274 lines
12 KiB
Python
274 lines
12 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from ..configuration_utils import ConfigMixin, register_to_config
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from ..modeling_utils import ModelMixin
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from ..utils import BaseOutput
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from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
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from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
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@dataclass
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class UNet2DOutput(BaseOutput):
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"""
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Args:
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sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Hidden states output. Output of last layer of model.
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"""
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sample: torch.FloatTensor
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class UNet2DModel(ModelMixin, ConfigMixin):
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r"""
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UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.
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This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
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implements for all the model (such as downloading or saving, etc.)
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Parameters:
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sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
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Height and width of input/output sample.
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in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image.
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out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
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center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
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time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
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freq_shift (`int`, *optional*, defaults to 0): Frequency shift for fourier time embedding.
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flip_sin_to_cos (`bool`, *optional*, defaults to :
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obj:`True`): Whether to flip sin to cos for fourier time embedding.
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down_block_types (`Tuple[str]`, *optional*, defaults to :
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obj:`("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block
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types.
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mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
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The mid block type. Choose from `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
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up_block_types (`Tuple[str]`, *optional*, defaults to :
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obj:`("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types.
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block_out_channels (`Tuple[int]`, *optional*, defaults to :
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obj:`(224, 448, 672, 896)`): Tuple of block output channels.
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layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
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mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
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downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
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act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
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attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
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norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for the normalization.
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norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for the normalization.
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resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
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for resnet blocks, see [`~models.resnet.ResnetBlock2D`]. Choose from `default` or `scale_shift`.
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"""
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@register_to_config
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def __init__(
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self,
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sample_size: Optional[Union[int, Tuple[int, int]]] = None,
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in_channels: int = 3,
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out_channels: int = 3,
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center_input_sample: bool = False,
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time_embedding_type: str = "positional",
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freq_shift: int = 0,
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flip_sin_to_cos: bool = True,
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down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
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up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
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block_out_channels: Tuple[int] = (224, 448, 672, 896),
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layers_per_block: int = 2,
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mid_block_scale_factor: float = 1,
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downsample_padding: int = 1,
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act_fn: str = "silu",
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attention_head_dim: int = 8,
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norm_num_groups: int = 32,
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norm_eps: float = 1e-5,
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resnet_time_scale_shift: str = "default",
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add_attention: bool = True,
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):
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super().__init__()
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self.sample_size = sample_size
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time_embed_dim = block_out_channels[0] * 4
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# input
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self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
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# time
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if time_embedding_type == "fourier":
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self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
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timestep_input_dim = 2 * block_out_channels[0]
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elif time_embedding_type == "positional":
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self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
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timestep_input_dim = block_out_channels[0]
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self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
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self.down_blocks = nn.ModuleList([])
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self.mid_block = None
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self.up_blocks = nn.ModuleList([])
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# down
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output_channel = block_out_channels[0]
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for i, down_block_type in enumerate(down_block_types):
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input_channel = output_channel
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output_channel = block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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down_block = get_down_block(
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down_block_type,
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num_layers=layers_per_block,
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in_channels=input_channel,
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out_channels=output_channel,
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temb_channels=time_embed_dim,
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add_downsample=not is_final_block,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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attn_num_head_channels=attention_head_dim,
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downsample_padding=downsample_padding,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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self.down_blocks.append(down_block)
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# mid
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self.mid_block = UNetMidBlock2D(
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in_channels=block_out_channels[-1],
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temb_channels=time_embed_dim,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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output_scale_factor=mid_block_scale_factor,
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resnet_time_scale_shift=resnet_time_scale_shift,
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attn_num_head_channels=attention_head_dim,
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resnet_groups=norm_num_groups,
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add_attention=add_attention,
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)
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# up
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reversed_block_out_channels = list(reversed(block_out_channels))
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output_channel = reversed_block_out_channels[0]
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for i, up_block_type in enumerate(up_block_types):
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prev_output_channel = output_channel
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output_channel = reversed_block_out_channels[i]
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input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
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is_final_block = i == len(block_out_channels) - 1
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up_block = get_up_block(
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up_block_type,
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num_layers=layers_per_block + 1,
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in_channels=input_channel,
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out_channels=output_channel,
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prev_output_channel=prev_output_channel,
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temb_channels=time_embed_dim,
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add_upsample=not is_final_block,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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attn_num_head_channels=attention_head_dim,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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self.up_blocks.append(up_block)
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prev_output_channel = output_channel
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# out
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num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
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self.conv_act = nn.SiLU()
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self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
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def forward(
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self,
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sample: torch.FloatTensor,
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timestep: Union[torch.Tensor, float, int],
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return_dict: bool = True,
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) -> Union[UNet2DOutput, Tuple]:
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r"""
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Args:
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sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
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timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.
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Returns:
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[`~models.unet_2d.UNet2DOutput`] or `tuple`: [`~models.unet_2d.UNet2DOutput`] if `return_dict` is True,
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otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
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"""
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# 0. center input if necessary
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if self.config.center_input_sample:
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sample = 2 * sample - 1.0
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# 1. time
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timesteps = timestep
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if not torch.is_tensor(timesteps):
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timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
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elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
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timesteps = timesteps[None].to(sample.device)
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# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)
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t_emb = self.time_proj(timesteps)
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# timesteps does not contain any weights and will always return f32 tensors
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# but time_embedding might actually be running in fp16. so we need to cast here.
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# there might be better ways to encapsulate this.
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t_emb = t_emb.to(dtype=self.dtype)
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emb = self.time_embedding(t_emb)
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# 2. pre-process
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skip_sample = sample
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sample = self.conv_in(sample)
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# 3. down
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down_block_res_samples = (sample,)
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for downsample_block in self.down_blocks:
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if hasattr(downsample_block, "skip_conv"):
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sample, res_samples, skip_sample = downsample_block(
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hidden_states=sample, temb=emb, skip_sample=skip_sample
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)
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else:
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sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
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down_block_res_samples += res_samples
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# 4. mid
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sample = self.mid_block(sample, emb)
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# 5. up
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skip_sample = None
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for upsample_block in self.up_blocks:
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res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
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down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
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if hasattr(upsample_block, "skip_conv"):
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sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
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else:
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sample = upsample_block(sample, res_samples, emb)
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# 6. post-process
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sample = self.conv_norm_out(sample)
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sample = self.conv_act(sample)
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sample = self.conv_out(sample)
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if skip_sample is not None:
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sample += skip_sample
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if self.config.time_embedding_type == "fourier":
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timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
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sample = sample / timesteps
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if not return_dict:
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return (sample,)
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return UNet2DOutput(sample=sample)
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