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# Copyright 2022 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 ..modeling_utils import ModelMixin
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
@dataclass
class UNet2DOutput(BaseOutput):
"""
Args:
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Hidden states output. Output of last layer of model.
"""
sample: torch.FloatTensor
class UNet2DModel(ModelMixin, ConfigMixin):
r"""
UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.
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:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
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.
center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
freq_shift (`int`, *optional*, defaults to 0): Frequency shift for fourier time embedding.
flip_sin_to_cos (`bool`, *optional*, defaults to :
obj:`True`): Whether to flip sin to cos for fourier time embedding.
down_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block
types.
mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
The mid block type. Choose from `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
up_block_types (`Tuple[str]`, *optional*, defaults to :
obj:`("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types.
block_out_channels (`Tuple[int]`, *optional*, defaults to :
obj:`(224, 448, 672, 896)`): Tuple of block output channels.
layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for the normalization.
norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for the normalization.
resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
for resnet blocks, see [`~models.resnet.ResnetBlock2D`]. Choose from `default` or `scale_shift`.
"""
@register_to_config
def __init__(
self,
sample_size: Optional[Union[int, Tuple[int, int]]] = None,
in_channels: int = 3,
out_channels: int = 3,
center_input_sample: bool = False,
time_embedding_type: str = "positional",
freq_shift: int = 0,
flip_sin_to_cos: bool = True,
down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
block_out_channels: Tuple[int] = (224, 448, 672, 896),
layers_per_block: int = 2,
mid_block_scale_factor: float = 1,
downsample_padding: int = 1,
act_fn: str = "silu",
attention_head_dim: int = 8,
norm_num_groups: int = 32,
norm_eps: float = 1e-5,
resnet_time_scale_shift: str = "default",
add_attention: bool = True,
):
super().__init__()
self.sample_size = sample_size
time_embed_dim = block_out_channels[0] * 4
# input
self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
# time
if time_embedding_type == "fourier":
self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
timestep_input_dim = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
self.down_blocks = nn.ModuleList([])
self.mid_block = None
self.up_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
attn_num_head_channels=attention_head_dim,
downsample_padding=downsample_padding,
resnet_time_scale_shift=resnet_time_scale_shift,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlock2D(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
output_scale_factor=mid_block_scale_factor,
resnet_time_scale_shift=resnet_time_scale_shift,
attn_num_head_channels=attention_head_dim,
resnet_groups=norm_num_groups,
add_attention=add_attention,
)
# up
reversed_block_out_channels = list(reversed(block_out_channels))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
is_final_block = i == len(block_out_channels) - 1
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsample=not is_final_block,
resnet_eps=norm_eps,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
attn_num_head_channels=attention_head_dim,
resnet_time_scale_shift=resnet_time_scale_shift,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
self.conv_act = nn.SiLU()
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
def forward(
self,
sample: torch.FloatTensor,
timestep: Union[torch.Tensor, float, int],
return_dict: bool = True,
) -> Union[UNet2DOutput, Tuple]:
r"""
Args:
sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.
Returns:
[`~models.unet_2d.UNet2DOutput`] or `tuple`: [`~models.unet_2d.UNet2DOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
"""
# 0. center input if necessary
if self.config.center_input_sample:
sample = 2 * sample - 1.0
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)
t_emb = self.time_proj(timesteps)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
t_emb = t_emb.to(dtype=self.dtype)
emb = self.time_embedding(t_emb)
# 2. pre-process
skip_sample = sample
sample = self.conv_in(sample)
# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "skip_conv"):
sample, res_samples, skip_sample = downsample_block(
hidden_states=sample, temb=emb, skip_sample=skip_sample
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
down_block_res_samples += res_samples
# 4. mid
sample = self.mid_block(sample, emb)
# 5. up
skip_sample = None
for upsample_block in self.up_blocks:
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
if hasattr(upsample_block, "skip_conv"):
sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
else:
sample = upsample_block(sample, res_samples, emb)
# 6. post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
if skip_sample is not None:
sample += skip_sample
if self.config.time_embedding_type == "fourier":
timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
sample = sample / timesteps
if not return_dict:
return (sample,)
return UNet2DOutput(sample=sample)
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