474 lines
21 KiB
Python
474 lines
21 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 List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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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, logging
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from .embeddings import TimestepEmbedding, Timesteps
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from .unet_2d_blocks import (
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CrossAttnDownBlock2D,
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CrossAttnUpBlock2D,
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DownBlock2D,
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UNetMidBlock2DCrossAttn,
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UNetMidBlock2DSimpleCrossAttn,
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UpBlock2D,
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get_down_block,
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get_up_block,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class UNet2DConditionOutput(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 conditioned on `encoder_hidden_states` input. Output of last layer of model.
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"""
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sample: torch.FloatTensor
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class UNet2DConditionModel(ModelMixin, ConfigMixin):
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r"""
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UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep
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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 models (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 4): The number of channels in the input sample.
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out_channels (`int`, *optional*, defaults to 4): The 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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flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
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Whether to flip the sin to cos in the time embedding.
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freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
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down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
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The tuple of downsample blocks to use.
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mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
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The mid block type. Choose from `UNetMidBlock2DCrossAttn` or `UNetMidBlock2DSimpleCrossAttn`.
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up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
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The tuple of upsample blocks to use.
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block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
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The tuple of output channels for each block.
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layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
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downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
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mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
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act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
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norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
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norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
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cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
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attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
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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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class_embed_type (`str`, *optional*, defaults to None): The type of class embedding to use which is ultimately
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summed with the time embeddings. Choose from `None`, `"timestep"`, or `"identity"`.
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"""
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_supports_gradient_checkpointing = True
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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[int] = None,
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in_channels: int = 4,
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out_channels: int = 4,
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center_input_sample: bool = False,
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flip_sin_to_cos: bool = True,
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freq_shift: int = 0,
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down_block_types: Tuple[str] = (
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D",
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),
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mid_block_type: str = "UNetMidBlock2DCrossAttn",
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up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
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only_cross_attention: Union[bool, Tuple[bool]] = False,
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block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
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layers_per_block: int = 2,
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downsample_padding: int = 1,
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mid_block_scale_factor: float = 1,
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act_fn: str = "silu",
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norm_num_groups: int = 32,
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norm_eps: float = 1e-5,
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cross_attention_dim: int = 1280,
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attention_head_dim: Union[int, Tuple[int]] = 8,
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dual_cross_attention: bool = False,
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use_linear_projection: bool = False,
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class_embed_type: Optional[str] = None,
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num_class_embeds: Optional[int] = None,
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upcast_attention: bool = False,
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resnet_time_scale_shift: str = "default",
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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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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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# class embedding
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if class_embed_type is None and num_class_embeds is not None:
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self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
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elif class_embed_type == "timestep":
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self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
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elif class_embed_type == "identity":
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self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
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else:
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self.class_embedding = None
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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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if isinstance(only_cross_attention, bool):
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only_cross_attention = [only_cross_attention] * len(down_block_types)
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if isinstance(attention_head_dim, int):
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attention_head_dim = (attention_head_dim,) * len(down_block_types)
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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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cross_attention_dim=cross_attention_dim,
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attn_num_head_channels=attention_head_dim[i],
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downsample_padding=downsample_padding,
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dual_cross_attention=dual_cross_attention,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention[i],
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upcast_attention=upcast_attention,
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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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if mid_block_type == "UNetMidBlock2DCrossAttn":
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self.mid_block = UNetMidBlock2DCrossAttn(
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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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cross_attention_dim=cross_attention_dim,
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attn_num_head_channels=attention_head_dim[-1],
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resnet_groups=norm_num_groups,
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dual_cross_attention=dual_cross_attention,
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use_linear_projection=use_linear_projection,
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upcast_attention=upcast_attention,
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)
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elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
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self.mid_block = UNetMidBlock2DSimpleCrossAttn(
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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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cross_attention_dim=cross_attention_dim,
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attn_num_head_channels=attention_head_dim[-1],
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resnet_groups=norm_num_groups,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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else:
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raise ValueError(f"unknown mid_block_type : {mid_block_type}")
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# count how many layers upsample the images
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self.num_upsamplers = 0
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# up
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reversed_block_out_channels = list(reversed(block_out_channels))
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reversed_attention_head_dim = list(reversed(attention_head_dim))
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only_cross_attention = list(reversed(only_cross_attention))
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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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is_final_block = i == len(block_out_channels) - 1
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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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# add upsample block for all BUT final layer
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if not is_final_block:
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add_upsample = True
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self.num_upsamplers += 1
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else:
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add_upsample = False
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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=add_upsample,
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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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cross_attention_dim=cross_attention_dim,
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attn_num_head_channels=reversed_attention_head_dim[i],
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dual_cross_attention=dual_cross_attention,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention[i],
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upcast_attention=upcast_attention,
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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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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, 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 set_attention_slice(self, slice_size):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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`"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
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provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
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must be a multiple of `slice_size`.
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"""
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sliceable_head_dims = []
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def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
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if hasattr(module, "set_attention_slice"):
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sliceable_head_dims.append(module.sliceable_head_dim)
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for child in module.children():
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fn_recursive_retrieve_slicable_dims(child)
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# retrieve number of attention layers
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for module in self.children():
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fn_recursive_retrieve_slicable_dims(module)
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num_slicable_layers = len(sliceable_head_dims)
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = [dim // 2 for dim in sliceable_head_dims]
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elif slice_size == "max":
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# make smallest slice possible
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slice_size = num_slicable_layers * [1]
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slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
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if len(slice_size) != len(sliceable_head_dims):
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raise ValueError(
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f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
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f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
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)
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for i in range(len(slice_size)):
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size = slice_size[i]
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dim = sliceable_head_dims[i]
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if size is not None and size > dim:
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raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
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# Recursively walk through all the children.
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# Any children which exposes the set_attention_slice method
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# gets the message
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def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
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if hasattr(module, "set_attention_slice"):
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module.set_attention_slice(slice_size.pop())
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for child in module.children():
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fn_recursive_set_attention_slice(child, slice_size)
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reversed_slice_size = list(reversed(slice_size))
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for module in self.children():
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fn_recursive_set_attention_slice(module, reversed_slice_size)
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def _set_gradient_checkpointing(self, module, value=False):
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if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D, CrossAttnUpBlock2D, UpBlock2D)):
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module.gradient_checkpointing = value
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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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encoder_hidden_states: torch.Tensor,
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class_labels: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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return_dict: bool = True,
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) -> Union[UNet2DConditionOutput, 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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encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
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Returns:
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[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
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[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
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returning a tuple, the first element is the sample tensor.
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"""
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# By default samples have to be AT least a multiple of the overall upsampling factor.
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# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
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# However, the upsampling interpolation output size can be forced to fit any upsampling size
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# on the fly if necessary.
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default_overall_up_factor = 2**self.num_upsamplers
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# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
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forward_upsample_size = False
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upsample_size = None
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if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
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logger.info("Forward upsample size to force interpolation output size.")
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forward_upsample_size = True
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# prepare attention_mask
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if attention_mask is not None:
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attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
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attention_mask = attention_mask.unsqueeze(1)
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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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# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
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# This would be a good case for the `match` statement (Python 3.10+)
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is_mps = sample.device.type == "mps"
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if isinstance(timestep, float):
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dtype = torch.float32 if is_mps else torch.float64
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else:
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dtype = torch.int32 if is_mps else torch.int64
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timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
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elif 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.expand(sample.shape[0])
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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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if self.class_embedding is not None:
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if class_labels is None:
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raise ValueError("class_labels should be provided when num_class_embeds > 0")
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if self.config.class_embed_type == "timestep":
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class_labels = self.time_proj(class_labels)
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class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
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emb = emb + class_emb
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# 2. pre-process
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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, "has_cross_attention") and downsample_block.has_cross_attention:
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sample, res_samples = downsample_block(
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hidden_states=sample,
|
|
temb=emb,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=attention_mask,
|
|
)
|
|
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, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
|
|
)
|
|
|
|
# 5. up
|
|
for i, upsample_block in enumerate(self.up_blocks):
|
|
is_final_block = i == len(self.up_blocks) - 1
|
|
|
|
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
|
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
|
|
|
# if we have not reached the final block and need to forward the
|
|
# upsample size, we do it here
|
|
if not is_final_block and forward_upsample_size:
|
|
upsample_size = down_block_res_samples[-1].shape[2:]
|
|
|
|
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
|
sample = upsample_block(
|
|
hidden_states=sample,
|
|
temb=emb,
|
|
res_hidden_states_tuple=res_samples,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
upsample_size=upsample_size,
|
|
attention_mask=attention_mask,
|
|
)
|
|
else:
|
|
sample = upsample_block(
|
|
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
|
|
)
|
|
# 6. post-process
|
|
sample = self.conv_norm_out(sample)
|
|
sample = self.conv_act(sample)
|
|
sample = self.conv_out(sample)
|
|
|
|
if not return_dict:
|
|
return (sample,)
|
|
|
|
return UNet2DConditionOutput(sample=sample)
|