transformer/transformer_3d_allegro.py

# 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
# --------------------------------------------------------


import json
import os
from dataclasses import dataclass
from functools import partial
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.configuration_utils import ConfigMixin, register_to_config
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, TimestepEmbedding, Timesteps
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormZero
from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_xformers_available
from diffusers.utils.torch_utils import maybe_allow_in_graph
from einops import rearrange, repeat
from torch import nn
from diffusers.models.embeddings import PixArtAlphaTextProjection


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)

class CombinedTimestepSizeEmbeddings(nn.Module):
    """
    For PixArt-Alpha.

    Reference:
    https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
    """

    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
        super().__init__()

        self.outdim = size_emb_dim
        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)

        self.use_additional_conditions = use_additional_conditions
        if use_additional_conditions:
            self.use_additional_conditions = True
            self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
            self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
            self.aspect_ratio_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)

    def apply_condition(self, size: torch.Tensor, batch_size: int, embedder: nn.Module):
        if size.ndim == 1:
            size = size[:, None]

        if size.shape[0] != batch_size:
            size = size.repeat(batch_size // size.shape[0], 1)
            if size.shape[0] != batch_size:
                raise ValueError(f"`batch_size` should be {size.shape[0]} but found {batch_size}.")

        current_batch_size, dims = size.shape[0], size.shape[1]
        size = size.reshape(-1)
        size_freq = self.additional_condition_proj(size).to(size.dtype)

        size_emb = embedder(size_freq)
        size_emb = size_emb.reshape(current_batch_size, dims * self.outdim)
        return size_emb

    def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)

        if self.use_additional_conditions:
            resolution = self.apply_condition(resolution, batch_size=batch_size, embedder=self.resolution_embedder)
            aspect_ratio = self.apply_condition(
                aspect_ratio, batch_size=batch_size, embedder=self.aspect_ratio_embedder
            )
            conditioning = timesteps_emb + torch.cat([resolution, aspect_ratio], dim=1)
        else:
            conditioning = timesteps_emb

        return conditioning


class PositionGetter3D(object):
    """ return positions of patches """

    def __init__(self, ):
        self.cache_positions = {}
        
    def __call__(self, b, t, h, w, device):
        if not (b, t,h,w) in self.cache_positions:
            x = torch.arange(w, device=device)
            y = torch.arange(h, device=device)
            z = torch.arange(t, device=device)
            pos = torch.cartesian_prod(z, y, x)
           
            pos = pos.reshape(t * h * w, 3).transpose(0, 1).reshape(3, 1, -1).contiguous().expand(3, b, -1).clone()
            poses = (pos[0].contiguous(), pos[1].contiguous(), pos[2].contiguous())
            max_poses = (int(poses[0].max()), int(poses[1].max()), int(poses[2].max()))

            self.cache_positions[b, t, h, w] = (poses, max_poses)
        pos = self.cache_positions[b, t, h, w]

        return pos
    

class RoPE3D(torch.nn.Module):

    def __init__(self, freq=10000.0, F0=1.0, interpolation_scale_thw=(1, 1, 1)):
        super().__init__()
        self.base = freq
        self.F0 = F0
        self.interpolation_scale_t = interpolation_scale_thw[0]
        self.interpolation_scale_h = interpolation_scale_thw[1]
        self.interpolation_scale_w = interpolation_scale_thw[2]
        self.cache = {}

    def get_cos_sin(self, D, seq_len, device, dtype, interpolation_scale=1):
        if (D, seq_len, device, dtype) not in self.cache:
            inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D))
            t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) / interpolation_scale
            freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype)
            freqs = torch.cat((freqs, freqs), dim=-1)
            cos = freqs.cos()  # (Seq, Dim)
            sin = freqs.sin()
            self.cache[D, seq_len, device, dtype] = (cos, sin)
        return self.cache[D, seq_len, device, dtype]

    @staticmethod
    def rotate_half(x):
        x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
        return torch.cat((-x2, x1), dim=-1)

    def apply_rope1d(self, tokens, pos1d, cos, sin):
        assert pos1d.ndim == 2

            # for (batch_size x ntokens x nheads x dim)
        cos = torch.nn.functional.embedding(pos1d, cos)[:, None, :, :]
        sin = torch.nn.functional.embedding(pos1d, sin)[:, None, :, :]
        return (tokens * cos) + (self.rotate_half(tokens) * sin)

    def forward(self, tokens, positions):
        """
        input:
            * tokens: batch_size x nheads x ntokens x dim
            * positions: batch_size x ntokens x 3 (t, y and x position of each token)
        output:
            * tokens after appplying RoPE3D (batch_size x nheads x ntokens x x dim)
        """
        assert tokens.size(3) % 3 == 0, "number of dimensions should be a multiple of three"
        D = tokens.size(3) // 3
        poses, max_poses = positions
        assert len(poses) == 3 and poses[0].ndim == 2# Batch, Seq, 3
        cos_t, sin_t = self.get_cos_sin(D, max_poses[0] + 1, tokens.device, tokens.dtype, self.interpolation_scale_t)
        cos_y, sin_y = self.get_cos_sin(D, max_poses[1] + 1, tokens.device, tokens.dtype, self.interpolation_scale_h)
        cos_x, sin_x = self.get_cos_sin(D, max_poses[2] + 1, tokens.device, tokens.dtype, self.interpolation_scale_w)
        # split features into three along the feature dimension, and apply rope1d on each half
        t, y, x = tokens.chunk(3, dim=-1)
        t = self.apply_rope1d(t, poses[0], cos_t, sin_t)
        y = self.apply_rope1d(y, poses[1], cos_y, sin_y)
        x = self.apply_rope1d(x, poses[2], cos_x, sin_x)
        tokens = torch.cat((t, y, x), dim=-1)
        return tokens

class PatchEmbed2D(nn.Module):
    """2D Image to Patch Embedding"""

    def __init__(
        self,
        num_frames=1, 
        height=224,
        width=224,
        patch_size_t=1,
        patch_size=16,
        in_channels=3,
        embed_dim=768,
        layer_norm=False,
        flatten=True,
        bias=True,
        interpolation_scale=(1, 1),
        interpolation_scale_t=1,
        use_abs_pos=False, 
    ):
        super().__init__()
        self.use_abs_pos = use_abs_pos
        self.flatten = flatten
        self.layer_norm = layer_norm

        self.proj = nn.Conv2d(
            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=(patch_size, patch_size), bias=bias
        )
        if layer_norm:
            self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
        else:
            self.norm = None

        self.patch_size_t = patch_size_t
        self.patch_size = patch_size

    def forward(self, latent):
        b, _, _, _, _ = latent.shape
        video_latent = None

        latent = rearrange(latent, 'b c t h w -> (b t) c h w')

        latent = self.proj(latent)
        if self.flatten:
            latent = latent.flatten(2).transpose(1, 2)  # BT C H W -> BT N C
        if self.layer_norm:
            latent = self.norm(latent)

        latent = rearrange(latent, '(b t) n c -> b (t n) c', b=b)
        video_latent = latent

        return video_latent


@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


@dataclass
class Transformer3DModelOutput(BaseOutput):
    """
    The output of [`Transformer2DModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
            distributions for the unnoised latent pixels.
    """

    sample: torch.FloatTensor


class AllegroTransformer3DModel(ModelMixin, ConfigMixin):
    _supports_gradient_checkpointing = True

    """
    A 2D Transformer model for image-like data.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
            This is fixed during training since it is used to learn a number of position embeddings.
        num_vector_embeds (`int`, *optional*):
            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
            Includes the class for the masked latent pixel.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
        num_embeds_ada_norm ( `int`, *optional*):
            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
            added to the hidden states.

            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
        attention_bias (`bool`, *optional*):
            Configure if the `TransformerBlocks` attention should contain a bias parameter.
    """

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        dropout: float = 0.0,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        sample_size: Optional[int] = None,
        sample_size_t: Optional[int] = None,
        patch_size: Optional[int] = None,
        patch_size_t: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        double_self_attention: bool = False,
        upcast_attention: bool = False,
        norm_type: str = "ada_norm",
        norm_elementwise_affine: bool = True,
        norm_eps: float = 1e-5,
        caption_channels: int = None,
        interpolation_scale_h: float = None,
        interpolation_scale_w: float = None,
        interpolation_scale_t: float = None,
        use_additional_conditions: Optional[bool] = None,
        sa_attention_mode: str = "flash", 
        ca_attention_mode: str = 'xformers', 
        downsampler: str = None, 
        use_rope: bool = False,
        model_max_length: int = 300,
    ):
        super().__init__()
        self.use_linear_projection = use_linear_projection
        self.interpolation_scale_t = interpolation_scale_t
        self.interpolation_scale_h = interpolation_scale_h
        self.interpolation_scale_w = interpolation_scale_w
        self.downsampler = downsampler
        self.caption_channels = caption_channels
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        inner_dim = num_attention_heads * attention_head_dim
        self.inner_dim = inner_dim
        self.in_channels = in_channels
        self.out_channels = in_channels if out_channels is None else out_channels
        self.use_rope = use_rope
        self.model_max_length = model_max_length
        self.num_layers = num_layers
        self.config.hidden_size = inner_dim


        # 1. Transformer3DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
        # Define whether input is continuous or discrete depending on configuration
        assert in_channels is not None and patch_size is not None

        # 2. Initialize the right blocks.
        # Initialize the output blocks and other projection blocks when necessary.

        assert self.config.sample_size_t is not None, "AllegroTransformer3DModel over patched input must provide sample_size_t"
        assert self.config.sample_size is not None, "AllegroTransformer3DModel over patched input must provide sample_size"
        #assert not (self.config.sample_size_t == 1 and self.config.patch_size_t == 2), "Image do not need patchfy in t-dim"

        self.num_frames = self.config.sample_size_t
        self.config.sample_size = to_2tuple(self.config.sample_size)
        self.height = self.config.sample_size[0]
        self.width = self.config.sample_size[1]
        self.patch_size_t = self.config.patch_size_t
        self.patch_size = self.config.patch_size
        interpolation_scale_t = ((self.config.sample_size_t - 1) // 16 + 1) if self.config.sample_size_t % 2 == 1 else self.config.sample_size_t / 16
        interpolation_scale_t = (
            self.config.interpolation_scale_t if self.config.interpolation_scale_t is not None else interpolation_scale_t
        )
        interpolation_scale = (
            self.config.interpolation_scale_h if self.config.interpolation_scale_h is not None else self.config.sample_size[0] / 30, 
            self.config.interpolation_scale_w if self.config.interpolation_scale_w is not None else self.config.sample_size[1] / 40, 
        )
        self.pos_embed = PatchEmbed2D(
            num_frames=self.config.sample_size_t,
            height=self.config.sample_size[0],
            width=self.config.sample_size[1],
            patch_size_t=self.config.patch_size_t,
            patch_size=self.config.patch_size,
            in_channels=self.in_channels,
            embed_dim=self.inner_dim,
            interpolation_scale=interpolation_scale, 
            interpolation_scale_t=interpolation_scale_t,
            use_abs_pos=not self.config.use_rope, 
        )
        interpolation_scale_thw = (interpolation_scale_t, *interpolation_scale)

        # 3. Define transformers blocks, spatial attention
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    num_attention_heads,
                    attention_head_dim,
                    dropout=dropout,
                    cross_attention_dim=cross_attention_dim,
                    activation_fn=activation_fn,
                    num_embeds_ada_norm=num_embeds_ada_norm,
                    attention_bias=attention_bias,
                    only_cross_attention=only_cross_attention,
                    double_self_attention=double_self_attention,
                    upcast_attention=upcast_attention,
                    norm_type=norm_type,
                    norm_elementwise_affine=norm_elementwise_affine,
                    norm_eps=norm_eps,
                    sa_attention_mode=sa_attention_mode,
                    ca_attention_mode=ca_attention_mode, 
                    use_rope=use_rope,
                    interpolation_scale_thw=interpolation_scale_thw, 
                    block_idx=d,
                )
                for d in range(num_layers)
            ]
        )

        # 4. Define output layers

        if norm_type != "ada_norm_single":
            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
            self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
            self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)
        elif norm_type == "ada_norm_single":
            self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6)
            self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5)
            self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels)

        # 5. PixArt-Alpha blocks.
        self.adaln_single = None
        self.use_additional_conditions = False
        if norm_type == "ada_norm_single":
            # self.use_additional_conditions = self.config.sample_size[0] == 128  # False, 128 -> 1024
            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
            # additional conditions until we find better name
            self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions)

        self.caption_projection = None
        if caption_channels is not None:
            self.caption_projection = PixArtAlphaTextProjection(
                in_features=caption_channels, hidden_size=inner_dim
            )
        
        self.gradient_checkpointing = False

    def _set_gradient_checkpointing(self, module, value=False):
        self.gradient_checkpointing = value


    def forward(
        self,
        hidden_states: torch.Tensor,
        timestep: Optional[torch.LongTensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        added_cond_kwargs: Dict[str, torch.Tensor] = None,
        class_labels: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ):
        """
        The [`Transformer2DModel`] forward method.

        Args:
            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, frame, channel, height, width)` if continuous):
                Input `hidden_states`.
            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.LongTensor`, *optional*):
                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                `AdaLayerZeroNorm`.
            added_cond_kwargs ( `Dict[str, Any]`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AdaLayerNormSingle`
            cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            attention_mask ( `torch.Tensor`, *optional*):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            encoder_attention_mask ( `torch.Tensor`, *optional*):
                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                    * Mask `(batch, sequence_length)` True = keep, False = discard.
                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                above. This bias will be added to the cross-attention scores.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
            `tuple` where the first element is the sample tensor.
        """
        batch_size, c, frame, h, w = hidden_states.shape

        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)        attention_mask_vid, attention_mask_img = None, None
        if attention_mask is not None and attention_mask.ndim == 4:
            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #   (keep = +0,     discard = -10000.0)
            # b, frame+use_image_num, h, w -> a video with images
            # b, 1, h, w -> only images
            attention_mask = attention_mask.to(self.dtype)
            attention_mask_vid = attention_mask[:, :frame]  # b, frame, h, w

            if attention_mask_vid.numel() > 0:
                attention_mask_vid = attention_mask_vid.unsqueeze(1)  # b 1 t h w
                attention_mask_vid = F.max_pool3d(attention_mask_vid, kernel_size=(self.patch_size_t, self.patch_size, self.patch_size), 
                                                  stride=(self.patch_size_t, self.patch_size, self.patch_size))
                attention_mask_vid = rearrange(attention_mask_vid, 'b 1 t h w -> (b 1) 1 (t h w)') 

            attention_mask_vid = (1 - attention_mask_vid.bool().to(self.dtype)) * -10000.0 if attention_mask_vid.numel() > 0 else None

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 3:  
            # b, 1+use_image_num, l -> a video with images
            # b, 1, l -> only images
            encoder_attention_mask = (1 - encoder_attention_mask.to(self.dtype)) * -10000.0
            encoder_attention_mask_vid = rearrange(encoder_attention_mask, 'b 1 l -> (b 1) 1 l') if encoder_attention_mask.numel() > 0 else None

        # 1. Input
        frame = frame // self.patch_size_t  # patchfy
        # print('frame', frame)
        height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size

        added_cond_kwargs = {"resolution": None, "aspect_ratio": None} if added_cond_kwargs is None else added_cond_kwargs
        hidden_states, encoder_hidden_states_vid, \
        timestep_vid, embedded_timestep_vid = self._operate_on_patched_inputs(
            hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, batch_size,
        )


        for _, block in enumerate(self.transformer_blocks):
            hidden_states = block(
                hidden_states,
                attention_mask_vid,
                encoder_hidden_states_vid,
                encoder_attention_mask_vid,
                timestep_vid,
                cross_attention_kwargs,
                class_labels,
                frame=frame, 
                height=height, 
                width=width, 
            )

         # 3. Output
        output = None 
        if hidden_states is not None:
            output = self._get_output_for_patched_inputs(
                hidden_states=hidden_states,
                timestep=timestep_vid,
                class_labels=class_labels,
                embedded_timestep=embedded_timestep_vid,
                num_frames=frame, 
                height=height,
                width=width,
            )  # b c t h w

        if not return_dict:
            return (output,)

        return Transformer3DModelOutput(sample=output)

    def _operate_on_patched_inputs(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs, batch_size):
            # batch_size = hidden_states.shape[0]
            hidden_states_vid = self.pos_embed(hidden_states.to(self.dtype))
            timestep_vid = None
            embedded_timestep_vid = None
            encoder_hidden_states_vid = None

            if self.adaln_single is not None:
                if self.use_additional_conditions and added_cond_kwargs is None:
                    raise ValueError(
                        "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
                    )
                timestep, embedded_timestep = self.adaln_single(
                    timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=self.dtype
                )  # b 6d, b d

                timestep_vid = timestep
                embedded_timestep_vid = embedded_timestep

            if self.caption_projection is not None:
                encoder_hidden_states = self.caption_projection(encoder_hidden_states)  # b, 1+use_image_num, l, d or b, 1, l, d
                encoder_hidden_states_vid = rearrange(encoder_hidden_states[:, :1], 'b 1 l d -> (b 1) l d')

            return hidden_states_vid, encoder_hidden_states_vid, timestep_vid, embedded_timestep_vid

    def _get_output_for_patched_inputs(
        self, hidden_states, timestep, class_labels, embedded_timestep, num_frames, height=None, width=None
    ):  
        # import ipdb;ipdb.set_trace()
        if self.config.norm_type != "ada_norm_single":
            conditioning = self.transformer_blocks[0].norm1.emb(
                timestep, class_labels, hidden_dtype=self.dtype
            )
            shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
            hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
            hidden_states = self.proj_out_2(hidden_states)
        elif self.config.norm_type == "ada_norm_single":
            shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
            hidden_states = self.norm_out(hidden_states)
            # Modulation
            hidden_states = hidden_states * (1 + scale) + shift
            hidden_states = self.proj_out(hidden_states)
            hidden_states = hidden_states.squeeze(1)

        # unpatchify
        if self.adaln_single is None:
            height = width = int(hidden_states.shape[1] ** 0.5)
        hidden_states = hidden_states.reshape(
            shape=(-1, num_frames, height, width, self.patch_size_t, self.patch_size, self.patch_size, self.out_channels)
        )
        hidden_states = torch.einsum("nthwopqc->nctohpwq", hidden_states)
        output = hidden_states.reshape(
            shape=(-1, self.out_channels, num_frames * self.patch_size_t, height * self.patch_size, width * self.patch_size)
        )
        return output