unet_2d_condition.py 46.8 KB
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
# 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.
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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 ..loaders import UNet2DConditionLoadersMixin
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from ..utils import BaseOutput, logging
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from .activations import get_activation
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from .attention_processor import AttentionProcessor, AttnProcessor
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from .embeddings import (
    GaussianFourierProjection,
    TextImageProjection,
    TextImageTimeEmbedding,
    TextTimeEmbedding,
    TimestepEmbedding,
    Timesteps,
)
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from .modeling_utils import ModelMixin
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from .unet_2d_blocks import (
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    CrossAttnDownBlock2D,
    CrossAttnUpBlock2D,
    DownBlock2D,
    UNetMidBlock2DCrossAttn,
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    UNetMidBlock2DSimpleCrossAttn,
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    UpBlock2D,
    get_down_block,
    get_up_block,
)
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logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


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@dataclass
class UNet2DConditionOutput(BaseOutput):
    """
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    The output of [`UNet2DConditionModel`].

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    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
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    """

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    sample: torch.FloatTensor = None
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class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
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    r"""
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    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
    shaped output.
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    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).
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    Parameters:
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        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample.
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        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 4): 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.
        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
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        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
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            Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or
            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
        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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        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
            Whether to include self-attention in the basic transformer blocks, see
            [`~models.attention.BasicTransformerBlock`].
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        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
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            If `None`, normalization and activation layers is skipped in post-processing.
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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` or `Tuple[int]`, *optional*, defaults to 1280):
            The dimension of the cross attention features.
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        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
        encoder_hid_dim (`int`, *optional*, defaults to None):
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            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
            dimension to `cross_attention_dim`.
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        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
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            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
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        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
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        num_attention_heads (`int`, *optional*):
            The number of attention heads. If not defined, defaults to `attention_head_dim`
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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`.
        class_embed_type (`str`, *optional*, defaults to `None`):
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            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
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            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
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        addition_embed_type (`str`, *optional*, defaults to `None`):
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            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
            "text". "text" will use the `TextTimeEmbedding` layer.
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        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
            Dimension for the timestep embeddings.
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        num_class_embeds (`int`, *optional*, defaults to `None`):
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            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
            class conditioning with `class_embed_type` equal to `None`.
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        time_embedding_type (`str`, *optional*, defaults to `positional`):
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            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
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        time_embedding_dim (`int`, *optional*, defaults to `None`):
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            An optional override for the dimension of the projected time embedding.
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        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
            Optional activation function to use only once on the time embeddings before they are passed to the rest of
            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
        timestep_post_act (`str`, *optional*, defaults to `None`):
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            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
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        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
            The dimension of `cond_proj` layer in the timestep embedding.
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        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
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        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
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            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
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        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
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            embeddings with the class embeddings.
        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
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            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
            otherwise.
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    """

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    _supports_gradient_checkpointing = True

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    @register_to_config
    def __init__(
        self,
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        sample_size: Optional[int] = None,
        in_channels: int = 4,
        out_channels: int = 4,
        center_input_sample: bool = False,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
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        mid_block_type: Optional[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: Union[int, Tuple[int]] = 2,
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        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        act_fn: str = "silu",
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        norm_num_groups: Optional[int] = 32,
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        norm_eps: float = 1e-5,
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        cross_attention_dim: Union[int, Tuple[int]] = 1280,
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        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
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        encoder_hid_dim: Optional[int] = None,
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        encoder_hid_dim_type: Optional[str] = None,
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        attention_head_dim: Union[int, Tuple[int]] = 8,
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        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
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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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        addition_embed_type: Optional[str] = None,
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        addition_time_embed_dim: Optional[int] = 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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        resnet_skip_time_act: bool = False,
        resnet_out_scale_factor: int = 1.0,
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        time_embedding_type: str = "positional",
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        time_embedding_dim: Optional[int] = None,
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        time_embedding_act_fn: Optional[str] = None,
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        timestep_post_act: Optional[str] = None,
        time_cond_proj_dim: Optional[int] = None,
        conv_in_kernel: int = 3,
        conv_out_kernel: int = 3,
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        projection_class_embeddings_input_dim: Optional[int] = None,
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        class_embeddings_concat: bool = False,
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        mid_block_only_cross_attention: Optional[bool] = None,
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        cross_attention_norm: Optional[str] = None,
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        addition_embed_type_num_heads=64,
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    ):
        super().__init__()

        self.sample_size = sample_size

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        # If `num_attention_heads` is not defined (which is the case for most models)
        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
        # The reason for this behavior is to correct for incorrectly named variables that were introduced
        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
        # which is why we correct for the naming here.
        num_attention_heads = num_attention_heads or attention_head_dim

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        # Check inputs
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
            )

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        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
            )

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        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
            )

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        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
            )

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        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
            )

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        # input
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        conv_in_padding = (conv_in_kernel - 1) // 2
        self.conv_in = nn.Conv2d(
            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
        )
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        # time
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        if time_embedding_type == "fourier":
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            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
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            if time_embed_dim % 2 != 0:
                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
            self.time_proj = GaussianFourierProjection(
                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
            )
            timestep_input_dim = time_embed_dim
        elif time_embedding_type == "positional":
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            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
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            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
            timestep_input_dim = block_out_channels[0]
        else:
            raise ValueError(
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                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
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            )
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        self.time_embedding = TimestepEmbedding(
            timestep_input_dim,
            time_embed_dim,
            act_fn=act_fn,
            post_act_fn=timestep_post_act,
            cond_proj_dim=time_cond_proj_dim,
        )
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        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
            encoder_hid_dim_type = "text_proj"
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            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
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            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")

        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
            raise ValueError(
                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
            )

        if encoder_hid_dim_type == "text_proj":
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            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
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        elif encoder_hid_dim_type == "text_image_proj":
            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
            self.encoder_hid_proj = TextImageProjection(
                text_embed_dim=encoder_hid_dim,
                image_embed_dim=cross_attention_dim,
                cross_attention_dim=cross_attention_dim,
            )

        elif encoder_hid_dim_type is not None:
            raise ValueError(
                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
            )
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        else:
            self.encoder_hid_proj = None

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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, act_fn=act_fn)
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        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
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        elif class_embed_type == "projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
                )
            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
            # 2. it projects from an arbitrary input dimension.
            #
            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
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        elif class_embed_type == "simple_projection":
            if projection_class_embeddings_input_dim is None:
                raise ValueError(
                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
                )
            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
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        else:
            self.class_embedding = None
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        if addition_embed_type == "text":
            if encoder_hid_dim is not None:
                text_time_embedding_from_dim = encoder_hid_dim
            else:
                text_time_embedding_from_dim = cross_attention_dim

            self.add_embedding = TextTimeEmbedding(
                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
            )
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        elif addition_embed_type == "text_image":
            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
            self.add_embedding = TextImageTimeEmbedding(
                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
            )
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        elif addition_embed_type == "text_time":
            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)

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        elif addition_embed_type is not None:
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            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
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        if time_embedding_act_fn is None:
            self.time_embed_act = None
        else:
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            self.time_embed_act = get_activation(time_embedding_act_fn)
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        self.down_blocks = nn.ModuleList([])
        self.up_blocks = nn.ModuleList([])

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        if isinstance(only_cross_attention, bool):
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            if mid_block_only_cross_attention is None:
                mid_block_only_cross_attention = only_cross_attention

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            only_cross_attention = [only_cross_attention] * len(down_block_types)

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        if mid_block_only_cross_attention is None:
            mid_block_only_cross_attention = False

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        if isinstance(num_attention_heads, int):
            num_attention_heads = (num_attention_heads,) * len(down_block_types)

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        if isinstance(attention_head_dim, int):
            attention_head_dim = (attention_head_dim,) * len(down_block_types)

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        if isinstance(cross_attention_dim, int):
            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)

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        if isinstance(layers_per_block, int):
            layers_per_block = [layers_per_block] * len(down_block_types)

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        if isinstance(transformer_layers_per_block, int):
            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)

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        if class_embeddings_concat:
            # The time embeddings are concatenated with the class embeddings. The dimension of the
            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
            # regular time embeddings
            blocks_time_embed_dim = time_embed_dim * 2
        else:
            blocks_time_embed_dim = time_embed_dim

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        # 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,
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                num_layers=layers_per_block[i],
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                transformer_layers_per_block=transformer_layers_per_block[i],
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                in_channels=input_channel,
                out_channels=output_channel,
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                temb_channels=blocks_time_embed_dim,
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                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
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                resnet_groups=norm_num_groups,
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                cross_attention_dim=cross_attention_dim[i],
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                num_attention_heads=num_attention_heads[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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                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
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                cross_attention_norm=cross_attention_norm,
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                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
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            )
            self.down_blocks.append(down_block)

        # mid
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        if mid_block_type == "UNetMidBlock2DCrossAttn":
            self.mid_block = UNetMidBlock2DCrossAttn(
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                transformer_layers_per_block=transformer_layers_per_block[-1],
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                in_channels=block_out_channels[-1],
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                temb_channels=blocks_time_embed_dim,
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                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
                resnet_time_scale_shift=resnet_time_scale_shift,
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                cross_attention_dim=cross_attention_dim[-1],
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                num_attention_heads=num_attention_heads[-1],
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                resnet_groups=norm_num_groups,
                dual_cross_attention=dual_cross_attention,
                use_linear_projection=use_linear_projection,
                upcast_attention=upcast_attention,
            )
        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
                in_channels=block_out_channels[-1],
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                temb_channels=blocks_time_embed_dim,
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                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                output_scale_factor=mid_block_scale_factor,
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                cross_attention_dim=cross_attention_dim[-1],
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                attention_head_dim=attention_head_dim[-1],
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                resnet_groups=norm_num_groups,
                resnet_time_scale_shift=resnet_time_scale_shift,
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                skip_time_act=resnet_skip_time_act,
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                only_cross_attention=mid_block_only_cross_attention,
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                cross_attention_norm=cross_attention_norm,
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            )
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        elif mid_block_type is None:
            self.mid_block = None
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        else:
            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
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        # count how many layers upsample the images
        self.num_upsamplers = 0

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        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
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        reversed_num_attention_heads = list(reversed(num_attention_heads))
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        reversed_layers_per_block = list(reversed(layers_per_block))
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        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
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        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
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        only_cross_attention = list(reversed(only_cross_attention))
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        output_channel = reversed_block_out_channels[0]
        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
            output_channel = reversed_block_out_channels[i]
            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
            if not is_final_block:
                add_upsample = True
                self.num_upsamplers += 1
            else:
                add_upsample = False
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            up_block = get_up_block(
                up_block_type,
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                num_layers=reversed_layers_per_block[i] + 1,
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                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
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                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
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                temb_channels=blocks_time_embed_dim,
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                add_upsample=add_upsample,
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                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
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                resnet_groups=norm_num_groups,
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                cross_attention_dim=reversed_cross_attention_dim[i],
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                num_attention_heads=reversed_num_attention_heads[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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                resnet_skip_time_act=resnet_skip_time_act,
                resnet_out_scale_factor=resnet_out_scale_factor,
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                cross_attention_norm=cross_attention_norm,
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                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
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            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
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        if norm_num_groups is not None:
            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 = get_activation(act_fn)
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        else:
            self.conv_norm_out = None
            self.conv_act = None

        conv_out_padding = (conv_out_kernel - 1) // 2
        self.conv_out = nn.Conv2d(
            block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding
        )
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    @property
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    def attn_processors(self) -> Dict[str, AttentionProcessor]:
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        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
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        # set recursively
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        processors = {}

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        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
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            if hasattr(module, "set_processor"):
                processors[f"{name}.processor"] = module.processor

            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

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    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
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        r"""
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        Sets the attention processor to use to compute attention.

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        Parameters:
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            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
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                The instantiated processor class or a dictionary of processor classes that will be set as the processor
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                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.
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        """
        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):
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            if hasattr(module, "set_processor"):
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                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))
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            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
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        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)
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    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.
        """
        self.set_attn_processor(AttnProcessor())

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    def set_attention_slice(self, slice_size):
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        r"""
        Enable sliced attention computation.
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        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
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        Args:
            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
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                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
                must be a multiple of `slice_size`.
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        """
        sliceable_head_dims = []

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        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
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            if hasattr(module, "set_attention_slice"):
                sliceable_head_dims.append(module.sliceable_head_dim)

            for child in module.children():
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                fn_recursive_retrieve_sliceable_dims(child)
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        # retrieve number of attention layers
        for module in self.children():
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            fn_recursive_retrieve_sliceable_dims(module)
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        num_sliceable_layers = len(sliceable_head_dims)
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        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = [dim // 2 for dim in sliceable_head_dims]
        elif slice_size == "max":
            # make smallest slice possible
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            slice_size = num_sliceable_layers * [1]
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        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
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        if len(slice_size) != len(sliceable_head_dims):
            raise ValueError(
                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
            )
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        for i in range(len(slice_size)):
            size = slice_size[i]
            dim = sliceable_head_dims[i]
            if size is not None and size > dim:
                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")

        # Recursively walk through all the children.
        # Any children which exposes the set_attention_slice method
        # gets the message
        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
            if hasattr(module, "set_attention_slice"):
                module.set_attention_slice(slice_size.pop())

            for child in module.children():
                fn_recursive_set_attention_slice(child, slice_size)

        reversed_slice_size = list(reversed(slice_size))
        for module in self.children():
            fn_recursive_set_attention_slice(module, reversed_slice_size)
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    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D, CrossAttnUpBlock2D, UpBlock2D)):
            module.gradient_checkpointing = value

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    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
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        class_labels: Optional[torch.Tensor] = None,
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        timestep_cond: Optional[torch.Tensor] = None,
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        attention_mask: Optional[torch.Tensor] = None,
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        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
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        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        mid_block_additional_residual: Optional[torch.Tensor] = None,
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        encoder_attention_mask: Optional[torch.Tensor] = None,
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        return_dict: bool = True,
    ) -> Union[UNet2DConditionOutput, Tuple]:
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        r"""
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        The [`UNet2DConditionModel`] forward method.

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        Args:
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            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, channel, height, width)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.FloatTensor`):
                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
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            encoder_attention_mask (`torch.Tensor`):
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                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
                which adds large negative values to the attention scores corresponding to "discard" tokens.
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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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            cross_attention_kwargs (`dict`, *optional*):
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                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
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            added_cond_kwargs: (`dict`, *optional*):
                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
                are passed along to the UNet blocks.
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        Returns:
            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
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                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
                a `tuple` is returned where 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 layers).
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        # However, the upsampling interpolation output size can be forced to fit any upsampling size
        # on the fly if necessary.
        default_overall_up_factor = 2**self.num_upsamplers

        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
        forward_upsample_size = False
        upsample_size = None

        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
            logger.info("Forward upsample size to force interpolation output size.")
            forward_upsample_size = True

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        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
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        if attention_mask is not None:
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            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #       (keep = +0,     discard = -10000.0)
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            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

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        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None:
            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

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        # 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):
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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+)
            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
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        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.
        # there might be better ways to encapsulate this.
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        t_emb = t_emb.to(dtype=sample.dtype)
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        emb = self.time_embedding(t_emb, timestep_cond)
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        aug_emb = None
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        if self.class_embedding is not None:
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            if class_labels is None:
                raise ValueError("class_labels should be provided when num_class_embeds > 0")
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            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

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                # `Timesteps` does not contain any weights and will always return f32 tensors
                # there might be better ways to encapsulate this.
                class_labels = class_labels.to(dtype=sample.dtype)

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            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
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            if self.config.class_embeddings_concat:
                emb = torch.cat([emb, class_emb], dim=-1)
            else:
                emb = emb + class_emb
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        if self.config.addition_embed_type == "text":
            aug_emb = self.add_embedding(encoder_hidden_states)
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        elif self.config.addition_embed_type == "text_image":
            # Kadinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
                )

            image_embs = added_cond_kwargs.get("image_embeds")
            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)

            aug_emb = self.add_embedding(text_embs, image_embs)
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        elif self.config.addition_embed_type == "text_time":
            if "text_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
                )
            text_embeds = added_cond_kwargs.get("text_embeds")
            if "time_ids" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
                )
            time_ids = added_cond_kwargs.get("time_ids")
            time_embeds = self.add_time_proj(time_ids.flatten())
            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))

            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
            add_embeds = add_embeds.to(emb.dtype)
            aug_emb = self.add_embedding(add_embeds)

        emb = emb + aug_emb if aug_emb is not None else emb
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        if self.time_embed_act is not None:
            emb = self.time_embed_act(emb)

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        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
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            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
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        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
            # Kadinsky 2.1 - style
            if "image_embeds" not in added_cond_kwargs:
                raise ValueError(
                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
                )

            image_embeds = added_cond_kwargs.get("image_embeds")
            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
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        # 2. pre-process
        sample = self.conv_in(sample)

        # 3. down
        down_block_res_samples = (sample,)
        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,
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                    attention_mask=attention_mask,
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                    cross_attention_kwargs=cross_attention_kwargs,
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                    encoder_attention_mask=encoder_attention_mask,
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                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

            down_block_res_samples += res_samples

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        if down_block_additional_residuals is not None:
            new_down_block_res_samples = ()

            for down_block_res_sample, down_block_additional_residual in zip(
                down_block_res_samples, down_block_additional_residuals
            ):
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                down_block_res_sample = down_block_res_sample + down_block_additional_residual
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                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
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            down_block_res_samples = new_down_block_res_samples

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        # 4. mid
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        if self.mid_block is not None:
            sample = self.mid_block(
                sample,
                emb,
                encoder_hidden_states=encoder_hidden_states,
                attention_mask=attention_mask,
                cross_attention_kwargs=cross_attention_kwargs,
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                encoder_attention_mask=encoder_attention_mask,
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            )
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        if mid_block_additional_residual is not None:
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            sample = sample + mid_block_additional_residual
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        # 5. up
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        for i, upsample_block in enumerate(self.up_blocks):
            is_final_block = i == len(self.up_blocks) - 1

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            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

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            # 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:]

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            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
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                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
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                    cross_attention_kwargs=cross_attention_kwargs,
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                    upsample_size=upsample_size,
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                    attention_mask=attention_mask,
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                    encoder_attention_mask=encoder_attention_mask,
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                )
            else:
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                sample = upsample_block(
                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
                )
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        # 6. post-process
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        if self.conv_norm_out:
            sample = self.conv_norm_out(sample)
            sample = self.conv_act(sample)
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        sample = self.conv_out(sample)

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        if not return_dict:
            return (sample,)
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        return UNet2DConditionOutput(sample=sample)