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# Copyright 2023 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 dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class VQEncoderOutput(BaseOutput):
"""
Output of VQModel encoding method.
Args:
latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Encoded output sample of the model. Output of the last layer of the model.
"""
latents: torch.FloatTensor
class VQModel(ModelMixin, ConfigMixin):
r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray
Kavukcuoglu.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.)
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types.
up_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to :
obj:`(64,)`): Tuple of block output channels.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): TODO
num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE.
vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE.
scaling_factor (`float`, *optional*, defaults to `0.18215`):
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
/ scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
"""
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
up_block_types: Tuple[str] = ("UpDecoderBlock2D",),
block_out_channels: Tuple[int] = (64,),
layers_per_block: int = 1,
act_fn: str = "silu",
latent_channels: int = 3,
sample_size: int = 32,
num_vq_embeddings: int = 256,
norm_num_groups: int = 32,
vq_embed_dim: Optional[int] = None,
scaling_factor: float = 0.18215,
):
super().__init__()
# pass init params to Encoder
self.encoder = Encoder(
in_channels=in_channels,
out_channels=latent_channels,
down_block_types=down_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
double_z=False,
)
vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels
self.quant_conv = nn.Conv2d(latent_channels, vq_embed_dim, 1)
self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False)
self.post_quant_conv = nn.Conv2d(vq_embed_dim, latent_channels, 1)
# pass init params to Decoder
self.decoder = Decoder(
in_channels=latent_channels,
out_channels=out_channels,
up_block_types=up_block_types,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
act_fn=act_fn,
norm_num_groups=norm_num_groups,
)
def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput:
h = self.encoder(x)
h = self.quant_conv(h)
if not return_dict:
return (h,)
return VQEncoderOutput(latents=h)
def decode(
self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True
) -> Union[DecoderOutput, torch.FloatTensor]:
# also go through quantization layer
if not force_not_quantize:
quant, emb_loss, info = self.quantize(h)
else:
quant = h
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
def forward(self, sample: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
r"""
Args:
sample (`torch.FloatTensor`): Input sample.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
x = sample
h = self.encode(x).latents
dec = self.decode(h).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
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