# Copyright 2025 Junyang Chen, Jinshan Pan, Jiangxin Dong, IMAG Lab Team
# and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import copy
import inspect
from collections import OrderedDict
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import cv2
import numpy as np
import PIL.Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import UNet2DConditionModel as OriginalUNet2DConditionModel
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import (
    FromSingleFileMixin,
    IPAdapterMixin,
    PeftAdapterMixin,
    StableDiffusionXLLoraLoaderMixin,
    TextualInversionLoaderMixin,
    UNet2DConditionLoadersMixin,
)
from diffusers.models import AutoencoderKL
from diffusers.models.attention_processor import (
    AttnProcessor2_0,
    FusedAttnProcessor2_0,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
from diffusers.schedulers import DDPMScheduler, KarrasDiffusionSchedulers
from diffusers.utils import (
    USE_PEFT_BACKEND,
    deprecate,
    is_invisible_watermark_available,
    is_torch_version,
    is_torch_xla_available,
    logging,
    replace_example_docstring,
    scale_lora_layers,
    unscale_lora_layers,
)
from diffusers.utils.outputs import BaseOutput
from diffusers.utils.torch_utils import randn_tensor


if is_invisible_watermark_available():
    from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker

if is_torch_xla_available():
    import torch_xla.core.xla_model as xm

    XLA_AVAILABLE = True
else:
    XLA_AVAILABLE = False


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import random
        >>> import numpy as np
        >>> import torch
        >>> from diffusers import DiffusionPipeline, AutoencoderKL, UniPCMultistepScheduler
        >>> from huggingface_hub import hf_hub_download
        >>> from diffusers.utils import load_image
        >>> from PIL import Image
        >>>
        >>> device = "cuda"
        >>> dtype = torch.float16
        >>> MAX_SEED = np.iinfo(np.int32).max
        >>>
        >>> # Download weights for additional unet layers
        >>> model_file = hf_hub_download(
        ...     "jychen9811/FaithDiff",
        ...     filename="FaithDiff.bin", local_dir="./proc_data/faithdiff", local_dir_use_symlinks=False
        ... )
        >>>
        >>> # Initialize the models and pipeline
        >>> vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
        >>>
        >>> model_id = "SG161222/RealVisXL_V4.0"
        >>> pipe = DiffusionPipeline.from_pretrained(
        ...     model_id,
        ...     torch_dtype=dtype,
        ...     vae=vae,
        ...     unet=None, #<- Do not load with original model.
        ...     custom_pipeline="mixture_tiling_sdxl",
        ...     use_safetensors=True,
        ...     variant="fp16",
        ... ).to(device)
        >>>
        >>> # Here we need use pipeline internal unet model
        >>> pipe.unet = pipe.unet_model.from_pretrained(model_id, subfolder="unet", variant="fp16", use_safetensors=True)
        >>>
        >>> # Load aditional layers to the model
        >>> pipe.unet.load_additional_layers(weight_path="proc_data/faithdiff/FaithDiff.bin", dtype=dtype)
        >>>
        >>> # Enable vae tiling
        >>> pipe.set_encoder_tile_settings()
        >>> pipe.enable_vae_tiling()
        >>>
        >>> # Optimization
        >>> pipe.enable_model_cpu_offload()
        >>>
        >>> # Set selected scheduler
        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
        >>>
        >>> #input params
        >>> prompt = "The image features a woman in her 55s with blonde hair and a white shirt, smiling at the camera. She appears to be in a good mood and is wearing a white scarf around her neck. "
        >>> upscale = 2 # scale here
        >>> start_point = "lr" # or "noise"
        >>> latent_tiled_overlap = 0.5
        >>> latent_tiled_size = 1024
        >>>
        >>> # Load image
        >>> lq_image = load_image("https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/woman.png")
        >>> original_height = lq_image.height
        >>> original_width = lq_image.width
        >>> print(f"Current resolution: H:{original_height} x W:{original_width}")
        >>>
        >>> width = original_width * int(upscale)
        >>> height = original_height * int(upscale)
        >>> print(f"Final resolution: H:{height} x W:{width}")
        >>>
        >>> # Restoration
        >>> image = lq_image.resize((width, height), Image.LANCZOS)
        >>> input_image, width_init, height_init, width_now, height_now = pipe.check_image_size(image)
        >>>
        >>> generator = torch.Generator(device=device).manual_seed(random.randint(0, MAX_SEED))
        >>> gen_image = pipe(lr_img=input_image,
        ...                 prompt = prompt,
        ...                 num_inference_steps=20,
        ...                 guidance_scale=5,
        ...                 generator=generator,
        ...                 start_point=start_point,
        ...                 height = height_now,
        ...                 width=width_now,
        ...                 overlap=latent_tiled_overlap,
        ...                 target_size=(latent_tiled_size, latent_tiled_size)
        ...                 ).images[0]
        >>>
        >>> cropped_image = gen_image.crop((0, 0, width_init, height_init))
        >>> cropped_image.save("data/result.png")
        ```
"""


def zero_module(module):
    """Zero out the parameters of a module and return it."""
    for p in module.parameters():
        nn.init.zeros_(p)
    return module


class Encoder(nn.Module):
    """Encoder layer of a variational autoencoder that encodes input into a latent representation."""

    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 4,
        down_block_types: Tuple[str, ...] = (
            "DownEncoderBlock2D",
            "DownEncoderBlock2D",
            "DownEncoderBlock2D",
            "DownEncoderBlock2D",
        ),
        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
        layers_per_block: int = 2,
        norm_num_groups: int = 32,
        act_fn: str = "silu",
        double_z: bool = True,
        mid_block_add_attention: bool = True,
    ):
        super().__init__()
        self.layers_per_block = layers_per_block

        self.conv_in = nn.Conv2d(
            in_channels,
            block_out_channels[0],
            kernel_size=3,
            stride=1,
            padding=1,
        )

        self.mid_block = None
        self.down_blocks = nn.ModuleList([])
        self.use_rgb = False
        self.down_block_type = down_block_types
        self.block_out_channels = block_out_channels

        self.tile_sample_min_size = 1024
        self.tile_latent_min_size = int(self.tile_sample_min_size / 8)
        self.tile_overlap_factor = 0.25
        self.use_tiling = False

        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=self.layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                add_downsample=not is_final_block,
                resnet_eps=1e-6,
                downsample_padding=0,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=output_channel,
                temb_channels=None,
            )
            self.down_blocks.append(down_block)

        self.mid_block = UNetMidBlock2D(
            in_channels=block_out_channels[-1],
            resnet_eps=1e-6,
            resnet_act_fn=act_fn,
            output_scale_factor=1,
            resnet_time_scale_shift="default",
            attention_head_dim=block_out_channels[-1],
            resnet_groups=norm_num_groups,
            temb_channels=None,
            add_attention=mid_block_add_attention,
        )

        self.gradient_checkpointing = False

    def to_rgb_init(self):
        """Initialize layers to convert features to RGB."""
        self.to_rgbs = nn.ModuleList([])
        self.use_rgb = True
        for i, down_block_type in enumerate(self.down_block_type):
            output_channel = self.block_out_channels[i]
            self.to_rgbs.append(nn.Conv2d(output_channel, 3, kernel_size=3, padding=1))

    def enable_tiling(self):
        """Enable tiling for large inputs."""
        self.use_tiling = True

    def encode(self, sample: torch.FloatTensor) -> torch.FloatTensor:
        """Encode the input tensor into a latent representation."""
        sample = self.conv_in(sample)
        if self.training and self.gradient_checkpointing:

            def create_custom_forward(module):
                def custom_forward(*inputs):
                    return module(*inputs)

                return custom_forward

            if is_torch_version(">=", "1.11.0"):
                for down_block in self.down_blocks:
                    sample = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(down_block), sample, use_reentrant=False
                    )
                sample = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(self.mid_block), sample, use_reentrant=False
                )
            else:
                for down_block in self.down_blocks:
                    sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample)
                sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample)
            return sample
        else:
            for down_block in self.down_blocks:
                sample = down_block(sample)
            sample = self.mid_block(sample)
            return sample

    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
        """Blend two tensors vertically with a smooth transition."""
        blend_extent = min(a.shape[2], b.shape[2], blend_extent)
        for y in range(blend_extent):
            b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
        return b

    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
        """Blend two tensors horizontally with a smooth transition."""
        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
        for x in range(blend_extent):
            b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
        return b

    def tiled_encode(self, x: torch.FloatTensor) -> torch.FloatTensor:
        """Encode the input tensor using tiling for large inputs."""
        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
        row_limit = self.tile_latent_min_size - blend_extent

        rows = []
        for i in range(0, x.shape[2], overlap_size):
            row = []
            for j in range(0, x.shape[3], overlap_size):
                tile = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                tile = self.encode(tile)
                row.append(tile)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[:, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=3))

        moments = torch.cat(result_rows, dim=2)
        return moments

    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
        """Forward pass of the encoder, using tiling if enabled for large inputs."""
        if self.use_tiling and (
            sample.shape[-1] > self.tile_latent_min_size or sample.shape[-2] > self.tile_latent_min_size
        ):
            return self.tiled_encode(sample)
        return self.encode(sample)


class ControlNetConditioningEmbedding(nn.Module):
    """A small network to preprocess conditioning inputs, inspired by ControlNet."""

    def __init__(self, conditioning_embedding_channels: int, conditioning_channels: int = 4):
        super().__init__()
        self.conv_in = nn.Conv2d(conditioning_channels, conditioning_channels, kernel_size=3, padding=1)
        self.norm_in = nn.GroupNorm(num_channels=conditioning_channels, num_groups=32, eps=1e-6)
        self.conv_out = zero_module(
            nn.Conv2d(conditioning_channels, conditioning_embedding_channels, kernel_size=3, padding=1)
        )

    def forward(self, conditioning):
        """Process the conditioning input through the network."""
        conditioning = self.norm_in(conditioning)
        embedding = self.conv_in(conditioning)
        embedding = F.silu(embedding)
        embedding = self.conv_out(embedding)
        return embedding


class QuickGELU(nn.Module):
    """A fast approximation of the GELU activation function."""

    def forward(self, x: torch.Tensor):
        """Apply the QuickGELU activation to the input tensor."""
        return x * torch.sigmoid(1.702 * x)


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        """Apply LayerNorm and preserve the input dtype."""
        orig_type = x.dtype
        ret = super().forward(x)
        return ret.type(orig_type)


class ResidualAttentionBlock(nn.Module):
    """A transformer-style block with self-attention and an MLP."""

    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
        super().__init__()
        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(
            OrderedDict(
                [
                    ("c_fc", nn.Linear(d_model, d_model * 2)),
                    ("gelu", QuickGELU()),
                    ("c_proj", nn.Linear(d_model * 2, d_model)),
                ]
            )
        )
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        """Apply self-attention to the input tensor."""
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]

    def forward(self, x: torch.Tensor):
        """Forward pass through the residual attention block."""
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


@dataclass
class UNet2DConditionOutput(BaseOutput):
    """The output of UnifiedUNet2DConditionModel."""

    sample: torch.FloatTensor = None


class UNet2DConditionModel(OriginalUNet2DConditionModel, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
    """A unified 2D UNet model extending OriginalUNet2DConditionModel with custom functionality."""

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 4,
        out_channels: int = 4,
        center_input_sample: bool = False,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
        only_cross_attention: Union[bool, Tuple[bool]] = False,
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        layers_per_block: Union[int, Tuple[int]] = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        dropout: float = 0.0,
        act_fn: str = "silu",
        norm_num_groups: Optional[int] = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: Union[int, Tuple[int]] = 1280,
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: Optional[str] = None,
        attention_head_dim: Union[int, Tuple[int]] = 8,
        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        class_embed_type: Optional[str] = None,
        addition_embed_type: Optional[str] = None,
        addition_time_embed_dim: Optional[int] = None,
        num_class_embeds: Optional[int] = None,
        upcast_attention: bool = False,
        resnet_time_scale_shift: str = "default",
        resnet_skip_time_act: bool = False,
        resnet_out_scale_factor: float = 1.0,
        time_embedding_type: str = "positional",
        time_embedding_dim: Optional[int] = None,
        time_embedding_act_fn: Optional[str] = None,
        timestep_post_act: Optional[str] = None,
        time_cond_proj_dim: Optional[int] = None,
        conv_in_kernel: int = 3,
        conv_out_kernel: int = 3,
        projection_class_embeddings_input_dim: Optional[int] = None,
        attention_type: str = "default",
        class_embeddings_concat: bool = False,
        mid_block_only_cross_attention: Optional[bool] = None,
        cross_attention_norm: Optional[str] = None,
        addition_embed_type_num_heads: int = 64,
    ):
        """Initialize the UnifiedUNet2DConditionModel."""
        super().__init__(
            sample_size=sample_size,
            in_channels=in_channels,
            out_channels=out_channels,
            center_input_sample=center_input_sample,
            flip_sin_to_cos=flip_sin_to_cos,
            freq_shift=freq_shift,
            down_block_types=down_block_types,
            mid_block_type=mid_block_type,
            up_block_types=up_block_types,
            only_cross_attention=only_cross_attention,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            downsample_padding=downsample_padding,
            mid_block_scale_factor=mid_block_scale_factor,
            dropout=dropout,
            act_fn=act_fn,
            norm_num_groups=norm_num_groups,
            norm_eps=norm_eps,
            cross_attention_dim=cross_attention_dim,
            transformer_layers_per_block=transformer_layers_per_block,
            reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
            encoder_hid_dim=encoder_hid_dim,
            encoder_hid_dim_type=encoder_hid_dim_type,
            attention_head_dim=attention_head_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            class_embed_type=class_embed_type,
            addition_embed_type=addition_embed_type,
            addition_time_embed_dim=addition_time_embed_dim,
            num_class_embeds=num_class_embeds,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resnet_skip_time_act=resnet_skip_time_act,
            resnet_out_scale_factor=resnet_out_scale_factor,
            time_embedding_type=time_embedding_type,
            time_embedding_dim=time_embedding_dim,
            time_embedding_act_fn=time_embedding_act_fn,
            timestep_post_act=timestep_post_act,
            time_cond_proj_dim=time_cond_proj_dim,
            conv_in_kernel=conv_in_kernel,
            conv_out_kernel=conv_out_kernel,
            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
            attention_type=attention_type,
            class_embeddings_concat=class_embeddings_concat,
            mid_block_only_cross_attention=mid_block_only_cross_attention,
            cross_attention_norm=cross_attention_norm,
            addition_embed_type_num_heads=addition_embed_type_num_heads,
        )

        # Additional attributes
        self.denoise_encoder = None
        self.information_transformer_layes = None
        self.condition_embedding = None
        self.agg_net = None
        self.spatial_ch_projs = None

    def init_vae_encoder(self, dtype):
        self.denoise_encoder = Encoder()
        if dtype is not None:
            self.denoise_encoder.dtype = dtype

    def init_information_transformer_layes(self):
        num_trans_channel = 640
        num_trans_head = 8
        num_trans_layer = 2
        num_proj_channel = 320
        self.information_transformer_layes = nn.Sequential(
            *[ResidualAttentionBlock(num_trans_channel, num_trans_head) for _ in range(num_trans_layer)]
        )
        self.spatial_ch_projs = zero_module(nn.Linear(num_trans_channel, num_proj_channel))

    def init_ControlNetConditioningEmbedding(self, channel=512):
        self.condition_embedding = ControlNetConditioningEmbedding(320, channel)

    def init_extra_weights(self):
        self.agg_net = nn.ModuleList()

    def load_additional_layers(
        self, dtype: Optional[torch.dtype] = torch.float16, channel: int = 512, weight_path: Optional[str] = None
    ):
        """Load additional layers and weights from a file.

        Args:
            weight_path (str): Path to the weight file.
            dtype (torch.dtype, optional): Data type for the loaded weights. Defaults to torch.float16.
            channel (int): Conditioning embedding channel out size. Defaults 512.
        """
        if self.denoise_encoder is None:
            self.init_vae_encoder(dtype)

        if self.information_transformer_layes is None:
            self.init_information_transformer_layes()

        if self.condition_embedding is None:
            self.init_ControlNetConditioningEmbedding(channel)

        if self.agg_net is None:
            self.init_extra_weights()

        # Load weights if provided
        if weight_path is not None:
            state_dict = torch.load(weight_path, weights_only=False)
            self.load_state_dict(state_dict, strict=True)

        # Move all modules to the same device and dtype as the model
        device = next(self.parameters()).device
        if dtype is not None or device is not None:
            self.to(device=device, dtype=dtype or next(self.parameters()).dtype)

    def to(self, *args, **kwargs):
        """Override to() to move all additional modules to the same device and dtype."""
        super().to(*args, **kwargs)
        for module in [
            self.denoise_encoder,
            self.information_transformer_layes,
            self.condition_embedding,
            self.agg_net,
            self.spatial_ch_projs,
        ]:
            if module is not None:
                module.to(*args, **kwargs)
        return self

    def load_state_dict(self, state_dict, strict=True):
        """Load state dictionary into the model.

        Args:
            state_dict (dict): State dictionary to load.
            strict (bool, optional): Whether to strictly enforce that all keys match. Defaults to True.
        """
        core_dict = {}
        additional_dicts = {
            "denoise_encoder": {},
            "information_transformer_layes": {},
            "condition_embedding": {},
            "agg_net": {},
            "spatial_ch_projs": {},
        }

        for key, value in state_dict.items():
            if key.startswith("denoise_encoder."):
                additional_dicts["denoise_encoder"][key[len("denoise_encoder.") :]] = value
            elif key.startswith("information_transformer_layes."):
                additional_dicts["information_transformer_layes"][key[len("information_transformer_layes.") :]] = value
            elif key.startswith("condition_embedding."):
                additional_dicts["condition_embedding"][key[len("condition_embedding.") :]] = value
            elif key.startswith("agg_net."):
                additional_dicts["agg_net"][key[len("agg_net.") :]] = value
            elif key.startswith("spatial_ch_projs."):
                additional_dicts["spatial_ch_projs"][key[len("spatial_ch_projs.") :]] = value
            else:
                core_dict[key] = value

        super().load_state_dict(core_dict, strict=False)
        for module_name, module_dict in additional_dicts.items():
            module = getattr(self, module_name, None)
            if module is not None and module_dict:
                module.load_state_dict(module_dict, strict=strict)

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        mid_block_additional_residual: Optional[torch.Tensor] = None,
        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        input_embedding: Optional[torch.Tensor] = None,
        add_sample: bool = True,
        return_dict: bool = True,
        use_condition_embedding: bool = True,
    ) -> Union[UNet2DConditionOutput, Tuple]:
        """Forward pass prioritizing the original modified implementation.

        Args:
            sample (torch.FloatTensor): The noisy input tensor with shape `(batch, channel, height, width)`.
            timestep (Union[torch.Tensor, float, int]): The number of timesteps to denoise an input.
            encoder_hidden_states (torch.Tensor): The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
            class_labels (torch.Tensor, optional): Optional class labels for conditioning.
            timestep_cond (torch.Tensor, optional): Conditional embeddings for timestep.
            attention_mask (torch.Tensor, optional): An attention mask of shape `(batch, key_tokens)`.
            cross_attention_kwargs (Dict[str, Any], optional): A kwargs dictionary for the AttentionProcessor.
            added_cond_kwargs (Dict[str, torch.Tensor], optional): Additional embeddings to add to the UNet blocks.
            down_block_additional_residuals (Tuple[torch.Tensor], optional): Residuals for down UNet blocks.
            mid_block_additional_residual (torch.Tensor, optional): Residual for the middle UNet block.
            down_intrablock_additional_residuals (Tuple[torch.Tensor], optional): Additional residuals within down blocks.
            encoder_attention_mask (torch.Tensor, optional): A cross-attention mask of shape `(batch, sequence_length)`.
            input_embedding (torch.Tensor, optional): Additional input embedding for preprocessing.
            add_sample (bool): Whether to add the sample to the processed embedding. Defaults to True.
            return_dict (bool): Whether to return a UNet2DConditionOutput. Defaults to True.
            use_condition_embedding (bool): Whether to use the condition embedding. Defaults to True.

        Returns:
            Union[UNet2DConditionOutput, Tuple]: The processed sample tensor, either as a UNet2DConditionOutput or tuple.
        """
        default_overall_up_factor = 2**self.num_upsamplers
        forward_upsample_size = False
        upsample_size = None

        for dim in sample.shape[-2:]:
            if dim % default_overall_up_factor != 0:
                forward_upsample_size = True
                break

        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        if encoder_attention_mask is not None:
            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        t_emb = self.get_time_embed(sample=sample, timestep=timestep)
        emb = self.time_embedding(t_emb, timestep_cond)
        aug_emb = None

        class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
        if class_emb is not None:
            if self.config.class_embeddings_concat:
                emb = torch.cat([emb, class_emb], dim=-1)
            else:
                emb = emb + class_emb

        aug_emb = self.get_aug_embed(
            emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )
        if self.config.addition_embed_type == "image_hint":
            aug_emb, hint = aug_emb
            sample = torch.cat([sample, hint], dim=1)

        emb = emb + aug_emb if aug_emb is not None else emb

        if self.time_embed_act is not None:
            emb = self.time_embed_act(emb)

        encoder_hidden_states = self.process_encoder_hidden_states(
            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )

        # 2. pre-process (following the original modified logic)
        sample = self.conv_in(sample)  # [B, 4, H, W] -> [B, 320, H, W]
        if (
            input_embedding is not None
            and self.condition_embedding is not None
            and self.information_transformer_layes is not None
        ):
            if use_condition_embedding:
                input_embedding = self.condition_embedding(input_embedding)  # [B, 320, H, W]
            batch_size, channel, height, width = input_embedding.shape
            concat_feat = (
                torch.cat([sample, input_embedding], dim=1)
                .view(batch_size, 2 * channel, height * width)
                .transpose(1, 2)
            )
            concat_feat = self.information_transformer_layes(concat_feat)
            feat_alpha = self.spatial_ch_projs(concat_feat).transpose(1, 2).view(batch_size, channel, height, width)
            sample = sample + feat_alpha if add_sample else feat_alpha  # Update sample as in the original version

        # 2.5 GLIGEN position net (kept from the original version)
        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
            cross_attention_kwargs = cross_attention_kwargs.copy()
            gligen_args = cross_attention_kwargs.pop("gligen")
            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}

        # 3. down (continues the standard flow)
        if cross_attention_kwargs is not None:
            cross_attention_kwargs = cross_attention_kwargs.copy()
            lora_scale = cross_attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

        if USE_PEFT_BACKEND:
            scale_lora_layers(self, lora_scale)

        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
        is_adapter = down_intrablock_additional_residuals is not None
        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
            deprecate(
                "T2I should not use down_block_additional_residuals",
                "1.3.0",
                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
                standard_warn=False,
            )
            down_intrablock_additional_residuals = down_block_additional_residuals
            is_adapter = True

        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                additional_residuals = {}
                if is_adapter and len(down_intrablock_additional_residuals) > 0:
                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                    encoder_attention_mask=encoder_attention_mask,
                    **additional_residuals,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
                if is_adapter and len(down_intrablock_additional_residuals) > 0:
                    sample += down_intrablock_additional_residuals.pop(0)
            down_block_res_samples += res_samples

        if is_controlnet:
            new_down_block_res_samples = ()
            for down_block_res_sample, down_block_additional_residual in zip(
                down_block_res_samples, down_block_additional_residuals
            ):
                down_block_res_sample = down_block_res_sample + down_block_additional_residual
                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
            down_block_res_samples = new_down_block_res_samples

        # 4. mid
        if self.mid_block is not None:
            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                    encoder_attention_mask=encoder_attention_mask,
                )
            else:
                sample = self.mid_block(sample, emb)
            if (
                is_adapter
                and len(down_intrablock_additional_residuals) > 0
                and sample.shape == down_intrablock_additional_residuals[0].shape
            ):
                sample += down_intrablock_additional_residuals.pop(0)

        if is_controlnet:
            sample = sample + mid_block_additional_residual

        # 5. up
        for i, upsample_block in enumerate(self.up_blocks):
            is_final_block = i == len(self.up_blocks) - 1
            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
            if not is_final_block and forward_upsample_size:
                upsample_size = down_block_res_samples[-1].shape[2:]
            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    upsample_size=upsample_size,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                )
            else:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    upsample_size=upsample_size,
                )

        # 6. post-process
        if self.conv_norm_out:
            sample = self.conv_norm_out(sample)
            sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if USE_PEFT_BACKEND:
            unscale_lora_layers(self, lora_scale)

        if not return_dict:
            return (sample,)
        return UNet2DConditionOutput(sample=sample)


class LocalAttention:
    """A class to handle local attention by splitting tensors into overlapping grids for processing."""

    def __init__(self, kernel_size=None, overlap=0.5):
        """Initialize the LocalAttention module.

        Args:
            kernel_size (tuple[int, int], optional): Size of the grid (height, width). Defaults to None.
            overlap (float): Overlap factor between adjacent grids (0.0 to 1.0). Defaults to 0.5.
        """
        super().__init__()
        self.kernel_size = kernel_size
        self.overlap = overlap

    def grids_list(self, x):
        """Split the input tensor into a list of non-overlapping grid patches.

        Args:
            x (torch.Tensor): Input tensor of shape (batch, channels, height, width).

        Returns:
            list[torch.Tensor]: List of tensor patches.
        """
        b, c, h, w = x.shape
        self.original_size = (b, c, h, w)
        assert b == 1
        k1, k2 = self.kernel_size
        if h < k1:
            k1 = h
        if w < k2:
            k2 = w
        num_row = (h - 1) // k1 + 1
        num_col = (w - 1) // k2 + 1
        self.nr = num_row
        self.nc = num_col

        import math

        step_j = k2 if num_col == 1 else math.ceil(k2 * self.overlap)
        step_i = k1 if num_row == 1 else math.ceil(k1 * self.overlap)
        parts = []
        idxes = []
        i = 0
        last_i = False
        while i < h and not last_i:
            j = 0
            if i + k1 >= h:
                i = h - k1
                last_i = True
            last_j = False
            while j < w and not last_j:
                if j + k2 >= w:
                    j = w - k2
                    last_j = True
                parts.append(x[:, :, i : i + k1, j : j + k2])
                idxes.append({"i": i, "j": j})
                j = j + step_j
            i = i + step_i
        return parts

    def grids(self, x):
        """Split the input tensor into overlapping grid patches and concatenate them.

        Args:
            x (torch.Tensor): Input tensor of shape (batch, channels, height, width).

        Returns:
            torch.Tensor: Concatenated tensor of all grid patches.
        """
        b, c, h, w = x.shape
        self.original_size = (b, c, h, w)
        assert b == 1
        k1, k2 = self.kernel_size
        if h < k1:
            k1 = h
        if w < k2:
            k2 = w
        self.tile_weights = self._gaussian_weights(k2, k1)
        num_row = (h - 1) // k1 + 1
        num_col = (w - 1) // k2 + 1
        self.nr = num_row
        self.nc = num_col

        import math

        step_j = k2 if num_col == 1 else math.ceil(k2 * self.overlap)
        step_i = k1 if num_row == 1 else math.ceil(k1 * self.overlap)
        parts = []
        idxes = []
        i = 0
        last_i = False
        while i < h and not last_i:
            j = 0
            if i + k1 >= h:
                i = h - k1
                last_i = True
            last_j = False
            while j < w and not last_j:
                if j + k2 >= w:
                    j = w - k2
                    last_j = True
                parts.append(x[:, :, i : i + k1, j : j + k2])
                idxes.append({"i": i, "j": j})
                j = j + step_j
            i = i + step_i
        self.idxes = idxes
        return torch.cat(parts, dim=0)

    def _gaussian_weights(self, tile_width, tile_height):
        """Generate a Gaussian weight mask for tile contributions.

        Args:
            tile_width (int): Width of the tile.
            tile_height (int): Height of the tile.

        Returns:
            torch.Tensor: Gaussian weight tensor of shape (channels, height, width).
        """
        import numpy as np
        from numpy import exp, pi, sqrt

        latent_width = tile_width
        latent_height = tile_height
        var = 0.01
        midpoint = (latent_width - 1) / 2
        x_probs = [
            exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)
            for x in range(latent_width)
        ]
        midpoint = latent_height / 2
        y_probs = [
            exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)
            for y in range(latent_height)
        ]
        weights = np.outer(y_probs, x_probs)
        return torch.tile(torch.tensor(weights, device=torch.device("cuda")), (4, 1, 1))

    def grids_inverse(self, outs):
        """Reconstruct the original tensor from processed grid patches with overlap blending.

        Args:
            outs (torch.Tensor): Processed grid patches.

        Returns:
            torch.Tensor: Reconstructed tensor of original size.
        """
        preds = torch.zeros(self.original_size).to(outs.device)
        b, c, h, w = self.original_size
        count_mt = torch.zeros((b, 4, h, w)).to(outs.device)
        k1, k2 = self.kernel_size

        for cnt, each_idx in enumerate(self.idxes):
            i = each_idx["i"]
            j = each_idx["j"]
            preds[0, :, i : i + k1, j : j + k2] += outs[cnt, :, :, :] * self.tile_weights
            count_mt[0, :, i : i + k1, j : j + k2] += self.tile_weights

        del outs
        torch.cuda.empty_cache()
        return preds / count_mt

    def _pad(self, x):
        """Pad the input tensor to align with kernel size.

        Args:
            x (torch.Tensor): Input tensor of shape (batch, channels, height, width).

        Returns:
            tuple: Padded tensor and padding values.
        """
        b, c, h, w = x.shape
        k1, k2 = self.kernel_size
        mod_pad_h = (k1 - h % k1) % k1
        mod_pad_w = (k2 - w % k2) % k2
        pad = (mod_pad_w // 2, mod_pad_w - mod_pad_w // 2, mod_pad_h // 2, mod_pad_h - mod_pad_h // 2)
        x = F.pad(x, pad, "reflect")
        return x, pad

    def forward(self, x):
        """Apply local attention by splitting into grids and reconstructing.

        Args:
            x (torch.Tensor): Input tensor of shape (batch, channels, height, width).

        Returns:
            torch.Tensor: Processed tensor of original size.
        """
        b, c, h, w = x.shape
        qkv = self.grids(x)
        out = self.grids_inverse(qkv)
        return out


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4

    Args:
        noise_cfg (torch.Tensor): Noise configuration tensor.
        noise_pred_text (torch.Tensor): Predicted noise from text-conditioned model.
        guidance_rescale (float): Rescaling factor for guidance. Defaults to 0.0.

    Returns:
        torch.Tensor: Rescaled noise configuration.
    """
    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
    return noise_cfg


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
    """Retrieve latents from an encoder output.

    Args:
        encoder_output (torch.Tensor): Output from an encoder (e.g., VAE).
        generator (torch.Generator, optional): Random generator for sampling. Defaults to None.
        sample_mode (str): Sampling mode ("sample" or "argmax"). Defaults to "sample".

    Returns:
        torch.Tensor: Retrieved latent tensor.
    """
    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
        return encoder_output.latent_dist.sample(generator)
    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
        return encoder_output.latent_dist.mode()
    elif hasattr(encoder_output, "latents"):
        return encoder_output.latents
    else:
        raise AttributeError("Could not access latents of provided encoder_output")


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
    scheduler,
    num_inference_steps: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    timesteps: Optional[List[int]] = None,
    **kwargs,
):
    """
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

    Args:
        scheduler (`SchedulerMixin`):
            The scheduler to get timesteps from.
        num_inference_steps (`int`):
            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
            must be `None`.
        device (`str` or `torch.device`, *optional*):
            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        timesteps (`List[int]`, *optional*):
                Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default
                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
                must be `None`.

    Returns:
        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
        second element is the number of inference steps.
    """
    if timesteps is not None:
        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
        if not accepts_timesteps:
            raise ValueError(
                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
                f" timestep schedules. Please check whether you are using the correct scheduler."
            )
        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
        timesteps = scheduler.timesteps
        num_inference_steps = len(timesteps)
    else:
        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
        timesteps = scheduler.timesteps
    return timesteps, num_inference_steps


class FaithDiffStableDiffusionXLPipeline(
    DiffusionPipeline,
    StableDiffusionMixin,
    FromSingleFileMixin,
    StableDiffusionXLLoraLoaderMixin,
    TextualInversionLoaderMixin,
    IPAdapterMixin,
):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion XL.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    The pipeline also inherits the following loading methods:
        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
        - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion XL uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        text_encoder_2 ([` CLIPTextModelWithProjection`]):
            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
            specifically the
            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
            variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        tokenizer_2 (`CLIPTokenizer`):
            Second Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
            Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
            `stabilityai/stable-diffusion-xl-base-1-0`.
        add_watermarker (`bool`, *optional*):
            Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
            watermark output images. If not defined, it will default to True if the package is installed, otherwise no
            watermarker will be used.
    """

    unet_model = UNet2DConditionModel
    model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
    _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2", "feature_extractor", "unet"]
    _callback_tensor_inputs = [
        "latents",
        "prompt_embeds",
        "negative_prompt_embeds",
        "add_text_embeds",
        "add_time_ids",
        "negative_pooled_prompt_embeds",
        "negative_add_time_ids",
    ]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        text_encoder_2: CLIPTextModelWithProjection,
        tokenizer: CLIPTokenizer,
        tokenizer_2: CLIPTokenizer,
        unet: OriginalUNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        force_zeros_for_empty_prompt: bool = True,
        add_watermarker: Optional[bool] = None,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            unet=unet,
            scheduler=scheduler,
        )
        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.DDPMScheduler = DDPMScheduler.from_config(self.scheduler.config, subfolder="scheduler")
        self.default_sample_size = self.unet.config.sample_size if unet is not None else 128
        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()

        if add_watermarker:
            self.watermark = StableDiffusionXLWatermarker()
        else:
            self.watermark = None

    def encode_prompt(
        self,
        prompt: str,
        prompt_2: Optional[str] = None,
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
        do_classifier_free_guidance: bool = True,
        negative_prompt: Optional[str] = None,
        negative_prompt_2: Optional[str] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        lora_scale: Optional[float] = None,
        clip_skip: Optional[int] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                used in both text-encoders
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            negative_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                input argument.
            lora_scale (`float`, *optional*):
                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.
        """
        device = "cuda"  # device or self._execution_device

        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            if self.text_encoder is not None:
                if not USE_PEFT_BACKEND:
                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
                else:
                    scale_lora_layers(self.text_encoder, lora_scale)

            if self.text_encoder_2 is not None:
                if not USE_PEFT_BACKEND:
                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
                else:
                    scale_lora_layers(self.text_encoder_2, lora_scale)

        prompt = [prompt] if isinstance(prompt, str) else prompt

        if prompt is not None:
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        # Define tokenizers and text encoders
        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
        text_encoders = (
            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
        )
        dtype = text_encoders[0].dtype
        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2

            # textual inversion: process multi-vector tokens if necessary
            prompt_embeds_list = []
            prompts = [prompt, prompt_2]
            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    prompt = self.maybe_convert_prompt(prompt, tokenizer)

                text_inputs = tokenizer(
                    prompt,
                    padding="max_length",
                    max_length=tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="pt",
                )

                text_input_ids = text_inputs.input_ids
                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids

                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                    text_input_ids, untruncated_ids
                ):
                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
                    logger.warning(
                        "The following part of your input was truncated because CLIP can only handle sequences up to"
                        f" {tokenizer.model_max_length} tokens: {removed_text}"
                    )
                text_encoder = text_encoder.to(dtype)
                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)

                # We are only ALWAYS interested in the pooled output of the final text encoder
                pooled_prompt_embeds = prompt_embeds[0]
                if clip_skip is None:
                    prompt_embeds = prompt_embeds.hidden_states[-2]
                else:
                    # "2" because SDXL always indexes from the penultimate layer.
                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]

                prompt_embeds_list.append(prompt_embeds)

            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)

        # get unconditional embeddings for classifier free guidance
        zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
        elif do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt_2 = negative_prompt_2 or negative_prompt

            # normalize str to list
            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
            negative_prompt_2 = (
                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
            )

            uncond_tokens: List[str]
            if prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = [negative_prompt, negative_prompt_2]

            negative_prompt_embeds_list = []
            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
                if isinstance(self, TextualInversionLoaderMixin):
                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)

                max_length = prompt_embeds.shape[1]
                uncond_input = tokenizer(
                    negative_prompt,
                    padding="max_length",
                    max_length=max_length,
                    truncation=True,
                    return_tensors="pt",
                )

                negative_prompt_embeds = text_encoder(
                    uncond_input.input_ids.to(device),
                    output_hidden_states=True,
                )
                # We are only ALWAYS interested in the pooled output of the final text encoder
                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]

                negative_prompt_embeds_list.append(negative_prompt_embeds)

            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)

        if self.text_encoder_2 is not None:
            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
        else:
            prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            if self.text_encoder_2 is not None:
                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
            else:
                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)

        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
            bs_embed * num_images_per_prompt, -1
        )
        if do_classifier_free_guidance:
            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
                bs_embed * num_images_per_prompt, -1
            )

        if self.text_encoder is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder, lora_scale)

        if self.text_encoder_2 is not None:
            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
                # Retrieve the original scale by scaling back the LoRA layers
                unscale_lora_layers(self.text_encoder_2, lora_scale)

        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_image_size(self, x, padder_size=8):
        # 获取图像的宽高
        width, height = x.size
        padder_size = padder_size
        # 计算需要填充的高度和宽度
        mod_pad_h = (padder_size - height % padder_size) % padder_size
        mod_pad_w = (padder_size - width % padder_size) % padder_size
        x_np = np.array(x)
        # 使用 ImageOps.expand 进行填充
        x_padded = cv2.copyMakeBorder(
            x_np, top=0, bottom=mod_pad_h, left=0, right=mod_pad_w, borderType=cv2.BORDER_REPLICATE
        )

        x = PIL.Image.fromarray(x_padded)
        # x = x.resize((width + mod_pad_w, height + mod_pad_h))

        return x, width, height, width + mod_pad_w, height + mod_pad_h

    def check_inputs(
        self,
        lr_img,
        prompt,
        prompt_2,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        negative_prompt_2=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        pooled_prompt_embeds=None,
        negative_pooled_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
    ):
        if lr_img is None:
            raise ValueError("`lr_image` must be provided!")
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if callback_on_step_end_tensor_inputs is not None and not all(
            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
        ):
            raise ValueError(
                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt_2 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

        if prompt_embeds is not None and pooled_prompt_embeds is None:
            raise ValueError(
                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
            )

        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
            raise ValueError(
                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
            )

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

        if latents is None:
            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def upcast_vae(self):
        dtype = self.vae.dtype
        self.vae.to(dtype=torch.float32)
        use_torch_2_0_or_xformers = isinstance(
            self.vae.decoder.mid_block.attentions[0].processor,
            (
                AttnProcessor2_0,
                XFormersAttnProcessor,
                LoRAXFormersAttnProcessor,
                LoRAAttnProcessor2_0,
                FusedAttnProcessor2_0,
            ),
        )
        # if xformers or torch_2_0 is used attention block does not need
        # to be in float32 which can save lots of memory
        if use_torch_2_0_or_xformers:
            self.vae.post_quant_conv.to(dtype)
            self.vae.decoder.conv_in.to(dtype)
            self.vae.decoder.mid_block.to(dtype)

    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
    def get_guidance_scale_embedding(
        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
    ) -> torch.FloatTensor:
        """
        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298

        Args:
            w (`torch.Tensor`):
                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
            embedding_dim (`int`, *optional*, defaults to 512):
                Dimension of the embeddings to generate.
            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
                Data type of the generated embeddings.

        Returns:
            `torch.FloatTensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
        """
        assert len(w.shape) == 1
        w = w * 1000.0

        half_dim = embedding_dim // 2
        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
        emb = w.to(dtype)[:, None] * emb[None, :]
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
        if embedding_dim % 2 == 1:  # zero pad
            emb = torch.nn.functional.pad(emb, (0, 1))
        assert emb.shape == (w.shape[0], embedding_dim)
        return emb

    def set_encoder_tile_settings(
        self,
        denoise_encoder_tile_sample_min_size=1024,
        denoise_encoder_sample_overlap_factor=0.25,
        vae_sample_size=1024,
        vae_tile_overlap_factor=0.25,
    ):
        self.unet.denoise_encoder.tile_sample_min_size = denoise_encoder_tile_sample_min_size
        self.unet.denoise_encoder.tile_overlap_factor = denoise_encoder_sample_overlap_factor
        self.vae.config.sample_size = vae_sample_size
        self.vae.tile_overlap_factor = vae_tile_overlap_factor

    def enable_vae_tiling(self):
        r"""
        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
        processing larger images.
        """
        self.vae.enable_tiling()
        self.unet.denoise_encoder.enable_tiling()

    def disable_vae_tiling(self):
        r"""
        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_tiling()
        self.unet.denoise_encoder.disable_tiling()

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def guidance_rescale(self):
        return self._guidance_rescale

    @property
    def clip_skip(self):
        return self._clip_skip

    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
    # corresponds to doing no classifier free guidance.
    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None

    @property
    def cross_attention_kwargs(self):
        return self._cross_attention_kwargs

    @property
    def denoising_end(self):
        return self._denoising_end

    @property
    def num_timesteps(self):
        return self._num_timesteps

    @property
    def interrupt(self):
        return self._interrupt

    def prepare_image_latents(
        self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None
    ):
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
            raise ValueError(
                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
            )

        image = image.to(device=device, dtype=dtype)

        batch_size = batch_size * num_images_per_prompt

        if image.shape[1] == 4:
            image_latents = image
        else:
            # make sure the VAE is in float32 mode, as it overflows in float16
            # needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
            # if needs_upcasting:
            #     image = image.float()
            #     self.upcast_vae()
            self.unet.denoise_encoder.to(device=image.device, dtype=image.dtype)
            image_latents = self.unet.denoise_encoder(image)
            self.unet.denoise_encoder.to("cpu")
            # cast back to fp16 if needed
            # if needs_upcasting:
            #     self.vae.to(dtype=torch.float16)

        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
            # expand image_latents for batch_size
            deprecation_message = (
                f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
                " your script to pass as many initial images as text prompts to suppress this warning."
            )
            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
            additional_image_per_prompt = batch_size // image_latents.shape[0]
            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            image_latents = torch.cat([image_latents], dim=0)

        if do_classifier_free_guidance:
            image_latents = image_latents

        if image_latents.dtype != self.vae.dtype:
            image_latents = image_latents.to(dtype=self.vae.dtype)

        return image_latents

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        lr_img: PipelineImageInput = None,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        start_point: Optional[str] = "noise",
        timesteps: List[int] = None,
        denoising_end: Optional[float] = None,
        overlap: float = 0.5,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        original_size: Optional[Tuple[int, int]] = None,
        target_size: Optional[Tuple[int, int]] = None,
        clip_skip: Optional[int] = None,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        add_sample: bool = True,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            lr_img (PipelineImageInput, optional): Low-resolution input image for conditioning the generation process.
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                used in both text-encoders
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image. This is set to 1024 by default for the best results.
                Anything below 512 pixels won't work well for
                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                and checkpoints that are not specifically fine-tuned on low resolutions.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image. This is set to 1024 by default for the best results.
                Anything below 512 pixels won't work well for
                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
                and checkpoints that are not specifically fine-tuned on low resolutions.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            start_point (str, *optional*):
                The starting point for the generation process. Can be "noise" (random noise) or "lr" (low-resolution image).
                Defaults to "noise".
            timesteps (`List[int]`, *optional*):
                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
                passed will be used. Must be in descending order.
            denoising_end (`float`, *optional*):
                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
                completed before it is intentionally prematurely terminated. As a result, the returned sample will
                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
            overlap (float):
                Overlap factor for local attention tiling (between 0.0 and 1.0). Controls the overlap between adjacent
                grid patches during processing. Defaults to 0.5.
            guidance_scale (`float`, *optional*, defaults to 5.0):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            negative_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
                of a plain tuple.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            guidance_rescale (`float`, *optional*, defaults to 0.0):
                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                Guidance rescale factor should fix overexposure when using zero terminal SNR.
            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
                explained in section 2.2 of
                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
                For most cases, `target_size` should be set to the desired height and width of the generated image. If
                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.
            add_sample (bool):
                Whether to include sample conditioning (e.g., low-resolution image) in the UNet during denoising.
                Defaults to True.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is a list with the generated images.
        """

        callback = kwargs.pop("callback", None)
        callback_steps = kwargs.pop("callback_steps", None)

        if callback is not None:
            deprecate(
                "callback",
                "1.0.0",
                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )
        if callback_steps is not None:
            deprecate(
                "callback_steps",
                "1.0.0",
                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
            )

        # 0. Default height and width to unet
        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor

        original_size = original_size or (height, width)
        target_size = target_size or (height, width)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            lr_img,
            prompt,
            prompt_2,
            height,
            width,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
            callback_on_step_end_tensor_inputs,
        )
        self._guidance_scale = guidance_scale
        self._guidance_rescale = guidance_rescale
        self._clip_skip = clip_skip
        self._cross_attention_kwargs = cross_attention_kwargs
        self._denoising_end = denoising_end
        self._interrupt = False
        self.tlc_vae_latents = LocalAttention((target_size[0] // 8, target_size[1] // 8), overlap)
        self.tlc_vae_img = LocalAttention((target_size[0] // 8, target_size[1] // 8), overlap)

        # 2. Define call parameters
        batch_size = 1
        num_images_per_prompt = 1

        device = torch.device("cuda")  # self._execution_device

        # 3. Encode input prompt
        lora_scale = (
            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
        )

        num_samples = num_images_per_prompt
        with torch.inference_mode():
            (
                prompt_embeds,
                negative_prompt_embeds,
                pooled_prompt_embeds,
                negative_pooled_prompt_embeds,
            ) = self.encode_prompt(
                prompt,
                num_images_per_prompt=num_samples,
                do_classifier_free_guidance=True,
                negative_prompt=negative_prompt,
                lora_scale=lora_scale,
            )

        lr_img_list = [lr_img]
        lr_img = self.image_processor.preprocess(lr_img_list, height=height, width=width).to(
            device, dtype=prompt_embeds.dtype
        )

        # 4. Prepare timesteps
        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
        image_latents = self.prepare_image_latents(
            lr_img, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, self.do_classifier_free_guidance
        )

        image_latents = self.tlc_vae_img.grids(image_latents)

        # 5. Prepare latent variables
        num_channels_latents = self.vae.config.latent_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )
        if start_point == "lr":
            latents_condition_image = self.vae.encode(lr_img * 2 - 1).latent_dist.sample()
            latents_condition_image = latents_condition_image * self.vae.config.scaling_factor
            start_steps_tensor = torch.randint(999, 999 + 1, (latents.shape[0],), device=latents.device)
            start_steps_tensor = start_steps_tensor.long()
            latents = self.DDPMScheduler.add_noise(latents_condition_image[0:1, ...], latents, start_steps_tensor)

        latents = self.tlc_vae_latents.grids(latents)

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        views_scheduler_status = [copy.deepcopy(self.scheduler.__dict__)] * image_latents.shape[0]

        # 7. Prepare added time ids & embeddings
        add_text_embeds = pooled_prompt_embeds

        if self.do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)

        prompt_embeds = prompt_embeds.to(device)
        add_text_embeds = add_text_embeds.to(device)

        # 8. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        # 8.1 Apply denoising_end
        if (
            self.denoising_end is not None
            and isinstance(self.denoising_end, float)
            and self.denoising_end > 0
            and self.denoising_end < 1
        ):
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        # 9. Optionally get Guidance Scale Embedding
        timestep_cond = None
        if self.unet.config.time_cond_proj_dim is not None:
            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
            timestep_cond = self.get_guidance_scale_embedding(
                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
            ).to(device=device, dtype=latents.dtype)

        self._num_timesteps = len(timesteps)
        sub_latents_num = latents.shape[0]
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if i >= 1:
                    latents = self.tlc_vae_latents.grids(latents).to(dtype=latents.dtype)
                if self.interrupt:
                    continue
                concat_grid = []
                for sub_num in range(sub_latents_num):
                    self.scheduler.__dict__.update(views_scheduler_status[sub_num])
                    sub_latents = latents[sub_num, :, :, :].unsqueeze(0)
                    img_sub_latents = image_latents[sub_num, :, :, :].unsqueeze(0)
                    latent_model_input = (
                        torch.cat([sub_latents] * 2) if self.do_classifier_free_guidance else sub_latents
                    )
                    img_sub_latents = (
                        torch.cat([img_sub_latents] * 2) if self.do_classifier_free_guidance else img_sub_latents
                    )
                    scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                    pos_height = self.tlc_vae_latents.idxes[sub_num]["i"]
                    pos_width = self.tlc_vae_latents.idxes[sub_num]["j"]
                    add_time_ids = [
                        torch.tensor([original_size]),
                        torch.tensor([[pos_height, pos_width]]),
                        torch.tensor([target_size]),
                    ]
                    add_time_ids = torch.cat(add_time_ids, dim=1).to(
                        img_sub_latents.device, dtype=img_sub_latents.dtype
                    )
                    add_time_ids = add_time_ids.repeat(2, 1).to(dtype=img_sub_latents.dtype)

                    # predict the noise residual
                    added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
                    with torch.amp.autocast(
                        device.type, dtype=latents.dtype, enabled=latents.dtype != self.unet.dtype
                    ):
                        noise_pred = self.unet(
                            scaled_latent_model_input,
                            t,
                            encoder_hidden_states=prompt_embeds,
                            timestep_cond=timestep_cond,
                            cross_attention_kwargs=self.cross_attention_kwargs,
                            input_embedding=img_sub_latents,
                            add_sample=add_sample,
                            added_cond_kwargs=added_cond_kwargs,
                            return_dict=False,
                        )[0]

                    # perform guidance
                    if self.do_classifier_free_guidance:
                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                        noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)

                    if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                        # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                        noise_pred = rescale_noise_cfg(
                            noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale
                        )

                    # compute the previous noisy sample x_t -> x_t-1
                    latents_dtype = sub_latents.dtype
                    sub_latents = self.scheduler.step(
                        noise_pred, t, sub_latents, **extra_step_kwargs, return_dict=False
                    )[0]

                    views_scheduler_status[sub_num] = copy.deepcopy(self.scheduler.__dict__)
                    concat_grid.append(sub_latents)
                    if latents.dtype != sub_latents:
                        if torch.backends.mps.is_available():
                            # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                            sub_latents = sub_latents.to(latents_dtype)

                    if callback_on_step_end is not None:
                        callback_kwargs = {}
                        for k in callback_on_step_end_tensor_inputs:
                            callback_kwargs[k] = locals()[k]
                        callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)

                        latents = callback_outputs.pop("latents", latents)
                        prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
                        negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
                        add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
                        negative_pooled_prompt_embeds = callback_outputs.pop(
                            "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
                        )
                        add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        step_idx = i // getattr(self.scheduler, "order", 1)
                        callback(step_idx, t, latents)

                if XLA_AVAILABLE:
                    xm.mark_step()

                latents = self.tlc_vae_latents.grids_inverse(torch.cat(concat_grid, dim=0)).to(sub_latents.dtype)

        if not output_type == "latent":
            # make sure the VAE is in float32 mode, as it overflows in float16
            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast

            if needs_upcasting:
                self.upcast_vae()
                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
            elif latents.dtype != self.vae.dtype:
                if torch.backends.mps.is_available():
                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
                    self.vae = self.vae.to(latents.dtype)

            # unscale/denormalize the latents
            # denormalize with the mean and std if available and not None
            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
            if has_latents_mean and has_latents_std:
                latents_mean = (
                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
                )
                latents_std = (
                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
                )
                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
            else:
                latents = latents / self.vae.config.scaling_factor

            image = self.vae.decode(latents, return_dict=False)[0]

            # cast back to fp16 if needed
            if needs_upcasting:
                self.vae.to(dtype=torch.float16)
        else:
            image = latents

        if not output_type == "latent":
            # apply watermark if available
            if self.watermark is not None:
                image = self.watermark.apply_watermark(image)

            image = self.image_processor.postprocess(image, output_type=output_type)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return StableDiffusionXLPipelineOutput(images=image)
