# 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,
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# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..loaders import FromOriginalVAEMixin
from ..utils import is_torch_version
from ..utils.accelerate_utils import apply_forward_hook
from .attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor
from .modeling_outputs import AutoencoderKLOutput
from .modeling_utils import ModelMixin
from .unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder
from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
class TemporalDecoder(nn.Module):
def __init__(
self,
in_channels: int = 4,
out_channels: int = 3,
block_out_channels: Tuple[int] = (128, 256, 512, 512),
layers_per_block: int = 2,
):
super().__init__()
self.layers_per_block = layers_per_block
self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
self.mid_block = MidBlockTemporalDecoder(
num_layers=self.layers_per_block,
in_channels=block_out_channels[-1],
out_channels=block_out_channels[-1],
attention_head_dim=block_out_channels[-1],
)
# up
self.up_blocks = nn.ModuleList([])
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i in range(len(block_out_channels)):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
up_block = UpBlockTemporalDecoder(
num_layers=self.layers_per_block + 1,
in_channels=prev_output_channel,
out_channels=output_channel,
add_upsample=not is_final_block,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-6)
self.conv_act = nn.SiLU()
self.conv_out = torch.nn.Conv2d(
in_channels=block_out_channels[0],
out_channels=out_channels,
kernel_size=3,
padding=1,
)
conv_out_kernel_size = (3, 1, 1)
padding = [int(k // 2) for k in conv_out_kernel_size]
self.time_conv_out = torch.nn.Conv3d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=conv_out_kernel_size,
padding=padding,
)
self.gradient_checkpointing = False
def forward(
self,
sample: torch.FloatTensor,
image_only_indicator: torch.FloatTensor,
num_frames: int = 1,
) -> torch.FloatTensor:
r"""The forward method of the `Decoder` class."""
sample = self.conv_in(sample)
upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
if is_torch_version(">=", "1.11.0"):
# middle
sample = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block),
sample,
image_only_indicator,
use_reentrant=False,
)
sample = sample.to(upscale_dtype)
# up
for up_block in self.up_blocks:
sample = torch.utils.checkpoint.checkpoint(
create_custom_forward(up_block),
sample,
image_only_indicator,
use_reentrant=False,
)
else:
# middle
sample = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block),
sample,
image_only_indicator,
)
sample = sample.to(upscale_dtype)
# up
for up_block in self.up_blocks:
sample = torch.utils.checkpoint.checkpoint(
create_custom_forward(up_block),
sample,
image_only_indicator,
)
else:
# middle
sample = self.mid_block(sample, image_only_indicator=image_only_indicator)
sample = sample.to(upscale_dtype)
# up
for up_block in self.up_blocks:
sample = up_block(sample, image_only_indicator=image_only_indicator)
# post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
batch_frames, channels, height, width = sample.shape
batch_size = batch_frames // num_frames
sample = sample[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
sample = self.time_conv_out(sample)
sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
return sample
class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
r"""
A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
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 `("DownEncoderBlock2D",)`):
Tuple of downsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
Tuple of block output channels.
layers_per_block: (`int`, *optional*, defaults to 1): Number of layers per block.
latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
sample_size (`int`, *optional*, defaults to `32`): Sample input size.
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.
force_upcast (`bool`, *optional*, default to `True`):
If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
can be fine-tuned / trained to a lower range without loosing too much precision in which case
`force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
block_out_channels: Tuple[int] = (64,),
layers_per_block: int = 1,
latent_channels: int = 4,
sample_size: int = 32,
scaling_factor: float = 0.18215,
force_upcast: float = True,
):
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,
double_z=True,
)
# pass init params to Decoder
self.decoder = TemporalDecoder(
in_channels=latent_channels,
out_channels=out_channels,
block_out_channels=block_out_channels,
layers_per_block=layers_per_block,
)
self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
sample_size = (
self.config.sample_size[0]
if isinstance(self.config.sample_size, (list, tuple))
else self.config.sample_size
)
self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
self.tile_overlap_factor = 0.25
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (Encoder, TemporalDecoder)):
module.gradient_checkpointing = value
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention processors: A dictionary containing all attention processors used in the model with
indexed by its weight name.
"""
# set recursively
processors = {}
def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
if hasattr(module, "get_processor"):
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(
self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
):
r"""
Sets the attention processor to use to compute attention.
Parameters:
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for **all** `Attention` layers.
If `processor` is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.
"""
count = len(self.attn_processors.keys())
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
)
def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
if hasattr(module, "set_processor"):
if not isinstance(processor, dict):
module.set_processor(processor, _remove_lora=_remove_lora)
else:
module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
for name, module in self.named_children():
fn_recursive_attn_processor(name, module, processor)
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnProcessor()
else:
raise ValueError(
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
)
self.set_attn_processor(processor, _remove_lora=True)
@apply_forward_hook
def encode(
self, x: torch.FloatTensor, return_dict: bool = True
) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
"""
Encode a batch of images into latents.
Args:
x (`torch.FloatTensor`): Input batch of images.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
Returns:
The latent representations of the encoded images. If `return_dict` is True, a
[`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
"""
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=posterior)
@apply_forward_hook
def decode(
self,
z: torch.FloatTensor,
num_frames: int,
return_dict: bool = True,
) -> Union[DecoderOutput, torch.FloatTensor]:
"""
Decode a batch of images.
Args:
z (`torch.FloatTensor`): Input batch of latent vectors.
return_dict (`bool`, *optional*, defaults to `True`):
Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
Returns:
[`~models.vae.DecoderOutput`] or `tuple`:
If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
returned.
"""
batch_size = z.shape[0] // num_frames
image_only_indicator = torch.zeros(batch_size, num_frames, dtype=z.dtype, device=z.device)
decoded = self.decoder(z, num_frames=num_frames, image_only_indicator=image_only_indicator)
if not return_dict:
return (decoded,)
return DecoderOutput(sample=decoded)
def forward(
self,
sample: torch.FloatTensor,
sample_posterior: bool = False,
return_dict: bool = True,
generator: Optional[torch.Generator] = None,
num_frames: int = 1,
) -> Union[DecoderOutput, torch.FloatTensor]:
r"""
Args:
sample (`torch.FloatTensor`): Input sample.
sample_posterior (`bool`, *optional*, defaults to `False`):
Whether to sample from the posterior.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
"""
x = sample
posterior = self.encode(x).latent_dist
if sample_posterior:
z = posterior.sample(generator=generator)
else:
z = posterior.mode()
dec = self.decode(z, num_frames=num_frames).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)