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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.utils.checkpoint | |
from ..configuration_utils import ConfigMixin, register_to_config | |
from ..loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin | |
from ..utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers | |
from .activations import get_activation | |
from .attention_processor import ( | |
ADDED_KV_ATTENTION_PROCESSORS, | |
CROSS_ATTENTION_PROCESSORS, | |
Attention, | |
AttentionProcessor, | |
AttnAddedKVProcessor, | |
AttnProcessor, | |
) | |
from .embeddings import ( | |
GaussianFourierProjection, | |
GLIGENTextBoundingboxProjection, | |
ImageHintTimeEmbedding, | |
ImageProjection, | |
ImageTimeEmbedding, | |
TextImageProjection, | |
TextImageTimeEmbedding, | |
TextTimeEmbedding, | |
TimestepEmbedding, | |
Timesteps, | |
) | |
from .modeling_utils import ModelMixin | |
from .unet_2d_blocks import ( | |
UNetMidBlock2D, | |
UNetMidBlock2DCrossAttn, | |
UNetMidBlock2DSimpleCrossAttn, | |
get_down_block, | |
get_up_block, | |
) | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
class UNet2DConditionOutput(BaseOutput): | |
""" | |
The output of [`UNet2DConditionModel`]. | |
Args: | |
sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): | |
The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. | |
""" | |
sample: torch.FloatTensor = None | |
class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin): | |
r""" | |
A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample | |
shaped output. | |
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented | |
for all models (such as downloading or saving). | |
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): Number of channels in the input sample. | |
out_channels (`int`, *optional*, defaults to 4): 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"`): | |
Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or | |
`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. | |
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. | |
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. | |
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`, normalization and activation layers is skipped 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. | |
transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *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`]. | |
reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): | |
The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling | |
blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for | |
[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], | |
[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. | |
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 are 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. | |
num_attention_heads (`int`, *optional*): | |
The number of attention heads. If not defined, defaults to `attention_head_dim` | |
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. | |
addition_time_embed_dim: (`int`, *optional*, defaults to `None`): | |
Dimension for the timestep embeddings. | |
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*, defaults to `positional`): | |
The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. | |
time_embedding_dim (`int`, *optional*, defaults to `None`): | |
An optional override for the dimension of the projected time embedding. | |
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`): | |
The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. | |
time_cond_proj_dim (`int`, *optional*, defaults to `None`): | |
The dimension of `cond_proj` layer in the 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 | |
`class_embed_type="projection"`. Required when `class_embed_type="projection"`. | |
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 is used as the value for `mid_block_only_cross_attention`. Default to `False` | |
otherwise. | |
""" | |
_supports_gradient_checkpointing = True | |
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, | |
dropout: float = 0.0, | |
act_fn: str = "silu", | |
norm_num_groups: Optional[int] = 32, | |
norm_eps: float = 1e-5, | |
cross_attention_dim: Union[int, Tuple[int]] = 1280, | |
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, | |
reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, | |
encoder_hid_dim: Optional[int] = None, | |
encoder_hid_dim_type: Optional[str] = None, | |
attention_head_dim: Union[int, Tuple[int]] = 8, | |
num_attention_heads: Optional[Union[int, Tuple[int]]] = None, | |
dual_cross_attention: bool = False, | |
use_linear_projection: bool = False, | |
class_embed_type: Optional[str] = None, | |
addition_embed_type: Optional[str] = None, | |
addition_time_embed_dim: Optional[int] = 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, | |
attention_type: str = "default", | |
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, | |
): | |
super().__init__() | |
self.sample_size = sample_size | |
if num_attention_heads is not None: | |
raise ValueError( | |
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." | |
) | |
# 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 | |
# 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(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}." | |
) | |
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}." | |
) | |
if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None: | |
for layer_number_per_block in transformer_layers_per_block: | |
if isinstance(layer_number_per_block, list): | |
raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") | |
# 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" | |
self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) | |
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 == "image_proj": | |
# Kandinsky 2.2 | |
self.encoder_hid_proj = ImageProjection( | |
image_embed_dim=encoder_hid_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 == "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) | |
elif addition_embed_type == "image": | |
# Kandinsky 2.2 | |
self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) | |
elif addition_embed_type == "image_hint": | |
# Kandinsky 2.2 ControlNet | |
self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_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 | |
else: | |
self.time_embed_act = get_activation(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(num_attention_heads, int): | |
num_attention_heads = (num_attention_heads,) * len(down_block_types) | |
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) | |
if isinstance(layers_per_block, int): | |
layers_per_block = [layers_per_block] * len(down_block_types) | |
if isinstance(transformer_layers_per_block, int): | |
transformer_layers_per_block = [transformer_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], | |
transformer_layers_per_block=transformer_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], | |
num_attention_heads=num_attention_heads[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, | |
attention_type=attention_type, | |
resnet_skip_time_act=resnet_skip_time_act, | |
resnet_out_scale_factor=resnet_out_scale_factor, | |
cross_attention_norm=cross_attention_norm, | |
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, | |
dropout=dropout, | |
) | |
self.down_blocks.append(down_block) | |
# mid | |
if mid_block_type == "UNetMidBlock2DCrossAttn": | |
self.mid_block = UNetMidBlock2DCrossAttn( | |
transformer_layers_per_block=transformer_layers_per_block[-1], | |
in_channels=block_out_channels[-1], | |
temb_channels=blocks_time_embed_dim, | |
dropout=dropout, | |
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], | |
num_attention_heads=num_attention_heads[-1], | |
resnet_groups=norm_num_groups, | |
dual_cross_attention=dual_cross_attention, | |
use_linear_projection=use_linear_projection, | |
upcast_attention=upcast_attention, | |
attention_type=attention_type, | |
) | |
elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": | |
self.mid_block = UNetMidBlock2DSimpleCrossAttn( | |
in_channels=block_out_channels[-1], | |
temb_channels=blocks_time_embed_dim, | |
dropout=dropout, | |
resnet_eps=norm_eps, | |
resnet_act_fn=act_fn, | |
output_scale_factor=mid_block_scale_factor, | |
cross_attention_dim=cross_attention_dim[-1], | |
attention_head_dim=attention_head_dim[-1], | |
resnet_groups=norm_num_groups, | |
resnet_time_scale_shift=resnet_time_scale_shift, | |
skip_time_act=resnet_skip_time_act, | |
only_cross_attention=mid_block_only_cross_attention, | |
cross_attention_norm=cross_attention_norm, | |
) | |
elif mid_block_type == "UNetMidBlock2D": | |
self.mid_block = UNetMidBlock2D( | |
in_channels=block_out_channels[-1], | |
temb_channels=blocks_time_embed_dim, | |
dropout=dropout, | |
num_layers=0, | |
resnet_eps=norm_eps, | |
resnet_act_fn=act_fn, | |
output_scale_factor=mid_block_scale_factor, | |
resnet_groups=norm_num_groups, | |
resnet_time_scale_shift=resnet_time_scale_shift, | |
add_attention=False, | |
) | |
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_num_attention_heads = list(reversed(num_attention_heads)) | |
reversed_layers_per_block = list(reversed(layers_per_block)) | |
reversed_cross_attention_dim = list(reversed(cross_attention_dim)) | |
reversed_transformer_layers_per_block = ( | |
list(reversed(transformer_layers_per_block)) | |
if reverse_transformer_layers_per_block is None | |
else reverse_transformer_layers_per_block | |
) | |
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, | |
transformer_layers_per_block=reversed_transformer_layers_per_block[i], | |
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, | |
resolution_idx=i, | |
resnet_groups=norm_num_groups, | |
cross_attention_dim=reversed_cross_attention_dim[i], | |
num_attention_heads=reversed_num_attention_heads[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, | |
attention_type=attention_type, | |
resnet_skip_time_act=resnet_skip_time_act, | |
resnet_out_scale_factor=resnet_out_scale_factor, | |
cross_attention_norm=cross_attention_norm, | |
attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, | |
dropout=dropout, | |
) | |
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 | |
) | |
self.conv_act = get_activation(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 attention_type in ["gated", "gated-text-image"]: | |
positive_len = 768 | |
if isinstance(cross_attention_dim, int): | |
positive_len = cross_attention_dim | |
elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list): | |
positive_len = cross_attention_dim[0] | |
feature_type = "text-only" if attention_type == "gated" else "text-image" | |
self.position_net = GLIGENTextBoundingboxProjection( | |
positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type | |
) | |
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, "get_processor"): | |
processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) | |
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""" | |
Sets the attention processor to use to compute attention. | |
Parameters: | |
processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): | |
The instantiated processor class or a dictionary of processor classes that will be set as the processor | |
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. | |
""" | |
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. | |
""" | |
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): | |
processor = AttnAddedKVProcessor() | |
elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): | |
processor = AttnProcessor() | |
else: | |
raise ValueError( | |
f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" | |
) | |
self.set_attn_processor(processor) | |
def set_attention_slice(self, slice_size): | |
r""" | |
Enable sliced attention computation. | |
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. | |
Args: | |
slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): | |
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`. | |
""" | |
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 hasattr(module, "gradient_checkpointing"): | |
module.gradient_checkpointing = value | |
def enable_freeu(self, s1, s2, b1, b2): | |
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. | |
The suffixes after the scaling factors represent the stage blocks where they are being applied. | |
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that | |
are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. | |
Args: | |
s1 (`float`): | |
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to | |
mitigate the "oversmoothing effect" in the enhanced denoising process. | |
s2 (`float`): | |
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to | |
mitigate the "oversmoothing effect" in the enhanced denoising process. | |
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. | |
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. | |
""" | |
for i, upsample_block in enumerate(self.up_blocks): | |
setattr(upsample_block, "s1", s1) | |
setattr(upsample_block, "s2", s2) | |
setattr(upsample_block, "b1", b1) | |
setattr(upsample_block, "b2", b2) | |
def disable_freeu(self): | |
"""Disables the FreeU mechanism.""" | |
freeu_keys = {"s1", "s2", "b1", "b2"} | |
for i, upsample_block in enumerate(self.up_blocks): | |
for k in freeu_keys: | |
if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None: | |
setattr(upsample_block, k, None) | |
def fuse_qkv_projections(self): | |
""" | |
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, | |
key, value) are fused. For cross-attention modules, key and value projection matrices are fused. | |
<Tip warning={true}> | |
This API is 🧪 experimental. | |
</Tip> | |
""" | |
self.original_attn_processors = None | |
for _, attn_processor in self.attn_processors.items(): | |
if "Added" in str(attn_processor.__class__.__name__): | |
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") | |
self.original_attn_processors = self.attn_processors | |
for module in self.modules(): | |
if isinstance(module, Attention): | |
module.fuse_projections(fuse=True) | |
def unfuse_qkv_projections(self): | |
"""Disables the fused QKV projection if enabled. | |
<Tip warning={true}> | |
This API is 🧪 experimental. | |
</Tip> | |
""" | |
if self.original_attn_processors is not None: | |
self.set_attn_processor(self.original_attn_processors) | |
def unload_lora(self): | |
"""Unloads LoRA weights.""" | |
deprecate( | |
"unload_lora", | |
"0.28.0", | |
"Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().", | |
) | |
for module in self.modules(): | |
if hasattr(module, "set_lora_layer"): | |
module.set_lora_layer(None) | |
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, | |
down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, | |
encoder_attention_mask: Optional[torch.Tensor] = None, | |
return_dict: bool = True, | |
) -> Union[UNet2DConditionOutput, Tuple]: | |
r""" | |
The [`UNet2DConditionModel`] forward method. | |
Args: | |
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)`. | |
class_labels (`torch.Tensor`, *optional*, defaults to `None`): | |
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. | |
timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): | |
Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed | |
through the `self.time_embedding` layer to obtain the timestep embeddings. | |
attention_mask (`torch.Tensor`, *optional*, defaults to `None`): | |
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask | |
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large | |
negative values to the attention scores corresponding to "discard" tokens. | |
cross_attention_kwargs (`dict`, *optional*): | |
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under | |
`self.processor` in | |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
added_cond_kwargs: (`dict`, *optional*): | |
A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that | |
are passed along to the UNet blocks. | |
down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): | |
A tuple of tensors that if specified are added to the residuals of down unet blocks. | |
mid_block_additional_residual: (`torch.Tensor`, *optional*): | |
A tensor that if specified is added to the residual of the middle unet block. | |
encoder_attention_mask (`torch.Tensor`): | |
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. | |
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 [`AttnProcessor`]. | |
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. | |
down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*): | |
additional residuals to be added to UNet long skip connections from down blocks to up blocks for | |
example from ControlNet side model(s) | |
mid_block_additional_residual (`torch.Tensor`, *optional*): | |
additional residual to be added to UNet mid block output, for example from ControlNet side model | |
down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): | |
additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) | |
Returns: | |
[`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: | |
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. | |
""" | |
# 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 | |
for dim in sample.shape[-2:]: | |
if dim % default_overall_up_factor != 0: | |
# Forward upsample size to force interpolation output size. | |
forward_upsample_size = True | |
break | |
# 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) | |
aug_emb = None | |
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) | |
elif self.config.addition_embed_type == "text_image": | |
# Kandinsky 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) | |
elif self.config.addition_embed_type == "text_time": | |
# SDXL - style | |
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) | |
elif self.config.addition_embed_type == "image": | |
# Kandinsky 2.2 - style | |
if "image_embeds" not in added_cond_kwargs: | |
raise ValueError( | |
f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" | |
) | |
image_embs = added_cond_kwargs.get("image_embeds") | |
aug_emb = self.add_embedding(image_embs) | |
elif self.config.addition_embed_type == "image_hint": | |
# Kandinsky 2.2 - style | |
if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: | |
raise ValueError( | |
f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" | |
) | |
image_embs = added_cond_kwargs.get("image_embeds") | |
hint = added_cond_kwargs.get("hint") | |
aug_emb, hint = self.add_embedding(image_embs, hint) | |
sample = torch.cat([sample, hint], dim=1) | |
emb = emb + aug_emb if aug_emb is not None else 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) | |
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": | |
# Kandinsky 2.2 - style | |
if "image_embeds" not in added_cond_kwargs: | |
raise ValueError( | |
f"{self.__class__} has the config param `encoder_hid_dim_type` set to '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(image_embeds) | |
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": | |
if "image_embeds" not in added_cond_kwargs: | |
raise ValueError( | |
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" | |
) | |
image_embeds = added_cond_kwargs.get("image_embeds") | |
image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype) | |
encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1) | |
# 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() | |
gligen_args = cross_attention_kwargs.pop("gligen") | |
cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)} | |
# 3. down | |
lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 | |
if USE_PEFT_BACKEND: | |
# weight the lora layers by setting `lora_scale` for each PEFT layer | |
scale_lora_layers(self, lora_scale) | |
is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None | |
# using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets | |
is_adapter = down_intrablock_additional_residuals is not None | |
# maintain backward compatibility for legacy usage, where | |
# T2I-Adapter and ControlNet both use down_block_additional_residuals arg | |
# but can only use one or the other | |
if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None: | |
deprecate( | |
"T2I should not use down_block_additional_residuals", | |
"1.3.0", | |
"Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ | |
and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ | |
for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", | |
standard_warn=False, | |
) | |
down_intrablock_additional_residuals = down_block_additional_residuals | |
is_adapter = True | |
down_block_res_samples = (sample,) | |
for downsample_block in self.down_blocks: | |
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: | |
# For t2i-adapter CrossAttnDownBlock2D | |
additional_residuals = {} | |
if is_adapter and len(down_intrablock_additional_residuals) > 0: | |
additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) | |
sample, res_samples = 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, | |
**additional_residuals, | |
) | |
else: | |
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale) | |
if is_adapter and len(down_intrablock_additional_residuals) > 0: | |
sample += down_intrablock_additional_residuals.pop(0) | |
down_block_res_samples += res_samples | |
if is_controlnet: | |
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 | |
if self.mid_block is not None: | |
if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: | |
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, | |
) | |
else: | |
sample = self.mid_block(sample, emb) | |
# To support T2I-Adapter-XL | |
if ( | |
is_adapter | |
and len(down_intrablock_additional_residuals) > 0 | |
and sample.shape == down_intrablock_additional_residuals[0].shape | |
): | |
sample += down_intrablock_additional_residuals.pop(0) | |
if is_controlnet: | |
sample = sample + mid_block_additional_residual | |
# 5. up | |
for i, upsample_block in enumerate(self.up_blocks): | |
is_final_block = i == len(self.up_blocks) - 1 | |
res_samples = down_block_res_samples[-len(upsample_block.resnets) :] | |
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] | |
# if we have not reached the final block and need to forward the | |
# upsample size, we do it here | |
if not is_final_block and forward_upsample_size: | |
upsample_size = down_block_res_samples[-1].shape[2:] | |
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: | |
sample = upsample_block( | |
hidden_states=sample, | |
temb=emb, | |
res_hidden_states_tuple=res_samples, | |
encoder_hidden_states=encoder_hidden_states, | |
cross_attention_kwargs=cross_attention_kwargs, | |
upsample_size=upsample_size, | |
attention_mask=attention_mask, | |
encoder_attention_mask=encoder_attention_mask, | |
) | |
else: | |
sample = upsample_block( | |
hidden_states=sample, | |
temb=emb, | |
res_hidden_states_tuple=res_samples, | |
upsample_size=upsample_size, | |
scale=lora_scale, | |
) | |
# 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 USE_PEFT_BACKEND: | |
# remove `lora_scale` from each PEFT layer | |
unscale_lora_layers(self, lora_scale) | |
if not return_dict: | |
return (sample,) | |
return UNet2DConditionOutput(sample=sample) | |