1095 lines
48 KiB
Python
1095 lines
48 KiB
Python
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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from diffusers import UNet2DConditionModel
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from diffusers.models.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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CrossAttnDownBlock2D,
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DownBlock2D,
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)
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from torch import nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.utils import BaseOutput, logging
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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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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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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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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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@dataclass
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class BrushNetOutput(BaseOutput):
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"""
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The output of [`BrushNetModel`].
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Args:
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up_block_res_samples (`tuple[torch.Tensor]`):
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A tuple of upsample activations at different resolutions for each upsampling block. Each tensor should
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be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
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used to condition the original UNet's upsampling activations.
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down_block_res_samples (`tuple[torch.Tensor]`):
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A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
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be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be
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used to condition the original UNet's downsampling activations.
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mid_down_block_re_sample (`torch.Tensor`):
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The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
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`(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`.
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Output can be used to condition the original UNet's middle block activation.
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"""
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up_block_res_samples: Tuple[torch.Tensor]
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down_block_res_samples: Tuple[torch.Tensor]
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mid_block_res_sample: torch.Tensor
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class BrushNetModel(ModelMixin, ConfigMixin):
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"""
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A BrushNet model.
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Args:
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in_channels (`int`, defaults to 4):
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The number of channels in the input sample.
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flip_sin_to_cos (`bool`, defaults to `True`):
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Whether to flip the sin to cos in the time embedding.
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freq_shift (`int`, defaults to 0):
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The frequency shift to apply to the time embedding.
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down_block_types (`tuple[str]`, 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 (`Union[bool, Tuple[bool]]`, defaults to `False`):
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block_out_channels (`tuple[int]`, 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`, defaults to 2):
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The number of layers per block.
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downsample_padding (`int`, defaults to 1):
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The padding to use for the downsampling convolution.
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mid_block_scale_factor (`float`, defaults to 1):
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The scale factor to use for the mid block.
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act_fn (`str`, defaults to "silu"):
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The activation function to use.
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norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups to use for the normalization. If None, normalization and activation layers is skipped
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in post-processing.
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norm_eps (`float`, defaults to 1e-5):
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The epsilon to use for the normalization.
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cross_attention_dim (`int`, defaults to 1280):
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The dimension of the cross attention features.
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transformer_layers_per_block (`int` or `Tuple[int]`, *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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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 (`Union[int, Tuple[int]]`, defaults to 8):
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The dimension of the attention heads.
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use_linear_projection (`bool`, defaults to `False`):
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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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num_class_embeds (`int`, *optional*, defaults to 0):
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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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upcast_attention (`bool`, defaults to `False`):
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resnet_time_scale_shift (`str`, defaults to `"default"`):
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Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
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projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
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The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
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`class_embed_type="projection"`.
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brushnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
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The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
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conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
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The tuple of output channel for each block in the `conditioning_embedding` layer.
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global_pool_conditions (`bool`, defaults to `False`):
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TODO(Patrick) - unused parameter.
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addition_embed_type_num_heads (`int`, defaults to 64):
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The number of heads to use for the `TextTimeEmbedding` layer.
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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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in_channels: int = 4,
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conditioning_channels: int = 5,
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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: int = 2,
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downsample_padding: int = 1,
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mid_block_scale_factor: float = 1,
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act_fn: str = "silu",
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norm_num_groups: Optional[int] = 32,
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norm_eps: float = 1e-5,
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cross_attention_dim: int = 1280,
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transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
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encoder_hid_dim: Optional[int] = None,
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encoder_hid_dim_type: Optional[str] = None,
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attention_head_dim: Union[int, Tuple[int, ...]] = 8,
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num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
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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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projection_class_embeddings_input_dim: Optional[int] = None,
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brushnet_conditioning_channel_order: str = "rgb",
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conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (
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16,
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32,
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96,
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256,
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),
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global_pool_conditions: bool = False,
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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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# 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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if len(down_block_types) != len(up_block_types):
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raise ValueError(
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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}."
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)
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if len(block_out_channels) != len(down_block_types):
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raise ValueError(
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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}."
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)
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if not isinstance(only_cross_attention, bool) and len(
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only_cross_attention
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) != len(down_block_types):
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raise ValueError(
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f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
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)
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if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(
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down_block_types
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):
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raise ValueError(
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f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
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)
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if isinstance(transformer_layers_per_block, int):
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transformer_layers_per_block = [transformer_layers_per_block] * len(
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down_block_types
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)
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# input
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conv_in_kernel = 3
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conv_in_padding = (conv_in_kernel - 1) // 2
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self.conv_in_condition = nn.Conv2d(
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in_channels + conditioning_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 = block_out_channels[0] * 4
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self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
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timestep_input_dim = block_out_channels[0]
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self.time_embedding = TimestepEmbedding(
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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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)
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if encoder_hid_dim_type is None and encoder_hid_dim is not None:
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encoder_hid_dim_type = "text_proj"
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self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
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logger.info(
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"encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined."
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)
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if encoder_hid_dim is None and encoder_hid_dim_type is not None:
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raise ValueError(
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f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
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)
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if encoder_hid_dim_type == "text_proj":
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self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
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elif encoder_hid_dim_type == "text_image_proj":
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# image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
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# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
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# case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
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self.encoder_hid_proj = TextImageProjection(
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text_embed_dim=encoder_hid_dim,
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image_embed_dim=cross_attention_dim,
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cross_attention_dim=cross_attention_dim,
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)
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elif encoder_hid_dim_type is not None:
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raise ValueError(
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f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
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)
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else:
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self.encoder_hid_proj = None
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# class embedding
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if class_embed_type is None and num_class_embeds is not None:
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self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
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elif class_embed_type == "timestep":
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self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
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elif class_embed_type == "identity":
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self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
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elif class_embed_type == "projection":
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if projection_class_embeddings_input_dim is None:
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raise ValueError(
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"`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
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)
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# The projection `class_embed_type` is the same as the timestep `class_embed_type` except
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# 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
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# 2. it projects from an arbitrary input dimension.
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#
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# Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
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# When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
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# As a result, `TimestepEmbedding` can be passed arbitrary vectors.
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self.class_embedding = TimestepEmbedding(
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projection_class_embeddings_input_dim, time_embed_dim
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)
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else:
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self.class_embedding = None
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if addition_embed_type == "text":
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if encoder_hid_dim is not None:
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text_time_embedding_from_dim = encoder_hid_dim
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else:
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text_time_embedding_from_dim = cross_attention_dim
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self.add_embedding = TextTimeEmbedding(
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text_time_embedding_from_dim,
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time_embed_dim,
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num_heads=addition_embed_type_num_heads,
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)
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elif addition_embed_type == "text_image":
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# text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
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# they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
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# case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
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self.add_embedding = TextImageTimeEmbedding(
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text_embed_dim=cross_attention_dim,
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image_embed_dim=cross_attention_dim,
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time_embed_dim=time_embed_dim,
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)
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elif addition_embed_type == "text_time":
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self.add_time_proj = Timesteps(
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addition_time_embed_dim, flip_sin_to_cos, freq_shift
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)
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self.add_embedding = TimestepEmbedding(
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projection_class_embeddings_input_dim, time_embed_dim
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)
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elif addition_embed_type is not None:
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raise ValueError(
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f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'."
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)
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self.down_blocks = nn.ModuleList([])
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self.brushnet_down_blocks = nn.ModuleList([])
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if isinstance(only_cross_attention, bool):
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only_cross_attention = [only_cross_attention] * len(down_block_types)
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if isinstance(attention_head_dim, int):
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attention_head_dim = (attention_head_dim,) * len(down_block_types)
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if isinstance(num_attention_heads, int):
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num_attention_heads = (num_attention_heads,) * len(down_block_types)
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# down
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output_channel = block_out_channels[0]
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brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
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brushnet_block = zero_module(brushnet_block)
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self.brushnet_down_blocks.append(brushnet_block)
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for i, down_block_type in enumerate(down_block_types):
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input_channel = output_channel
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output_channel = block_out_channels[i]
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is_final_block = i == len(block_out_channels) - 1
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down_block = get_down_block(
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down_block_type,
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num_layers=layers_per_block,
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transformer_layers_per_block=transformer_layers_per_block[i],
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in_channels=input_channel,
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out_channels=output_channel,
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temb_channels=time_embed_dim,
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add_downsample=not is_final_block,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
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resnet_groups=norm_num_groups,
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cross_attention_dim=cross_attention_dim,
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num_attention_heads=num_attention_heads[i],
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attention_head_dim=attention_head_dim[i]
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if attention_head_dim[i] is not None
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else output_channel,
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downsample_padding=downsample_padding,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention[i],
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upcast_attention=upcast_attention,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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self.down_blocks.append(down_block)
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for _ in range(layers_per_block):
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brushnet_block = nn.Conv2d(
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output_channel, output_channel, kernel_size=1
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)
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brushnet_block = zero_module(brushnet_block)
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self.brushnet_down_blocks.append(brushnet_block)
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if not is_final_block:
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brushnet_block = nn.Conv2d(
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output_channel, output_channel, kernel_size=1
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)
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brushnet_block = zero_module(brushnet_block)
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self.brushnet_down_blocks.append(brushnet_block)
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# mid
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mid_block_channel = block_out_channels[-1]
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brushnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
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brushnet_block = zero_module(brushnet_block)
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self.brushnet_mid_block = brushnet_block
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self.mid_block = get_mid_block(
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mid_block_type,
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transformer_layers_per_block=transformer_layers_per_block[-1],
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in_channels=mid_block_channel,
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temb_channels=time_embed_dim,
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resnet_eps=norm_eps,
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resnet_act_fn=act_fn,
|
|
output_scale_factor=mid_block_scale_factor,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_attention_heads=num_attention_heads[-1],
|
|
resnet_groups=norm_num_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
|
|
# 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_transformer_layers_per_block = list(
|
|
reversed(transformer_layers_per_block)
|
|
)
|
|
only_cross_attention = list(reversed(only_cross_attention))
|
|
|
|
output_channel = reversed_block_out_channels[0]
|
|
|
|
self.up_blocks = nn.ModuleList([])
|
|
self.brushnet_up_blocks = nn.ModuleList([])
|
|
|
|
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=layers_per_block + 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=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=cross_attention_dim,
|
|
num_attention_heads=reversed_num_attention_heads[i],
|
|
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_head_dim=attention_head_dim[i]
|
|
if attention_head_dim[i] is not None
|
|
else output_channel,
|
|
)
|
|
self.up_blocks.append(up_block)
|
|
prev_output_channel = output_channel
|
|
|
|
for _ in range(layers_per_block + 1):
|
|
brushnet_block = nn.Conv2d(
|
|
output_channel, output_channel, kernel_size=1
|
|
)
|
|
brushnet_block = zero_module(brushnet_block)
|
|
self.brushnet_up_blocks.append(brushnet_block)
|
|
|
|
if not is_final_block:
|
|
brushnet_block = nn.Conv2d(
|
|
output_channel, output_channel, kernel_size=1
|
|
)
|
|
brushnet_block = zero_module(brushnet_block)
|
|
self.brushnet_up_blocks.append(brushnet_block)
|
|
|
|
@classmethod
|
|
def from_unet(
|
|
cls,
|
|
unet: UNet2DConditionModel,
|
|
brushnet_conditioning_channel_order: str = "rgb",
|
|
conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (
|
|
16,
|
|
32,
|
|
96,
|
|
256,
|
|
),
|
|
load_weights_from_unet: bool = True,
|
|
conditioning_channels: int = 5,
|
|
):
|
|
r"""
|
|
Instantiate a [`BrushNetModel`] from [`UNet2DConditionModel`].
|
|
|
|
Parameters:
|
|
unet (`UNet2DConditionModel`):
|
|
The UNet model weights to copy to the [`BrushNetModel`]. All configuration options are also copied
|
|
where applicable.
|
|
"""
|
|
transformer_layers_per_block = (
|
|
unet.config.transformer_layers_per_block
|
|
if "transformer_layers_per_block" in unet.config
|
|
else 1
|
|
)
|
|
encoder_hid_dim = (
|
|
unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None
|
|
)
|
|
encoder_hid_dim_type = (
|
|
unet.config.encoder_hid_dim_type
|
|
if "encoder_hid_dim_type" in unet.config
|
|
else None
|
|
)
|
|
addition_embed_type = (
|
|
unet.config.addition_embed_type
|
|
if "addition_embed_type" in unet.config
|
|
else None
|
|
)
|
|
addition_time_embed_dim = (
|
|
unet.config.addition_time_embed_dim
|
|
if "addition_time_embed_dim" in unet.config
|
|
else None
|
|
)
|
|
|
|
brushnet = cls(
|
|
in_channels=unet.config.in_channels,
|
|
conditioning_channels=conditioning_channels,
|
|
flip_sin_to_cos=unet.config.flip_sin_to_cos,
|
|
freq_shift=unet.config.freq_shift,
|
|
# down_block_types=['DownBlock2D','DownBlock2D','DownBlock2D','DownBlock2D'],
|
|
down_block_types=[
|
|
"CrossAttnDownBlock2D",
|
|
"CrossAttnDownBlock2D",
|
|
"CrossAttnDownBlock2D",
|
|
"DownBlock2D",
|
|
],
|
|
# mid_block_type='MidBlock2D',
|
|
mid_block_type="UNetMidBlock2DCrossAttn",
|
|
# up_block_types=['UpBlock2D','UpBlock2D','UpBlock2D','UpBlock2D'],
|
|
up_block_types=[
|
|
"UpBlock2D",
|
|
"CrossAttnUpBlock2D",
|
|
"CrossAttnUpBlock2D",
|
|
"CrossAttnUpBlock2D",
|
|
],
|
|
only_cross_attention=unet.config.only_cross_attention,
|
|
block_out_channels=unet.config.block_out_channels,
|
|
layers_per_block=unet.config.layers_per_block,
|
|
downsample_padding=unet.config.downsample_padding,
|
|
mid_block_scale_factor=unet.config.mid_block_scale_factor,
|
|
act_fn=unet.config.act_fn,
|
|
norm_num_groups=unet.config.norm_num_groups,
|
|
norm_eps=unet.config.norm_eps,
|
|
cross_attention_dim=unet.config.cross_attention_dim,
|
|
transformer_layers_per_block=transformer_layers_per_block,
|
|
encoder_hid_dim=encoder_hid_dim,
|
|
encoder_hid_dim_type=encoder_hid_dim_type,
|
|
attention_head_dim=unet.config.attention_head_dim,
|
|
num_attention_heads=unet.config.num_attention_heads,
|
|
use_linear_projection=unet.config.use_linear_projection,
|
|
class_embed_type=unet.config.class_embed_type,
|
|
addition_embed_type=addition_embed_type,
|
|
addition_time_embed_dim=addition_time_embed_dim,
|
|
num_class_embeds=unet.config.num_class_embeds,
|
|
upcast_attention=unet.config.upcast_attention,
|
|
resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
|
|
projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
|
|
brushnet_conditioning_channel_order=brushnet_conditioning_channel_order,
|
|
conditioning_embedding_out_channels=conditioning_embedding_out_channels,
|
|
)
|
|
|
|
if load_weights_from_unet:
|
|
conv_in_condition_weight = torch.zeros_like(
|
|
brushnet.conv_in_condition.weight
|
|
)
|
|
conv_in_condition_weight[:, :4, ...] = unet.conv_in.weight
|
|
conv_in_condition_weight[:, 4:8, ...] = unet.conv_in.weight
|
|
brushnet.conv_in_condition.weight = torch.nn.Parameter(
|
|
conv_in_condition_weight
|
|
)
|
|
brushnet.conv_in_condition.bias = unet.conv_in.bias
|
|
|
|
brushnet.time_proj.load_state_dict(unet.time_proj.state_dict())
|
|
brushnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
|
|
|
|
if brushnet.class_embedding:
|
|
brushnet.class_embedding.load_state_dict(
|
|
unet.class_embedding.state_dict()
|
|
)
|
|
|
|
brushnet.down_blocks.load_state_dict(
|
|
unet.down_blocks.state_dict(), strict=False
|
|
)
|
|
brushnet.mid_block.load_state_dict(
|
|
unet.mid_block.state_dict(), strict=False
|
|
)
|
|
brushnet.up_blocks.load_state_dict(
|
|
unet.up_blocks.state_dict(), strict=False
|
|
)
|
|
|
|
return brushnet.to(unet.dtype)
|
|
|
|
@property
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
|
|
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
|
|
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
|
|
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)
|
|
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_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)
|
|
|
|
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice
|
|
def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None:
|
|
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: bool = False) -> None:
|
|
if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
|
|
module.gradient_checkpointing = value
|
|
|
|
def forward(
|
|
self,
|
|
sample: torch.FloatTensor,
|
|
timestep: Union[torch.Tensor, float, int],
|
|
encoder_hidden_states: torch.Tensor,
|
|
brushnet_cond: torch.FloatTensor,
|
|
conditioning_scale: float = 1.0,
|
|
class_labels: Optional[torch.Tensor] = None,
|
|
timestep_cond: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
guess_mode: bool = False,
|
|
return_dict: bool = True,
|
|
) -> Union[BrushNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]:
|
|
"""
|
|
The [`BrushNetModel`] forward method.
|
|
|
|
Args:
|
|
sample (`torch.FloatTensor`):
|
|
The noisy input tensor.
|
|
timestep (`Union[torch.Tensor, float, int]`):
|
|
The number of timesteps to denoise an input.
|
|
encoder_hidden_states (`torch.Tensor`):
|
|
The encoder hidden states.
|
|
brushnet_cond (`torch.FloatTensor`):
|
|
The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
|
|
conditioning_scale (`float`, defaults to `1.0`):
|
|
The scale factor for BrushNet outputs.
|
|
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`):
|
|
Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
|
|
timestep_embedding passed through the `self.time_embedding` layer to obtain the final 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.
|
|
added_cond_kwargs (`dict`):
|
|
Additional conditions for the Stable Diffusion XL UNet.
|
|
cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
|
|
A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
|
|
guess_mode (`bool`, defaults to `False`):
|
|
In this mode, the BrushNet encoder tries its best to recognize the input content of the input even if
|
|
you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
|
|
return_dict (`bool`, defaults to `True`):
|
|
Whether or not to return a [`~models.brushnet.BrushNetOutput`] instead of a plain tuple.
|
|
|
|
Returns:
|
|
[`~models.brushnet.BrushNetOutput`] **or** `tuple`:
|
|
If `return_dict` is `True`, a [`~models.brushnet.BrushNetOutput`] is returned, otherwise a tuple is
|
|
returned where the first element is the sample tensor.
|
|
"""
|
|
# check channel order
|
|
channel_order = self.config.brushnet_conditioning_channel_order
|
|
|
|
if channel_order == "rgb":
|
|
# in rgb order by default
|
|
...
|
|
elif channel_order == "bgr":
|
|
brushnet_cond = torch.flip(brushnet_cond, dims=[1])
|
|
else:
|
|
raise ValueError(
|
|
f"unknown `brushnet_conditioning_channel_order`: {channel_order}"
|
|
)
|
|
|
|
# prepare attention_mask
|
|
if attention_mask is not None:
|
|
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
|
attention_mask = attention_mask.unsqueeze(1)
|
|
|
|
# 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)
|
|
|
|
class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
|
|
emb = emb + class_emb
|
|
|
|
if self.config.addition_embed_type is not None:
|
|
if self.config.addition_embed_type == "text":
|
|
aug_emb = self.add_embedding(encoder_hidden_states)
|
|
|
|
elif self.config.addition_embed_type == "text_time":
|
|
if "text_embeds" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
|
|
)
|
|
text_embeds = added_cond_kwargs.get("text_embeds")
|
|
if "time_ids" not in added_cond_kwargs:
|
|
raise ValueError(
|
|
f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
|
|
)
|
|
time_ids = added_cond_kwargs.get("time_ids")
|
|
time_embeds = self.add_time_proj(time_ids.flatten())
|
|
time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
|
|
|
|
add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
|
|
add_embeds = add_embeds.to(emb.dtype)
|
|
aug_emb = self.add_embedding(add_embeds)
|
|
|
|
emb = emb + aug_emb if aug_emb is not None else emb
|
|
|
|
# 2. pre-process
|
|
brushnet_cond = torch.concat([sample, brushnet_cond], 1)
|
|
sample = self.conv_in_condition(brushnet_cond)
|
|
|
|
# 3. down
|
|
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
|
|
):
|
|
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,
|
|
)
|
|
else:
|
|
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
|
|
|
down_block_res_samples += res_samples
|
|
|
|
# 4. PaintingNet down blocks
|
|
brushnet_down_block_res_samples = ()
|
|
for down_block_res_sample, brushnet_down_block in zip(
|
|
down_block_res_samples, self.brushnet_down_blocks
|
|
):
|
|
down_block_res_sample = brushnet_down_block(down_block_res_sample)
|
|
brushnet_down_block_res_samples = brushnet_down_block_res_samples + (
|
|
down_block_res_sample,
|
|
)
|
|
|
|
# 5. 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,
|
|
)
|
|
else:
|
|
sample = self.mid_block(sample, emb)
|
|
|
|
# 6. BrushNet mid blocks
|
|
brushnet_mid_block_res_sample = self.brushnet_mid_block(sample)
|
|
|
|
# 7. up
|
|
up_block_res_samples = ()
|
|
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:
|
|
upsample_size = down_block_res_samples[-1].shape[2:]
|
|
|
|
if (
|
|
hasattr(upsample_block, "has_cross_attention")
|
|
and upsample_block.has_cross_attention
|
|
):
|
|
sample, up_res_samples = 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,
|
|
return_res_samples=True,
|
|
)
|
|
else:
|
|
sample, up_res_samples = upsample_block(
|
|
hidden_states=sample,
|
|
temb=emb,
|
|
res_hidden_states_tuple=res_samples,
|
|
upsample_size=upsample_size,
|
|
return_res_samples=True,
|
|
)
|
|
|
|
up_block_res_samples += up_res_samples
|
|
|
|
# 8. BrushNet up blocks
|
|
brushnet_up_block_res_samples = ()
|
|
for up_block_res_sample, brushnet_up_block in zip(
|
|
up_block_res_samples, self.brushnet_up_blocks
|
|
):
|
|
up_block_res_sample = brushnet_up_block(up_block_res_sample)
|
|
brushnet_up_block_res_samples = brushnet_up_block_res_samples + (
|
|
up_block_res_sample,
|
|
)
|
|
|
|
# 6. scaling
|
|
if guess_mode and not self.config.global_pool_conditions:
|
|
scales = torch.logspace(
|
|
-1,
|
|
0,
|
|
len(brushnet_down_block_res_samples)
|
|
+ 1
|
|
+ len(brushnet_up_block_res_samples),
|
|
device=sample.device,
|
|
) # 0.1 to 1.0
|
|
scales = scales * conditioning_scale
|
|
|
|
brushnet_down_block_res_samples = [
|
|
sample * scale
|
|
for sample, scale in zip(
|
|
brushnet_down_block_res_samples,
|
|
scales[: len(brushnet_down_block_res_samples)],
|
|
)
|
|
]
|
|
brushnet_mid_block_res_sample = (
|
|
brushnet_mid_block_res_sample
|
|
* scales[len(brushnet_down_block_res_samples)]
|
|
)
|
|
brushnet_up_block_res_samples = [
|
|
sample * scale
|
|
for sample, scale in zip(
|
|
brushnet_up_block_res_samples,
|
|
scales[len(brushnet_down_block_res_samples) + 1 :],
|
|
)
|
|
]
|
|
else:
|
|
brushnet_down_block_res_samples = [
|
|
sample * conditioning_scale
|
|
for sample in brushnet_down_block_res_samples
|
|
]
|
|
brushnet_mid_block_res_sample = (
|
|
brushnet_mid_block_res_sample * conditioning_scale
|
|
)
|
|
brushnet_up_block_res_samples = [
|
|
sample * conditioning_scale for sample in brushnet_up_block_res_samples
|
|
]
|
|
|
|
if self.config.global_pool_conditions:
|
|
brushnet_down_block_res_samples = [
|
|
torch.mean(sample, dim=(2, 3), keepdim=True)
|
|
for sample in brushnet_down_block_res_samples
|
|
]
|
|
brushnet_mid_block_res_sample = torch.mean(
|
|
brushnet_mid_block_res_sample, dim=(2, 3), keepdim=True
|
|
)
|
|
brushnet_up_block_res_samples = [
|
|
torch.mean(sample, dim=(2, 3), keepdim=True)
|
|
for sample in brushnet_up_block_res_samples
|
|
]
|
|
|
|
if not return_dict:
|
|
return (
|
|
brushnet_down_block_res_samples,
|
|
brushnet_mid_block_res_sample,
|
|
brushnet_up_block_res_samples,
|
|
)
|
|
|
|
return BrushNetOutput(
|
|
down_block_res_samples=brushnet_down_block_res_samples,
|
|
mid_block_res_sample=brushnet_mid_block_res_sample,
|
|
up_block_res_samples=brushnet_up_block_res_samples,
|
|
)
|
|
|
|
|
|
def zero_module(module):
|
|
for p in module.parameters():
|
|
nn.init.zeros_(p)
|
|
return module
|