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# coding=utf-8
# Copyright 2024 The Emu team, BAAI and The HuggingFace Inc. 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.
""" Emu3VisionVQ model configuration """
from typing import List
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Emu3VisionVQConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Emu3VisionVQ`]. It is used to instantiate an video movq
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a configuration to the VQ model presented in Emu3 paper.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
codebook_size (`int`, *optional*, defaults to 32768):
Codebook size of the VQ model.
embed_dim (`int`, *optional*, defaults to 4):
Dimension of the quantized vector in codebook.
z_channels (`int`, *optional*, defaults to 4):
Dimension of the output channel of encoder and the input channel of decoder
double_z (`bool`, *optional*, defaults to False):
Whether double the output dim of the encoder.
in_channels (`int`, *optional*, defaults to 3):
Input channel of encoder.
out_channels (`int`, *optional*, defaults to 3):
Output channel of decoder.
temporal_downsample_factor (`int`, *optional*, defaults to 4):
Temporal downsample factor.
ch (`int`, *optional*, defaults to 256):
Basic channel number of the intermediate blocks.
ch_mult (`List[int]`, *optional*, defaults to `[1, 2, 2, 4]`):
Channel scaling factor of the intermediate blocks.
num_res_blocks (`int`, *optional*, defaults to 2):
Residual block number in each stage.
attn_resolutions (`List[int]`, *optional*, defaults to 3):
Stage indices to apply attention.
dropout (`float`, *optional*, defaults to 0.0):
Dropout probability.
```python
>>> from transformers import Emu3VisionVQ, Emu3VisionVQConfig
>>> # Initializing a video VQ model of Emu3 configuration
>>> configuration = Emu3VisionVQConfig()
>>> # Initializing a model from the Emu3 VQ model style configuration
>>> model = Emu3VisionVQModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "Emu3VisionVQ"
def __init__(
self,
codebook_size: int = 32768,
embed_dim: int = 4,
z_channels: int = 4,
double_z: bool = False,
in_channels: int = 3,
out_channels: int = 3,
temporal_downsample_factor: int = 4,
ch: int = 256,
ch_mult: List[int] = [1, 2, 2, 4],
num_res_blocks: int = 2,
attn_resolutions: List[int] = [3],
dropout: float = 0.0,
**kwargs,
):
super().__init__(**kwargs)
self.codebook_size = codebook_size
self.embed_dim = embed_dim
self.z_channels = z_channels
self.double_z = double_z
self.in_channels = in_channels
self.out_channels = out_channels
self.temporal_downsample_factor = temporal_downsample_factor
self.ch = ch
self.ch_mult = ch_mult
self.num_res_blocks = num_res_blocks
self.attn_resolutions = attn_resolutions
self.dropout = dropout
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