diff --git "a/v0.33.0/pipeline_faithdiff_stable_diffusion_xl.py" "b/v0.33.0/pipeline_faithdiff_stable_diffusion_xl.py"
new file mode 100644--- /dev/null
+++ "b/v0.33.0/pipeline_faithdiff_stable_diffusion_xl.py"
@@ -0,0 +1,2269 @@
+# 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)