1475 lines
69 KiB
Python
1475 lines
69 KiB
Python
# Copyright 2025 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 ...configuration_utils import ConfigMixin, register_to_config
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from ...loaders import UNet2DConditionLoadersMixin
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from ...models.activations import get_activation
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from ...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 ...models.embeddings import (
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TimestepEmbedding,
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Timesteps,
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)
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from ...models.modeling_utils import ModelMixin
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from ...models.resnet import Downsample2D, ResnetBlock2D, Upsample2D
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from ...models.transformers.transformer_2d import Transformer2DModel
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from ...models.unets.unet_2d_blocks import DownBlock2D, UpBlock2D
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from ...models.unets.unet_2d_condition import UNet2DConditionOutput
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from ...utils import BaseOutput, logging
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def add_special_tokens(hidden_states, attention_mask, sos_token, eos_token):
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batch_size = hidden_states.shape[0]
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if attention_mask is not None:
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# Add two more steps to attn mask
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new_attn_mask_step = attention_mask.new_ones((batch_size, 1))
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attention_mask = torch.concat([new_attn_mask_step, attention_mask, new_attn_mask_step], dim=-1)
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# Add the SOS / EOS tokens at the start / end of the sequence respectively
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sos_token = sos_token.expand(batch_size, 1, -1)
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eos_token = eos_token.expand(batch_size, 1, -1)
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hidden_states = torch.concat([sos_token, hidden_states, eos_token], dim=1)
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return hidden_states, attention_mask
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@dataclass
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class AudioLDM2ProjectionModelOutput(BaseOutput):
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"""
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Args:
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Class for AudioLDM2 projection layer's outputs.
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hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states obtained by linearly projecting the hidden-states for each of the text
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encoders and subsequently concatenating them together.
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices, formed by concatenating the attention masks
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for the two text encoders together. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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"""
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hidden_states: torch.Tensor
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attention_mask: Optional[torch.LongTensor] = None
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class AudioLDM2ProjectionModel(ModelMixin, ConfigMixin):
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"""
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A simple linear projection model to map two text embeddings to a shared latent space. It also inserts learned
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embedding vectors at the start and end of each text embedding sequence respectively. Each variable appended with
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`_1` refers to that corresponding to the second text encoder. Otherwise, it is from the first.
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Args:
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text_encoder_dim (`int`):
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Dimensionality of the text embeddings from the first text encoder (CLAP).
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text_encoder_1_dim (`int`):
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Dimensionality of the text embeddings from the second text encoder (T5 or VITS).
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langauge_model_dim (`int`):
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Dimensionality of the text embeddings from the language model (GPT2).
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"""
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@register_to_config
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def __init__(
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self,
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text_encoder_dim,
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text_encoder_1_dim,
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langauge_model_dim,
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use_learned_position_embedding=None,
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max_seq_length=None,
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):
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super().__init__()
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# additional projection layers for each text encoder
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self.projection = nn.Linear(text_encoder_dim, langauge_model_dim)
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self.projection_1 = nn.Linear(text_encoder_1_dim, langauge_model_dim)
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# learnable SOS / EOS token embeddings for each text encoder
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self.sos_embed = nn.Parameter(torch.ones(langauge_model_dim))
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self.eos_embed = nn.Parameter(torch.ones(langauge_model_dim))
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self.sos_embed_1 = nn.Parameter(torch.ones(langauge_model_dim))
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self.eos_embed_1 = nn.Parameter(torch.ones(langauge_model_dim))
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self.use_learned_position_embedding = use_learned_position_embedding
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# learable positional embedding for vits encoder
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if self.use_learned_position_embedding is not None:
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self.learnable_positional_embedding = torch.nn.Parameter(
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torch.zeros((1, text_encoder_1_dim, max_seq_length))
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)
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def forward(
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self,
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hidden_states: Optional[torch.Tensor] = None,
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hidden_states_1: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.LongTensor] = None,
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attention_mask_1: Optional[torch.LongTensor] = None,
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):
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hidden_states = self.projection(hidden_states)
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hidden_states, attention_mask = add_special_tokens(
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hidden_states, attention_mask, sos_token=self.sos_embed, eos_token=self.eos_embed
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)
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# Add positional embedding for Vits hidden state
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if self.use_learned_position_embedding is not None:
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hidden_states_1 = (hidden_states_1.permute(0, 2, 1) + self.learnable_positional_embedding).permute(0, 2, 1)
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hidden_states_1 = self.projection_1(hidden_states_1)
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hidden_states_1, attention_mask_1 = add_special_tokens(
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hidden_states_1, attention_mask_1, sos_token=self.sos_embed_1, eos_token=self.eos_embed_1
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)
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# concatenate clap and t5 text encoding
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hidden_states = torch.cat([hidden_states, hidden_states_1], dim=1)
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# concatenate attention masks
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if attention_mask is None and attention_mask_1 is not None:
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attention_mask = attention_mask_1.new_ones((hidden_states[:2]))
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elif attention_mask is not None and attention_mask_1 is None:
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attention_mask_1 = attention_mask.new_ones((hidden_states_1[:2]))
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if attention_mask is not None and attention_mask_1 is not None:
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attention_mask = torch.cat([attention_mask, attention_mask_1], dim=-1)
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else:
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attention_mask = None
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return AudioLDM2ProjectionModelOutput(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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)
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class AudioLDM2UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
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r"""
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A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
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shaped output. Compared to the vanilla [`UNet2DConditionModel`], this variant optionally includes an additional
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self-attention layer in each Transformer block, as well as multiple cross-attention layers. It also allows for up
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to two cross-attention embeddings, `encoder_hidden_states` and `encoder_hidden_states_1`.
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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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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 only be `UNetMidBlock2DCrossAttn` for AudioLDM2.
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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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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` 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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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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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.
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conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
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projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
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`class_embed_type="projection"`. Required when `class_embed_type="projection"`.
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class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
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embeddings with the class embeddings.
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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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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] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
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only_cross_attention: Union[bool, Tuple[bool]] = False,
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block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
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layers_per_block: Union[int, Tuple[int]] = 2,
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downsample_padding: int = 1,
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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: Union[int, Tuple[int]] = 1280,
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transformer_layers_per_block: Union[int, Tuple[int]] = 1,
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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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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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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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class_embeddings_concat: bool = False,
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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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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(only_cross_attention) != 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(down_block_types):
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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 not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
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raise ValueError(
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f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
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)
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if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
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raise ValueError(
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f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
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)
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if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
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raise ValueError(
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f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
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)
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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, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
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)
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# time
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if time_embedding_type == "positional":
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time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
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self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
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timestep_input_dim = block_out_channels[0]
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else:
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raise ValueError(f"{time_embedding_type} does not exist. Please make sure to use `positional`.")
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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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# class embedding
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if class_embed_type is None and num_class_embeds is not None:
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self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
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elif class_embed_type == "timestep":
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self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
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elif class_embed_type == "identity":
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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(projection_class_embeddings_input_dim, time_embed_dim)
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elif class_embed_type == "simple_projection":
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if projection_class_embeddings_input_dim is None:
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raise ValueError(
|
|
"`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
|
|
)
|
|
self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
|
|
else:
|
|
self.class_embedding = None
|
|
|
|
if time_embedding_act_fn is None:
|
|
self.time_embed_act = None
|
|
else:
|
|
self.time_embed_act = get_activation(time_embedding_act_fn)
|
|
|
|
self.down_blocks = nn.ModuleList([])
|
|
self.up_blocks = nn.ModuleList([])
|
|
|
|
if isinstance(only_cross_attention, bool):
|
|
only_cross_attention = [only_cross_attention] * len(down_block_types)
|
|
|
|
if isinstance(num_attention_heads, int):
|
|
num_attention_heads = (num_attention_heads,) * len(down_block_types)
|
|
|
|
if isinstance(cross_attention_dim, int):
|
|
cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
|
|
|
|
if isinstance(layers_per_block, int):
|
|
layers_per_block = [layers_per_block] * len(down_block_types)
|
|
|
|
if isinstance(transformer_layers_per_block, int):
|
|
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
|
|
|
|
if class_embeddings_concat:
|
|
# The time embeddings are concatenated with the class embeddings. The dimension of the
|
|
# time embeddings passed to the down, middle, and up blocks is twice the dimension of the
|
|
# regular time embeddings
|
|
blocks_time_embed_dim = time_embed_dim * 2
|
|
else:
|
|
blocks_time_embed_dim = time_embed_dim
|
|
|
|
# down
|
|
output_channel = block_out_channels[0]
|
|
for i, down_block_type in enumerate(down_block_types):
|
|
input_channel = output_channel
|
|
output_channel = block_out_channels[i]
|
|
is_final_block = i == len(block_out_channels) - 1
|
|
|
|
down_block = get_down_block(
|
|
down_block_type,
|
|
num_layers=layers_per_block[i],
|
|
transformer_layers_per_block=transformer_layers_per_block[i],
|
|
in_channels=input_channel,
|
|
out_channels=output_channel,
|
|
temb_channels=blocks_time_embed_dim,
|
|
add_downsample=not is_final_block,
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
resnet_groups=norm_num_groups,
|
|
cross_attention_dim=cross_attention_dim[i],
|
|
num_attention_heads=num_attention_heads[i],
|
|
downsample_padding=downsample_padding,
|
|
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,
|
|
)
|
|
self.down_blocks.append(down_block)
|
|
|
|
# mid
|
|
if mid_block_type == "UNetMidBlock2DCrossAttn":
|
|
self.mid_block = UNetMidBlock2DCrossAttn(
|
|
transformer_layers_per_block=transformer_layers_per_block[-1],
|
|
in_channels=block_out_channels[-1],
|
|
temb_channels=blocks_time_embed_dim,
|
|
resnet_eps=norm_eps,
|
|
resnet_act_fn=act_fn,
|
|
output_scale_factor=mid_block_scale_factor,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
cross_attention_dim=cross_attention_dim[-1],
|
|
num_attention_heads=num_attention_heads[-1],
|
|
resnet_groups=norm_num_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
upcast_attention=upcast_attention,
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"unknown mid_block_type : {mid_block_type}. Should be `UNetMidBlock2DCrossAttn` for AudioLDM2."
|
|
)
|
|
|
|
# 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))
|
|
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,
|
|
resnet_groups=norm_num_groups,
|
|
cross_attention_dim=reversed_cross_attention_dim[i],
|
|
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,
|
|
)
|
|
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
|
|
)
|
|
|
|
@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()
|
|
|
|
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):
|
|
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 forward(
|
|
self,
|
|
sample: torch.Tensor,
|
|
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,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
return_dict: bool = True,
|
|
encoder_hidden_states_1: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask_1: Optional[torch.Tensor] = None,
|
|
) -> Union[UNet2DConditionOutput, Tuple]:
|
|
r"""
|
|
The [`AudioLDM2UNet2DConditionModel`] forward method.
|
|
|
|
Args:
|
|
sample (`torch.Tensor`):
|
|
The noisy input tensor with the following shape `(batch, channel, height, width)`.
|
|
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
|
|
encoder_hidden_states (`torch.Tensor`):
|
|
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
|
|
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`].
|
|
encoder_hidden_states_1 (`torch.Tensor`, *optional*):
|
|
A second set of encoder hidden states with shape `(batch, sequence_length_2, feature_dim_2)`. Can be
|
|
used to condition the model on a different set of embeddings to `encoder_hidden_states`.
|
|
encoder_attention_mask_1 (`torch.Tensor`, *optional*):
|
|
A cross-attention mask of shape `(batch, sequence_length_2)` is applied to `encoder_hidden_states_1`.
|
|
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.
|
|
|
|
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
|
|
|
|
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
|
|
logger.info("Forward upsample size to force interpolation output size.")
|
|
forward_upsample_size = True
|
|
|
|
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension
|
|
# expects mask of shape:
|
|
# [batch, key_tokens]
|
|
# adds singleton query_tokens dimension:
|
|
# [batch, 1, key_tokens]
|
|
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
|
|
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
|
|
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
|
|
if attention_mask is not None:
|
|
# assume that mask is expressed as:
|
|
# (1 = keep, 0 = discard)
|
|
# convert mask into a bias that can be added to attention scores:
|
|
# (keep = +0, discard = -10000.0)
|
|
attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
|
|
attention_mask = attention_mask.unsqueeze(1)
|
|
|
|
# convert encoder_attention_mask to a bias the same way we do for attention_mask
|
|
if encoder_attention_mask is not None:
|
|
encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
|
|
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
|
|
|
|
if encoder_attention_mask_1 is not None:
|
|
encoder_attention_mask_1 = (1 - encoder_attention_mask_1.to(sample.dtype)) * -10000.0
|
|
encoder_attention_mask_1 = encoder_attention_mask_1.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"
|
|
is_npu = sample.device.type == "npu"
|
|
if isinstance(timestep, float):
|
|
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
|
|
else:
|
|
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
|
|
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
|
|
elif len(timesteps.shape) == 0:
|
|
timesteps = timesteps[None].to(sample.device)
|
|
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timesteps = timesteps.expand(sample.shape[0])
|
|
|
|
t_emb = self.time_proj(timesteps)
|
|
|
|
# `Timesteps` does not contain any weights and will always return f32 tensors
|
|
# but time_embedding might actually be running in fp16. so we need to cast here.
|
|
# there might be better ways to encapsulate this.
|
|
t_emb = t_emb.to(dtype=sample.dtype)
|
|
|
|
emb = self.time_embedding(t_emb, timestep_cond)
|
|
aug_emb = None
|
|
|
|
if self.class_embedding is not None:
|
|
if class_labels is None:
|
|
raise ValueError("class_labels should be provided when num_class_embeds > 0")
|
|
|
|
if self.config.class_embed_type == "timestep":
|
|
class_labels = self.time_proj(class_labels)
|
|
|
|
# `Timesteps` does not contain any weights and will always return f32 tensors
|
|
# there might be better ways to encapsulate this.
|
|
class_labels = class_labels.to(dtype=sample.dtype)
|
|
|
|
class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
|
|
|
|
if self.config.class_embeddings_concat:
|
|
emb = torch.cat([emb, class_emb], dim=-1)
|
|
else:
|
|
emb = emb + class_emb
|
|
|
|
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)
|
|
|
|
# 2. pre-process
|
|
sample = self.conv_in(sample)
|
|
|
|
# 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,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
encoder_hidden_states_1=encoder_hidden_states_1,
|
|
encoder_attention_mask_1=encoder_attention_mask_1,
|
|
)
|
|
else:
|
|
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
|
|
|
|
down_block_res_samples += res_samples
|
|
|
|
# 4. mid
|
|
if self.mid_block is not None:
|
|
sample = self.mid_block(
|
|
sample,
|
|
emb,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=attention_mask,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
encoder_hidden_states_1=encoder_hidden_states_1,
|
|
encoder_attention_mask_1=encoder_attention_mask_1,
|
|
)
|
|
|
|
# 5. up
|
|
for i, upsample_block in enumerate(self.up_blocks):
|
|
is_final_block = i == len(self.up_blocks) - 1
|
|
|
|
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
|
|
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
|
|
|
|
# if we have not reached the final block and need to forward the
|
|
# upsample size, we do it here
|
|
if not is_final_block and forward_upsample_size:
|
|
upsample_size = down_block_res_samples[-1].shape[2:]
|
|
|
|
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
|
|
sample = upsample_block(
|
|
hidden_states=sample,
|
|
temb=emb,
|
|
res_hidden_states_tuple=res_samples,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
upsample_size=upsample_size,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
encoder_hidden_states_1=encoder_hidden_states_1,
|
|
encoder_attention_mask_1=encoder_attention_mask_1,
|
|
)
|
|
else:
|
|
sample = upsample_block(
|
|
hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
|
|
)
|
|
|
|
# 6. post-process
|
|
if self.conv_norm_out:
|
|
sample = self.conv_norm_out(sample)
|
|
sample = self.conv_act(sample)
|
|
sample = self.conv_out(sample)
|
|
|
|
if not return_dict:
|
|
return (sample,)
|
|
|
|
return UNet2DConditionOutput(sample=sample)
|
|
|
|
|
|
def get_down_block(
|
|
down_block_type,
|
|
num_layers,
|
|
in_channels,
|
|
out_channels,
|
|
temb_channels,
|
|
add_downsample,
|
|
resnet_eps,
|
|
resnet_act_fn,
|
|
transformer_layers_per_block=1,
|
|
num_attention_heads=None,
|
|
resnet_groups=None,
|
|
cross_attention_dim=None,
|
|
downsample_padding=None,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
resnet_time_scale_shift="default",
|
|
):
|
|
down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
|
|
if down_block_type == "DownBlock2D":
|
|
return DownBlock2D(
|
|
num_layers=num_layers,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
add_downsample=add_downsample,
|
|
resnet_eps=resnet_eps,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
downsample_padding=downsample_padding,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
)
|
|
elif down_block_type == "CrossAttnDownBlock2D":
|
|
if cross_attention_dim is None:
|
|
raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D")
|
|
return CrossAttnDownBlock2D(
|
|
num_layers=num_layers,
|
|
transformer_layers_per_block=transformer_layers_per_block,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
add_downsample=add_downsample,
|
|
resnet_eps=resnet_eps,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
downsample_padding=downsample_padding,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_attention_heads=num_attention_heads,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
)
|
|
raise ValueError(f"{down_block_type} does not exist.")
|
|
|
|
|
|
def get_up_block(
|
|
up_block_type,
|
|
num_layers,
|
|
in_channels,
|
|
out_channels,
|
|
prev_output_channel,
|
|
temb_channels,
|
|
add_upsample,
|
|
resnet_eps,
|
|
resnet_act_fn,
|
|
transformer_layers_per_block=1,
|
|
num_attention_heads=None,
|
|
resnet_groups=None,
|
|
cross_attention_dim=None,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
resnet_time_scale_shift="default",
|
|
):
|
|
up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
|
|
if up_block_type == "UpBlock2D":
|
|
return UpBlock2D(
|
|
num_layers=num_layers,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
prev_output_channel=prev_output_channel,
|
|
temb_channels=temb_channels,
|
|
add_upsample=add_upsample,
|
|
resnet_eps=resnet_eps,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
)
|
|
elif up_block_type == "CrossAttnUpBlock2D":
|
|
if cross_attention_dim is None:
|
|
raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D")
|
|
return CrossAttnUpBlock2D(
|
|
num_layers=num_layers,
|
|
transformer_layers_per_block=transformer_layers_per_block,
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
prev_output_channel=prev_output_channel,
|
|
temb_channels=temb_channels,
|
|
add_upsample=add_upsample,
|
|
resnet_eps=resnet_eps,
|
|
resnet_act_fn=resnet_act_fn,
|
|
resnet_groups=resnet_groups,
|
|
cross_attention_dim=cross_attention_dim,
|
|
num_attention_heads=num_attention_heads,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
resnet_time_scale_shift=resnet_time_scale_shift,
|
|
)
|
|
raise ValueError(f"{up_block_type} does not exist.")
|
|
|
|
|
|
class CrossAttnDownBlock2D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
transformer_layers_per_block: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
num_attention_heads=1,
|
|
cross_attention_dim=1280,
|
|
output_scale_factor=1.0,
|
|
downsample_padding=1,
|
|
add_downsample=True,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
attentions = []
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
if isinstance(cross_attention_dim, int):
|
|
cross_attention_dim = (cross_attention_dim,)
|
|
if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:
|
|
raise ValueError(
|
|
"Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention "
|
|
f"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}"
|
|
)
|
|
self.cross_attention_dim = cross_attention_dim
|
|
|
|
for i in range(num_layers):
|
|
in_channels = in_channels if i == 0 else out_channels
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
for j in range(len(cross_attention_dim)):
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=transformer_layers_per_block,
|
|
cross_attention_dim=cross_attention_dim[j],
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
double_self_attention=True if cross_attention_dim[j] is None else False,
|
|
)
|
|
)
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_downsample:
|
|
self.downsamplers = nn.ModuleList(
|
|
[
|
|
Downsample2D(
|
|
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.downsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states_1: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask_1: Optional[torch.Tensor] = None,
|
|
):
|
|
output_states = ()
|
|
num_layers = len(self.resnets)
|
|
num_attention_per_layer = len(self.attentions) // num_layers
|
|
|
|
encoder_hidden_states_1 = (
|
|
encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
|
|
)
|
|
encoder_attention_mask_1 = (
|
|
encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
|
|
)
|
|
|
|
for i in range(num_layers):
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states = self._gradient_checkpointing_func(self.resnets[i], hidden_states, temb)
|
|
for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
|
|
if cross_attention_dim is not None and idx <= 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states
|
|
forward_encoder_attention_mask = encoder_attention_mask
|
|
elif cross_attention_dim is not None and idx > 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states_1
|
|
forward_encoder_attention_mask = encoder_attention_mask_1
|
|
else:
|
|
forward_encoder_hidden_states = None
|
|
forward_encoder_attention_mask = None
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
self.attentions[i * num_attention_per_layer + idx],
|
|
hidden_states,
|
|
forward_encoder_hidden_states,
|
|
None, # timestep
|
|
None, # class_labels
|
|
cross_attention_kwargs,
|
|
attention_mask,
|
|
forward_encoder_attention_mask,
|
|
)[0]
|
|
else:
|
|
hidden_states = self.resnets[i](hidden_states, temb)
|
|
for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
|
|
if cross_attention_dim is not None and idx <= 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states
|
|
forward_encoder_attention_mask = encoder_attention_mask
|
|
elif cross_attention_dim is not None and idx > 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states_1
|
|
forward_encoder_attention_mask = encoder_attention_mask_1
|
|
else:
|
|
forward_encoder_hidden_states = None
|
|
forward_encoder_attention_mask = None
|
|
hidden_states = self.attentions[i * num_attention_per_layer + idx](
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=forward_encoder_hidden_states,
|
|
encoder_attention_mask=forward_encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class UNetMidBlock2DCrossAttn(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
transformer_layers_per_block: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
num_attention_heads=1,
|
|
output_scale_factor=1.0,
|
|
cross_attention_dim=1280,
|
|
use_linear_projection=False,
|
|
upcast_attention=False,
|
|
):
|
|
super().__init__()
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
|
|
|
|
if isinstance(cross_attention_dim, int):
|
|
cross_attention_dim = (cross_attention_dim,)
|
|
if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:
|
|
raise ValueError(
|
|
"Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention "
|
|
f"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}"
|
|
)
|
|
self.cross_attention_dim = cross_attention_dim
|
|
|
|
# there is always at least one resnet
|
|
resnets = [
|
|
ResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
]
|
|
attentions = []
|
|
|
|
for i in range(num_layers):
|
|
for j in range(len(cross_attention_dim)):
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
in_channels // num_attention_heads,
|
|
in_channels=in_channels,
|
|
num_layers=transformer_layers_per_block,
|
|
cross_attention_dim=cross_attention_dim[j],
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
upcast_attention=upcast_attention,
|
|
double_self_attention=True if cross_attention_dim[j] is None else False,
|
|
)
|
|
)
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=in_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states_1: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask_1: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.resnets[0](hidden_states, temb)
|
|
num_attention_per_layer = len(self.attentions) // (len(self.resnets) - 1)
|
|
|
|
encoder_hidden_states_1 = (
|
|
encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
|
|
)
|
|
encoder_attention_mask_1 = (
|
|
encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
|
|
)
|
|
|
|
for i in range(len(self.resnets[1:])):
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
|
|
if cross_attention_dim is not None and idx <= 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states
|
|
forward_encoder_attention_mask = encoder_attention_mask
|
|
elif cross_attention_dim is not None and idx > 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states_1
|
|
forward_encoder_attention_mask = encoder_attention_mask_1
|
|
else:
|
|
forward_encoder_hidden_states = None
|
|
forward_encoder_attention_mask = None
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
self.attentions[i * num_attention_per_layer + idx],
|
|
hidden_states,
|
|
forward_encoder_hidden_states,
|
|
None, # timestep
|
|
None, # class_labels
|
|
cross_attention_kwargs,
|
|
attention_mask,
|
|
forward_encoder_attention_mask,
|
|
)[0]
|
|
hidden_states = self._gradient_checkpointing_func(self.resnets[i + 1], hidden_states, temb)
|
|
else:
|
|
for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
|
|
if cross_attention_dim is not None and idx <= 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states
|
|
forward_encoder_attention_mask = encoder_attention_mask
|
|
elif cross_attention_dim is not None and idx > 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states_1
|
|
forward_encoder_attention_mask = encoder_attention_mask_1
|
|
else:
|
|
forward_encoder_hidden_states = None
|
|
forward_encoder_attention_mask = None
|
|
hidden_states = self.attentions[i * num_attention_per_layer + idx](
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=forward_encoder_hidden_states,
|
|
encoder_attention_mask=forward_encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
hidden_states = self.resnets[i + 1](hidden_states, temb)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class CrossAttnUpBlock2D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
prev_output_channel: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
transformer_layers_per_block: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
num_attention_heads=1,
|
|
cross_attention_dim=1280,
|
|
output_scale_factor=1.0,
|
|
add_upsample=True,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
attentions = []
|
|
|
|
self.has_cross_attention = True
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
if isinstance(cross_attention_dim, int):
|
|
cross_attention_dim = (cross_attention_dim,)
|
|
if isinstance(cross_attention_dim, (list, tuple)) and len(cross_attention_dim) > 4:
|
|
raise ValueError(
|
|
"Only up to 4 cross-attention layers are supported. Ensure that the length of cross-attention "
|
|
f"dims is less than or equal to 4. Got cross-attention dims {cross_attention_dim} of length {len(cross_attention_dim)}"
|
|
)
|
|
self.cross_attention_dim = cross_attention_dim
|
|
|
|
for i in range(num_layers):
|
|
res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=resnet_in_channels + res_skip_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
for j in range(len(cross_attention_dim)):
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
num_attention_heads,
|
|
out_channels // num_attention_heads,
|
|
in_channels=out_channels,
|
|
num_layers=transformer_layers_per_block,
|
|
cross_attention_dim=cross_attention_dim[j],
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
double_self_attention=True if cross_attention_dim[j] is None else False,
|
|
)
|
|
)
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
|
|
if add_upsample:
|
|
self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
upsample_size: Optional[int] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states_1: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask_1: Optional[torch.Tensor] = None,
|
|
):
|
|
num_layers = len(self.resnets)
|
|
num_attention_per_layer = len(self.attentions) // num_layers
|
|
|
|
encoder_hidden_states_1 = (
|
|
encoder_hidden_states_1 if encoder_hidden_states_1 is not None else encoder_hidden_states
|
|
)
|
|
encoder_attention_mask_1 = (
|
|
encoder_attention_mask_1 if encoder_hidden_states_1 is not None else encoder_attention_mask
|
|
)
|
|
|
|
for i in range(num_layers):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states = self._gradient_checkpointing_func(self.resnets[i], hidden_states, temb)
|
|
for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
|
|
if cross_attention_dim is not None and idx <= 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states
|
|
forward_encoder_attention_mask = encoder_attention_mask
|
|
elif cross_attention_dim is not None and idx > 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states_1
|
|
forward_encoder_attention_mask = encoder_attention_mask_1
|
|
else:
|
|
forward_encoder_hidden_states = None
|
|
forward_encoder_attention_mask = None
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
self.attentions[i * num_attention_per_layer + idx],
|
|
hidden_states,
|
|
forward_encoder_hidden_states,
|
|
None, # timestep
|
|
None, # class_labels
|
|
cross_attention_kwargs,
|
|
attention_mask,
|
|
forward_encoder_attention_mask,
|
|
)[0]
|
|
else:
|
|
hidden_states = self.resnets[i](hidden_states, temb)
|
|
for idx, cross_attention_dim in enumerate(self.cross_attention_dim):
|
|
if cross_attention_dim is not None and idx <= 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states
|
|
forward_encoder_attention_mask = encoder_attention_mask
|
|
elif cross_attention_dim is not None and idx > 1:
|
|
forward_encoder_hidden_states = encoder_hidden_states_1
|
|
forward_encoder_attention_mask = encoder_attention_mask_1
|
|
else:
|
|
forward_encoder_hidden_states = None
|
|
forward_encoder_attention_mask = None
|
|
hidden_states = self.attentions[i * num_attention_per_layer + idx](
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
encoder_hidden_states=forward_encoder_hidden_states,
|
|
encoder_attention_mask=forward_encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size)
|
|
|
|
return hidden_states
|