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llm-grounded-diffusion / models /unet_2d_condition.py

Tony Lian

Add stage 2

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	# Copyright 2023 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	from dataclasses import dataclass
	from typing import Any, Dict, List, Optional, Tuple, Union

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.utils.checkpoint

	from diffusers.configuration_utils import ConfigMixin, register_to_config
	from diffusers.loaders import UNet2DConditionLoadersMixin
	from diffusers.utils import BaseOutput, logging
	from diffusers.models.embeddings import (
	GaussianFourierProjection,
	TextImageProjection,
	TextImageTimeEmbedding,
	TextTimeEmbedding,
	TimestepEmbedding,
	Timesteps,
	)
	from diffusers.models.modeling_utils import ModelMixin
	from .unet_2d_blocks import (
	CrossAttnDownBlock2D,
	CrossAttnUpBlock2D,
	DownBlock2D,
	UNetMidBlock2DCrossAttn,
	UpBlock2D,
	get_down_block,
	get_up_block,
	)
	from .attention_processor import AttentionProcessor, AttnProcessor


	logger = logging.get_logger(__name__) # pylint: disable=invalid-name


	@dataclass
	class UNet2DConditionOutput(BaseOutput):
	"""
	Args:
	sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
	Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
	"""

	sample: torch.FloatTensor
	cross_attention_probs_down: List[Any]
	cross_attention_probs_mid: List[Any]
	cross_attention_probs_up: List[Any]


	class FourierEmbedder(nn.Module):
	def __init__(self, num_freqs=64, temperature=100):
	super().__init__()

	self.num_freqs = num_freqs
	self.temperature = temperature

	freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
	freq_bands = freq_bands[None, None, None]
	self.register_buffer('freq_bands', freq_bands, persistent=False)

	def __call__(self, x):
	x = self.freq_bands * x.unsqueeze(-1)
	return torch.stack((x.sin(), x.cos()), dim=-1).permute(0, 1, 3, 4, 2).reshape(*x.shape[:2], -1)


	class PositionNet(nn.Module):
	def __init__(self, positive_len, out_dim, fourier_freqs=8):
	super().__init__()
	self.positive_len = positive_len
	self.out_dim = out_dim

	self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
	self.position_dim = fourier_freqs * 2 * 4 # 2: sin/cos, 4: xyxy

	self.linears = nn.Sequential(
	nn.Linear(self.positive_len + self.position_dim, 512),
	nn.SiLU(),
	nn.Linear(512, 512),
	nn.SiLU(),
	nn.Linear(512, out_dim),
	)

	self.null_positive_feature = torch.nn.Parameter(torch.zeros([self.positive_len]))
	self.null_position_feature = torch.nn.Parameter(torch.zeros([self.position_dim]))

	def forward(self, boxes, masks, positive_embeddings):
	masks = masks.unsqueeze(-1)

	# embedding position (it may includes padding as placeholder)
	xyxy_embedding = self.fourier_embedder(boxes) # BN4 -> BNC

	# learnable null embedding
	positive_null = self.null_positive_feature.view(1, 1, -1)
	xyxy_null = self.null_position_feature.view(1, 1, -1)

	# replace padding with learnable null embedding
	positive_embeddings = positive_embeddings * masks + (1 - masks) * positive_null
	xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null

	objs = self.linears(torch.cat([positive_embeddings, xyxy_embedding], dim=-1))
	return objs



	class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
	r"""
	UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep
	and returns sample shaped output.

	This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
	implements for all the models (such as downloading or saving, etc.)

	Parameters:
	sample_size (`int` or `Tuple[int, int]`, optional, defaults to `None`):
	Height and width of input/output sample.
	in_channels (`int`, optional, defaults to 4): The number of channels in the input sample.
	out_channels (`int`, optional, defaults to 4): The number of channels in the output.
	center_input_sample (`bool`, optional, defaults to `False`): Whether to center the input sample.
	flip_sin_to_cos (`bool`, optional, defaults to `False`):
	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.
	mid_block_type (`str`, optional, defaults to `"UNetMidBlock2DCrossAttn"`):
	The mid block type. Choose from `UNetMidBlock2DCrossAttn` or `UNetMidBlock2DSimpleCrossAttn`, will skip the
	mid block layer if `None`.
	up_block_types (`Tuple[str]`, optional, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
	The tuple of upsample blocks to use.
	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`].
	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.
	If `None`, it will skip the normalization and activation layers in post-processing
	norm_eps (`float`, optional, defaults to 1e-5): The epsilon to use for the normalization.
	cross_attention_dim (`int` or `Tuple[int]`, optional, defaults to 1280):
	The dimension of the cross attention features.
	encoder_hid_dim (`int`, optional, defaults to None):
	If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
	dimension to `cross_attention_dim`.
	encoder_hid_dim_type (`str`, optional, defaults to None):
	If given, the `encoder_hidden_states` and potentially other embeddings will be down-projected to text
	embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
	attention_head_dim (`int`, optional, defaults to 8): The dimension of the attention heads.
	resnet_time_scale_shift (`str`, optional, defaults to `"default"`): Time scale shift config
	for resnet blocks, see [`~models.resnet.ResnetBlock2D`]. Choose from `default` or `scale_shift`.
	class_embed_type (`str`, optional, defaults to None):
	The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
	`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
	addition_embed_type (`str`, optional, defaults to None):
	Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
	"text". "text" will use the `TextTimeEmbedding` layer.
	num_class_embeds (`int`, optional, defaults to None):
	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`.
	time_embedding_type (`str`, optional, default to `positional`):
	The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
	time_embedding_dim (`int`, optional, default to `None`):
	An optional override for the dimension of the projected time embedding.
	time_embedding_act_fn (`str`, optional, default to `None`):
	Optional activation function to use on the time embeddings only one time before they as passed to the rest
	of the unet. Choose from `silu`, `mish`, `gelu`, and `swish`.
	timestep_post_act (`str, optional, default to `None`):
	The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
	time_cond_proj_dim (`int`, optional, default to `None`):
	The dimension of `cond_proj` layer in timestep embedding.
	conv_in_kernel (`int`, optional, default to `3`): The kernel size of `conv_in` layer.
	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
	using the "projection" `class_embed_type`. Required when using the "projection" `class_embed_type`.
	class_embeddings_concat (`bool`, optional, defaults to `False`): Whether to concatenate the time
	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
	`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is None, the
	`only_cross_attention` value will be used as the value for `mid_block_only_cross_attention`. Else, it will
	default to `False`.
	"""

	_supports_gradient_checkpointing = True

	@register_to_config
	def __init__(
	self,
	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",
	),
	mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
	up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
	only_cross_attention: Union[bool, Tuple[bool]] = False,
	block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
	layers_per_block: Union[int, Tuple[int]] = 2,
	downsample_padding: int = 1,
	mid_block_scale_factor: float = 1,
	act_fn: str = "silu",
	norm_num_groups: Optional[int] = 32,
	norm_eps: float = 1e-5,
	cross_attention_dim: Union[int, Tuple[int]] = 1280,
	encoder_hid_dim: Optional[int] = None,
	encoder_hid_dim_type: Optional[str] = None,
	attention_head_dim: Union[int, Tuple[int]] = 8,
	dual_cross_attention: bool = False,
	use_linear_projection: bool = False,
	class_embed_type: Optional[str] = None,
	addition_embed_type: Optional[str] = None,
	num_class_embeds: Optional[int] = None,
	upcast_attention: bool = False,
	resnet_time_scale_shift: str = "default",
	resnet_skip_time_act: bool = False,
	resnet_out_scale_factor: int = 1.0,
	time_embedding_type: str = "positional",
	time_embedding_dim: Optional[int] = None,
	time_embedding_act_fn: Optional[str] = None,
	timestep_post_act: Optional[str] = None,
	time_cond_proj_dim: Optional[int] = None,
	conv_in_kernel: int = 3,
	conv_out_kernel: int = 3,
	projection_class_embeddings_input_dim: Optional[int] = None,
	class_embeddings_concat: bool = False,
	mid_block_only_cross_attention: Optional[bool] = None,
	cross_attention_norm: Optional[str] = None,
	addition_embed_type_num_heads=64,
	use_gated_attention: bool = False,
	):
	super().__init__()

	self.sample_size = sample_size

	# 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}."
	)

	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}."
	)

	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}."
	)

	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}."
	)

	# input
	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
	)

	# time
	if time_embedding_type == "fourier":
	time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
	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":
	time_embed_dim = time_embedding_dim or block_out_channels[0] * 4

	self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
	timestep_input_dim = block_out_channels[0]
	else:
	raise ValueError(
	f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
	)

	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,
	)

	if encoder_hid_dim_type is None and encoder_hid_dim is not None:
	encoder_hid_dim_type = "text_proj"
	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":
	self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
	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'."
	)
	else:
	self.encoder_hid_proj = None

	# class embedding
	if class_embed_type is None and num_class_embeds is not None:
	self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
	elif class_embed_type == "timestep":
	self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
	elif class_embed_type == "identity":
	self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
	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)
	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)
	else:
	self.class_embedding = None

	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
	)
	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
	)
	elif addition_embed_type is not None:
	raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")

	if time_embedding_act_fn is None:
	self.time_embed_act = None
	elif time_embedding_act_fn == "swish":
	self.time_embed_act = lambda x: F.silu(x)
	elif time_embedding_act_fn == "mish":
	self.time_embed_act = nn.Mish()
	elif time_embedding_act_fn == "silu":
	self.time_embed_act = nn.SiLU()
	elif time_embedding_act_fn == "gelu":
	self.time_embed_act = nn.GELU()
	else:
	raise ValueError(f"Unsupported activation function: {time_embedding_act_fn}")

	self.down_blocks = nn.ModuleList([])
	self.up_blocks = nn.ModuleList([])

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

	only_cross_attention = [only_cross_attention] * len(down_block_types)

	if mid_block_only_cross_attention is None:
	mid_block_only_cross_attention = False

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

	if isinstance(cross_attention_dim, int):
	cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
	else:
	assert not use_gated_attention, f"use_gated_attention is not supported with varying cross_attention_dim: {cross_attention_dim}"

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

	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

	# down
	output_channel = block_out_channels[0]
	for i, down_block_type in enumerate(down_block_types):
	input_channel = output_channel
	output_channel = block_out_channels[i]
	is_final_block = i == len(block_out_channels) - 1

	down_block = get_down_block(
	down_block_type,
	num_layers=layers_per_block[i],
	in_channels=input_channel,
	out_channels=output_channel,
	temb_channels=blocks_time_embed_dim,
	add_downsample=not is_final_block,
	resnet_eps=norm_eps,
	resnet_act_fn=act_fn,
	resnet_groups=norm_num_groups,
	cross_attention_dim=cross_attention_dim[i],
	attn_num_head_channels=attention_head_dim[i],
	downsample_padding=downsample_padding,
	dual_cross_attention=dual_cross_attention,
	use_linear_projection=use_linear_projection,
	only_cross_attention=only_cross_attention[i],
	upcast_attention=upcast_attention,
	resnet_time_scale_shift=resnet_time_scale_shift,
	resnet_skip_time_act=resnet_skip_time_act,
	resnet_out_scale_factor=resnet_out_scale_factor,
	cross_attention_norm=cross_attention_norm,
	use_gated_attention=use_gated_attention,
	)
	self.down_blocks.append(down_block)

	# mid
	if mid_block_type == "UNetMidBlock2DCrossAttn":
	self.mid_block = UNetMidBlock2DCrossAttn(
	in_channels=block_out_channels[-1],
	temb_channels=blocks_time_embed_dim,
	resnet_eps=norm_eps,
	resnet_act_fn=act_fn,
	output_scale_factor=mid_block_scale_factor,
	resnet_time_scale_shift=resnet_time_scale_shift,
	cross_attention_dim=cross_attention_dim[-1],
	attn_num_head_channels=attention_head_dim[-1],
	resnet_groups=norm_num_groups,
	dual_cross_attention=dual_cross_attention,
	use_linear_projection=use_linear_projection,
	upcast_attention=upcast_attention,
	use_gated_attention=use_gated_attention,
	)
	elif mid_block_type is None:
	self.mid_block = None
	else:
	raise ValueError(f"unknown mid_block_type : {mid_block_type}")

	# count how many layers upsample the images
	self.num_upsamplers = 0

	# up
	reversed_block_out_channels = list(reversed(block_out_channels))
	reversed_attention_head_dim = list(reversed(attention_head_dim))
	reversed_layers_per_block = list(reversed(layers_per_block))
	reversed_cross_attention_dim = list(reversed(cross_attention_dim))
	only_cross_attention = list(reversed(only_cross_attention))

	output_channel = reversed_block_out_channels[0]
	for i, up_block_type in enumerate(up_block_types):
	is_final_block = i == len(block_out_channels) - 1

	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)]

	# add upsample block for all BUT final layer
	if not is_final_block:
	add_upsample = True
	self.num_upsamplers += 1
	else:
	add_upsample = False

	up_block = get_up_block(
	up_block_type,
	num_layers=reversed_layers_per_block[i] + 1,
	in_channels=input_channel,
	out_channels=output_channel,
	prev_output_channel=prev_output_channel,
	temb_channels=blocks_time_embed_dim,
	add_upsample=add_upsample,
	resnet_eps=norm_eps,
	resnet_act_fn=act_fn,
	resnet_groups=norm_num_groups,
	cross_attention_dim=reversed_cross_attention_dim[i],
	attn_num_head_channels=reversed_attention_head_dim[i],
	dual_cross_attention=dual_cross_attention,
	use_linear_projection=use_linear_projection,
	only_cross_attention=only_cross_attention[i],
	upcast_attention=upcast_attention,
	resnet_time_scale_shift=resnet_time_scale_shift,
	resnet_skip_time_act=resnet_skip_time_act,
	resnet_out_scale_factor=resnet_out_scale_factor,
	cross_attention_norm=cross_attention_norm,
	use_gated_attention=use_gated_attention,
	)
	self.up_blocks.append(up_block)
	prev_output_channel = output_channel

	# out
	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
	)

	if act_fn == "swish":
	self.conv_act = lambda x: F.silu(x)
	elif act_fn == "mish":
	self.conv_act = nn.Mish()
	elif act_fn == "silu":
	self.conv_act = nn.SiLU()
	elif act_fn == "gelu":
	self.conv_act = nn.GELU()
	else:
	raise ValueError(f"Unsupported activation function: {act_fn}")

	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
	)

	if use_gated_attention:
	self.position_net = PositionNet(positive_len=768, out_dim=cross_attention_dim[-1])


	@property
	def attn_processors(self) -> Dict[str, AttentionProcessor]:
	r"""
	Returns:
	`dict` of attention processors: A dictionary containing all attention processors used in the model with
	indexed by its weight name.
	"""
	# set recursively
	processors = {}

	def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
	if hasattr(module, "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

	def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
	r"""
	Parameters:
	`processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):
	The instantiated processor class or a dictionary of processor classes that will be set as the processor
	of all `Attention` layers.
	In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors.:

	"""
	count = len(self.attn_processors.keys())

	if isinstance(processor, dict) and len(processor) != count:
	raise ValueError(
	f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
	f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
	)

	def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
	if hasattr(module, "set_processor"):
	if not isinstance(processor, dict):
	module.set_processor(processor)
	else:
	module.set_processor(processor.pop(f"{name}.processor"))

	for sub_name, child in module.named_children():
	fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

	for name, module in self.named_children():
	fn_recursive_attn_processor(name, module, processor)

	def set_default_attn_processor(self):
	"""
	Disables custom attention processors and sets the default attention implementation.
	"""
	self.set_attn_processor(AttnProcessor())

	def set_attention_slice(self, slice_size):
	r"""
	Enable sliced attention computation.

	When this option is enabled, the attention module will split the input tensor in slices, to compute attention
	in several steps. This is useful to save some memory in exchange for a small speed decrease.

	Args:
	slice_size (`str` or `int` or `list(int)`, optional, defaults to `"auto"`):
	When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
	`"max"`, maximum amount of memory will be 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`.
	"""
	sliceable_head_dims = []

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

	for child in module.children():
	fn_recursive_retrieve_sliceable_dims(child)

	# retrieve number of attention layers
	for module in self.children():
	fn_recursive_retrieve_sliceable_dims(module)

	num_sliceable_layers = len(sliceable_head_dims)

	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
	slice_size = num_sliceable_layers * [1]

	slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size

	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)}."
	)

	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)

	def _set_gradient_checkpointing(self, module, value=False):
	if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D, CrossAttnUpBlock2D, UpBlock2D)):
	module.gradient_checkpointing = value

	def forward(
	self,
	sample: torch.FloatTensor,
	timestep: Union[torch.Tensor, float, int],
	encoder_hidden_states: torch.Tensor,
	class_labels: Optional[torch.Tensor] = None,
	timestep_cond: Optional[torch.Tensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	cross_attention_kwargs: Optional[Dict[str, Any]] = None,
	added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
	down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
	mid_block_additional_residual: Optional[torch.Tensor] = None,
	encoder_attention_mask: Optional[torch.Tensor] = None,
	return_dict: bool = True,
	return_cross_attention_probs: bool = False
	) -> Union[UNet2DConditionOutput, Tuple]:
	r"""
	Args:
	sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
	timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
	encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
	encoder_attention_mask (`torch.Tensor`):
	(batch, sequence_length) cross-attention mask, applied to encoder_hidden_states. True = keep, False =
	discard. Mask will be converted into a bias, which adds large negative values to attention scores
	corresponding to "discard" tokens.
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
	cross_attention_kwargs (`dict`, optional):
	A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
	`self.processor` in
	[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
	added_cond_kwargs (`dict`, optional):
	A kwargs dictionary that if specified includes additonal conditions that can be used for additonal time
	embeddings or encoder hidden states projections. See the configurations `encoder_hid_dim_type` and
	`addition_embed_type` for more information.

	Returns:
	[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
	[`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
	returning a tuple, the first element is the sample tensor.
	"""
	# By default samples have to be AT least a multiple of the overall upsampling factor.
	# The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
	# 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

	# 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)
	if attention_mask is not None:
	# 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)
	attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
	attention_mask = attention_mask.unsqueeze(1)

	# 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)

	# 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):
	# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
	# This would be a good case for the `match` statement (Python 3.10+)
	is_mps = sample.device.type == "mps"
	if isinstance(timestep, float):
	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:
	timesteps = timesteps[None].to(sample.device)

	# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
	timesteps = timesteps.expand(sample.shape[0])

	t_emb = self.time_proj(timesteps)

	# `Timesteps` does not contain any weights and will always return f32 tensors
	# but time_embedding might actually be running in fp16. so we need to cast here.
	# there might be better ways to encapsulate this.
	t_emb = t_emb.to(dtype=sample.dtype)

	emb = self.time_embedding(t_emb, timestep_cond)

	if self.class_embedding is not None:
	if class_labels is None:
	raise ValueError("class_labels should be provided when num_class_embeds > 0")

	if self.config.class_embed_type == "timestep":
	class_labels = self.time_proj(class_labels)

	# `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)

	class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)

	if self.config.class_embeddings_concat:
	emb = torch.cat([emb, class_emb], dim=-1)
	else:
	emb = emb + class_emb

	if self.config.addition_embed_type == "text":
	aug_emb = self.add_embedding(encoder_hidden_states)
	emb = emb + aug_emb
	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)
	emb = emb + aug_emb

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

	if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
	encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
	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)

	# 2. pre-process
	sample = self.conv_in(sample)

	# 2.5 GLIGEN position net
	if cross_attention_kwargs is not None and cross_attention_kwargs.get('gligen', None) is not None:
	cross_attention_kwargs = cross_attention_kwargs.copy()
	cross_attention_kwargs['gligen'] = {
	'objs': self.position_net(
	boxes=cross_attention_kwargs['gligen']['boxes'],
	masks=cross_attention_kwargs['gligen']['masks'],
	positive_embeddings=cross_attention_kwargs['gligen']['positive_embeddings']
	),
	'fuser_attn_kwargs': cross_attention_kwargs['gligen'].get('fuser_attn_kwargs', {})
	}

	# 3. down
	down_block_res_samples = (sample,)
	cross_attention_probs_down = []
	if cross_attention_kwargs is None:
	cross_attention_kwargs = {}

	for i, downsample_block in enumerate(self.down_blocks):
	cross_attention_kwargs["attn_key"] = ["down", i]

	if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
	downsample_block_output = downsample_block(
	hidden_states=sample,
	temb=emb,
	encoder_hidden_states=encoder_hidden_states,
	attention_mask=attention_mask,
	cross_attention_kwargs=cross_attention_kwargs,
	encoder_attention_mask=encoder_attention_mask,
	return_cross_attention_probs=return_cross_attention_probs,
	)
	if return_cross_attention_probs:
	sample, res_samples, cross_attention_probs = downsample_block_output
	cross_attention_probs_down.append(cross_attention_probs)
	else:
	sample, res_samples = downsample_block_output
	else:
	sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

	down_block_res_samples += res_samples

	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
	):
	down_block_res_sample = down_block_res_sample + down_block_additional_residual
	new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)

	down_block_res_samples = new_down_block_res_samples

	# 4. mid
	cross_attention_probs_mid = []
	if self.mid_block is not None:
	cross_attention_kwargs["attn_key"] = ["mid", 0]

	sample = self.mid_block(
	sample,
	emb,
	encoder_hidden_states=encoder_hidden_states,
	attention_mask=attention_mask,
	cross_attention_kwargs=cross_attention_kwargs,
	encoder_attention_mask=encoder_attention_mask,
	return_cross_attention_probs=return_cross_attention_probs,
	)
	if return_cross_attention_probs:
	sample, cross_attention_probs = sample
	cross_attention_probs_mid.append(cross_attention_probs)


	if mid_block_additional_residual is not None:
	sample = sample + mid_block_additional_residual

	cross_attention_probs_up = []
	# 5. up
	for i, upsample_block in enumerate(self.up_blocks):
	cross_attention_kwargs["attn_key"] = ["up", i]

	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,
	cross_attention_kwargs=cross_attention_kwargs,
	upsample_size=upsample_size,
	attention_mask=attention_mask,
	encoder_attention_mask=encoder_attention_mask,
	return_cross_attention_probs=return_cross_attention_probs,
	)
	if return_cross_attention_probs:
	sample, cross_attention_probs = sample
	cross_attention_probs_up.append(cross_attention_probs)
	else:
	sample = upsample_block(
	hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
	)

	# 6. post-process
	if self.conv_norm_out:
	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, cross_attention_probs_down=cross_attention_probs_down, cross_attention_probs_mid=cross_attention_probs_mid, cross_attention_probs_up=cross_attention_probs_up)