# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
import torch
import torch.nn as nn
import torch.nn.functional as F

class AdaLayerNorm(nn.Module):
    def __init__(self, embedding_dim: int, time_embedding_dim: int = None):
        super().__init__()

        if time_embedding_dim is None:
            time_embedding_dim = embedding_dim

        self.silu = nn.SiLU()
        self.linear = nn.Linear(time_embedding_dim, 2 * embedding_dim, bias=True)
        nn.init.zeros_(self.linear.weight)
        nn.init.zeros_(self.linear.bias)

        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)

    def forward(
        self, x: torch.Tensor, timestep_embedding: torch.Tensor
    ):
        emb = self.linear(self.silu(timestep_embedding))
        shift, scale = emb.view(len(x), 1, -1).chunk(2, dim=-1)
        x = self.norm(x) * (1 + scale) + shift
        return x


class AttnProcessor(nn.Module):
    r"""
    Default processor for performing attention-related computations.
    """

    def __init__(
        self,
        hidden_size=None,
        cross_attention_dim=None,
    ):
        super().__init__()

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
    ):
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class IPAttnProcessor(nn.Module):
    r"""
    Attention processor for IP-Adapater.
    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        scale (`float`, defaults to 1.0):
            the weight scale of image prompt.
        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
            The context length of the image features.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.scale = scale
        self.num_tokens = num_tokens

        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
    ):
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        else:
            # get encoder_hidden_states, ip_hidden_states
            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
            encoder_hidden_states, ip_hidden_states = (
                encoder_hidden_states[:, :end_pos, :],
                encoder_hidden_states[:, end_pos:, :],
            )
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # for ip-adapter
        ip_key = self.to_k_ip(ip_hidden_states)
        ip_value = self.to_v_ip(ip_hidden_states)

        ip_key = attn.head_to_batch_dim(ip_key)
        ip_value = attn.head_to_batch_dim(ip_value)

        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)

        hidden_states = hidden_states + self.scale * ip_hidden_states

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class TA_IPAttnProcessor(nn.Module):
    r"""
    Attention processor for IP-Adapater.
    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        scale (`float`, defaults to 1.0):
            the weight scale of image prompt.
        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
            The context length of the image features.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.scale = scale
        self.num_tokens = num_tokens

        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

        self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim)
        self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
    ):
        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."

        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        else:
            # get encoder_hidden_states, ip_hidden_states
            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
            encoder_hidden_states, ip_hidden_states = (
                encoder_hidden_states[:, :end_pos, :],
                encoder_hidden_states[:, end_pos:, :],
            )
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # for ip-adapter
        ip_key = self.to_k_ip(ip_hidden_states)
        ip_value = self.to_v_ip(ip_hidden_states)

        # time-dependent adaLN
        ip_key = self.ln_k_ip(ip_key, temb)
        ip_value = self.ln_v_ip(ip_value, temb)

        ip_key = attn.head_to_batch_dim(ip_key)
        ip_value = attn.head_to_batch_dim(ip_value)

        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)

        hidden_states = hidden_states + self.scale * ip_hidden_states

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class AttnProcessor2_0(torch.nn.Module):
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(
        self,
        hidden_size=None,
        cross_attention_dim=None,
    ):
        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        external_kv=None,
        temb=None,
    ):
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        if external_kv:
            key = torch.cat([key, external_kv.k], axis=1)
            value = torch.cat([value, external_kv.v], axis=1)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class split_AttnProcessor2_0(torch.nn.Module):
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(
        self,
        hidden_size=None,
        cross_attention_dim=None,
        time_embedding_dim=None,
    ):
        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        external_kv=None,
        temb=None,
        cat_dim=-2,
        original_shape=None,
    ):
        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:
            # 2d to sequence.
            height, width = hidden_states.shape[-2:]
            if cat_dim==-2 or cat_dim==2:
                hidden_states_0 = hidden_states[:, :, :height//2, :]
                hidden_states_1 = hidden_states[:, :, -(height//2):, :]
            elif cat_dim==-1 or cat_dim==3:
                hidden_states_0 = hidden_states[:, :, :, :width//2]
                hidden_states_1 = hidden_states[:, :, :, -(width//2):]
            batch_size, channel, height, width = hidden_states_0.shape
            hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2)
            hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2)
        else:
            # directly split sqeuence according to concat dim.
            single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1]
            hidden_states_0 = hidden_states[:, :single_dim*single_dim,:]
            hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:]

        hidden_states = torch.cat([hidden_states_0, hidden_states_1], dim=1)
        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)
        key = attn.to_k(hidden_states)
        value = attn.to_v(hidden_states)

        if external_kv:
            key = torch.cat([key, external_kv.k], dim=1)
            value = torch.cat([value, external_kv.v], dim=1)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        # spatially split.
        hidden_states_0, hidden_states_1 = hidden_states.chunk(2, dim=1)

        if input_ndim == 4:
            hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width)
            hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width)

            if cat_dim==-2 or cat_dim==2:
                hidden_states_pad = torch.zeros(batch_size, channel, 1, width)
            elif cat_dim==-1 or cat_dim==3:
                hidden_states_pad = torch.zeros(batch_size, channel, height, 1)
            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim)
            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"
        else:
            batch_size, sequence_length, inner_dim = hidden_states.shape
            hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim)
            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1)
            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class sep_split_AttnProcessor2_0(torch.nn.Module):
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(
        self,
        hidden_size=None,
        cross_attention_dim=None,
        time_embedding_dim=None,
    ):
        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
        self.ln_k_ref = AdaLayerNorm(hidden_size, time_embedding_dim)
        self.ln_v_ref = AdaLayerNorm(hidden_size, time_embedding_dim)
        # self.hidden_size = hidden_size
        # self.cross_attention_dim = cross_attention_dim
        # self.scale = scale
        # self.num_tokens = num_tokens

        # self.to_q_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        # self.to_k_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        # self.to_v_ref = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        external_kv=None,
        temb=None,
        cat_dim=-2,
        original_shape=None,
        ref_scale=1.0,
    ):
        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:
            # 2d to sequence.
            height, width = hidden_states.shape[-2:]
            if cat_dim==-2 or cat_dim==2:
                hidden_states_0 = hidden_states[:, :, :height//2, :]
                hidden_states_1 = hidden_states[:, :, -(height//2):, :]
            elif cat_dim==-1 or cat_dim==3:
                hidden_states_0 = hidden_states[:, :, :, :width//2]
                hidden_states_1 = hidden_states[:, :, :, -(width//2):]
            batch_size, channel, height, width = hidden_states_0.shape
            hidden_states_0 = hidden_states_0.view(batch_size, channel, height * width).transpose(1, 2)
            hidden_states_1 = hidden_states_1.view(batch_size, channel, height * width).transpose(1, 2)
        else:
            # directly split sqeuence according to concat dim.
            single_dim = original_shape[2] if cat_dim==-2 or cat_dim==2 else original_shape[1]
            hidden_states_0 = hidden_states[:, :single_dim*single_dim,:]
            hidden_states_1 = hidden_states[:, single_dim*(single_dim+1):,:]

        batch_size, sequence_length, _ = (
            hidden_states_0.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states_0 = attn.group_norm(hidden_states_0.transpose(1, 2)).transpose(1, 2)
            hidden_states_1 = attn.group_norm(hidden_states_1.transpose(1, 2)).transpose(1, 2)

        query_0 = attn.to_q(hidden_states_0)
        query_1 = attn.to_q(hidden_states_1)
        key_0 = attn.to_k(hidden_states_0)
        key_1 = attn.to_k(hidden_states_1)
        value_0 = attn.to_v(hidden_states_0)
        value_1 = attn.to_v(hidden_states_1)

        # time-dependent adaLN
        key_1 = self.ln_k_ref(key_1, temb)
        value_1 = self.ln_v_ref(value_1, temb)

        if external_kv:
            key_1 = torch.cat([key_1, external_kv.k], dim=1)
            value_1 = torch.cat([value_1, external_kv.v], dim=1)

        inner_dim = key_0.shape[-1]
        head_dim = inner_dim // attn.heads

        query_0 = query_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        query_1 = query_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key_0 = key_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key_1 = key_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value_0 = value_0.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value_1 = value_1.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states_0 = F.scaled_dot_product_attention(
            query_0, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states_1 = F.scaled_dot_product_attention(
            query_1, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        # cross-attn
        _hidden_states_0 = F.scaled_dot_product_attention(
            query_0, key_1, value_1, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states_0 = hidden_states_0 + ref_scale * _hidden_states_0 * 10

        # TODO: drop this cross-attn
        _hidden_states_1 = F.scaled_dot_product_attention(
            query_1, key_0, value_0, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states_1 = hidden_states_1 + ref_scale * _hidden_states_1

        hidden_states_0 = hidden_states_0.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states_1 = hidden_states_1.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states_0 = hidden_states_0.to(query_0.dtype)
        hidden_states_1 = hidden_states_1.to(query_1.dtype)


        # linear proj
        hidden_states_0 = attn.to_out[0](hidden_states_0)
        hidden_states_1 = attn.to_out[0](hidden_states_1)
        # dropout
        hidden_states_0 = attn.to_out[1](hidden_states_0)
        hidden_states_1 = attn.to_out[1](hidden_states_1)


        if input_ndim == 4:
            hidden_states_0 = hidden_states_0.transpose(-1, -2).reshape(batch_size, channel, height, width)
            hidden_states_1 = hidden_states_1.transpose(-1, -2).reshape(batch_size, channel, height, width)

            if cat_dim==-2 or cat_dim==2:
                hidden_states_pad = torch.zeros(batch_size, channel, 1, width)
            elif cat_dim==-1 or cat_dim==3:
                hidden_states_pad = torch.zeros(batch_size, channel, height, 1)
            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=cat_dim)
            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"
        else:
            batch_size, sequence_length, inner_dim = hidden_states.shape
            hidden_states_pad = torch.zeros(batch_size, single_dim, inner_dim)
            hidden_states_pad = hidden_states_pad.to(hidden_states_0.device, dtype=hidden_states_0.dtype)
            hidden_states = torch.cat([hidden_states_0, hidden_states_pad, hidden_states_1], dim=1)
            assert hidden_states.shape == residual.shape, f"{hidden_states.shape} != {residual.shape}"

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class AdditiveKV_AttnProcessor2_0(torch.nn.Module):
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(
        self,
        hidden_size: int = None,
        cross_attention_dim: int = None,
        time_embedding_dim: int = None,
        additive_scale: float = 1.0,
    ):
        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
        self.additive_scale = additive_scale

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        external_kv=None,
        attention_mask=None,
        temb=None,
    ):
        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."
        
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        if external_kv:
            key = external_kv.k
            value = external_kv.v

            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)

            external_attn_output = F.scaled_dot_product_attention(
                query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
            )

            external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
            hidden_states = hidden_states + self.additive_scale * external_attn_output

        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 TA_AdditiveKV_AttnProcessor2_0(torch.nn.Module):
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(
        self,
        hidden_size: int = None,
        cross_attention_dim: int = None,
        time_embedding_dim: int = None,
        additive_scale: float = 1.0,
    ):
        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
        self.ln_k = AdaLayerNorm(hidden_size, time_embedding_dim)
        self.ln_v = AdaLayerNorm(hidden_size, time_embedding_dim)
        self.additive_scale = additive_scale

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        external_kv=None,
        attention_mask=None,
        temb=None,
    ):
        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."
        
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)

        if external_kv:
            key = external_kv.k
            value = external_kv.v

            # time-dependent adaLN
            key = self.ln_k(key, temb)
            value = self.ln_v(value, temb)

            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)

            external_attn_output = F.scaled_dot_product_attention(
                query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
            )

            external_attn_output = external_attn_output.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
            hidden_states = hidden_states + self.additive_scale * external_attn_output

        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 IPAttnProcessor2_0(torch.nn.Module):
    r"""
    Attention processor for IP-Adapater for PyTorch 2.0.
    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        scale (`float`, defaults to 1.0):
            the weight scale of image prompt.
        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
            The context length of the image features.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.scale = scale
        self.num_tokens = num_tokens

        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
    ):
        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)

        if isinstance(encoder_hidden_states, tuple):
            # FIXME: now hard coded to single image prompt.
            batch_size, _, hid_dim = encoder_hidden_states[0].shape
            ip_tokens = encoder_hidden_states[1][0]
            encoder_hidden_states = torch.cat([encoder_hidden_states[0], ip_tokens], dim=1)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        else:
            # get encoder_hidden_states, ip_hidden_states
            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
            encoder_hidden_states, ip_hidden_states = (
                encoder_hidden_states[:, :end_pos, :],
                encoder_hidden_states[:, end_pos:, :],
            )
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # for ip-adapter
        ip_key = self.to_k_ip(ip_hidden_states)
        ip_value = self.to_v_ip(ip_hidden_states)

        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        ip_hidden_states = F.scaled_dot_product_attention(
            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
        )

        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        ip_hidden_states = ip_hidden_states.to(query.dtype)

        hidden_states = hidden_states + self.scale * ip_hidden_states

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class TA_IPAttnProcessor2_0(torch.nn.Module):
    r"""
    Attention processor for IP-Adapater for PyTorch 2.0.
    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        scale (`float`, defaults to 1.0):
            the weight scale of image prompt.
        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
            The context length of the image features.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, time_embedding_dim: int = None, scale=1.0, num_tokens=4):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.scale = scale
        self.num_tokens = num_tokens

        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.ln_k_ip = AdaLayerNorm(hidden_size, time_embedding_dim)
        self.ln_v_ip = AdaLayerNorm(hidden_size, time_embedding_dim)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        external_kv=None,
        temb=None,
    ):
        assert temb is not None, "Timestep embedding is needed for a time-aware attention processor."

        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)

        if not isinstance(encoder_hidden_states, tuple):
            # get encoder_hidden_states, ip_hidden_states
            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
            encoder_hidden_states, ip_hidden_states = (
                encoder_hidden_states[:, :end_pos, :],
                encoder_hidden_states[:, end_pos:, :],
            )
        else:
            # FIXME: now hard coded to single image prompt.
            batch_size, _, hid_dim = encoder_hidden_states[0].shape
            ip_hidden_states = encoder_hidden_states[1][0]
            encoder_hidden_states = encoder_hidden_states[0]
        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        else:
            if 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)

        if external_kv:
            key = torch.cat([key, external_kv.k], axis=1)
            value = torch.cat([value, external_kv.v], axis=1)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # for ip-adapter
        ip_key = self.to_k_ip(ip_hidden_states)
        ip_value = self.to_v_ip(ip_hidden_states)
        
        # time-dependent adaLN
        ip_key = self.ln_k_ip(ip_key, temb)
        ip_value = self.ln_v_ip(ip_value, temb)

        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        ip_hidden_states = F.scaled_dot_product_attention(
            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
        )

        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        ip_hidden_states = ip_hidden_states.to(query.dtype)

        hidden_states = hidden_states + self.scale * ip_hidden_states

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


## for controlnet
class CNAttnProcessor:
    r"""
    Default processor for performing attention-related computations.
    """

    def __init__(self, num_tokens=4):
        self.num_tokens = num_tokens

    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        else:
            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class CNAttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(self, num_tokens=4):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
        self.num_tokens = num_tokens

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
    ):
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        else:
            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


def init_attn_proc(unet, ip_adapter_tokens=16, use_lcm=False, use_adaln=True, use_external_kv=False):
    attn_procs = {}
    unet_sd = unet.state_dict()
    for name in unet.attn_processors.keys():
        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
        if name.startswith("mid_block"):
            hidden_size = unet.config.block_out_channels[-1]
        elif name.startswith("up_blocks"):
            block_id = int(name[len("up_blocks.")])
            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
        elif name.startswith("down_blocks"):
            block_id = int(name[len("down_blocks.")])
            hidden_size = unet.config.block_out_channels[block_id]
        if cross_attention_dim is None:
            if use_external_kv:
                attn_procs[name] = AdditiveKV_AttnProcessor2_0(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                    time_embedding_dim=1280,
                ) if hasattr(F, "scaled_dot_product_attention") else AdditiveKV_AttnProcessor()
            else:
                attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()
        else:
            if use_adaln:
                layer_name = name.split(".processor")[0]
                if use_lcm:
                    weights = {
                        "to_k_ip.weight": unet_sd[layer_name + ".to_k.base_layer.weight"],
                        "to_v_ip.weight": unet_sd[layer_name + ".to_v.base_layer.weight"],
                    }
                else:
                    weights = {
                        "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
                        "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
                    }
                attn_procs[name] = TA_IPAttnProcessor2_0(
                        hidden_size=hidden_size,
                        cross_attention_dim=cross_attention_dim,
                        num_tokens=ip_adapter_tokens,
                        time_embedding_dim=1280,
                    ) if hasattr(F, "scaled_dot_product_attention") else \
                    TA_IPAttnProcessor(
                        hidden_size=hidden_size,
                        cross_attention_dim=cross_attention_dim,
                        num_tokens=ip_adapter_tokens,
                        time_embedding_dim=1280,
                    )
                attn_procs[name].load_state_dict(weights, strict=False)
            else:
                attn_procs[name] = AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnProcessor()

    return attn_procs


def init_aggregator_attn_proc(unet, use_adaln=False, split_attn=False):
    attn_procs = {}
    unet_sd = unet.state_dict()
    for name in unet.attn_processors.keys():
        # get layer name and hidden dim
        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
        if name.startswith("mid_block"):
            hidden_size = unet.config.block_out_channels[-1]
        elif name.startswith("up_blocks"):
            block_id = int(name[len("up_blocks.")])
            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
        elif name.startswith("down_blocks"):
            block_id = int(name[len("down_blocks.")])
            hidden_size = unet.config.block_out_channels[block_id]
        # init attn proc
        if split_attn:
            # layer_name = name.split(".processor")[0]
            # weights = {
            #     "to_q_ref.weight": unet_sd[layer_name + ".to_q.weight"],
            #     "to_k_ref.weight": unet_sd[layer_name + ".to_k.weight"],
            #     "to_v_ref.weight": unet_sd[layer_name + ".to_v.weight"],
            # }
            attn_procs[name] = (
                sep_split_AttnProcessor2_0(
                    hidden_size=hidden_size,
                    cross_attention_dim=hidden_size,
                    time_embedding_dim=1280,
                )
                if use_adaln
                else split_AttnProcessor2_0(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                    time_embedding_dim=1280,
                )
            )
            # attn_procs[name].load_state_dict(weights, strict=False)
        else:
            attn_procs[name] = (
                AttnProcessor2_0(
                    hidden_size=hidden_size,
                    cross_attention_dim=hidden_size,
                )
                if hasattr(F, "scaled_dot_product_attention")
                else AttnProcessor(
                    hidden_size=hidden_size,
                    cross_attention_dim=hidden_size,
                )
            )

    return attn_procs