1544 lines
71 KiB
Python
1544 lines
71 KiB
Python
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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BaseOutput,
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deprecate,
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logging,
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scale_lora_layers,
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unscale_lora_layers,
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)
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from diffusers.models.activations import get_activation
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from diffusers.models.attention_processor import (
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ADDED_KV_ATTENTION_PROCESSORS,
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CROSS_ATTENTION_PROCESSORS,
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Attention,
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AttentionProcessor,
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AttnAddedKVProcessor,
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AttnProcessor,
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)
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from diffusers.models.embeddings import (
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GaussianFourierProjection,
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GLIGENTextBoundingboxProjection,
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ImageHintTimeEmbedding,
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ImageProjection,
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ImageTimeEmbedding,
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TextImageProjection,
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TextImageTimeEmbedding,
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TextTimeEmbedding,
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TimestepEmbedding,
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Timesteps,
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)
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from diffusers.models.modeling_utils import ModelMixin
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from .unet_2d_blocks import (
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get_down_block,
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get_mid_block,
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get_up_block,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class UNet2DConditionOutput(BaseOutput):
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"""
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The output of [`UNet2DConditionModel`].
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Args:
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sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
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"""
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sample: torch.FloatTensor = None
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class UNet2DConditionModel(
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ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin
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):
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r"""
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A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
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shaped output.
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This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
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for all models (such as downloading or saving).
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Parameters:
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sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
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Height and width of input/output sample.
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in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
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out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
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center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
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flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
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Whether to flip the sin to cos in the time embedding.
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freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
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down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
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The tuple of downsample blocks to use.
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mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
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Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
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`UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
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up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
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The tuple of upsample blocks to use.
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only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
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Whether to include self-attention in the basic transformer blocks, see
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[`~models.attention.BasicTransformerBlock`].
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block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
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The tuple of output channels for each block.
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layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
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downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
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mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
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act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
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norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
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If `None`, normalization and activation layers is skipped in post-processing.
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norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
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cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
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The dimension of the cross attention features.
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transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
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The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
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[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
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[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
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reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
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The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
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blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
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[`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
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[`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
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encoder_hid_dim (`int`, *optional*, defaults to None):
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If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
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dimension to `cross_attention_dim`.
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encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
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If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
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embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
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attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
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num_attention_heads (`int`, *optional*):
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The number of attention heads. If not defined, defaults to `attention_head_dim`
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resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
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for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
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class_embed_type (`str`, *optional*, defaults to `None`):
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The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
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`"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
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addition_embed_type (`str`, *optional*, defaults to `None`):
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Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
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"text". "text" will use the `TextTimeEmbedding` layer.
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addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
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Dimension for the timestep embeddings.
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num_class_embeds (`int`, *optional*, defaults to `None`):
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Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
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class conditioning with `class_embed_type` equal to `None`.
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time_embedding_type (`str`, *optional*, defaults to `positional`):
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The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
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time_embedding_dim (`int`, *optional*, defaults to `None`):
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An optional override for the dimension of the projected time embedding.
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time_embedding_act_fn (`str`, *optional*, defaults to `None`):
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Optional activation function to use only once on the time embeddings before they are passed to the rest of
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the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
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timestep_post_act (`str`, *optional*, defaults to `None`):
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The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
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time_cond_proj_dim (`int`, *optional*, defaults to `None`):
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The dimension of `cond_proj` layer in the timestep embedding.
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conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
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*optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
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*optional*): The dimension of the `class_labels` input when
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`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
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class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
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embeddings with the class embeddings.
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mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
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Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
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`only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
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`only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
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otherwise.
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"""
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_supports_gradient_checkpointing = True
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@register_to_config
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def __init__(
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self,
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sample_size: Optional[int] = None,
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in_channels: int = 4,
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out_channels: int = 4,
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center_input_sample: bool = False,
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flip_sin_to_cos: bool = True,
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freq_shift: int = 0,
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down_block_types: Tuple[str] = (
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D",
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),
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mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
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up_block_types: Tuple[str] = (
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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),
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only_cross_attention: Union[bool, Tuple[bool]] = False,
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block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
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layers_per_block: Union[int, Tuple[int]] = 2,
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downsample_padding: int = 1,
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mid_block_scale_factor: float = 1,
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dropout: float = 0.0,
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act_fn: str = "silu",
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norm_num_groups: Optional[int] = 32,
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norm_eps: float = 1e-5,
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cross_attention_dim: Union[int, Tuple[int]] = 1280,
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transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
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reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
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encoder_hid_dim: Optional[int] = None,
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encoder_hid_dim_type: Optional[str] = None,
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attention_head_dim: Union[int, Tuple[int]] = 8,
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num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
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dual_cross_attention: bool = False,
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use_linear_projection: bool = False,
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class_embed_type: Optional[str] = None,
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addition_embed_type: Optional[str] = None,
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addition_time_embed_dim: Optional[int] = None,
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num_class_embeds: Optional[int] = None,
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upcast_attention: bool = False,
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resnet_time_scale_shift: str = "default",
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resnet_skip_time_act: bool = False,
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resnet_out_scale_factor: float = 1.0,
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time_embedding_type: str = "positional",
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time_embedding_dim: Optional[int] = None,
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time_embedding_act_fn: Optional[str] = None,
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timestep_post_act: Optional[str] = None,
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time_cond_proj_dim: Optional[int] = None,
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conv_in_kernel: int = 3,
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conv_out_kernel: int = 3,
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projection_class_embeddings_input_dim: Optional[int] = None,
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attention_type: str = "default",
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class_embeddings_concat: bool = False,
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mid_block_only_cross_attention: Optional[bool] = None,
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cross_attention_norm: Optional[str] = None,
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addition_embed_type_num_heads: int = 64,
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):
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super().__init__()
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self.sample_size = sample_size
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if num_attention_heads is not None:
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raise ValueError(
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"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."
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)
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# If `num_attention_heads` is not defined (which is the case for most models)
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# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
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# The reason for this behavior is to correct for incorrectly named variables that were introduced
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# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
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# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
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# which is why we correct for the naming here.
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num_attention_heads = num_attention_heads or attention_head_dim
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# Check inputs
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self._check_config(
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down_block_types=down_block_types,
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up_block_types=up_block_types,
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only_cross_attention=only_cross_attention,
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block_out_channels=block_out_channels,
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layers_per_block=layers_per_block,
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cross_attention_dim=cross_attention_dim,
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transformer_layers_per_block=transformer_layers_per_block,
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reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
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attention_head_dim=attention_head_dim,
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num_attention_heads=num_attention_heads,
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)
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# input
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conv_in_padding = (conv_in_kernel - 1) // 2
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self.conv_in = nn.Conv2d(
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in_channels,
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block_out_channels[0],
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kernel_size=conv_in_kernel,
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padding=conv_in_padding,
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)
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# time
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time_embed_dim, timestep_input_dim = self._set_time_proj(
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time_embedding_type,
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block_out_channels=block_out_channels,
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flip_sin_to_cos=flip_sin_to_cos,
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freq_shift=freq_shift,
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time_embedding_dim=time_embedding_dim,
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)
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self.time_embedding = TimestepEmbedding(
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timestep_input_dim,
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time_embed_dim,
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act_fn=act_fn,
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post_act_fn=timestep_post_act,
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cond_proj_dim=time_cond_proj_dim,
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)
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self._set_encoder_hid_proj(
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encoder_hid_dim_type,
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cross_attention_dim=cross_attention_dim,
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encoder_hid_dim=encoder_hid_dim,
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)
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# class embedding
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self._set_class_embedding(
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class_embed_type,
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act_fn=act_fn,
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num_class_embeds=num_class_embeds,
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projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
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time_embed_dim=time_embed_dim,
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timestep_input_dim=timestep_input_dim,
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)
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self._set_add_embedding(
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addition_embed_type,
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addition_embed_type_num_heads=addition_embed_type_num_heads,
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addition_time_embed_dim=addition_time_embed_dim,
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cross_attention_dim=cross_attention_dim,
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encoder_hid_dim=encoder_hid_dim,
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flip_sin_to_cos=flip_sin_to_cos,
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freq_shift=freq_shift,
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projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
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time_embed_dim=time_embed_dim,
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)
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if time_embedding_act_fn is None:
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self.time_embed_act = None
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else:
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self.time_embed_act = get_activation(time_embedding_act_fn)
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self.down_blocks = nn.ModuleList([])
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self.up_blocks = nn.ModuleList([])
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if isinstance(only_cross_attention, bool):
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if mid_block_only_cross_attention is None:
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mid_block_only_cross_attention = only_cross_attention
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only_cross_attention = [only_cross_attention] * len(down_block_types)
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if mid_block_only_cross_attention is None:
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mid_block_only_cross_attention = False
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if isinstance(num_attention_heads, int):
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num_attention_heads = (num_attention_heads,) * len(down_block_types)
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if isinstance(attention_head_dim, int):
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attention_head_dim = (attention_head_dim,) * len(down_block_types)
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if isinstance(cross_attention_dim, int):
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cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
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if isinstance(layers_per_block, int):
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layers_per_block = [layers_per_block] * len(down_block_types)
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||
|
if isinstance(transformer_layers_per_block, int):
|
||
|
transformer_layers_per_block = [transformer_layers_per_block] * len(
|
||
|
down_block_types
|
||
|
)
|
||
|
|
||
|
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
|
||
|
self.mid_block = get_mid_block(
|
||
|
mid_block_type,
|
||
|
temb_channels=blocks_time_embed_dim,
|
||
|
in_channels=block_out_channels[-1],
|
||
|
resnet_eps=norm_eps,
|
||
|
resnet_act_fn=act_fn,
|
||
|
resnet_groups=norm_num_groups,
|
||
|
output_scale_factor=mid_block_scale_factor,
|
||
|
transformer_layers_per_block=transformer_layers_per_block[-1],
|
||
|
num_attention_heads=num_attention_heads[-1],
|
||
|
cross_attention_dim=cross_attention_dim[-1],
|
||
|
dual_cross_attention=dual_cross_attention,
|
||
|
use_linear_projection=use_linear_projection,
|
||
|
mid_block_only_cross_attention=mid_block_only_cross_attention,
|
||
|
upcast_attention=upcast_attention,
|
||
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
||
|
attention_type=attention_type,
|
||
|
resnet_skip_time_act=resnet_skip_time_act,
|
||
|
cross_attention_norm=cross_attention_norm,
|
||
|
attention_head_dim=attention_head_dim[-1],
|
||
|
dropout=dropout,
|
||
|
)
|
||
|
|
||
|
# 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,
|
||
|
)
|
||
|
|
||
|
self._set_pos_net_if_use_gligen(
|
||
|
attention_type=attention_type, cross_attention_dim=cross_attention_dim
|
||
|
)
|
||
|
|
||
|
def _check_config(
|
||
|
self,
|
||
|
down_block_types: Tuple[str],
|
||
|
up_block_types: Tuple[str],
|
||
|
only_cross_attention: Union[bool, Tuple[bool]],
|
||
|
block_out_channels: Tuple[int],
|
||
|
layers_per_block: Union[int, Tuple[int]],
|
||
|
cross_attention_dim: Union[int, Tuple[int]],
|
||
|
transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]],
|
||
|
reverse_transformer_layers_per_block: bool,
|
||
|
attention_head_dim: int,
|
||
|
num_attention_heads: Optional[Union[int, Tuple[int]]],
|
||
|
):
|
||
|
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."
|
||
|
)
|
||
|
|
||
|
def _set_time_proj(
|
||
|
self,
|
||
|
time_embedding_type: str,
|
||
|
block_out_channels: int,
|
||
|
flip_sin_to_cos: bool,
|
||
|
freq_shift: float,
|
||
|
time_embedding_dim: int,
|
||
|
) -> Tuple[int, int]:
|
||
|
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`."
|
||
|
)
|
||
|
|
||
|
return time_embed_dim, timestep_input_dim
|
||
|
|
||
|
def _set_encoder_hid_proj(
|
||
|
self,
|
||
|
encoder_hid_dim_type: Optional[str],
|
||
|
cross_attention_dim: Union[int, Tuple[int]],
|
||
|
encoder_hid_dim: Optional[int],
|
||
|
):
|
||
|
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
|
||
|
|
||
|
def _set_class_embedding(
|
||
|
self,
|
||
|
class_embed_type: Optional[str],
|
||
|
act_fn: str,
|
||
|
num_class_embeds: Optional[int],
|
||
|
projection_class_embeddings_input_dim: Optional[int],
|
||
|
time_embed_dim: int,
|
||
|
timestep_input_dim: int,
|
||
|
):
|
||
|
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
|
||
|
|
||
|
def _set_add_embedding(
|
||
|
self,
|
||
|
addition_embed_type: str,
|
||
|
addition_embed_type_num_heads: int,
|
||
|
addition_time_embed_dim: Optional[int],
|
||
|
flip_sin_to_cos: bool,
|
||
|
freq_shift: float,
|
||
|
cross_attention_dim: Optional[int],
|
||
|
encoder_hid_dim: Optional[int],
|
||
|
projection_class_embeddings_input_dim: Optional[int],
|
||
|
time_embed_dim: int,
|
||
|
):
|
||
|
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'."
|
||
|
)
|
||
|
|
||
|
def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int):
|
||
|
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,
|
||
|
)
|
||
|
|
||
|
@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, "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: Union[str, int, List[int]] = "auto"):
|
||
|
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: float, s2: float, b1: float, b2: float):
|
||
|
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 get_time_embed(
|
||
|
self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int]
|
||
|
) -> Optional[torch.Tensor]:
|
||
|
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)
|
||
|
return t_emb
|
||
|
|
||
|
def get_class_embed(
|
||
|
self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]
|
||
|
) -> Optional[torch.Tensor]:
|
||
|
class_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)
|
||
|
return class_emb
|
||
|
|
||
|
def get_aug_embed(
|
||
|
self,
|
||
|
emb: torch.Tensor,
|
||
|
encoder_hidden_states: torch.Tensor,
|
||
|
added_cond_kwargs: Dict[str, Any],
|
||
|
) -> Optional[torch.Tensor]:
|
||
|
aug_emb = None
|
||
|
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 = self.add_embedding(image_embs, hint)
|
||
|
return aug_emb
|
||
|
|
||
|
def process_encoder_hidden_states(
|
||
|
self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any]
|
||
|
) -> torch.Tensor:
|
||
|
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)
|
||
|
encoder_hidden_states = (encoder_hidden_states, image_embeds)
|
||
|
return encoder_hidden_states
|
||
|
|
||
|
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,
|
||
|
down_block_add_samples: Optional[Tuple[torch.Tensor]] = None,
|
||
|
mid_block_add_sample: Optional[Tuple[torch.Tensor]] = None,
|
||
|
up_block_add_samples: Optional[Tuple[torch.Tensor]] = None,
|
||
|
) -> 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.unets.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.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
|
||
|
If `return_dict` is True, an [`~models.unets.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
|
||
|
t_emb = self.get_time_embed(sample=sample, timestep=timestep)
|
||
|
emb = self.time_embedding(t_emb, timestep_cond)
|
||
|
aug_emb = None
|
||
|
|
||
|
class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
|
||
|
if class_emb is not None:
|
||
|
if self.config.class_embeddings_concat:
|
||
|
emb = torch.cat([emb, class_emb], dim=-1)
|
||
|
else:
|
||
|
emb = emb + class_emb
|
||
|
|
||
|
aug_emb = self.get_aug_embed(
|
||
|
emb=emb,
|
||
|
encoder_hidden_states=encoder_hidden_states,
|
||
|
added_cond_kwargs=added_cond_kwargs,
|
||
|
)
|
||
|
if self.config.addition_embed_type == "image_hint":
|
||
|
aug_emb, hint = aug_emb
|
||
|
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)
|
||
|
|
||
|
encoder_hidden_states = self.process_encoder_hidden_states(
|
||
|
encoder_hidden_states=encoder_hidden_states,
|
||
|
added_cond_kwargs=added_cond_kwargs,
|
||
|
)
|
||
|
|
||
|
# 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
|
||
|
is_brushnet = (
|
||
|
down_block_add_samples is not None
|
||
|
and mid_block_add_sample is not None
|
||
|
and up_block_add_samples is not None
|
||
|
)
|
||
|
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,)
|
||
|
|
||
|
if is_brushnet:
|
||
|
sample = sample + down_block_add_samples.pop(0)
|
||
|
|
||
|
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)
|
||
|
)
|
||
|
|
||
|
if is_brushnet and len(down_block_add_samples) > 0:
|
||
|
additional_residuals["down_block_add_samples"] = [
|
||
|
down_block_add_samples.pop(0)
|
||
|
for _ in range(
|
||
|
len(downsample_block.resnets)
|
||
|
+ (downsample_block.downsamplers != None)
|
||
|
)
|
||
|
]
|
||
|
|
||
|
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:
|
||
|
additional_residuals = {}
|
||
|
if is_brushnet and len(down_block_add_samples) > 0:
|
||
|
additional_residuals["down_block_add_samples"] = [
|
||
|
down_block_add_samples.pop(0)
|
||
|
for _ in range(
|
||
|
len(downsample_block.resnets)
|
||
|
+ (downsample_block.downsamplers != None)
|
||
|
)
|
||
|
]
|
||
|
|
||
|
sample, res_samples = downsample_block(
|
||
|
hidden_states=sample,
|
||
|
temb=emb,
|
||
|
scale=lora_scale,
|
||
|
**additional_residuals,
|
||
|
)
|
||
|
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
|
||
|
|
||
|
if is_brushnet:
|
||
|
sample = sample + mid_block_add_sample
|
||
|
|
||
|
# 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
|
||
|
):
|
||
|
additional_residuals = {}
|
||
|
if is_brushnet and len(up_block_add_samples) > 0:
|
||
|
additional_residuals["up_block_add_samples"] = [
|
||
|
up_block_add_samples.pop(0)
|
||
|
for _ in range(
|
||
|
len(upsample_block.resnets)
|
||
|
+ (upsample_block.upsamplers != None)
|
||
|
)
|
||
|
]
|
||
|
|
||
|
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,
|
||
|
**additional_residuals,
|
||
|
)
|
||
|
else:
|
||
|
additional_residuals = {}
|
||
|
if is_brushnet and len(up_block_add_samples) > 0:
|
||
|
additional_residuals["up_block_add_samples"] = [
|
||
|
up_block_add_samples.pop(0)
|
||
|
for _ in range(
|
||
|
len(upsample_block.resnets)
|
||
|
+ (upsample_block.upsamplers != None)
|
||
|
)
|
||
|
]
|
||
|
|
||
|
sample = upsample_block(
|
||
|
hidden_states=sample,
|
||
|
temb=emb,
|
||
|
res_hidden_states_tuple=res_samples,
|
||
|
upsample_size=upsample_size,
|
||
|
scale=lora_scale,
|
||
|
**additional_residuals,
|
||
|
)
|
||
|
|
||
|
# 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)
|