# 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 from importlib import import_module from typing import Callable, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..image_processor import IPAdapterMaskProcessor from ..utils import USE_PEFT_BACKEND, deprecate, logging from ..utils.import_utils import is_xformers_available from ..utils.torch_utils import maybe_allow_in_graph from .lora import LoRACompatibleLinear, LoRALinearLayer logger = logging.get_logger(__name__) # pylint: disable=invalid-name 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'" ) if USE_PEFT_BACKEND: linear_cls = nn.Linear else: linear_cls = LoRACompatibleLinear self.linear_cls = linear_cls self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias) if not self.only_cross_attention: # only relevant for the `AddedKVProcessor` classes self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias) self.to_v = linear_cls(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 = linear_cls(added_kv_proj_dim, self.inner_dim) self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim) self.to_out = nn.ModuleList([]) self.to_out.append(linear_cls(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_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 posssible 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 = self.linear_cls(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 = self.linear_cls(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, scale: float = 1.0, ) -> torch.Tensor: residual = hidden_states args = () if USE_PEFT_BACKEND else (scale,) 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, *args) 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, *args) value = attn.to_v(encoder_hidden_states, *args) 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, *args) # 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, scale: float = 1.0, ) -> torch.Tensor: residual = hidden_states args = () if USE_PEFT_BACKEND else (scale,) 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, *args) query = attn.head_to_batch_dim(query) encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states, *args) encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states, *args) 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, *args) value = attn.to_v(hidden_states, *args) 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, *args) # 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, scale: float = 1.0, ) -> torch.Tensor: residual = hidden_states args = () if USE_PEFT_BACKEND else (scale,) 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, *args) 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, *args) value = attn.to_v(hidden_states, *args) 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, *args) # 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, scale: float = 1.0, ) -> torch.FloatTensor: residual = hidden_states args = () if USE_PEFT_BACKEND else (scale,) 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, *args) 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, *args) value = attn.to_v(encoder_hidden_states, *args) 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, *args) # 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, scale: float = 1.0, ) -> torch.FloatTensor: 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) args = () if USE_PEFT_BACKEND else (scale,) query = attn.to_q(hidden_states, *args) 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, *args) value = attn.to_v(encoder_hidden_states, *args) 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, *args) # 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, scale: float = 1.0, ) -> torch.FloatTensor: 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) args = () if USE_PEFT_BACKEND else (scale,) if encoder_hidden_states is None: qkv = attn.to_qkv(hidden_states, *args) 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, *args) kv = attn.to_kv(encoder_hidden_states, *args) 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, *args) # 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, *args, **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, *args, **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, *args, **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, *args, **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, *args, **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, *args, **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, *args, **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, *args, **kwargs) class IPAdapterAttnProcessor(nn.Module): r""" Attention processor for Multiple IP-Adapater. 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 supress 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, torch.Tensor) or ip_adapter_masks.ndim != 4: raise ValueError( " ip_adapter_mask should be a tensor with shape [num_ip_adapter, 1, height, width]." " Please use `IPAdapterMaskProcessor` to preprocess your mask" ) if len(ip_adapter_masks) != len(self.scale): raise ValueError( f"Number of ip_adapter_masks ({len(ip_adapter_masks)}) must match number of IP-Adapters ({len(self.scale)})" ) 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 ): 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) if mask is not None: mask_downsample = IPAdapterMaskProcessor.downsample( mask, 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) current_ip_hidden_states = current_ip_hidden_states * mask_downsample 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-Adapater 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 supress 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, torch.Tensor) or ip_adapter_masks.ndim != 4: raise ValueError( " ip_adapter_mask should be a tensor with shape [num_ip_adapter, 1, height, width]." " Please use `IPAdapterMaskProcessor` to preprocess your mask" ) if len(ip_adapter_masks) != len(self.scale): raise ValueError( f"Number of ip_adapter_masks ({len(ip_adapter_masks)}) must match number of IP-Adapters ({len(self.scale)})" ) 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 ): 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) if mask is not None: mask_downsample = IPAdapterMaskProcessor.downsample( mask, 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) current_ip_hidden_states = current_ip_hidden_states * mask_downsample 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, ]