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import re
import torch
import torch.nn as nn
from dataclasses import dataclass

from diffusers.models.attention_processor import Attention
from diffusers import (
    DDPMScheduler,
    UNet2DConditionModel,
    AutoencoderKL
)
from diffusers.loaders import AttnProcsLayers


class LoRALinearLayerwBias(nn.Module):
    r"""
    A linear layer that is used with LoRA, can be used with bias.

    Parameters:
        in_features (`int`):
            Number of input features.
        out_features (`int`):
            Number of output features.
        rank (`int`, `optional`, defaults to 4):
            The rank of the LoRA layer.
        network_alpha (`float`, `optional`, defaults to `None`):
            The value of the network alpha used for stable learning and preventing underflow. This value has the same
            meaning as the `--network_alpha` option in the kohya-ss trainer script. See
            https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
        device (`torch.device`, `optional`, defaults to `None`):
            The device to use for the layer's weights.
        dtype (`torch.dtype`, `optional`, defaults to `None`):
            The dtype to use for the layer's weights.
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        rank: int = 4,
        network_alpha=None,
        device=None,
        dtype=None,
        with_bias: bool = False
    ):
        super().__init__()

        self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)
        self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)
        if with_bias:
            self.bias = nn.Parameter(torch.zeros([1, 1, out_features], device=device, dtype=dtype))
        self.with_bias = with_bias

        # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
        # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
        self.network_alpha = network_alpha
        self.rank = rank
        self.out_features = out_features
        self.in_features = in_features

        nn.init.normal_(self.down.weight, std=1 / rank)
        nn.init.zeros_(self.up.weight)


    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_dtype = hidden_states.dtype
        dtype = self.down.weight.dtype

        down_hidden_states = self.down(hidden_states.to(dtype))
        up_hidden_states = self.up(down_hidden_states)
        if self.with_bias:
            up_hidden_states = up_hidden_states + self.bias

        if self.network_alpha is not None:
            up_hidden_states *= self.network_alpha / self.rank

        return up_hidden_states.to(orig_dtype)
    
class TriplaneLoRAConv2dLayer(nn.Module):
    r"""
    A convolutional layer that is used with LoRA.

    Parameters:
        in_features (`int`):
            Number of input features.
        out_features (`int`):
            Number of output features.
        rank (`int`, `optional`, defaults to 4):
            The rank of the LoRA layer.
        kernel_size (`int` or `tuple` of two `int`, `optional`, defaults to 1):
            The kernel size of the convolution.
        stride (`int` or `tuple` of two `int`, `optional`, defaults to 1):
            The stride of the convolution.
        padding (`int` or `tuple` of two `int` or `str`, `optional`, defaults to 0):
            The padding of the convolution.
        network_alpha (`float`, `optional`, defaults to `None`):
            The value of the network alpha used for stable learning and preventing underflow. This value has the same
            meaning as the `--network_alpha` option in the kohya-ss trainer script. See
            https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        rank: int = 4,
        kernel_size = (1, 1),
        stride = (1, 1),
        padding = 0,
        network_alpha = None,
        with_bias: bool = False,
        locon_type: str = "hexa_v1", #hexa_v2, vanilla_v1, vanilla_v2
    ):
        super().__init__()

        assert locon_type in ["hexa_v1", "hexa_v2", "vanilla_v1", "vanilla_v2"], "The LoCON type is not supported."
        if locon_type == "hexa_v1":
            self.down_xy_geo = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
            self.down_xz_geo = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
            self.down_yz_geo = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
            self.down_xy_tex = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
            self.down_xz_tex = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
            self.down_yz_tex = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
            # according to the official kohya_ss trainer kernel_size are always fixed for the up layer
            # # see: https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L129
            self.up_xy_geo = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=with_bias)
            self.up_xz_geo = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=with_bias)
            self.up_yz_geo = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=with_bias)
            self.up_xy_tex = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=with_bias)
            self.up_xz_tex = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=with_bias)
            self.up_yz_tex = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=with_bias)

        # This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
        # See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning

        elif locon_type == "hexa_v2":
            self.down_xy_geo = nn.Conv2d(in_features, rank, kernel_size=(1, 1), stride=(1, 1),padding=padding, bias=False)
            self.down_xz_geo = nn.Conv2d(in_features, rank, kernel_size=(1, 1), stride=(1, 1),padding=padding, bias=False)
            self.down_yz_geo = nn.Conv2d(in_features, rank, kernel_size=(1, 1), stride=(1, 1),padding=padding, bias=False)
            self.down_xy_tex = nn.Conv2d(in_features, rank, kernel_size=(1, 1), stride=(1, 1),padding=padding, bias=False)
            self.down_xz_tex = nn.Conv2d(in_features, rank, kernel_size=(1, 1), stride=(1, 1),padding=padding, bias=False)
            self.down_yz_tex = nn.Conv2d(in_features, rank, kernel_size=(1, 1), stride=(1, 1),padding=padding, bias=False)

            self.up_xy_geo = nn.Conv2d(rank, out_features, kernel_size=kernel_size, stride=stride, bias=with_bias)
            self.up_xz_geo = nn.Conv2d(rank, out_features, kernel_size=kernel_size, stride=stride, bias=with_bias)
            self.up_yz_geo = nn.Conv2d(rank, out_features, kernel_size=kernel_size, stride=stride, bias=with_bias)
            self.up_xy_tex = nn.Conv2d(rank, out_features, kernel_size=kernel_size, stride=stride, bias=with_bias)
            self.up_xz_tex = nn.Conv2d(rank, out_features, kernel_size=kernel_size, stride=stride, bias=with_bias)
            self.up_yz_tex = nn.Conv2d(rank, out_features, kernel_size=kernel_size, stride=stride, bias=with_bias)

        elif locon_type == "vanilla_v1":
            self.down = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
            self.up = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=with_bias)

        elif locon_type == "vanilla_v2":
            self.down = nn.Conv2d(in_features, rank, kernel_size=(1, 1), stride=(1, 1), padding=padding, bias=False)
            self.up = nn.Conv2d(rank, out_features, kernel_size=kernel_size, stride=stride, bias=with_bias)

        self.network_alpha = network_alpha
        self.rank = rank
        self.locon_type = locon_type
        self._init_weights()

    def _init_weights(self):
        for layer in [
            "down_xy_geo", "down_xz_geo", "down_yz_geo", "down_xy_tex", "down_xz_tex", "down_yz_tex", # in case of hexa_vX
            "up_xy", "up_xz", "up_yz", "up_xy_tex", "up_xz_tex", "up_yz_tex", # in case of hexa_vX
            "down", "up" # in case of vanilla
        ]:
            if hasattr(self, layer):
                # initialize the weights
                if "down" in layer: 
                    nn.init.normal_(getattr(self, layer).weight, std=1 / self.rank)
                elif "up" in layer:
                    nn.init.zeros_(getattr(self, layer).weight)
                # initialize the bias
                if getattr(self, layer).bias is not None:
                    nn.init.zeros_(getattr(self, layer).bias)


    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_dtype = hidden_states.dtype
        dtype = self.down_xy_geo.weight.dtype if "hexa" in self.locon_type else self.down.weight.dtype

        if "hexa" in self.locon_type:
            # xy plane
            hidden_states_xy_geo = self.up_xy_geo(self.down_xy_geo(hidden_states[0::6].to(dtype)))
            hidden_states_xy_tex = self.up_xy_tex(self.down_xy_tex(hidden_states[3::6].to(dtype)))

            lora_hidden_states = torch.concat(
                [torch.zeros_like(hidden_states_xy_tex)] * 6,
                dim=0
            )

            lora_hidden_states[0::6] = hidden_states_xy_geo
            lora_hidden_states[3::6] = hidden_states_xy_tex

            # xz plane
            lora_hidden_states[1::6] = self.up_xz_geo(self.down_xz_geo(hidden_states[1::6].to(dtype)))
            lora_hidden_states[4::6] = self.up_xz_tex(self.down_xz_tex(hidden_states[4::6].to(dtype)))
            # yz plane
            lora_hidden_states[2::6] = self.up_yz_geo(self.down_yz_geo(hidden_states[2::6].to(dtype)))
            lora_hidden_states[5::6] = self.up_yz_tex(self.down_yz_tex(hidden_states[5::6].to(dtype)))

        elif "vanilla" in self.locon_type:
            lora_hidden_states = self.up(self.down(hidden_states.to(dtype)))

        if self.network_alpha is not None:
            lora_hidden_states *= self.network_alpha / self.rank

        return lora_hidden_states.to(orig_dtype)

class TriplaneSelfAttentionLoRAAttnProcessor(nn.Module):
    """
    Perform for implementing the Triplane Self-Attention LoRA Attention Processor.
    """

    def __init__(
        self,
        hidden_size: int,
        rank: int = 4,
        network_alpha=None,
        with_bias: bool = False,
        lora_type: str = "hexa_v1", # vanilla, 
    ):
        super().__init__()

        assert lora_type in ["hexa_v1", "vanilla", "none", "basic"], "The LoRA type is not supported."

        self.hidden_size = hidden_size
        self.rank = rank
        self.lora_type = lora_type

        if lora_type in ["hexa_v1"]:
            # lora for 1st plane geometry
            self.to_q_xy_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xy_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xy_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xy_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 1st plane texture
            self.to_q_xy_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xy_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xy_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xy_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            
            # lora for 2nd plane geometry
            self.to_q_xz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 2nd plane texture
            self.to_q_xz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 3nd plane geometry
            self.to_q_yz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_yz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_yz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_yz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 3nd plane texture
            self.to_q_yz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_yz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_yz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_yz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

        elif lora_type in ["vanilla", "basic"]:
            self.to_q_lora = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_lora = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_lora = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_lora = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

    def __call__(
        self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None
    ):
        assert encoder_hidden_states is None, "The encoder_hidden_states should be 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
        if self.lora_type in ["hexa_v1",]:
            query = attn.to_q(hidden_states)
            _query_new = torch.zeros_like(query)
            # lora for xy plane geometry
            _query_new[0::6] = self.to_q_xy_lora_geo(hidden_states[0::6])
            # lora for xy plane texture
            _query_new[3::6] = self.to_q_xy_lora_tex(hidden_states[3::6])
            # lora for xz plane geometry
            _query_new[1::6] = self.to_q_xz_lora_geo(hidden_states[1::6])
            # lora for xz plane texture
            _query_new[4::6] = self.to_q_xz_lora_tex(hidden_states[4::6])
            # lora for yz plane geometry
            _query_new[2::6] = self.to_q_yz_lora_geo(hidden_states[2::6])
            # lora for yz plane texture
            _query_new[5::6] = self.to_q_yz_lora_tex(hidden_states[5::6])
            query = query + scale * _query_new

            # # speed up inference
            # query[0::6] += self.to_q_xy_lora_geo(hidden_states[0::6]) * scale
            # query[3::6] += self.to_q_xy_lora_tex(hidden_states[3::6]) * scale
            # query[1::6] += self.to_q_xz_lora_geo(hidden_states[1::6]) * scale
            # query[4::6] += self.to_q_xz_lora_tex(hidden_states[4::6]) * scale
            # query[2::6] += self.to_q_yz_lora_geo(hidden_states[2::6]) * scale
            # query[5::6] += self.to_q_yz_lora_tex(hidden_states[5::6]) * scale

        elif self.lora_type in ["vanilla", "basic"]:
            query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
        elif self.lora_type in ["none"]:
            query = attn.to_q(hidden_states)
        else:
            raise NotImplementedError("The LoRA type is not supported for the query in HplaneSelfAttentionLoRAAttnProcessor.")

        ############################################################################################################

        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 and value
        if self.lora_type in ["hexa_v1",]:
            key = attn.to_k(encoder_hidden_states)
            _key_new = torch.zeros_like(key)
            # lora for xy plane geometry
            _key_new[0::6] = self.to_k_xy_lora_geo(encoder_hidden_states[0::6])
            # lora for xy plane texture
            _key_new[3::6] = self.to_k_xy_lora_tex(encoder_hidden_states[3::6])
            # lora for xz plane geometry
            _key_new[1::6] = self.to_k_xz_lora_geo(encoder_hidden_states[1::6])
            # lora for xz plane texture
            _key_new[4::6] = self.to_k_xz_lora_tex(encoder_hidden_states[4::6])
            # lora for yz plane geometry
            _key_new[2::6] = self.to_k_yz_lora_geo(encoder_hidden_states[2::6])
            # lora for yz plane texture
            _key_new[5::6] = self.to_k_yz_lora_tex(encoder_hidden_states[5::6])
            key = key + scale * _key_new

            # # speed up inference
            # key[0::6] += self.to_k_xy_lora_geo(encoder_hidden_states[0::6]) * scale
            # key[3::6] += self.to_k_xy_lora_tex(encoder_hidden_states[3::6]) * scale
            # key[1::6] += self.to_k_xz_lora_geo(encoder_hidden_states[1::6]) * scale
            # key[4::6] += self.to_k_xz_lora_tex(encoder_hidden_states[4::6]) * scale
            # key[2::6] += self.to_k_yz_lora_geo(encoder_hidden_states[2::6]) * scale
            # key[5::6] += self.to_k_yz_lora_tex(encoder_hidden_states[5::6]) * scale

            value = attn.to_v(encoder_hidden_states)
            _value_new = torch.zeros_like(value)
            # lora for xy plane geometry
            _value_new[0::6] = self.to_v_xy_lora_geo(encoder_hidden_states[0::6])
            # lora for xy plane texture
            _value_new[3::6] = self.to_v_xy_lora_tex(encoder_hidden_states[3::6])
            # lora for xz plane geometry
            _value_new[1::6] = self.to_v_xz_lora_geo(encoder_hidden_states[1::6])
            # lora for xz plane texture
            _value_new[4::6] = self.to_v_xz_lora_tex(encoder_hidden_states[4::6])
            # lora for yz plane geometry
            _value_new[2::6] = self.to_v_yz_lora_geo(encoder_hidden_states[2::6])
            # lora for yz plane texture
            _value_new[5::6] = self.to_v_yz_lora_tex(encoder_hidden_states[5::6])
            value = value + scale * _value_new

            # # speed up inference
            # value[0::6] += self.to_v_xy_lora_geo(encoder_hidden_states[0::6]) * scale
            # value[3::6] += self.to_v_xy_lora_tex(encoder_hidden_states[3::6]) * scale
            # value[1::6] += self.to_v_xz_lora_geo(encoder_hidden_states[1::6]) * scale
            # value[4::6] += self.to_v_xz_lora_tex(encoder_hidden_states[4::6]) * scale
            # value[2::6] += self.to_v_yz_lora_geo(encoder_hidden_states[2::6]) * scale
            # value[5::6] += self.to_v_yz_lora_tex(encoder_hidden_states[5::6]) * scale

        elif self.lora_type in ["vanilla", "basic"]:
            key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
            
        elif self.lora_type in ["none", ]:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        else:
            raise NotImplementedError("The LoRA type is not supported for the key and value in HplaneSelfAttentionLoRAAttnProcessor.")

        ############################################################################################################
        # attention scores

        # in self-attention, query of each plane should be used to calculate the attention scores of all planes
        if self.lora_type in ["hexa_v1", "vanilla",]:
            query = attn.head_to_batch_dim(
                query.view(batch_size // 6, sequence_length * 6, self.hidden_size)
            ) 
            key = attn.head_to_batch_dim(
                key.view(batch_size // 6, sequence_length * 6, self.hidden_size)
            )
            value = attn.head_to_batch_dim(
                value.view(batch_size // 6, sequence_length * 6, self.hidden_size)
            )
            # calculate the attention scores
            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)
            # split the hidden states into 6 planes
            hidden_states = hidden_states.view(batch_size, sequence_length, self.hidden_size)
        elif self.lora_type in ["none", "basic"]:
            query = attn.head_to_batch_dim(query)
            key = attn.head_to_batch_dim(key)
            value = attn.head_to_batch_dim(value)
            # calculate the attention scores
            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)
        else:
            raise NotImplementedError("The LoRA type is not supported for attention scores calculation in HplaneSelfAttentionLoRAAttnProcessor.")

        ############################################################################################################
        # linear proj
        if self.lora_type in ["hexa_v1", ]:
            hidden_states = attn.to_out[0](hidden_states)
            _hidden_states_new = torch.zeros_like(hidden_states)
            # lora for xy plane geometry
            _hidden_states_new[0::6] = self.to_out_xy_lora_geo(hidden_states[0::6])
            # lora for xy plane texture
            _hidden_states_new[3::6] = self.to_out_xy_lora_tex(hidden_states[3::6])
            # lora for xz plane geometry
            _hidden_states_new[1::6] = self.to_out_xz_lora_geo(hidden_states[1::6])
            # lora for xz plane texture
            _hidden_states_new[4::6] = self.to_out_xz_lora_tex(hidden_states[4::6])
            # lora for yz plane geometry
            _hidden_states_new[2::6] = self.to_out_yz_lora_geo(hidden_states[2::6])
            # lora for yz plane texture
            _hidden_states_new[5::6] = self.to_out_yz_lora_tex(hidden_states[5::6])
            hidden_states = hidden_states + scale * _hidden_states_new

            # # speed up inference
            # hidden_states[0::6] += self.to_out_xy_lora_geo(hidden_states[0::6]) * scale
            # hidden_states[3::6] += self.to_out_xy_lora_tex(hidden_states[3::6]) * scale
            # hidden_states[1::6] += self.to_out_xz_lora_geo(hidden_states[1::6]) * scale
            # hidden_states[4::6] += self.to_out_xz_lora_tex(hidden_states[4::6]) * scale
            # hidden_states[2::6] += self.to_out_yz_lora_geo(hidden_states[2::6]) * scale
            # hidden_states[5::6] += self.to_out_yz_lora_tex(hidden_states[5::6]) * scale

        elif self.lora_type in ["vanilla", "basic"]:
            hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
        elif self.lora_type in ["none",]:
            hidden_states = attn.to_out[0](hidden_states)
        else:
            raise NotImplementedError("The LoRA type is not supported for the to_out layer in HplaneSelfAttentionLoRAAttnProcessor.")

        # 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 TriplaneCrossAttentionLoRAAttnProcessor(nn.Module):
    """
    Perform for implementing the Triplane Cross-Attention LoRA Attention Processor.
    """

    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: int,
        rank: int = 4,
        network_alpha = None,
        with_bias: bool = False,
        lora_type: str = "hexa_v1", # vanilla,
    ):
        super().__init__()

        assert lora_type in ["hexa_v1", "vanilla", "none"], "The LoRA type is not supported."

        self.hidden_size = hidden_size
        self.rank = rank
        self.lora_type = lora_type

        if lora_type in ["hexa_v1"]:
            # lora for 1st plane geometry
            self.to_q_xy_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xy_lora_geo = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xy_lora_geo = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xy_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 1st plane texture
            self.to_q_xy_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xy_lora_tex = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xy_lora_tex = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xy_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 2nd plane geometry
            self.to_q_xz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xz_lora_geo = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xz_lora_geo = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 2nd plane texture
            self.to_q_xz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_xz_lora_tex = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_xz_lora_tex = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_xz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 3nd plane geometry
            self.to_q_yz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_yz_lora_geo = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_yz_lora_geo = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_yz_lora_geo = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

            # lora for 3nd plane texture
            self.to_q_yz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_yz_lora_tex = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_yz_lora_tex = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_yz_lora_tex = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

        elif lora_type in ["vanilla"]:
            # lora for all planes
            self.to_q_lora = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_k_lora = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_v_lora = LoRALinearLayerwBias(cross_attention_dim, hidden_size, rank, network_alpha, with_bias=with_bias)
            self.to_out_lora = LoRALinearLayerwBias(hidden_size, hidden_size, rank, network_alpha, with_bias=with_bias)

    def __call__(
        self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0, temb=None
    ):
        assert encoder_hidden_states is not None, "The encoder_hidden_states should not be 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
        if self.lora_type in ["hexa_v1",]:
            query = attn.to_q(hidden_states)
            _query_new = torch.zeros_like(query)        
            # lora for xy plane geometry
            _query_new[0::6] = self.to_q_xy_lora_geo(hidden_states[0::6])
            # lora for xy plane texture
            _query_new[3::6] = self.to_q_xy_lora_tex(hidden_states[3::6])
            # lora for xz plane geometry
            _query_new[1::6] = self.to_q_xz_lora_geo(hidden_states[1::6])
            # lora for xz plane texture
            _query_new[4::6] = self.to_q_xz_lora_tex(hidden_states[4::6])
            # lora for yz plane geometry
            _query_new[2::6] = self.to_q_yz_lora_geo(hidden_states[2::6])
            # lora for yz plane texture
            _query_new[5::6] = self.to_q_yz_lora_tex(hidden_states[5::6])
            query = query + scale * _query_new

        elif self.lora_type == "vanilla":
            query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
        
        elif self.lora_type == "none":
            query = attn.to_q(hidden_states)

        query = attn.head_to_batch_dim(query)
        ############################################################################################################

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

        ############################################################################################################
        # key and value
        if self.lora_type in ["hexa_v1",]:
            key = attn.to_k(encoder_hidden_states)
            _key_new = torch.zeros_like(key)
            # lora for xy plane geometry
            _key_new[0::6] = self.to_k_xy_lora_geo(encoder_hidden_states[0::6])
            # lora for xy plane texture
            _key_new[3::6] = self.to_k_xy_lora_tex(encoder_hidden_states[3::6])
            # lora for xz plane geometry
            _key_new[1::6] = self.to_k_xz_lora_geo(encoder_hidden_states[1::6])
            # lora for xz plane texture
            _key_new[4::6] = self.to_k_xz_lora_tex(encoder_hidden_states[4::6])
            # lora for yz plane geometry
            _key_new[2::6] = self.to_k_yz_lora_geo(encoder_hidden_states[2::6])
            # lora for yz plane texture
            _key_new[5::6] = self.to_k_yz_lora_tex(encoder_hidden_states[5::6])
            key = key + scale * _key_new

            value = attn.to_v(encoder_hidden_states)
            _value_new = torch.zeros_like(value)
            # lora for xy plane geometry
            _value_new[0::6] = self.to_v_xy_lora_geo(encoder_hidden_states[0::6])
            # lora for xy plane texture
            _value_new[3::6] = self.to_v_xy_lora_tex(encoder_hidden_states[3::6])
            # lora for xz plane geometry
            _value_new[1::6] = self.to_v_xz_lora_geo(encoder_hidden_states[1::6])
            # lora for xz plane texture
            _value_new[4::6] = self.to_v_xz_lora_tex(encoder_hidden_states[4::6])
            # lora for yz plane geometry
            _value_new[2::6] = self.to_v_yz_lora_geo(encoder_hidden_states[2::6])
            # lora for yz plane texture
            _value_new[5::6] = self.to_v_yz_lora_tex(encoder_hidden_states[5::6])
            value = value + scale * _value_new

        elif self.lora_type in ["vanilla",]:
            key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)

        elif self.lora_type in ["none",]:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

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

        # calculate the attention scores        
        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
        if self.lora_type in ["hexa_v1", ]:
            hidden_states = attn.to_out[0](hidden_states)
            _hidden_states_new = torch.zeros_like(hidden_states)
            # lora for xy plane geometry
            _hidden_states_new[0::6] = self.to_out_xy_lora_geo(hidden_states[0::6])
            # lora for xy plane texture
            _hidden_states_new[3::6] = self.to_out_xy_lora_tex(hidden_states[3::6])
            # lora for xz plane geometry
            _hidden_states_new[1::6] = self.to_out_xz_lora_geo(hidden_states[1::6])
            # lora for xz plane texture
            _hidden_states_new[4::6] = self.to_out_xz_lora_tex(hidden_states[4::6])
            # lora for yz plane geometry
            _hidden_states_new[2::6] = self.to_out_yz_lora_geo(hidden_states[2::6])
            # lora for yz plane texture
            _hidden_states_new[5::6] = self.to_out_yz_lora_tex(hidden_states[5::6])
            hidden_states = hidden_states + scale * _hidden_states_new
        elif self.lora_type in ["vanilla",]:
            hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
        elif self.lora_type in ["none",]:
            hidden_states = attn.to_out[0](hidden_states)
        else:
            raise NotImplementedError("The LoRA type is not supported for the to_out layer in HplaneCrossAttentionLoRAAttnProcessor.")

        # 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

@dataclass
class GeneratorConfig:
    training_type: str = "self_lora_rank_16-cross_lora_rank_16-locon_rank_16"
    output_dim: int = 32
    self_lora_type: str = "hexa_v1"
    cross_lora_type: str = "hexa_v1"
    locon_type: str = "vanilla_v1"
    vae_attn_type: str = "basic"
    prompt_bias: bool = False

class OneStepTriplaneDualStableDiffusion(nn.Module):
    """
    One-step Triplane Stable Diffusion module.
    """
    def __init__(
        self, 
        config,
        vae: AutoencoderKL,
        unet: UNet2DConditionModel,
    ):
        super().__init__()
        # Convert dict to GeneratorConfig if needed
        self.cfg = GeneratorConfig(**config) if isinstance(config, dict) else config
        self.output_dim = self.cfg.output_dim

        # Load models
        self.unet = unet
        self.vae = vae
        
        # Get device from one of the models
        self.device = next(self.unet.parameters()).device
        
        # Remove unused components
        del vae.encoder
        del vae.quant_conv 

        # Get training type from config
        training_type = self.cfg.training_type

        # save trainable parameters
        if not "full" in training_type: # then paramter-efficient training

            trainable_params = {}

            assert "lora" in training_type or "locon" in training_type, "The training type is not supported."
            @dataclass
            class SubModules:
                unet: UNet2DConditionModel
                vae: AutoencoderKL

            self.submodules = SubModules(
                unet=unet.to(self.device),
                vae=vae.to(self.device),
            )

            # free all the parameters
            for param in self.unet.parameters():
                param.requires_grad_(False)
            for param in self.vae.parameters():
                param.requires_grad_(False)

                ############################################################
                # overwrite the unet and vae with the customized processors

            if "lora" in training_type:

                # parse the rank from the training type, with the template "lora_rank_{}"
                assert "self_lora_rank" in training_type, "The self_lora_rank is not specified."
                rank = re.search(r"self_lora_rank_(\d+)", training_type).group(1)
                self.self_lora_rank = int(rank)

                assert "cross_lora_rank" in training_type, "The cross_lora_rank is not specified."
                rank = re.search(r"cross_lora_rank_(\d+)", training_type).group(1)
                self.cross_lora_rank = int(rank)

                # if the finetuning is with bias
                self.w_lora_bias = False
                if "with_bias" in training_type:
                    self.w_lora_bias = True

                # specify the attn_processor for unet
                lora_attn_procs = self._set_attn_processor(
                    self.unet, 
                    self_attn_name="attn1.processor",
                    self_lora_type=self.cfg.self_lora_type,
                    cross_lora_type=self.cfg.cross_lora_type
                )
                self.unet.set_attn_processor(lora_attn_procs)
                # update the trainable parameters
                trainable_params.update(self.unet.attn_processors)

                # specify the attn_processor for vae
                lora_attn_procs = self._set_attn_processor(
                    self.vae, 
                    self_attn_name="processor",
                    self_lora_type=self.cfg.vae_attn_type, # hard-coded for vae 
                    cross_lora_type="vanilla"
                )
                self.vae.set_attn_processor(lora_attn_procs)
                # update the trainable parameters
                trainable_params.update(self.vae.attn_processors)
            else:
                raise NotImplementedError("The training type is not supported.")

            if "locon" in training_type:
                # parse the rank from the training type, with the template "locon_rank_{}"
                rank = re.search(r"locon_rank_(\d+)", training_type).group(1)
                self.locon_rank = int(rank)

                # if the finetuning is with bias
                self.w_locon_bias = False
                if "with_bias" in training_type:
                    self.w_locon_bias = True

                # specify the conv_processor for unet
                locon_procs = self._set_conv_processor(
                    self.unet,
                    locon_type=self.cfg.locon_type
                )

                # update the trainable parameters
                trainable_params.update(locon_procs)

                # specify the conv_processor for vae
                locon_procs = self._set_conv_processor(
                    self.vae,
                    locon_type="vanilla_v1", # hard-coded for vae decoder
                )
                # update the trainable parameters
                trainable_params.update(locon_procs)
            else:
                raise NotImplementedError("The training type is not supported.")

            # overwrite the outconv
            # conv_out_orig = self.vae.decoder.conv_out
            conv_out_new = nn.Conv2d(
                in_channels=128, # conv_out_orig.in_channels, hard-coded
                out_channels=self.cfg.output_dim, kernel_size=3, padding=1
            )

            # update the trainable parameters
            self.vae.decoder.conv_out = conv_out_new
            trainable_params["vae.decoder.conv_out"] = conv_out_new

            # save the trainable parameters
            self.peft_layers = AttnProcsLayers(trainable_params).to(self.device)
            self.peft_layers._load_state_dict_pre_hooks.clear()
            self.peft_layers._state_dict_hooks.clear()    

        # hard-coded for now
        self.num_planes = 6

        if self.cfg.prompt_bias:
            self.prompt_bias = nn.Parameter(torch.zeros(self.num_planes, 77, 1024))

    @property
    def unet(self):
        return self.submodules.unet

    @property
    def vae(self):
        return self.submodules.vae

    def _set_conv_processor(
        self,
        module,
        conv_name: str = "LoRACompatibleConv",
        locon_type: str = "vanilla_v1",
    ):
        locon_procs = {}
        for _name, _module in module.named_modules():
            if _module.__class__.__name__ == conv_name:
                # append the locon processor to the module
                locon_proc = TriplaneLoRAConv2dLayer(
                    in_features=_module.in_channels,
                    out_features=_module.out_channels,
                    rank=self.locon_rank,
                    kernel_size=_module.kernel_size,
                    stride=_module.stride,
                    padding=_module.padding,
                    with_bias = self.w_locon_bias,
                    locon_type= locon_type,
                )
                # add the locon processor to the module
                _module.lora_layer = locon_proc
                # update the trainable parameters
                key_name = f"{_name}.lora_layer"
                locon_procs[key_name] = locon_proc
        return locon_procs



    def _set_attn_processor(
            self, 
            module,
            self_attn_name: str = "attn1.processor",
            self_attn_procs = TriplaneSelfAttentionLoRAAttnProcessor,
            self_lora_type: str = "hexa_v1",
            cross_attn_procs = TriplaneCrossAttentionLoRAAttnProcessor,
            cross_lora_type: str = "hexa_v1",
        ):
        lora_attn_procs = {}
        for name in module.attn_processors.keys():

            if name.startswith("mid_block"):
                hidden_size = module.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(module.config.block_out_channels))[
                    block_id
                ]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = module.config.block_out_channels[block_id]
            elif name.startswith("decoder"):
                # special case for decoder in SD
                hidden_size = 512

            if name.endswith(self_attn_name):
                # it is self-attention
                cross_attention_dim = None
                lora_attn_procs[name] = self_attn_procs(
                    hidden_size, self.self_lora_rank, with_bias = self.w_lora_bias,
                    lora_type = self_lora_type
                )
            else:
                # it is cross-attention
                cross_attention_dim = module.config.cross_attention_dim
                lora_attn_procs[name] = cross_attn_procs(
                    hidden_size, cross_attention_dim, self.cross_lora_rank, with_bias = self.w_lora_bias,
                    lora_type = cross_lora_type
                )
        return lora_attn_procs
    
    def forward(
        self,
        text_embed,
        styles,
    ):
        return None
    def forward_denoise(
        self, 
        text_embed,
        noisy_input,
        t,
    ):

        batch_size = text_embed.size(0)
        noise_shape = noisy_input.size(-2)

        if text_embed.ndim == 3:
            # same text_embed for all planes
            # text_embed = text_embed.repeat(self.num_planes, 1, 1) # wrong!!!
            text_embed = text_embed.repeat_interleave(self.num_planes, dim=0)
        elif text_embed.ndim == 4:
            # different text_embed for each plane
            text_embed = text_embed.view(batch_size * self.num_planes, *text_embed.shape[-2:])
        else:
            raise ValueError("The text_embed should be either 3D or 4D.")
        
        if hasattr(self, "prompt_bias"):
            text_embed = text_embed + self.prompt_bias.repeat(batch_size, 1, 1) * self.cfg.prompt_bias_lr_multiplier

        noisy_input = noisy_input.view(-1, 4, noise_shape, noise_shape)
        noise_pred = self.unet(
            noisy_input,
            t,
            encoder_hidden_states=text_embed
        ).sample


        return noise_pred

    def forward_decode(
        self,
        latents,
    ):
        latents = latents.view(-1, 4, *latents.shape[-2:])
        triplane = self.vae.decode(latents).sample
        triplane = triplane.view(-1, self.num_planes, self.cfg.output_dim, *triplane.shape[-2:])

        return triplane