Delete allegro/models/transformers/block.py

allegro/models/transformers/block.py

@@ -1,1195 +0,0 @@
-# Adapted from Open-Sora-Plan
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# --------------------------------------------------------
-# References:
-# Open-Sora-Plan: https://github.com/PKU-YuanGroup/Open-Sora-Plan
-# --------------------------------------------------------
-
-
-from importlib import import_module
-from typing import Any, Callable, Dict, Optional, Tuple
-
-import numpy as np
-import torch
-import collections
-import torch.nn.functional as F
-from torch.nn.attention import SDPBackend, sdpa_kernel
-from diffusers.models.activations import GEGLU, GELU, ApproximateGELU
-from diffusers.models.attention_processor import (
-    AttnAddedKVProcessor,
-    AttnAddedKVProcessor2_0,
-    AttnProcessor,
-    CustomDiffusionAttnProcessor,
-    CustomDiffusionAttnProcessor2_0,
-    CustomDiffusionXFormersAttnProcessor,
-    LoRAAttnAddedKVProcessor,
-    LoRAAttnProcessor,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    SlicedAttnAddedKVProcessor,
-    SlicedAttnProcessor,
-    SpatialNorm,
-    XFormersAttnAddedKVProcessor,
-    XFormersAttnProcessor,
-)
-from diffusers.models.embeddings import SinusoidalPositionalEmbedding
-from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormZero
-from diffusers.utils import USE_PEFT_BACKEND,  deprecate, is_xformers_available
-from diffusers.utils.torch_utils import maybe_allow_in_graph
-from torch import nn
-
-from allegro.models.transformers.rope import RoPE3D, PositionGetter3D
-from allegro.models.transformers.embedding import CombinedTimestepSizeEmbeddings
-
-if is_xformers_available():
-    import xformers
-    import xformers.ops
-else:
-    xformers = None
-
-from diffusers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-def to_2tuple(x):
-    if isinstance(x, collections.abc.Iterable):
-        return x
-    return (x, x)
-
-
-@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,
-        attention_mode: str = "xformers",
-        use_rope: bool = False,
-        interpolation_scale_thw=None, 
-    ):
-        super().__init__()
-        self.inner_dim = dim_head * heads
-        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.use_rope = use_rope
-
-        # 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 = 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'"
-            )
-
-        linear_cls = nn.Linear
-
-
-        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, query_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(
-                    attention_mode,
-                    use_rope,
-                    interpolation_scale_thw=interpolation_scale_thw,
-                )
-                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")
-        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", _remove_lora: bool = False) -> None:
-        r"""
-        Set the attention processor to use.
-
-        Args:
-            processor (`AttnProcessor`):
-                The attention processor to use.
-            _remove_lora (`bool`, *optional*, defaults to `False`):
-                Set to `True` to remove LoRA layers from the model.
-        """
-        if not USE_PEFT_BACKEND and hasattr(self, "processor") and _remove_lora and self.to_q.lora_layer is not None:
-            deprecate(
-                "set_processor to offload LoRA",
-                "0.26.0",
-                "In detail, removing LoRA layers via calling `set_default_attn_processor` is deprecated. Please make sure to call `pipe.unload_lora_weights()` instead.",
-            )
-            # TODO(Patrick, Sayak) - this can be deprecated once PEFT LoRA integration is complete
-            # We need to remove all LoRA layers
-            # Don't forget to remove ALL `_remove_lora` from the codebase
-            for module in self.modules():
-                if hasattr(module, "set_lora_layer"):
-                    module.set_lora_layer(None)
-
-        # 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):
-        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
-        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
-        batch_size, seq_len, dim = tensor.shape
-        tensor = tensor.reshape(batch_size, seq_len, 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, 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, head_size = None, 
-    ) -> 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 = head_size if head_size is not None else 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
-
-    def _init_compress(self):
-        self.sr.bias.data.zero_()
-        self.norm = nn.LayerNorm(self.inner_dim)
-
-
-class AttnProcessor2_0(nn.Module):
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(self, attention_mode="xformers", use_rope=False, interpolation_scale_thw=None):
-        super().__init__()
-        self.attention_mode = attention_mode
-        self.use_rope = use_rope
-        self.interpolation_scale_thw = interpolation_scale_thw
-
-        if self.use_rope:
-            self._init_rope(interpolation_scale_thw)
-
-        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 _init_rope(self, interpolation_scale_thw):
-        self.rope = RoPE3D(interpolation_scale_thw=interpolation_scale_thw)
-        self.position_getter = PositionGetter3D()
-        
-    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,
-        frame: int = 8, 
-        height: int = 16, 
-        width: int = 16, 
-    ) -> 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 and self.attention_mode == 'xformers':
-            attention_heads = attn.heads
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, head_size=attention_heads)
-            attention_mask = attention_mask.view(batch_size, attention_heads, -1, attention_mask.shape[-1])
-        else:
-            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)
-
-       
-       
-        attn_heads = attn.heads
-
-        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)
-        
-
-        if self.use_rope:
-            # require the shape of (batch_size x nheads x ntokens x dim)
-            pos_thw = self.position_getter(batch_size, t=frame, h=height, w=width, device=query.device)
-            query = self.rope(query, pos_thw)
-            key = self.rope(key, pos_thw)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        if self.attention_mode == 'flash':
-                # assert attention_mask is None, 'flash-attn do not support attention_mask'
-                with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
-                    hidden_states = F.scaled_dot_product_attention(
-                        query, key, value, dropout_p=0.0, is_causal=False
-                    )
-        elif self.attention_mode == 'xformers':
-            with sdpa_kernel(SDPBackend.EFFICIENT_ATTENTION):
-                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 FeedForward(nn.Module):
-    r"""
-    A feed-forward layer.
-
-    Parameters:
-        dim (`int`): The number of channels in the input.
-        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
-        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        mult: int = 4,
-        dropout: float = 0.0,
-        activation_fn: str = "geglu",
-        final_dropout: bool = False,
-    ):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = dim_out if dim_out is not None else dim
-        linear_cls = nn.Linear
-
-        if activation_fn == "gelu":
-            act_fn = GELU(dim, inner_dim)
-        if activation_fn == "gelu-approximate":
-            act_fn = GELU(dim, inner_dim, approximate="tanh")
-        elif activation_fn == "geglu":
-            act_fn = GEGLU(dim, inner_dim)
-        elif activation_fn == "geglu-approximate":
-            act_fn = ApproximateGELU(dim, inner_dim)
-
-        self.net = nn.ModuleList([])
-        # project in
-        self.net.append(act_fn)
-        # project dropout
-        self.net.append(nn.Dropout(dropout))
-        # project out
-        self.net.append(linear_cls(inner_dim, dim_out))
-        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
-        if final_dropout:
-            self.net.append(nn.Dropout(dropout))
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        for module in self.net:
-            hidden_states = module(hidden_states)
-        return hidden_states
-
-
-@maybe_allow_in_graph
-class BasicTransformerBlock(nn.Module):
-    r"""
-    A basic Transformer block.
-
-    Parameters:
-        dim (`int`): The number of channels in the input and output.
-        num_attention_heads (`int`): The number of heads to use for multi-head attention.
-        attention_head_dim (`int`): The number of channels in each head.
-        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
-        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
-        num_embeds_ada_norm (:
-            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
-        attention_bias (:
-            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
-        only_cross_attention (`bool`, *optional*):
-            Whether to use only cross-attention layers. In this case two cross attention layers are used.
-        double_self_attention (`bool`, *optional*):
-            Whether to use two self-attention layers. In this case no cross attention layers are used.
-        upcast_attention (`bool`, *optional*):
-            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
-        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
-            Whether to use learnable elementwise affine parameters for normalization.
-        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
-            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
-        final_dropout (`bool` *optional*, defaults to False):
-            Whether to apply a final dropout after the last feed-forward layer.
-        positional_embeddings (`str`, *optional*, defaults to `None`):
-            The type of positional embeddings to apply to.
-        num_positional_embeddings (`int`, *optional*, defaults to `None`):
-            The maximum number of positional embeddings to apply.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_attention_heads: int,
-        attention_head_dim: int,
-        dropout=0.0,
-        cross_attention_dim: Optional[int] = None,
-        activation_fn: str = "geglu",
-        num_embeds_ada_norm: Optional[int] = None,
-        attention_bias: bool = False,
-        only_cross_attention: bool = False,
-        double_self_attention: bool = False,
-        upcast_attention: bool = False,
-        norm_elementwise_affine: bool = True,
-        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
-        norm_eps: float = 1e-5,
-        final_dropout: bool = False,
-        positional_embeddings: Optional[str] = None,
-        num_positional_embeddings: Optional[int] = None,
-        sa_attention_mode: str = "flash", 
-        ca_attention_mode: str = "xformers", 
-        use_rope: bool = False,
-        interpolation_scale_thw: Tuple[int] = (1, 1, 1), 
-        block_idx: Optional[int] = None,
-    ):
-        super().__init__()
-        self.only_cross_attention = only_cross_attention
-
-        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
-        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
-        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
-        self.use_layer_norm = norm_type == "layer_norm"
-
-        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
-            raise ValueError(
-                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
-                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
-            )
-
-        if positional_embeddings and (num_positional_embeddings is None):
-            raise ValueError(
-                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
-            )
-
-        if positional_embeddings == "sinusoidal":
-            self.pos_embed = SinusoidalPositionalEmbedding(dim, max_seq_length=num_positional_embeddings)
-        else:
-            self.pos_embed = None
-
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        if self.use_ada_layer_norm:
-            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
-        elif self.use_ada_layer_norm_zero:
-            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
-        else:
-            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-
-        self.attn1 = Attention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            dropout=dropout,
-            bias=attention_bias,
-            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
-            upcast_attention=upcast_attention,
-            attention_mode=sa_attention_mode, 
-            use_rope=use_rope,
-            interpolation_scale_thw=interpolation_scale_thw, 
-        )
-
-        # 2. Cross-Attn
-        if cross_attention_dim is not None or double_self_attention:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            self.norm2 = (
-                AdaLayerNorm(dim, num_embeds_ada_norm)
-                if self.use_ada_layer_norm
-                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-            )
-            self.attn2 = Attention(
-                query_dim=dim,
-                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
-                heads=num_attention_heads,
-                dim_head=attention_head_dim,
-                dropout=dropout,
-                bias=attention_bias,
-                upcast_attention=upcast_attention,
-                attention_mode=ca_attention_mode,  # only xformers support attention_mask
-                use_rope=False,  # do not position in cross attention
-                interpolation_scale_thw=interpolation_scale_thw, 
-            )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.norm2 = None
-            self.attn2 = None
-
-        # 3. Feed-forward
-
-        if not self.use_ada_layer_norm_single:
-            self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
-
-        self.ff = FeedForward(
-            dim,
-            dropout=dropout,
-            activation_fn=activation_fn,
-            final_dropout=final_dropout,
-        )
-
-        # 5. Scale-shift for PixArt-Alpha.
-        if self.use_ada_layer_norm_single:
-            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
-
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        timestep: Optional[torch.LongTensor] = None,
-        cross_attention_kwargs: Dict[str, Any] = None,
-        class_labels: Optional[torch.LongTensor] = None,
-        frame: int = None, 
-        height: int = None, 
-        width: int = None, 
-    ) -> torch.FloatTensor:
-        # Notice that normalization is always applied before the real computation in the following blocks.
-        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
-
-        # 0. Self-Attention
-        batch_size = hidden_states.shape[0]
-
-        if self.use_ada_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states, timestep)
-        elif self.use_ada_layer_norm_zero:
-            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
-                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
-            )
-        elif self.use_layer_norm:
-            norm_hidden_states = self.norm1(hidden_states)
-        elif self.use_ada_layer_norm_single:
-            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
-            ).chunk(6, dim=1)
-            norm_hidden_states = self.norm1(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
-            norm_hidden_states = norm_hidden_states.squeeze(1)
-        else:
-            raise ValueError("Incorrect norm used")
-
-        if self.pos_embed is not None:
-            norm_hidden_states = self.pos_embed(norm_hidden_states)
-        
-        attn_output = self.attn1(
-            norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
-            attention_mask=attention_mask, 
-            frame=frame,
-            height=height,
-            width=width, 
-            **cross_attention_kwargs,
-        )
-        if self.use_ada_layer_norm_zero:
-            attn_output = gate_msa.unsqueeze(1) * attn_output
-        elif self.use_ada_layer_norm_single:
-            attn_output = gate_msa * attn_output
-
-        hidden_states = attn_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        # 1. Cross-Attention
-        if self.attn2 is not None:
-
-            if self.use_ada_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states, timestep)
-            elif self.use_ada_layer_norm_zero or self.use_layer_norm:
-                norm_hidden_states = self.norm2(hidden_states)
-            elif self.use_ada_layer_norm_single:
-                # For PixArt norm2 isn't applied here:
-                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
-                norm_hidden_states = hidden_states
-            else:
-                raise ValueError("Incorrect norm")
-
-            if self.pos_embed is not None and self.use_ada_layer_norm_single is False:
-                norm_hidden_states = self.pos_embed(norm_hidden_states)
-
-            attn_output = self.attn2(
-                norm_hidden_states,
-                encoder_hidden_states=encoder_hidden_states,
-                attention_mask=encoder_attention_mask,
-                **cross_attention_kwargs,
-            )
-            hidden_states = attn_output + hidden_states
-
-
-        # 2. Feed-forward
-        if not self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm3(hidden_states)
-
-        if self.use_ada_layer_norm_zero:
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
-
-        if self.use_ada_layer_norm_single:
-            norm_hidden_states = self.norm2(hidden_states)
-            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
-
-        ff_output = self.ff(norm_hidden_states)
-
-        if self.use_ada_layer_norm_zero:
-            ff_output = gate_mlp.unsqueeze(1) * ff_output
-        elif self.use_ada_layer_norm_single:
-            ff_output = gate_mlp * ff_output
-
-
-        hidden_states = ff_output + hidden_states
-        if hidden_states.ndim == 4:
-            hidden_states = hidden_states.squeeze(1)
-
-        return hidden_states
-    
-
-class AdaLayerNormSingle(nn.Module):
-    r"""
-    Norm layer adaptive layer norm single (adaLN-single).
-
-    As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
-
-    Parameters:
-        embedding_dim (`int`): The size of each embedding vector.
-        use_additional_conditions (`bool`): To use additional conditions for normalization or not.
-    """
-
-    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False):
-        super().__init__()
-
-        self.emb = CombinedTimestepSizeEmbeddings(
-            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions
-        )
-
-        self.silu = nn.SiLU()
-        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
-
-    def forward(
-        self,
-        timestep: torch.Tensor,
-        added_cond_kwargs: Dict[str, torch.Tensor] = None,
-        batch_size: int = None,
-        hidden_dtype: Optional[torch.dtype] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-        # No modulation happening here.
-        embedded_timestep = self.emb(
-            timestep, batch_size=batch_size, hidden_dtype=hidden_dtype, resolution=None, aspect_ratio=None
-        )
-        return self.linear(self.silu(embedded_timestep)), embedded_timestep