z_channels=16,
z_factor=1,
num_groups=1,
legacy_mode=False,
spatial_compression=16,
temporal_compression=8,
embedding_dim=6,
levels=[8, 8, 8, 5, 5, 5],
name="CausalDiscreteFactorizedVideoTokenizer",
)
return L(DiscreteVideoFSQStateDictTokenizer)(
enc_fp=ckpt_path.replace("ema.jit", "encoder.jit"),
dec_fp=ckpt_path.replace("ema.jit", "decoder.jit"),
tokenizer_module=CausalDiscreteFactorizedVideoTokenizerConfig,
name="discrete_video_fsq",
latent_ch=6,
is_bf16=True,
pixel_chunk_duration=pixel_chunk_duration,
latent_chunk_duration=1 + (pixel_chunk_duration - 1) // compression_ratio[0],
max_enc_batch_size=8,
max_dec_batch_size=4,
levels=[8, 8, 8, 5, 5, 5],
compression_ratio=compression_ratio,
)
@attrs.define(slots=False)
class TextTokenizerConfig:
"""
Text tokenizer config
Args:
config: Config file to define the text tokenizer class.
data_key (str): The input key from data_dict that will be passed to the text tokenizer.
tokenize_here (bool): Whether to use the tokenizer to perform online tokenization.
tokenizer_offset (int): Offset that is added to the tokens.
vocab_size (int): Vocabulary size of the tokenizer.
"""
config: LazyDict
data_key: str = ""
tokenize_here: bool = False
tokenizer_offset: int = 0
vocab_size: int = 0
@attrs.define(slots=False)
class VideoTokenizerConfig:
"""
Video tokenizer config
Args:
config: Config file to define the video tokenizer class.
data_key (str): The input key from data_dict that will be passed to the video tokenizer.
tokenize_here (bool): Whether to use the tokenizer to perform online tokenization.
tokenizer_offset (int): Offset that is added to the tokens. In case of joint text-video tokenizers, we
add an offset to make sure that video tokens and text tokens don't overlap.
vocab_size (int): Vocabulary size of the tokenizer.
max_seq_len (int): Maximum token length for an input video.
"""
config: LazyDict
data_key: str = ""
tokenize_here: bool = True
tokenizer_offset: int = 0
vocab_size: int = 0
max_seq_len: int = -1
@attrs.define(slots=False)
class TokenizerConfig:
"""
Joint tokenizer config
Args:
text_tokenizer (TextTokenizerConfig): Text tokenizer config file
class_tokenizer (ClassTokenizerConfig): Class tokenizer config file
video_tokenizer (VideoTokenizerConfig): Video tokenizer config file
image_tokenizer (ImageTokenizerConfig): Image tokenizer config file
seq_len (int): Final token sequence length
training_type (str): Type of training we use. Supports ["text_only", "text_to_video", "class_to_image", "image_text_interleaved"]
add_special_tokens (bool): Whether to add special tokens to the output tokens
pad_to_multiple_of (int): Pad the token sequence length to the nearest multiple of this number. Defaults to 64.
"""
text_tokenizer: Optional[TextTokenizerConfig] = None
video_tokenizer: Optional[VideoTokenizerConfig] = None
seq_len: int = 4096
training_type: str = None
add_special_tokens: bool = True
pad_to_multiple_of: Optional[int] = 64
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