diff --git "a/diffusers/models/attention_processor.py" "b/diffusers/models/attention_processor.py" deleted file mode 100644--- "a/diffusers/models/attention_processor.py" +++ /dev/null @@ -1,2699 +0,0 @@ -# Copyright 2024 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import inspect -import math -from importlib import import_module -from typing import Callable, List, Optional, Union - -import torch -import torch.nn.functional as F -from torch import nn - -from ..image_processor import IPAdapterMaskProcessor -from ..utils import deprecate, logging -from ..utils.import_utils import is_torch_npu_available, is_xformers_available -from ..utils.torch_utils import maybe_allow_in_graph -from .lora import LoRALinearLayer - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -if is_torch_npu_available(): - import torch_npu - -if is_xformers_available(): - import xformers - import xformers.ops -else: - xformers = None - - -@maybe_allow_in_graph -class Attention(nn.Module): - r""" - A cross attention layer. - - Parameters: - query_dim (`int`): - The number of channels in the query. - cross_attention_dim (`int`, *optional*): - The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`. - heads (`int`, *optional*, defaults to 8): - The number of heads to use for multi-head attention. - dim_head (`int`, *optional*, defaults to 64): - The number of channels in each head. - dropout (`float`, *optional*, defaults to 0.0): - The dropout probability to use. - bias (`bool`, *optional*, defaults to False): - Set to `True` for the query, key, and value linear layers to contain a bias parameter. - upcast_attention (`bool`, *optional*, defaults to False): - Set to `True` to upcast the attention computation to `float32`. - upcast_softmax (`bool`, *optional*, defaults to False): - Set to `True` to upcast the softmax computation to `float32`. - cross_attention_norm (`str`, *optional*, defaults to `None`): - The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`. - cross_attention_norm_num_groups (`int`, *optional*, defaults to 32): - The number of groups to use for the group norm in the cross attention. - added_kv_proj_dim (`int`, *optional*, defaults to `None`): - The number of channels to use for the added key and value projections. If `None`, no projection is used. - norm_num_groups (`int`, *optional*, defaults to `None`): - The number of groups to use for the group norm in the attention. - spatial_norm_dim (`int`, *optional*, defaults to `None`): - The number of channels to use for the spatial normalization. - out_bias (`bool`, *optional*, defaults to `True`): - Set to `True` to use a bias in the output linear layer. - scale_qk (`bool`, *optional*, defaults to `True`): - Set to `True` to scale the query and key by `1 / sqrt(dim_head)`. - only_cross_attention (`bool`, *optional*, defaults to `False`): - Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if - `added_kv_proj_dim` is not `None`. - eps (`float`, *optional*, defaults to 1e-5): - An additional value added to the denominator in group normalization that is used for numerical stability. - rescale_output_factor (`float`, *optional*, defaults to 1.0): - A factor to rescale the output by dividing it with this value. - residual_connection (`bool`, *optional*, defaults to `False`): - Set to `True` to add the residual connection to the output. - _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`): - Set to `True` if the attention block is loaded from a deprecated state dict. - processor (`AttnProcessor`, *optional*, defaults to `None`): - The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and - `AttnProcessor` otherwise. - """ - - def __init__( - self, - query_dim: int, - cross_attention_dim: Optional[int] = None, - heads: int = 8, - dim_head: int = 64, - dropout: float = 0.0, - bias: bool = False, - upcast_attention: bool = False, - upcast_softmax: bool = False, - cross_attention_norm: Optional[str] = None, - cross_attention_norm_num_groups: int = 32, - added_kv_proj_dim: Optional[int] = None, - norm_num_groups: Optional[int] = None, - spatial_norm_dim: Optional[int] = None, - out_bias: bool = True, - scale_qk: bool = True, - only_cross_attention: bool = False, - eps: float = 1e-5, - rescale_output_factor: float = 1.0, - residual_connection: bool = False, - _from_deprecated_attn_block: bool = False, - processor: Optional["AttnProcessor"] = None, - out_dim: int = None, - ): - super().__init__() - self.inner_dim = out_dim if out_dim is not None else dim_head * heads - self.query_dim = query_dim - self.use_bias = bias - self.is_cross_attention = cross_attention_dim is not None - self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim - self.upcast_attention = upcast_attention - self.upcast_softmax = upcast_softmax - self.rescale_output_factor = rescale_output_factor - self.residual_connection = residual_connection - self.dropout = dropout - self.fused_projections = False - self.out_dim = out_dim if out_dim is not None else query_dim - - # we make use of this private variable to know whether this class is loaded - # with an deprecated state dict so that we can convert it on the fly - self._from_deprecated_attn_block = _from_deprecated_attn_block - - self.scale_qk = scale_qk - self.scale = dim_head**-0.5 if self.scale_qk else 1.0 - - self.heads = out_dim // dim_head if out_dim is not None else heads - # for slice_size > 0 the attention score computation - # is split across the batch axis to save memory - # You can set slice_size with `set_attention_slice` - self.sliceable_head_dim = heads - - self.added_kv_proj_dim = added_kv_proj_dim - self.only_cross_attention = only_cross_attention - - if self.added_kv_proj_dim is None and self.only_cross_attention: - raise ValueError( - "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." - ) - - if norm_num_groups is not None: - self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True) - else: - self.group_norm = None - - if spatial_norm_dim is not None: - self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim) - else: - self.spatial_norm = None - - if cross_attention_norm is None: - self.norm_cross = None - elif cross_attention_norm == "layer_norm": - self.norm_cross = nn.LayerNorm(self.cross_attention_dim) - elif cross_attention_norm == "group_norm": - if self.added_kv_proj_dim is not None: - # The given `encoder_hidden_states` are initially of shape - # (batch_size, seq_len, added_kv_proj_dim) before being projected - # to (batch_size, seq_len, cross_attention_dim). The norm is applied - # before the projection, so we need to use `added_kv_proj_dim` as - # the number of channels for the group norm. - norm_cross_num_channels = added_kv_proj_dim - else: - norm_cross_num_channels = self.cross_attention_dim - - self.norm_cross = nn.GroupNorm( - num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True - ) - else: - raise ValueError( - f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'" - ) - - self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias) - - if not self.only_cross_attention: - # only relevant for the `AddedKVProcessor` classes - self.to_k = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias) - self.to_v = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias) - else: - self.to_k = None - self.to_v = None - - if self.added_kv_proj_dim is not None: - self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_dim) - self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_dim) - - self.to_out = nn.ModuleList([]) - self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)) - self.to_out.append(nn.Dropout(dropout)) - - # set attention processor - # We use the AttnProcessor2_0 by default when torch 2.x is used which uses - # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention - # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 - if processor is None: - processor = ( - AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() - ) - self.set_processor(processor) - - def set_use_npu_flash_attention(self, use_npu_flash_attention: bool) -> None: - r""" - Set whether to use npu flash attention from `torch_npu` or not. - - """ - if use_npu_flash_attention: - processor = AttnProcessorNPU() - else: - # set attention processor - # We use the AttnProcessor2_0 by default when torch 2.x is used which uses - # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention - # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 - processor = ( - AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() - ) - self.set_processor(processor) - - def set_use_memory_efficient_attention_xformers( - self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None - ) -> None: - r""" - Set whether to use memory efficient attention from `xformers` or not. - - Args: - use_memory_efficient_attention_xformers (`bool`): - Whether to use memory efficient attention from `xformers` or not. - attention_op (`Callable`, *optional*): - The attention operation to use. Defaults to `None` which uses the default attention operation from - `xformers`. - """ - is_lora = hasattr(self, "processor") and isinstance( - self.processor, - LORA_ATTENTION_PROCESSORS, - ) - is_custom_diffusion = hasattr(self, "processor") and isinstance( - self.processor, - (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0), - ) - is_added_kv_processor = hasattr(self, "processor") and isinstance( - self.processor, - ( - AttnAddedKVProcessor, - AttnAddedKVProcessor2_0, - SlicedAttnAddedKVProcessor, - XFormersAttnAddedKVProcessor, - LoRAAttnAddedKVProcessor, - ), - ) - - if use_memory_efficient_attention_xformers: - if is_added_kv_processor and (is_lora or is_custom_diffusion): - raise NotImplementedError( - f"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}" - ) - if not is_xformers_available(): - raise ModuleNotFoundError( - ( - "Refer to https://github.com/facebookresearch/xformers for more information on how to install" - " xformers" - ), - name="xformers", - ) - elif not torch.cuda.is_available(): - raise ValueError( - "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is" - " only available for GPU " - ) - else: - try: - # Make sure we can run the memory efficient attention - _ = xformers.ops.memory_efficient_attention( - torch.randn((1, 2, 40), device="cuda"), - torch.randn((1, 2, 40), device="cuda"), - torch.randn((1, 2, 40), device="cuda"), - ) - except Exception as e: - raise e - - if is_lora: - # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers - # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0? - processor = LoRAXFormersAttnProcessor( - hidden_size=self.processor.hidden_size, - cross_attention_dim=self.processor.cross_attention_dim, - rank=self.processor.rank, - attention_op=attention_op, - ) - processor.load_state_dict(self.processor.state_dict()) - processor.to(self.processor.to_q_lora.up.weight.device) - elif is_custom_diffusion: - processor = CustomDiffusionXFormersAttnProcessor( - train_kv=self.processor.train_kv, - train_q_out=self.processor.train_q_out, - hidden_size=self.processor.hidden_size, - cross_attention_dim=self.processor.cross_attention_dim, - attention_op=attention_op, - ) - processor.load_state_dict(self.processor.state_dict()) - if hasattr(self.processor, "to_k_custom_diffusion"): - processor.to(self.processor.to_k_custom_diffusion.weight.device) - elif is_added_kv_processor: - # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP - # which uses this type of cross attention ONLY because the attention mask of format - # [0, ..., -10.000, ..., 0, ...,] is not supported - # throw warning - logger.info( - "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation." - ) - processor = XFormersAttnAddedKVProcessor(attention_op=attention_op) - else: - processor = XFormersAttnProcessor(attention_op=attention_op) - else: - if is_lora: - attn_processor_class = ( - LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor - ) - processor = attn_processor_class( - hidden_size=self.processor.hidden_size, - cross_attention_dim=self.processor.cross_attention_dim, - rank=self.processor.rank, - ) - processor.load_state_dict(self.processor.state_dict()) - processor.to(self.processor.to_q_lora.up.weight.device) - elif is_custom_diffusion: - attn_processor_class = ( - CustomDiffusionAttnProcessor2_0 - if hasattr(F, "scaled_dot_product_attention") - else CustomDiffusionAttnProcessor - ) - processor = attn_processor_class( - train_kv=self.processor.train_kv, - train_q_out=self.processor.train_q_out, - hidden_size=self.processor.hidden_size, - cross_attention_dim=self.processor.cross_attention_dim, - ) - processor.load_state_dict(self.processor.state_dict()) - if hasattr(self.processor, "to_k_custom_diffusion"): - processor.to(self.processor.to_k_custom_diffusion.weight.device) - else: - # set attention processor - # We use the AttnProcessor2_0 by default when torch 2.x is used which uses - # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention - # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 - processor = ( - AttnProcessor2_0() - if hasattr(F, "scaled_dot_product_attention") and self.scale_qk - else AttnProcessor() - ) - - self.set_processor(processor) - - def set_attention_slice(self, slice_size: int) -> None: - r""" - Set the slice size for attention computation. - - Args: - slice_size (`int`): - The slice size for attention computation. - """ - if slice_size is not None and slice_size > self.sliceable_head_dim: - raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.") - - if slice_size is not None and self.added_kv_proj_dim is not None: - processor = SlicedAttnAddedKVProcessor(slice_size) - elif slice_size is not None: - processor = SlicedAttnProcessor(slice_size) - elif self.added_kv_proj_dim is not None: - processor = AttnAddedKVProcessor() - else: - # set attention processor - # We use the AttnProcessor2_0 by default when torch 2.x is used which uses - # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention - # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1 - processor = ( - AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor() - ) - - self.set_processor(processor) - - def set_processor(self, processor: "AttnProcessor") -> None: - r""" - Set the attention processor to use. - - Args: - processor (`AttnProcessor`): - The attention processor to use. - """ - # if current processor is in `self._modules` and if passed `processor` is not, we need to - # pop `processor` from `self._modules` - if ( - hasattr(self, "processor") - and isinstance(self.processor, torch.nn.Module) - and not isinstance(processor, torch.nn.Module) - ): - logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}") - self._modules.pop("processor") - - self.processor = processor - - def get_processor(self, return_deprecated_lora: bool = False) -> "AttentionProcessor": - r""" - Get the attention processor in use. - - Args: - return_deprecated_lora (`bool`, *optional*, defaults to `False`): - Set to `True` to return the deprecated LoRA attention processor. - - Returns: - "AttentionProcessor": The attention processor in use. - """ - if not return_deprecated_lora: - return self.processor - - # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible - # serialization format for LoRA Attention Processors. It should be deleted once the integration - # with PEFT is completed. - is_lora_activated = { - name: module.lora_layer is not None - for name, module in self.named_modules() - if hasattr(module, "lora_layer") - } - - # 1. if no layer has a LoRA activated we can return the processor as usual - if not any(is_lora_activated.values()): - return self.processor - - # If doesn't apply LoRA do `add_k_proj` or `add_v_proj` - is_lora_activated.pop("add_k_proj", None) - is_lora_activated.pop("add_v_proj", None) - # 2. else it is not possible that only some layers have LoRA activated - if not all(is_lora_activated.values()): - raise ValueError( - f"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}" - ) - - # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor - non_lora_processor_cls_name = self.processor.__class__.__name__ - lora_processor_cls = getattr(import_module(__name__), "LoRA" + non_lora_processor_cls_name) - - hidden_size = self.inner_dim - - # now create a LoRA attention processor from the LoRA layers - if lora_processor_cls in [LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor]: - kwargs = { - "cross_attention_dim": self.cross_attention_dim, - "rank": self.to_q.lora_layer.rank, - "network_alpha": self.to_q.lora_layer.network_alpha, - "q_rank": self.to_q.lora_layer.rank, - "q_hidden_size": self.to_q.lora_layer.out_features, - "k_rank": self.to_k.lora_layer.rank, - "k_hidden_size": self.to_k.lora_layer.out_features, - "v_rank": self.to_v.lora_layer.rank, - "v_hidden_size": self.to_v.lora_layer.out_features, - "out_rank": self.to_out[0].lora_layer.rank, - "out_hidden_size": self.to_out[0].lora_layer.out_features, - } - - if hasattr(self.processor, "attention_op"): - kwargs["attention_op"] = self.processor.attention_op - - lora_processor = lora_processor_cls(hidden_size, **kwargs) - lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) - lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) - lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) - lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict()) - elif lora_processor_cls == LoRAAttnAddedKVProcessor: - lora_processor = lora_processor_cls( - hidden_size, - cross_attention_dim=self.add_k_proj.weight.shape[0], - rank=self.to_q.lora_layer.rank, - network_alpha=self.to_q.lora_layer.network_alpha, - ) - lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict()) - lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict()) - lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict()) - lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict()) - - # only save if used - if self.add_k_proj.lora_layer is not None: - lora_processor.add_k_proj_lora.load_state_dict(self.add_k_proj.lora_layer.state_dict()) - lora_processor.add_v_proj_lora.load_state_dict(self.add_v_proj.lora_layer.state_dict()) - else: - lora_processor.add_k_proj_lora = None - lora_processor.add_v_proj_lora = None - else: - raise ValueError(f"{lora_processor_cls} does not exist.") - - return lora_processor - - def forward( - self, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - **cross_attention_kwargs, - ) -> torch.Tensor: - r""" - The forward method of the `Attention` class. - - Args: - hidden_states (`torch.Tensor`): - The hidden states of the query. - encoder_hidden_states (`torch.Tensor`, *optional*): - The hidden states of the encoder. - attention_mask (`torch.Tensor`, *optional*): - The attention mask to use. If `None`, no mask is applied. - **cross_attention_kwargs: - Additional keyword arguments to pass along to the cross attention. - - Returns: - `torch.Tensor`: The output of the attention layer. - """ - # The `Attention` class can call different attention processors / attention functions - # here we simply pass along all tensors to the selected processor class - # For standard processors that are defined here, `**cross_attention_kwargs` is empty - - attn_parameters = set(inspect.signature(self.processor.__call__).parameters.keys()) - unused_kwargs = [k for k, _ in cross_attention_kwargs.items() if k not in attn_parameters] - if len(unused_kwargs) > 0: - logger.warning( - f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored." - ) - cross_attention_kwargs = {k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters} - - return self.processor( - self, - hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - - def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor: - r""" - Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads` - is the number of heads initialized while constructing the `Attention` class. - - Args: - tensor (`torch.Tensor`): The tensor to reshape. - - Returns: - `torch.Tensor`: The reshaped tensor. - """ - head_size = self.heads - batch_size, seq_len, dim = tensor.shape - tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim) - tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size) - return tensor - - def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor: - r""" - Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is - the number of heads initialized while constructing the `Attention` class. - - Args: - tensor (`torch.Tensor`): The tensor to reshape. - out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is - reshaped to `[batch_size * heads, seq_len, dim // heads]`. - - Returns: - `torch.Tensor`: The reshaped tensor. - """ - head_size = self.heads - if tensor.ndim == 3: - batch_size, seq_len, dim = tensor.shape - extra_dim = 1 - else: - batch_size, extra_dim, seq_len, dim = tensor.shape - tensor = tensor.reshape(batch_size, seq_len * extra_dim, head_size, dim // head_size) - tensor = tensor.permute(0, 2, 1, 3) - - if out_dim == 3: - tensor = tensor.reshape(batch_size * head_size, seq_len * extra_dim, dim // head_size) - - return tensor - - def get_attention_scores( - self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None - ) -> torch.Tensor: - r""" - Compute the attention scores. - - Args: - query (`torch.Tensor`): The query tensor. - key (`torch.Tensor`): The key tensor. - attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied. - - Returns: - `torch.Tensor`: The attention probabilities/scores. - """ - dtype = query.dtype - if self.upcast_attention: - query = query.float() - key = key.float() - - if attention_mask is None: - baddbmm_input = torch.empty( - query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device - ) - beta = 0 - else: - baddbmm_input = attention_mask - beta = 1 - - attention_scores = torch.baddbmm( - baddbmm_input, - query, - key.transpose(-1, -2), - beta=beta, - alpha=self.scale, - ) - del baddbmm_input - - if self.upcast_softmax: - attention_scores = attention_scores.float() - - attention_probs = attention_scores.softmax(dim=-1) - del attention_scores - - attention_probs = attention_probs.to(dtype) - - return attention_probs - - def prepare_attention_mask( - self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3 - ) -> torch.Tensor: - r""" - Prepare the attention mask for the attention computation. - - Args: - attention_mask (`torch.Tensor`): - The attention mask to prepare. - target_length (`int`): - The target length of the attention mask. This is the length of the attention mask after padding. - batch_size (`int`): - The batch size, which is used to repeat the attention mask. - out_dim (`int`, *optional*, defaults to `3`): - The output dimension of the attention mask. Can be either `3` or `4`. - - Returns: - `torch.Tensor`: The prepared attention mask. - """ - head_size = self.heads - if attention_mask is None: - return attention_mask - - current_length: int = attention_mask.shape[-1] - if current_length != target_length: - if attention_mask.device.type == "mps": - # HACK: MPS: Does not support padding by greater than dimension of input tensor. - # Instead, we can manually construct the padding tensor. - padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length) - padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device) - attention_mask = torch.cat([attention_mask, padding], dim=2) - else: - # TODO: for pipelines such as stable-diffusion, padding cross-attn mask: - # we want to instead pad by (0, remaining_length), where remaining_length is: - # remaining_length: int = target_length - current_length - # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding - attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) - - if out_dim == 3: - if attention_mask.shape[0] < batch_size * head_size: - attention_mask = attention_mask.repeat_interleave(head_size, dim=0) - elif out_dim == 4: - attention_mask = attention_mask.unsqueeze(1) - attention_mask = attention_mask.repeat_interleave(head_size, dim=1) - - return attention_mask - - def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor: - r""" - Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the - `Attention` class. - - Args: - encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder. - - Returns: - `torch.Tensor`: The normalized encoder hidden states. - """ - assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states" - - if isinstance(self.norm_cross, nn.LayerNorm): - encoder_hidden_states = self.norm_cross(encoder_hidden_states) - elif isinstance(self.norm_cross, nn.GroupNorm): - # Group norm norms along the channels dimension and expects - # input to be in the shape of (N, C, *). In this case, we want - # to norm along the hidden dimension, so we need to move - # (batch_size, sequence_length, hidden_size) -> - # (batch_size, hidden_size, sequence_length) - encoder_hidden_states = encoder_hidden_states.transpose(1, 2) - encoder_hidden_states = self.norm_cross(encoder_hidden_states) - encoder_hidden_states = encoder_hidden_states.transpose(1, 2) - else: - assert False - - return encoder_hidden_states - - @torch.no_grad() - def fuse_projections(self, fuse=True): - device = self.to_q.weight.data.device - dtype = self.to_q.weight.data.dtype - - if not self.is_cross_attention: - # fetch weight matrices. - concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data]) - in_features = concatenated_weights.shape[1] - out_features = concatenated_weights.shape[0] - - # create a new single projection layer and copy over the weights. - self.to_qkv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype) - self.to_qkv.weight.copy_(concatenated_weights) - if self.use_bias: - concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data]) - self.to_qkv.bias.copy_(concatenated_bias) - - else: - concatenated_weights = torch.cat([self.to_k.weight.data, self.to_v.weight.data]) - in_features = concatenated_weights.shape[1] - out_features = concatenated_weights.shape[0] - - self.to_kv = nn.Linear(in_features, out_features, bias=self.use_bias, device=device, dtype=dtype) - self.to_kv.weight.copy_(concatenated_weights) - if self.use_bias: - concatenated_bias = torch.cat([self.to_k.bias.data, self.to_v.bias.data]) - self.to_kv.bias.copy_(concatenated_bias) - - self.fused_projections = fuse - - -class AttnProcessor: - r""" - Default processor for performing attention-related computations. - """ - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> torch.Tensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - query = attn.head_to_batch_dim(query) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class CustomDiffusionAttnProcessor(nn.Module): - r""" - Processor for implementing attention for the Custom Diffusion method. - - Args: - train_kv (`bool`, defaults to `True`): - Whether to newly train the key and value matrices corresponding to the text features. - train_q_out (`bool`, defaults to `True`): - Whether to newly train query matrices corresponding to the latent image features. - hidden_size (`int`, *optional*, defaults to `None`): - The hidden size of the attention layer. - cross_attention_dim (`int`, *optional*, defaults to `None`): - The number of channels in the `encoder_hidden_states`. - out_bias (`bool`, defaults to `True`): - Whether to include the bias parameter in `train_q_out`. - dropout (`float`, *optional*, defaults to 0.0): - The dropout probability to use. - """ - - def __init__( - self, - train_kv: bool = True, - train_q_out: bool = True, - hidden_size: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - out_bias: bool = True, - dropout: float = 0.0, - ): - super().__init__() - self.train_kv = train_kv - self.train_q_out = train_q_out - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - - # `_custom_diffusion` id for easy serialization and loading. - if self.train_kv: - self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - if self.train_q_out: - self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False) - self.to_out_custom_diffusion = nn.ModuleList([]) - self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias)) - self.to_out_custom_diffusion.append(nn.Dropout(dropout)) - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.Tensor: - batch_size, sequence_length, _ = hidden_states.shape - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - if self.train_q_out: - query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype) - else: - query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype)) - - if encoder_hidden_states is None: - crossattn = False - encoder_hidden_states = hidden_states - else: - crossattn = True - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - if self.train_kv: - key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype)) - value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype)) - key = key.to(attn.to_q.weight.dtype) - value = value.to(attn.to_q.weight.dtype) - else: - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - if crossattn: - detach = torch.ones_like(key) - detach[:, :1, :] = detach[:, :1, :] * 0.0 - key = detach * key + (1 - detach) * key.detach() - value = detach * value + (1 - detach) * value.detach() - - query = attn.head_to_batch_dim(query) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - if self.train_q_out: - # linear proj - hidden_states = self.to_out_custom_diffusion[0](hidden_states) - # dropout - hidden_states = self.to_out_custom_diffusion[1](hidden_states) - else: - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - return hidden_states - - -class AttnAddedKVProcessor: - r""" - Processor for performing attention-related computations with extra learnable key and value matrices for the text - encoder. - """ - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> torch.Tensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - residual = hidden_states - - hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) - batch_size, sequence_length, _ = hidden_states.shape - - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - query = attn.head_to_batch_dim(query) - - encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) - encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) - encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj) - encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj) - - if not attn.only_cross_attention: - key = attn.to_k(hidden_states) - value = attn.to_v(hidden_states) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - key = torch.cat([encoder_hidden_states_key_proj, key], dim=1) - value = torch.cat([encoder_hidden_states_value_proj, value], dim=1) - else: - key = encoder_hidden_states_key_proj - value = encoder_hidden_states_value_proj - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) - hidden_states = hidden_states + residual - - return hidden_states - - -class AttnAddedKVProcessor2_0: - r""" - Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra - learnable key and value matrices for the text encoder. - """ - - def __init__(self): - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError( - "AttnAddedKVProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." - ) - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> torch.Tensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - residual = hidden_states - - hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) - batch_size, sequence_length, _ = hidden_states.shape - - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - query = attn.head_to_batch_dim(query, out_dim=4) - - encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) - encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) - encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4) - encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4) - - if not attn.only_cross_attention: - key = attn.to_k(hidden_states) - value = attn.to_v(hidden_states) - key = attn.head_to_batch_dim(key, out_dim=4) - value = attn.head_to_batch_dim(value, out_dim=4) - key = torch.cat([encoder_hidden_states_key_proj, key], dim=2) - value = torch.cat([encoder_hidden_states_value_proj, value], dim=2) - else: - key = encoder_hidden_states_key_proj - value = encoder_hidden_states_value_proj - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1]) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) - hidden_states = hidden_states + residual - - return hidden_states - - -class XFormersAttnAddedKVProcessor: - r""" - Processor for implementing memory efficient attention using xFormers. - - Args: - attention_op (`Callable`, *optional*, defaults to `None`): - The base - [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to - use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best - operator. - """ - - def __init__(self, attention_op: Optional[Callable] = None): - self.attention_op = attention_op - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.Tensor: - residual = hidden_states - hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) - batch_size, sequence_length, _ = hidden_states.shape - - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - query = attn.head_to_batch_dim(query) - - encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) - encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) - encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj) - encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj) - - if not attn.only_cross_attention: - key = attn.to_k(hidden_states) - value = attn.to_v(hidden_states) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - key = torch.cat([encoder_hidden_states_key_proj, key], dim=1) - value = torch.cat([encoder_hidden_states_value_proj, value], dim=1) - else: - key = encoder_hidden_states_key_proj - value = encoder_hidden_states_value_proj - - hidden_states = xformers.ops.memory_efficient_attention( - query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale - ) - hidden_states = hidden_states.to(query.dtype) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) - hidden_states = hidden_states + residual - - return hidden_states - - -class XFormersAttnProcessor: - r""" - Processor for implementing memory efficient attention using xFormers. - - Args: - attention_op (`Callable`, *optional*, defaults to `None`): - The base - [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to - use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best - operator. - """ - - def __init__(self, attention_op: Optional[Callable] = None): - self.attention_op = attention_op - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, key_tokens, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size) - if attention_mask is not None: - # expand our mask's singleton query_tokens dimension: - # [batch*heads, 1, key_tokens] -> - # [batch*heads, query_tokens, key_tokens] - # so that it can be added as a bias onto the attention scores that xformers computes: - # [batch*heads, query_tokens, key_tokens] - # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. - _, query_tokens, _ = hidden_states.shape - attention_mask = attention_mask.expand(-1, query_tokens, -1) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - query = attn.head_to_batch_dim(query).contiguous() - key = attn.head_to_batch_dim(key).contiguous() - value = attn.head_to_batch_dim(value).contiguous() - - hidden_states = xformers.ops.memory_efficient_attention( - query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale - ) - hidden_states = hidden_states.to(query.dtype) - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class AttnProcessorNPU: - - r""" - Processor for implementing flash attention using torch_npu. Torch_npu supports only fp16 and bf16 data types. If - fp32 is used, F.scaled_dot_product_attention will be used for computation, but the acceleration effect on NPU is - not significant. - - """ - - def __init__(self): - if not is_torch_npu_available(): - raise ImportError("AttnProcessorNPU requires torch_npu extensions and is supported only on npu devices.") - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - residual = hidden_states - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - if query.dtype in (torch.float16, torch.bfloat16): - hidden_states = torch_npu.npu_fusion_attention( - query, - key, - value, - attn.heads, - input_layout="BNSD", - pse=None, - atten_mask=attention_mask, - scale=1.0 / math.sqrt(query.shape[-1]), - pre_tockens=65536, - next_tockens=65536, - keep_prob=1.0, - sync=False, - inner_precise=0, - )[0] - else: - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class AttnProcessor2_0: - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). - """ - - def __init__(self): - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - residual = hidden_states - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class FusedAttnProcessor2_0: - r""" - Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). It uses - fused projection layers. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. - For cross-attention modules, key and value projection matrices are fused. - - - - This API is currently 🧪 experimental in nature and can change in future. - - - """ - - def __init__(self): - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError( - "FusedAttnProcessor2_0 requires at least PyTorch 2.0, to use it. Please upgrade PyTorch to > 2.0." - ) - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - *args, - **kwargs, - ) -> torch.FloatTensor: - if len(args) > 0 or kwargs.get("scale", None) is not None: - deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`." - deprecate("scale", "1.0.0", deprecation_message) - - residual = hidden_states - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - if encoder_hidden_states is None: - qkv = attn.to_qkv(hidden_states) - split_size = qkv.shape[-1] // 3 - query, key, value = torch.split(qkv, split_size, dim=-1) - else: - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - query = attn.to_q(hidden_states) - - kv = attn.to_kv(encoder_hidden_states) - split_size = kv.shape[-1] // 2 - key, value = torch.split(kv, split_size, dim=-1) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class CustomDiffusionXFormersAttnProcessor(nn.Module): - r""" - Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method. - - Args: - train_kv (`bool`, defaults to `True`): - Whether to newly train the key and value matrices corresponding to the text features. - train_q_out (`bool`, defaults to `True`): - Whether to newly train query matrices corresponding to the latent image features. - hidden_size (`int`, *optional*, defaults to `None`): - The hidden size of the attention layer. - cross_attention_dim (`int`, *optional*, defaults to `None`): - The number of channels in the `encoder_hidden_states`. - out_bias (`bool`, defaults to `True`): - Whether to include the bias parameter in `train_q_out`. - dropout (`float`, *optional*, defaults to 0.0): - The dropout probability to use. - attention_op (`Callable`, *optional*, defaults to `None`): - The base - [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to use - as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best operator. - """ - - def __init__( - self, - train_kv: bool = True, - train_q_out: bool = False, - hidden_size: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - out_bias: bool = True, - dropout: float = 0.0, - attention_op: Optional[Callable] = None, - ): - super().__init__() - self.train_kv = train_kv - self.train_q_out = train_q_out - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.attention_op = attention_op - - # `_custom_diffusion` id for easy serialization and loading. - if self.train_kv: - self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - if self.train_q_out: - self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False) - self.to_out_custom_diffusion = nn.ModuleList([]) - self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias)) - self.to_out_custom_diffusion.append(nn.Dropout(dropout)) - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if self.train_q_out: - query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype) - else: - query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype)) - - if encoder_hidden_states is None: - crossattn = False - encoder_hidden_states = hidden_states - else: - crossattn = True - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - if self.train_kv: - key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype)) - value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype)) - key = key.to(attn.to_q.weight.dtype) - value = value.to(attn.to_q.weight.dtype) - else: - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - if crossattn: - detach = torch.ones_like(key) - detach[:, :1, :] = detach[:, :1, :] * 0.0 - key = detach * key + (1 - detach) * key.detach() - value = detach * value + (1 - detach) * value.detach() - - query = attn.head_to_batch_dim(query).contiguous() - key = attn.head_to_batch_dim(key).contiguous() - value = attn.head_to_batch_dim(value).contiguous() - - hidden_states = xformers.ops.memory_efficient_attention( - query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale - ) - hidden_states = hidden_states.to(query.dtype) - hidden_states = attn.batch_to_head_dim(hidden_states) - - if self.train_q_out: - # linear proj - hidden_states = self.to_out_custom_diffusion[0](hidden_states) - # dropout - hidden_states = self.to_out_custom_diffusion[1](hidden_states) - else: - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - return hidden_states - - -class CustomDiffusionAttnProcessor2_0(nn.Module): - r""" - Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled - dot-product attention. - - Args: - train_kv (`bool`, defaults to `True`): - Whether to newly train the key and value matrices corresponding to the text features. - train_q_out (`bool`, defaults to `True`): - Whether to newly train query matrices corresponding to the latent image features. - hidden_size (`int`, *optional*, defaults to `None`): - The hidden size of the attention layer. - cross_attention_dim (`int`, *optional*, defaults to `None`): - The number of channels in the `encoder_hidden_states`. - out_bias (`bool`, defaults to `True`): - Whether to include the bias parameter in `train_q_out`. - dropout (`float`, *optional*, defaults to 0.0): - The dropout probability to use. - """ - - def __init__( - self, - train_kv: bool = True, - train_q_out: bool = True, - hidden_size: Optional[int] = None, - cross_attention_dim: Optional[int] = None, - out_bias: bool = True, - dropout: float = 0.0, - ): - super().__init__() - self.train_kv = train_kv - self.train_q_out = train_q_out - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - - # `_custom_diffusion` id for easy serialization and loading. - if self.train_kv: - self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) - if self.train_q_out: - self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False) - self.to_out_custom_diffusion = nn.ModuleList([]) - self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias)) - self.to_out_custom_diffusion.append(nn.Dropout(dropout)) - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - batch_size, sequence_length, _ = hidden_states.shape - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - if self.train_q_out: - query = self.to_q_custom_diffusion(hidden_states) - else: - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - crossattn = False - encoder_hidden_states = hidden_states - else: - crossattn = True - if attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - if self.train_kv: - key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype)) - value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype)) - key = key.to(attn.to_q.weight.dtype) - value = value.to(attn.to_q.weight.dtype) - - else: - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - if crossattn: - detach = torch.ones_like(key) - detach[:, :1, :] = detach[:, :1, :] * 0.0 - key = detach * key + (1 - detach) * key.detach() - value = detach * value + (1 - detach) * value.detach() - - inner_dim = hidden_states.shape[-1] - - head_dim = inner_dim // attn.heads - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - if self.train_q_out: - # linear proj - hidden_states = self.to_out_custom_diffusion[0](hidden_states) - # dropout - hidden_states = self.to_out_custom_diffusion[1](hidden_states) - else: - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - return hidden_states - - -class SlicedAttnProcessor: - r""" - Processor for implementing sliced attention. - - Args: - slice_size (`int`, *optional*): - The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and - `attention_head_dim` must be a multiple of the `slice_size`. - """ - - def __init__(self, slice_size: int): - self.slice_size = slice_size - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - residual = hidden_states - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - dim = query.shape[-1] - query = attn.head_to_batch_dim(query) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - batch_size_attention, query_tokens, _ = query.shape - hidden_states = torch.zeros( - (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype - ) - - for i in range(batch_size_attention // self.slice_size): - start_idx = i * self.slice_size - end_idx = (i + 1) * self.slice_size - - query_slice = query[start_idx:end_idx] - key_slice = key[start_idx:end_idx] - attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None - - attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) - - attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) - - hidden_states[start_idx:end_idx] = attn_slice - - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class SlicedAttnAddedKVProcessor: - r""" - Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder. - - Args: - slice_size (`int`, *optional*): - The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and - `attention_head_dim` must be a multiple of the `slice_size`. - """ - - def __init__(self, slice_size): - self.slice_size = slice_size - - def __call__( - self, - attn: "Attention", - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - ) -> torch.FloatTensor: - residual = hidden_states - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2) - - batch_size, sequence_length, _ = hidden_states.shape - - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - dim = query.shape[-1] - query = attn.head_to_batch_dim(query) - - encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) - encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) - - encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj) - encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj) - - if not attn.only_cross_attention: - key = attn.to_k(hidden_states) - value = attn.to_v(hidden_states) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - key = torch.cat([encoder_hidden_states_key_proj, key], dim=1) - value = torch.cat([encoder_hidden_states_value_proj, value], dim=1) - else: - key = encoder_hidden_states_key_proj - value = encoder_hidden_states_value_proj - - batch_size_attention, query_tokens, _ = query.shape - hidden_states = torch.zeros( - (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype - ) - - for i in range(batch_size_attention // self.slice_size): - start_idx = i * self.slice_size - end_idx = (i + 1) * self.slice_size - - query_slice = query[start_idx:end_idx] - key_slice = key[start_idx:end_idx] - attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None - - attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice) - - attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx]) - - hidden_states[start_idx:end_idx] = attn_slice - - hidden_states = attn.batch_to_head_dim(hidden_states) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape) - hidden_states = hidden_states + residual - - return hidden_states - - -class SpatialNorm(nn.Module): - """ - Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002. - - Args: - f_channels (`int`): - The number of channels for input to group normalization layer, and output of the spatial norm layer. - zq_channels (`int`): - The number of channels for the quantized vector as described in the paper. - """ - - def __init__( - self, - f_channels: int, - zq_channels: int, - ): - super().__init__() - self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=32, eps=1e-6, affine=True) - self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0) - self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0) - - def forward(self, f: torch.FloatTensor, zq: torch.FloatTensor) -> torch.FloatTensor: - f_size = f.shape[-2:] - zq = F.interpolate(zq, size=f_size, mode="nearest") - norm_f = self.norm_layer(f) - new_f = norm_f * self.conv_y(zq) + self.conv_b(zq) - return new_f - - -class LoRAAttnProcessor(nn.Module): - def __init__( - self, - hidden_size: int, - cross_attention_dim: Optional[int] = None, - rank: int = 4, - network_alpha: Optional[int] = None, - **kwargs, - ): - deprecation_message = "Using LoRAAttnProcessor is deprecated. Please use the PEFT backend for all things LoRA. You can install PEFT by running `pip install peft`." - deprecate("LoRAAttnProcessor", "0.30.0", deprecation_message, standard_warn=False) - - super().__init__() - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.rank = rank - - q_rank = kwargs.pop("q_rank", None) - q_hidden_size = kwargs.pop("q_hidden_size", None) - q_rank = q_rank if q_rank is not None else rank - q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size - - v_rank = kwargs.pop("v_rank", None) - v_hidden_size = kwargs.pop("v_hidden_size", None) - v_rank = v_rank if v_rank is not None else rank - v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size - - out_rank = kwargs.pop("out_rank", None) - out_hidden_size = kwargs.pop("out_hidden_size", None) - out_rank = out_rank if out_rank is not None else rank - out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size - - self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha) - self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) - self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha) - self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha) - - def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, **kwargs) -> torch.FloatTensor: - self_cls_name = self.__class__.__name__ - deprecate( - self_cls_name, - "0.26.0", - ( - f"Make sure use {self_cls_name[4:]} instead by setting" - "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using" - " `LoraLoaderMixin.load_lora_weights`" - ), - ) - attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device) - attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device) - attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device) - attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device) - - attn._modules.pop("processor") - attn.processor = AttnProcessor() - return attn.processor(attn, hidden_states, **kwargs) - - -class LoRAAttnProcessor2_0(nn.Module): - def __init__( - self, - hidden_size: int, - cross_attention_dim: Optional[int] = None, - rank: int = 4, - network_alpha: Optional[int] = None, - **kwargs, - ): - deprecation_message = "Using LoRAAttnProcessor is deprecated. Please use the PEFT backend for all things LoRA. You can install PEFT by running `pip install peft`." - deprecate("LoRAAttnProcessor2_0", "0.30.0", deprecation_message, standard_warn=False) - - super().__init__() - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.rank = rank - - q_rank = kwargs.pop("q_rank", None) - q_hidden_size = kwargs.pop("q_hidden_size", None) - q_rank = q_rank if q_rank is not None else rank - q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size - - v_rank = kwargs.pop("v_rank", None) - v_hidden_size = kwargs.pop("v_hidden_size", None) - v_rank = v_rank if v_rank is not None else rank - v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size - - out_rank = kwargs.pop("out_rank", None) - out_hidden_size = kwargs.pop("out_hidden_size", None) - out_rank = out_rank if out_rank is not None else rank - out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size - - self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha) - self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) - self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha) - self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha) - - def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, **kwargs) -> torch.FloatTensor: - self_cls_name = self.__class__.__name__ - deprecate( - self_cls_name, - "0.26.0", - ( - f"Make sure use {self_cls_name[4:]} instead by setting" - "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using" - " `LoraLoaderMixin.load_lora_weights`" - ), - ) - attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device) - attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device) - attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device) - attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device) - - attn._modules.pop("processor") - attn.processor = AttnProcessor2_0() - return attn.processor(attn, hidden_states, **kwargs) - - -class LoRAXFormersAttnProcessor(nn.Module): - r""" - Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers. - - Args: - hidden_size (`int`, *optional*): - The hidden size of the attention layer. - cross_attention_dim (`int`, *optional*): - The number of channels in the `encoder_hidden_states`. - rank (`int`, defaults to 4): - The dimension of the LoRA update matrices. - attention_op (`Callable`, *optional*, defaults to `None`): - The base - [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to - use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best - operator. - network_alpha (`int`, *optional*): - Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs. - kwargs (`dict`): - Additional keyword arguments to pass to the `LoRALinearLayer` layers. - """ - - def __init__( - self, - hidden_size: int, - cross_attention_dim: int, - rank: int = 4, - attention_op: Optional[Callable] = None, - network_alpha: Optional[int] = None, - **kwargs, - ): - super().__init__() - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.rank = rank - self.attention_op = attention_op - - q_rank = kwargs.pop("q_rank", None) - q_hidden_size = kwargs.pop("q_hidden_size", None) - q_rank = q_rank if q_rank is not None else rank - q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size - - v_rank = kwargs.pop("v_rank", None) - v_hidden_size = kwargs.pop("v_hidden_size", None) - v_rank = v_rank if v_rank is not None else rank - v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size - - out_rank = kwargs.pop("out_rank", None) - out_hidden_size = kwargs.pop("out_hidden_size", None) - out_rank = out_rank if out_rank is not None else rank - out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size - - self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha) - self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) - self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha) - self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha) - - def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, **kwargs) -> torch.FloatTensor: - self_cls_name = self.__class__.__name__ - deprecate( - self_cls_name, - "0.26.0", - ( - f"Make sure use {self_cls_name[4:]} instead by setting" - "LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using" - " `LoraLoaderMixin.load_lora_weights`" - ), - ) - attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device) - attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device) - attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device) - attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device) - - attn._modules.pop("processor") - attn.processor = XFormersAttnProcessor() - return attn.processor(attn, hidden_states, **kwargs) - - -class LoRAAttnAddedKVProcessor(nn.Module): - r""" - Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text - encoder. - - Args: - hidden_size (`int`, *optional*): - The hidden size of the attention layer. - cross_attention_dim (`int`, *optional*, defaults to `None`): - The number of channels in the `encoder_hidden_states`. - rank (`int`, defaults to 4): - The dimension of the LoRA update matrices. - network_alpha (`int`, *optional*): - Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs. - kwargs (`dict`): - Additional keyword arguments to pass to the `LoRALinearLayer` layers. - """ - - def __init__( - self, - hidden_size: int, - cross_attention_dim: Optional[int] = None, - rank: int = 4, - network_alpha: Optional[int] = None, - ): - super().__init__() - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - self.rank = rank - - self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) - self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) - self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha) - self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) - self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) - self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha) - - def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, **kwargs) -> torch.FloatTensor: - self_cls_name = self.__class__.__name__ - deprecate( - self_cls_name, - "0.26.0", - ( - f"Make sure use {self_cls_name[4:]} instead by setting" - "LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using" - " `LoraLoaderMixin.load_lora_weights`" - ), - ) - attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device) - attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device) - attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device) - attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device) - - attn._modules.pop("processor") - attn.processor = AttnAddedKVProcessor() - return attn.processor(attn, hidden_states, **kwargs) - - -class IPAdapterAttnProcessor(nn.Module): - r""" - Attention processor for Multiple IP-Adapters. - - Args: - hidden_size (`int`): - The hidden size of the attention layer. - cross_attention_dim (`int`): - The number of channels in the `encoder_hidden_states`. - num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`): - The context length of the image features. - scale (`float` or List[`float`], defaults to 1.0): - the weight scale of image prompt. - """ - - def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0): - super().__init__() - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - - if not isinstance(num_tokens, (tuple, list)): - num_tokens = [num_tokens] - self.num_tokens = num_tokens - - if not isinstance(scale, list): - scale = [scale] * len(num_tokens) - if len(scale) != len(num_tokens): - raise ValueError("`scale` should be a list of integers with the same length as `num_tokens`.") - self.scale = scale - - self.to_k_ip = nn.ModuleList( - [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))] - ) - self.to_v_ip = nn.ModuleList( - [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))] - ) - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - scale: float = 1.0, - ip_adapter_masks: Optional[torch.FloatTensor] = None, - ): - residual = hidden_states - - # separate ip_hidden_states from encoder_hidden_states - if encoder_hidden_states is not None: - if isinstance(encoder_hidden_states, tuple): - encoder_hidden_states, ip_hidden_states = encoder_hidden_states - else: - deprecation_message = ( - "You have passed a tensor as `encoder_hidden_states`. This is deprecated and will be removed in a future release." - " Please make sure to update your script to pass `encoder_hidden_states` as a tuple to suppress this warning." - ) - deprecate("encoder_hidden_states not a tuple", "1.0.0", deprecation_message, standard_warn=False) - end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0] - encoder_hidden_states, ip_hidden_states = ( - encoder_hidden_states[:, :end_pos, :], - [encoder_hidden_states[:, end_pos:, :]], - ) - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - query = attn.head_to_batch_dim(query) - key = attn.head_to_batch_dim(key) - value = attn.head_to_batch_dim(value) - - attention_probs = attn.get_attention_scores(query, key, attention_mask) - hidden_states = torch.bmm(attention_probs, value) - hidden_states = attn.batch_to_head_dim(hidden_states) - - if ip_adapter_masks is not None: - if not isinstance(ip_adapter_masks, List): - # for backward compatibility, we accept `ip_adapter_mask` as a tensor of shape [num_ip_adapter, 1, height, width] - ip_adapter_masks = list(ip_adapter_masks.unsqueeze(1)) - if not (len(ip_adapter_masks) == len(self.scale) == len(ip_hidden_states)): - raise ValueError( - f"Length of ip_adapter_masks array ({len(ip_adapter_masks)}) must match " - f"length of self.scale array ({len(self.scale)}) and number of ip_hidden_states " - f"({len(ip_hidden_states)})" - ) - else: - for index, (mask, scale, ip_state) in enumerate(zip(ip_adapter_masks, self.scale, ip_hidden_states)): - if not isinstance(mask, torch.Tensor) or mask.ndim != 4: - raise ValueError( - "Each element of the ip_adapter_masks array should be a tensor with shape " - "[1, num_images_for_ip_adapter, height, width]." - " Please use `IPAdapterMaskProcessor` to preprocess your mask" - ) - if mask.shape[1] != ip_state.shape[1]: - raise ValueError( - f"Number of masks ({mask.shape[1]}) does not match " - f"number of ip images ({ip_state.shape[1]}) at index {index}" - ) - if isinstance(scale, list) and not len(scale) == mask.shape[1]: - raise ValueError( - f"Number of masks ({mask.shape[1]}) does not match " - f"number of scales ({len(scale)}) at index {index}" - ) - else: - ip_adapter_masks = [None] * len(self.scale) - - # for ip-adapter - for current_ip_hidden_states, scale, to_k_ip, to_v_ip, mask in zip( - ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip, ip_adapter_masks - ): - skip = False - if isinstance(scale, list): - if all(s == 0 for s in scale): - skip = True - elif scale == 0: - skip = True - if not skip: - if mask is not None: - if not isinstance(scale, list): - scale = [scale] * mask.shape[1] - - current_num_images = mask.shape[1] - for i in range(current_num_images): - ip_key = to_k_ip(current_ip_hidden_states[:, i, :, :]) - ip_value = to_v_ip(current_ip_hidden_states[:, i, :, :]) - - ip_key = attn.head_to_batch_dim(ip_key) - ip_value = attn.head_to_batch_dim(ip_value) - - ip_attention_probs = attn.get_attention_scores(query, ip_key, None) - _current_ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) - _current_ip_hidden_states = attn.batch_to_head_dim(_current_ip_hidden_states) - - mask_downsample = IPAdapterMaskProcessor.downsample( - mask[:, i, :, :], - batch_size, - _current_ip_hidden_states.shape[1], - _current_ip_hidden_states.shape[2], - ) - - mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device) - - hidden_states = hidden_states + scale[i] * (_current_ip_hidden_states * mask_downsample) - else: - ip_key = to_k_ip(current_ip_hidden_states) - ip_value = to_v_ip(current_ip_hidden_states) - - ip_key = attn.head_to_batch_dim(ip_key) - ip_value = attn.head_to_batch_dim(ip_value) - - ip_attention_probs = attn.get_attention_scores(query, ip_key, None) - current_ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) - current_ip_hidden_states = attn.batch_to_head_dim(current_ip_hidden_states) - - hidden_states = hidden_states + scale * current_ip_hidden_states - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -class IPAdapterAttnProcessor2_0(torch.nn.Module): - r""" - Attention processor for IP-Adapter for PyTorch 2.0. - - Args: - hidden_size (`int`): - The hidden size of the attention layer. - cross_attention_dim (`int`): - The number of channels in the `encoder_hidden_states`. - num_tokens (`int`, `Tuple[int]` or `List[int]`, defaults to `(4,)`): - The context length of the image features. - scale (`float` or `List[float]`, defaults to 1.0): - the weight scale of image prompt. - """ - - def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0): - super().__init__() - - if not hasattr(F, "scaled_dot_product_attention"): - raise ImportError( - f"{self.__class__.__name__} requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0." - ) - - self.hidden_size = hidden_size - self.cross_attention_dim = cross_attention_dim - - if not isinstance(num_tokens, (tuple, list)): - num_tokens = [num_tokens] - self.num_tokens = num_tokens - - if not isinstance(scale, list): - scale = [scale] * len(num_tokens) - if len(scale) != len(num_tokens): - raise ValueError("`scale` should be a list of integers with the same length as `num_tokens`.") - self.scale = scale - - self.to_k_ip = nn.ModuleList( - [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))] - ) - self.to_v_ip = nn.ModuleList( - [nn.Linear(cross_attention_dim, hidden_size, bias=False) for _ in range(len(num_tokens))] - ) - - def __call__( - self, - attn: Attention, - hidden_states: torch.FloatTensor, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - temb: Optional[torch.FloatTensor] = None, - scale: float = 1.0, - ip_adapter_masks: Optional[torch.FloatTensor] = None, - ): - residual = hidden_states - - # separate ip_hidden_states from encoder_hidden_states - if encoder_hidden_states is not None: - if isinstance(encoder_hidden_states, tuple): - encoder_hidden_states, ip_hidden_states = encoder_hidden_states - else: - deprecation_message = ( - "You have passed a tensor as `encoder_hidden_states`. This is deprecated and will be removed in a future release." - " Please make sure to update your script to pass `encoder_hidden_states` as a tuple to suppress this warning." - ) - deprecate("encoder_hidden_states not a tuple", "1.0.0", deprecation_message, standard_warn=False) - end_pos = encoder_hidden_states.shape[1] - self.num_tokens[0] - encoder_hidden_states, ip_hidden_states = ( - encoder_hidden_states[:, :end_pos, :], - [encoder_hidden_states[:, end_pos:, :]], - ) - - if attn.spatial_norm is not None: - hidden_states = attn.spatial_norm(hidden_states, temb) - - input_ndim = hidden_states.ndim - - if input_ndim == 4: - batch_size, channel, height, width = hidden_states.shape - hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) - - if attn.group_norm is not None: - hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) - - query = attn.to_q(hidden_states) - - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - elif attn.norm_cross: - encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) - - key = attn.to_k(encoder_hidden_states) - value = attn.to_v(encoder_hidden_states) - - inner_dim = key.shape[-1] - head_dim = inner_dim // attn.heads - - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - hidden_states = F.scaled_dot_product_attention( - query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False - ) - - hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) - hidden_states = hidden_states.to(query.dtype) - - if ip_adapter_masks is not None: - if not isinstance(ip_adapter_masks, List): - # for backward compatibility, we accept `ip_adapter_mask` as a tensor of shape [num_ip_adapter, 1, height, width] - ip_adapter_masks = list(ip_adapter_masks.unsqueeze(1)) - if not (len(ip_adapter_masks) == len(self.scale) == len(ip_hidden_states)): - raise ValueError( - f"Length of ip_adapter_masks array ({len(ip_adapter_masks)}) must match " - f"length of self.scale array ({len(self.scale)}) and number of ip_hidden_states " - f"({len(ip_hidden_states)})" - ) - else: - for index, (mask, scale, ip_state) in enumerate(zip(ip_adapter_masks, self.scale, ip_hidden_states)): - if not isinstance(mask, torch.Tensor) or mask.ndim != 4: - raise ValueError( - "Each element of the ip_adapter_masks array should be a tensor with shape " - "[1, num_images_for_ip_adapter, height, width]." - " Please use `IPAdapterMaskProcessor` to preprocess your mask" - ) - if mask.shape[1] != ip_state.shape[1]: - raise ValueError( - f"Number of masks ({mask.shape[1]}) does not match " - f"number of ip images ({ip_state.shape[1]}) at index {index}" - ) - if isinstance(scale, list) and not len(scale) == mask.shape[1]: - raise ValueError( - f"Number of masks ({mask.shape[1]}) does not match " - f"number of scales ({len(scale)}) at index {index}" - ) - else: - ip_adapter_masks = [None] * len(self.scale) - - # for ip-adapter - for current_ip_hidden_states, scale, to_k_ip, to_v_ip, mask in zip( - ip_hidden_states, self.scale, self.to_k_ip, self.to_v_ip, ip_adapter_masks - ): - skip = False - if isinstance(scale, list): - if all(s == 0 for s in scale): - skip = True - elif scale == 0: - skip = True - if not skip: - if mask is not None: - if not isinstance(scale, list): - scale = [scale] * mask.shape[1] - - current_num_images = mask.shape[1] - for i in range(current_num_images): - ip_key = to_k_ip(current_ip_hidden_states[:, i, :, :]) - ip_value = to_v_ip(current_ip_hidden_states[:, i, :, :]) - - ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - _current_ip_hidden_states = F.scaled_dot_product_attention( - query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False - ) - - _current_ip_hidden_states = _current_ip_hidden_states.transpose(1, 2).reshape( - batch_size, -1, attn.heads * head_dim - ) - _current_ip_hidden_states = _current_ip_hidden_states.to(query.dtype) - - mask_downsample = IPAdapterMaskProcessor.downsample( - mask[:, i, :, :], - batch_size, - _current_ip_hidden_states.shape[1], - _current_ip_hidden_states.shape[2], - ) - - mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device) - hidden_states = hidden_states + scale[i] * (_current_ip_hidden_states * mask_downsample) - else: - ip_key = to_k_ip(current_ip_hidden_states) - ip_value = to_v_ip(current_ip_hidden_states) - - ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) - - # the output of sdp = (batch, num_heads, seq_len, head_dim) - # TODO: add support for attn.scale when we move to Torch 2.1 - current_ip_hidden_states = F.scaled_dot_product_attention( - query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False - ) - - current_ip_hidden_states = current_ip_hidden_states.transpose(1, 2).reshape( - batch_size, -1, attn.heads * head_dim - ) - current_ip_hidden_states = current_ip_hidden_states.to(query.dtype) - - hidden_states = hidden_states + scale * current_ip_hidden_states - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - # dropout - hidden_states = attn.to_out[1](hidden_states) - - if input_ndim == 4: - hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) - - if attn.residual_connection: - hidden_states = hidden_states + residual - - hidden_states = hidden_states / attn.rescale_output_factor - - return hidden_states - - -LORA_ATTENTION_PROCESSORS = ( - LoRAAttnProcessor, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - LoRAAttnAddedKVProcessor, -) - -ADDED_KV_ATTENTION_PROCESSORS = ( - AttnAddedKVProcessor, - SlicedAttnAddedKVProcessor, - AttnAddedKVProcessor2_0, - XFormersAttnAddedKVProcessor, - LoRAAttnAddedKVProcessor, -) - -CROSS_ATTENTION_PROCESSORS = ( - AttnProcessor, - AttnProcessor2_0, - XFormersAttnProcessor, - SlicedAttnProcessor, - LoRAAttnProcessor, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - IPAdapterAttnProcessor, - IPAdapterAttnProcessor2_0, -) - -AttentionProcessor = Union[ - AttnProcessor, - AttnProcessor2_0, - FusedAttnProcessor2_0, - XFormersAttnProcessor, - SlicedAttnProcessor, - AttnAddedKVProcessor, - SlicedAttnAddedKVProcessor, - AttnAddedKVProcessor2_0, - XFormersAttnAddedKVProcessor, - CustomDiffusionAttnProcessor, - CustomDiffusionXFormersAttnProcessor, - CustomDiffusionAttnProcessor2_0, - # deprecated - LoRAAttnProcessor, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - LoRAAttnAddedKVProcessor, -]