From 21a03f93ef0fbfa5f7a7d97708f75149b1d1b3b0 Mon Sep 17 00:00:00 2001
From: Dhruv Nair <dhruv.nair@gmail.com>
Date: Mon, 10 Nov 2025 08:07:26 +0100
Subject: [PATCH 1/2] update

---
 examples/community/lpw_stable_diffusion_xl.py |   16 +-
 .../masked_stable_diffusion_xl_img2img.py     |    2 +-
 examples/community/mixture_tiling_sdxl.py     | 2455 ++++++++---------
 .../community/mod_controlnet_tile_sr_sdxl.py  |   23 +-
 .../pipeline_controlnet_xl_kolors.py          |   22 +-
 .../pipeline_controlnet_xl_kolors_img2img.py  |   22 +-
 .../pipeline_controlnet_xl_kolors_inpaint.py  |   22 +-
 .../community/pipeline_demofusion_sdxl.py     |   19 +-
 .../pipeline_faithdiff_stable_diffusion_xl.py |   30 +-
 .../pipeline_kolors_differential_img2img.py   |   22 +-
 .../community/pipeline_kolors_inpainting.py   |   26 +-
 .../community/pipeline_sdxl_style_aligned.py  |   21 +-
 ...pipeline_stable_diffusion_upscale_ldm3d.py |    2 +-
 ...ne_stable_diffusion_xl_attentive_eraser.py |   26 +-
 ..._stable_diffusion_xl_controlnet_adapter.py |   17 +-
 ...diffusion_xl_controlnet_adapter_inpaint.py |   17 +-
 ...table_diffusion_xl_differential_img2img.py |   22 +-
 ...e_stable_diffusion_xl_instandid_img2img.py |    2 +-
 .../pipeline_stable_diffusion_xl_instantid.py |    2 +-
 .../pipeline_stable_diffusion_xl_ipex.py      |   24 +-
 ...table_diffusion_xl_controlnet_reference.py |    4 +-
 .../stable_diffusion_xl_reference.py          |    4 +-
 .../animatediff/pipeline_animatediff_sdxl.py  |   25 +-
 .../pipelines/aura_flow/pipeline_aura_flow.py |   21 +-
 .../pipeline_controlnet_inpaint_sd_xl.py      |   22 +-
 .../controlnet/pipeline_controlnet_sd_xl.py   |   22 +-
 .../pipeline_controlnet_sd_xl_img2img.py      |   22 +-
 ...pipeline_controlnet_union_inpaint_sd_xl.py |   22 +-
 .../pipeline_controlnet_union_sd_xl.py        |   23 +-
 ...pipeline_controlnet_union_sd_xl_img2img.py |   22 +-
 .../pipeline_controlnet_xs_sd_xl.py           |   25 +-
 .../pipelines/kolors/pipeline_kolors.py       |   22 +-
 .../kolors/pipeline_kolors_img2img.py         |   22 +-
 .../pipeline_leditspp_stable_diffusion_xl.py  |   24 +-
 .../pag/pipeline_pag_controlnet_sd_xl.py      |   23 +-
 .../pipeline_pag_controlnet_sd_xl_img2img.py  |   23 +-
 .../pipelines/pag/pipeline_pag_kolors.py      |   22 +-
 .../pipelines/pag/pipeline_pag_sd_xl.py       |   25 +-
 .../pag/pipeline_pag_sd_xl_img2img.py         |   23 +-
 .../pag/pipeline_pag_sd_xl_inpaint.py         |   23 +-
 .../pipeline_stable_diffusion_upscale.py      |   22 +-
 ...ipeline_stable_diffusion_xl_k_diffusion.py |   25 +-
 .../pipeline_stable_diffusion_xl.py           |   24 +-
 .../pipeline_stable_diffusion_xl_img2img.py   |   22 +-
 .../pipeline_stable_diffusion_xl_inpaint.py   |   22 +-
 ...ne_stable_diffusion_xl_instruct_pix2pix.py |   26 +-
 .../pipeline_stable_diffusion_xl_adapter.py   |   23 +-
 .../pipeline_text_to_video_zero_sdxl.py       |   25 +-
 48 files changed, 1335 insertions(+), 2065 deletions(-)

diff --git a/examples/community/lpw_stable_diffusion_xl.py b/examples/community/lpw_stable_diffusion_xl.py
index 95d3405df50d..35157492508a 100644
--- a/examples/community/lpw_stable_diffusion_xl.py
+++ b/examples/community/lpw_stable_diffusion_xl.py
@@ -29,7 +29,6 @@
     TextualInversionLoaderMixin,
 )
 from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from diffusers.models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -1328,18 +1327,7 @@ def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, d
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
@@ -1921,7 +1909,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/examples/community/masked_stable_diffusion_xl_img2img.py b/examples/community/masked_stable_diffusion_xl_img2img.py
index 14d8c7c2da78..5a5024064d6c 100644
--- a/examples/community/masked_stable_diffusion_xl_img2img.py
+++ b/examples/community/masked_stable_diffusion_xl_img2img.py
@@ -481,7 +481,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
             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
diff --git a/examples/community/mixture_tiling_sdxl.py b/examples/community/mixture_tiling_sdxl.py
index 66c338b5b2a2..98fbbc246476 100644
--- a/examples/community/mixture_tiling_sdxl.py
+++ b/examples/community/mixture_tiling_sdxl.py
@@ -1,1237 +1,1218 @@
-# Copyright 2025 The DEVAIEXP 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 inspect
-from enum import Enum
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-from transformers import (
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-)
-
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.loaders import (
-    FromSingleFileMixin,
-    StableDiffusionXLLoraLoaderMixin,
-    TextualInversionLoaderMixin,
-)
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
-from diffusers.models.lora import adjust_lora_scale_text_encoder
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
-from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
-from diffusers.schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    is_invisible_watermark_available,
-    is_torch_xla_available,
-    logging,
-    replace_example_docstring,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.utils.torch_utils import randn_tensor
-
-
-try:
-    from ligo.segments import segment
-except ImportError:
-    raise ImportError("Please install transformers and ligo-segments to use the mixture pipeline")
-
-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 torch
-        >>> from diffusers import StableDiffusionXLPipeline
-
-        >>> pipe = StableDiffusionXLPipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-
-        >>> prompt = "a photo of an astronaut riding a horse on mars"
-        >>> image = pipe(prompt).images[0]
-        ```
-"""
-
-
-def _tile2pixel_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
-    """Given a tile row and column numbers returns the range of pixels affected by that tiles in the overall image
-
-    Returns a tuple with:
-        - Starting coordinates of rows in pixel space
-        - Ending coordinates of rows in pixel space
-        - Starting coordinates of columns in pixel space
-        - Ending coordinates of columns in pixel space
-    """
-    px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap)
-    px_row_end = px_row_init + tile_height
-    px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap)
-    px_col_end = px_col_init + tile_width
-    return px_row_init, px_row_end, px_col_init, px_col_end
-
-
-def _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end):
-    """Translates coordinates in pixel space to coordinates in latent space"""
-    return px_row_init // 8, px_row_end // 8, px_col_init // 8, px_col_end // 8
-
-
-def _tile2latent_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
-    """Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image
-
-    Returns a tuple with:
-        - Starting coordinates of rows in latent space
-        - Ending coordinates of rows in latent space
-        - Starting coordinates of columns in latent space
-        - Ending coordinates of columns in latent space
-    """
-    px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
-        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
-    )
-    return _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end)
-
-
-def _tile2latent_exclusive_indices(
-    tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, rows, columns
-):
-    """Given a tile row and column numbers returns the range of latents affected only by that tile in the overall image
-
-    Returns a tuple with:
-        - Starting coordinates of rows in latent space
-        - Ending coordinates of rows in latent space
-        - Starting coordinates of columns in latent space
-        - Ending coordinates of columns in latent space
-    """
-    row_init, row_end, col_init, col_end = _tile2latent_indices(
-        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
-    )
-    row_segment = segment(row_init, row_end)
-    col_segment = segment(col_init, col_end)
-    # Iterate over the rest of tiles, clipping the region for the current tile
-    for row in range(rows):
-        for column in range(columns):
-            if row != tile_row and column != tile_col:
-                clip_row_init, clip_row_end, clip_col_init, clip_col_end = _tile2latent_indices(
-                    row, column, tile_width, tile_height, tile_row_overlap, tile_col_overlap
-                )
-                row_segment = row_segment - segment(clip_row_init, clip_row_end)
-                col_segment = col_segment - segment(clip_col_init, clip_col_end)
-    # return row_init, row_end, col_init, col_end
-    return row_segment[0], row_segment[1], col_segment[0], col_segment[1]
-
-
-def _get_crops_coords_list(num_rows, num_cols, output_width):
-    """
-    Generates a list of lists of `crops_coords_top_left` tuples for focusing on
-    different horizontal parts of an image, and repeats this list for the specified
-    number of rows in the output structure.
-
-    This function calculates `crops_coords_top_left` tuples to create horizontal
-    focus variations (like left, center, right focus) based on `output_width`
-    and `num_cols` (which represents the number of horizontal focus points/columns).
-    It then repeats the *list* of these horizontal focus tuples `num_rows` times to
-    create the final list of lists output structure.
-
-    Args:
-        num_rows (int): The desired number of rows in the output list of lists.
-                          This determines how many times the list of horizontal
-                          focus variations will be repeated.
-        num_cols (int): The number of horizontal focus points (columns) to generate.
-                          This determines how many horizontal focus variations are
-                          created based on dividing the `output_width`.
-        output_width (int): The desired width of the output image.
-
-    Returns:
-        list[list[tuple[int, int]]]: A list of lists of tuples. Each inner list
-                                     contains `num_cols` tuples of `(ctop, cleft)`,
-                                     representing horizontal focus points. The outer list
-                                     contains `num_rows` such inner lists.
-    """
-    crops_coords_list = []
-    if num_cols <= 0:
-        crops_coords_list = []
-    elif num_cols == 1:
-        crops_coords_list = [(0, 0)]
-    else:
-        section_width = output_width / num_cols
-        for i in range(num_cols):
-            cleft = int(round(i * section_width))
-            crops_coords_list.append((0, cleft))
-
-    result_list = []
-    for _ in range(num_rows):
-        result_list.append(list(crops_coords_list))
-
-    return result_list
-
-
-# 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):
-    r"""
-    Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
-    Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
-    Flawed](https://huggingface.co/papers/2305.08891).
-
-    Args:
-        noise_cfg (`torch.Tensor`):
-            The predicted noise tensor for the guided diffusion process.
-        noise_pred_text (`torch.Tensor`):
-            The predicted noise tensor for the text-guided diffusion process.
-        guidance_rescale (`float`, *optional*, defaults to 0.0):
-            A rescale factor applied to the noise predictions.
-
-    Returns:
-        noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
-    """
-    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.retrieve_timesteps
-def retrieve_timesteps(
-    scheduler,
-    num_inference_steps: Optional[int] = None,
-    device: Optional[Union[str, torch.device]] = None,
-    timesteps: Optional[List[int]] = None,
-    sigmas: Optional[List[float]] = None,
-    **kwargs,
-):
-    r"""
-    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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
-            `num_inference_steps` and `sigmas` must be `None`.
-        sigmas (`List[float]`, *optional*):
-            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
-            `num_inference_steps` and `timesteps` 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 and sigmas is not None:
-        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
-    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)
-    elif sigmas is not None:
-        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
-        if not accept_sigmas:
-            raise ValueError(
-                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
-                f" sigmas schedules. Please check whether you are using the correct scheduler."
-            )
-        scheduler.set_timesteps(sigmas=sigmas, 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 StableDiffusionXLTilingPipeline(
-    DiffusionPipeline,
-    StableDiffusionMixin,
-    FromSingleFileMixin,
-    StableDiffusionXLLoraLoaderMixin,
-    TextualInversionLoaderMixin,
-):
-    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
-
-    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.
-    """
-
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
-    _optional_components = [
-        "tokenizer",
-        "tokenizer_2",
-        "text_encoder",
-        "text_encoder_2",
-    ]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        text_encoder_2: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        tokenizer_2: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        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) if getattr(self, "vae", None) else 8
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-        self.default_sample_size = (
-            self.unet.config.sample_size
-            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
-            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
-
-    class SeedTilesMode(Enum):
-        """Modes in which the latents of a particular tile can be re-seeded"""
-
-        FULL = "full"
-        EXCLUSIVE = "exclusive"
-
-    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.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.Tensor] = 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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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 = 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]
-        )
-
-        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}"
-                    )
-
-                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
-                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
-                    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
-                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
-                    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://huggingface.co/papers/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_inputs(self, prompt, height, width, grid_cols, seed_tiles_mode, tiles_mode):
-        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 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)}")
-
-        if not isinstance(prompt, list) or not all(isinstance(row, list) for row in prompt):
-            raise ValueError(f"`prompt` has to be a list of lists but is {type(prompt)}")
-
-        if not all(len(row) == grid_cols for row in prompt):
-            raise ValueError("All prompt rows must have the same number of prompt columns")
-
-        if not isinstance(seed_tiles_mode, str) and (
-            not isinstance(seed_tiles_mode, list) or not all(isinstance(row, list) for row in seed_tiles_mode)
-        ):
-            raise ValueError(f"`seed_tiles_mode` has to be a string or list of lists but is {type(prompt)}")
-
-        if any(mode not in tiles_mode for row in seed_tiles_mode for mode in row):
-            raise ValueError(f"Seed tiles mode must be one of {tiles_mode}")
-
-    def _get_add_time_ids(
-        self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
-    ):
-        add_time_ids = list(original_size + crops_coords_top_left + target_size)
-
-        passed_add_embed_dim = (
-            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
-        )
-        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
-
-        if expected_add_embed_dim != passed_add_embed_dim:
-            raise ValueError(
-                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
-            )
-
-        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
-        return add_time_ids
-
-    def _gaussian_weights(self, tile_width, tile_height, nbatches, device, dtype):
-        """Generates a gaussian mask of weights for tile contributions"""
-        import numpy as np
-        from numpy import exp, pi, sqrt
-
-        latent_width = tile_width // 8
-        latent_height = tile_height // 8
-
-        var = 0.01
-        midpoint = (latent_width - 1) / 2  # -1 because index goes from 0 to latent_width - 1
-        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 = np.outer(y_probs, x_probs)
-        weights_torch = torch.tensor(weights_np, device=device)
-        weights_torch = weights_torch.to(dtype)
-        return torch.tile(weights_torch, (nbatches, self.unet.config.in_channels, 1, 1))
-
-    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,
-                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.Tensor:
-        """
-        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.Tensor`: 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
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @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://huggingface.co/papers/2205.11487 . `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 num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 5.0,
-        negative_prompt: 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,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        original_size: Optional[Tuple[int, int]] = None,
-        crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
-        target_size: Optional[Tuple[int, int]] = None,
-        negative_original_size: Optional[Tuple[int, int]] = None,
-        negative_crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
-        negative_target_size: Optional[Tuple[int, int]] = None,
-        clip_skip: Optional[int] = None,
-        tile_height: Optional[int] = 1024,
-        tile_width: Optional[int] = 1024,
-        tile_row_overlap: Optional[int] = 128,
-        tile_col_overlap: Optional[int] = 128,
-        guidance_scale_tiles: Optional[List[List[float]]] = None,
-        seed_tiles: Optional[List[List[int]]] = None,
-        seed_tiles_mode: Optional[Union[str, List[List[str]]]] = "full",
-        seed_reroll_regions: Optional[List[Tuple[int, int, int, int, int]]] = None,
-        **kwargs,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            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.
-            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.
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://huggingface.co/papers/2205.11487). 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`).
-            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://huggingface.co/papers/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.
-            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).
-            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).
-            crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
-                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
-                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
-                `crops_coords_top_left` to (0, 0). 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).
-            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a specific image resolution. 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). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
-                To negatively condition the generation process based on a specific crop coordinates. 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). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a target image resolution. It should be as same
-                as the `target_size` for most cases. 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). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            tile_height (`int`, *optional*, defaults to 1024):
-                Height of each grid tile in pixels.
-            tile_width (`int`, *optional*, defaults to 1024):
-                Width of each grid tile in pixels.
-            tile_row_overlap (`int`, *optional*, defaults to 128):
-                Number of overlapping pixels between tiles in consecutive rows.
-            tile_col_overlap (`int`, *optional*, defaults to 128):
-                Number of overlapping pixels between tiles in consecutive columns.
-            guidance_scale_tiles (`List[List[float]]`, *optional*):
-                Specific weights for classifier-free guidance in each tile. If `None`, the value provided in `guidance_scale` will be used.
-            seed_tiles (`List[List[int]]`, *optional*):
-                Specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard `generator` parameter.
-            seed_tiles_mode (`Union[str, List[List[str]]]`, *optional*, defaults to `"full"`):
-                Mode for seeding tiles, can be `"full"` or `"exclusive"`. If `"full"`, all the latents affected by the tile will be overridden. If `"exclusive"`, only the latents that are exclusively affected by this tile (and no other tiles) will be overridden.
-            seed_reroll_regions (`List[Tuple[int, int, int, int, int]]`, *optional*):
-                A list of tuples in the form of `(start_row, end_row, start_column, end_column, seed)` defining regions in pixel space for which the latents will be overridden using the given seed. Takes priority over `seed_tiles`.
-            **kwargs (`Dict[str, Any]`, *optional*):
-                 Additional optional keyword arguments to be passed to the `unet.__call__` and `scheduler.step` functions.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLTilingPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLTilingPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is a list with the generated images.
-        """
-
-        # 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)
-        negative_original_size = negative_original_size or (height, width)
-        negative_target_size = negative_target_size or (height, width)
-
-        self._guidance_scale = guidance_scale
-        self._clip_skip = clip_skip
-        self._cross_attention_kwargs = cross_attention_kwargs
-        self._interrupt = False
-
-        grid_rows = len(prompt)
-        grid_cols = len(prompt[0])
-
-        tiles_mode = [mode.value for mode in self.SeedTilesMode]
-
-        if isinstance(seed_tiles_mode, str):
-            seed_tiles_mode = [[seed_tiles_mode for _ in range(len(row))] for row in prompt]
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            height,
-            width,
-            grid_cols,
-            seed_tiles_mode,
-            tiles_mode,
-        )
-
-        if seed_reroll_regions is None:
-            seed_reroll_regions = []
-
-        batch_size = 1
-
-        device = self._execution_device
-
-        # update crops coords list
-        crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)
-        if negative_original_size is not None and negative_target_size is not None:
-            negative_crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)
-
-        # update height and width tile size and tile overlap size
-        height = tile_height + (grid_rows - 1) * (tile_height - tile_row_overlap)
-        width = tile_width + (grid_cols - 1) * (tile_width - tile_col_overlap)
-
-        # 3. Encode input prompt
-        lora_scale = (
-            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
-        )
-        text_embeddings = [
-            [
-                self.encode_prompt(
-                    prompt=col,
-                    device=device,
-                    num_images_per_prompt=num_images_per_prompt,
-                    do_classifier_free_guidance=self.do_classifier_free_guidance,
-                    negative_prompt=negative_prompt,
-                    prompt_embeds=None,
-                    negative_prompt_embeds=None,
-                    pooled_prompt_embeds=None,
-                    negative_pooled_prompt_embeds=None,
-                    lora_scale=lora_scale,
-                    clip_skip=self.clip_skip,
-                )
-                for col in row
-            ]
-            for row in prompt
-        ]
-
-        # 3. Prepare latents
-        latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
-        dtype = text_embeddings[0][0][0].dtype
-        latents = randn_tensor(latents_shape, generator=generator, device=device, dtype=dtype)
-
-        # 3.1 overwrite latents for specific tiles if provided
-        if seed_tiles is not None:
-            for row in range(grid_rows):
-                for col in range(grid_cols):
-                    if (seed_tile := seed_tiles[row][col]) is not None:
-                        mode = seed_tiles_mode[row][col]
-                        if mode == self.SeedTilesMode.FULL.value:
-                            row_init, row_end, col_init, col_end = _tile2latent_indices(
-                                row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
-                            )
-                        else:
-                            row_init, row_end, col_init, col_end = _tile2latent_exclusive_indices(
-                                row,
-                                col,
-                                tile_width,
-                                tile_height,
-                                tile_row_overlap,
-                                tile_col_overlap,
-                                grid_rows,
-                                grid_cols,
-                            )
-                        tile_generator = torch.Generator(device).manual_seed(seed_tile)
-                        tile_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
-                        latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
-                            tile_shape, generator=tile_generator, device=device
-                        )
-
-        # 3.2 overwrite again for seed reroll regions
-        for row_init, row_end, col_init, col_end, seed_reroll in seed_reroll_regions:
-            row_init, row_end, col_init, col_end = _pixel2latent_indices(
-                row_init, row_end, col_init, col_end
-            )  # to latent space coordinates
-            reroll_generator = torch.Generator(device).manual_seed(seed_reroll)
-            region_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
-            latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
-                region_shape, generator=reroll_generator, device=device
-            )
-
-        # 4. Prepare timesteps
-        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
-        extra_set_kwargs = {}
-        if accepts_offset:
-            extra_set_kwargs["offset"] = 1
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler, num_inference_steps, device, None, None, **extra_set_kwargs
-        )
-
-        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
-        if isinstance(self.scheduler, LMSDiscreteScheduler):
-            latents = latents * self.scheduler.sigmas[0]
-
-        # 5. 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)
-
-        # 6. Prepare added time ids & embeddings
-        # text_embeddings order: prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
-        embeddings_and_added_time = []
-        for row in range(grid_rows):
-            addition_embed_type_row = []
-            for col in range(grid_cols):
-                # extract generated values
-                prompt_embeds = text_embeddings[row][col][0]
-                negative_prompt_embeds = text_embeddings[row][col][1]
-                pooled_prompt_embeds = text_embeddings[row][col][2]
-                negative_pooled_prompt_embeds = text_embeddings[row][col][3]
-
-                add_text_embeds = pooled_prompt_embeds
-                if self.text_encoder_2 is None:
-                    text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
-                else:
-                    text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
-                add_time_ids = self._get_add_time_ids(
-                    original_size,
-                    crops_coords_top_left[row][col],
-                    target_size,
-                    dtype=prompt_embeds.dtype,
-                    text_encoder_projection_dim=text_encoder_projection_dim,
-                )
-                if negative_original_size is not None and negative_target_size is not None:
-                    negative_add_time_ids = self._get_add_time_ids(
-                        negative_original_size,
-                        negative_crops_coords_top_left[row][col],
-                        negative_target_size,
-                        dtype=prompt_embeds.dtype,
-                        text_encoder_projection_dim=text_encoder_projection_dim,
-                    )
-                else:
-                    negative_add_time_ids = add_time_ids
-
-                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)
-                    add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
-
-                prompt_embeds = prompt_embeds.to(device)
-                add_text_embeds = add_text_embeds.to(device)
-                add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
-                addition_embed_type_row.append((prompt_embeds, add_text_embeds, add_time_ids))
-            embeddings_and_added_time.append(addition_embed_type_row)
-
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
-        # 7. Mask for tile weights strength
-        tile_weights = self._gaussian_weights(tile_width, tile_height, batch_size, device, torch.float32)
-
-        # 8. Denoising loop
-        self._num_timesteps = len(timesteps)
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # Diffuse each tile
-                noise_preds = []
-                for row in range(grid_rows):
-                    noise_preds_row = []
-                    for col in range(grid_cols):
-                        if self.interrupt:
-                            continue
-                        px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
-                            row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
-                        )
-                        tile_latents = latents[:, :, px_row_init:px_row_end, px_col_init:px_col_end]
-                        # expand the latents if we are doing classifier free guidance
-                        latent_model_input = (
-                            torch.cat([tile_latents] * 2) if self.do_classifier_free_guidance else tile_latents
-                        )
-                        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                        # predict the noise residual
-                        added_cond_kwargs = {
-                            "text_embeds": embeddings_and_added_time[row][col][1],
-                            "time_ids": embeddings_and_added_time[row][col][2],
-                        }
-                        with torch.amp.autocast(device.type, dtype=dtype, enabled=dtype != self.unet.dtype):
-                            noise_pred = self.unet(
-                                latent_model_input,
-                                t,
-                                encoder_hidden_states=embeddings_and_added_time[row][col][0],
-                                cross_attention_kwargs=self.cross_attention_kwargs,
-                                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)
-                            guidance = (
-                                guidance_scale
-                                if guidance_scale_tiles is None or guidance_scale_tiles[row][col] is None
-                                else guidance_scale_tiles[row][col]
-                            )
-                            noise_pred_tile = noise_pred_uncond + guidance * (noise_pred_text - noise_pred_uncond)
-                            noise_preds_row.append(noise_pred_tile)
-                    noise_preds.append(noise_preds_row)
-
-                # Stitch noise predictions for all tiles
-                noise_pred = torch.zeros(latents.shape, device=device)
-                contributors = torch.zeros(latents.shape, device=device)
-
-                # Add each tile contribution to overall latents
-                for row in range(grid_rows):
-                    for col in range(grid_cols):
-                        px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
-                            row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
-                        )
-                        noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += (
-                            noise_preds[row][col] * tile_weights
-                        )
-                        contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights
-
-                # Average overlapping areas with more than 1 contributor
-                noise_pred /= contributors
-                noise_pred = noise_pred.to(dtype)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents_dtype = latents.dtype
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-                if latents.dtype != latents_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
-                        latents = latents.to(latents_dtype)
-
-                # update progress bar
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-                if XLA_AVAILABLE:
-                    xm.mark_step()
-
-        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)
+# Copyright 2025 The DEVAIEXP 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 inspect
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from transformers import (
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+)
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    is_invisible_watermark_available,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+
+
+try:
+    from ligo.segments import segment
+except ImportError:
+    raise ImportError("Please install transformers and ligo-segments to use the mixture pipeline")
+
+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 torch
+        >>> from diffusers import StableDiffusionXLPipeline
+
+        >>> pipe = StableDiffusionXLPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ... )
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "a photo of an astronaut riding a horse on mars"
+        >>> image = pipe(prompt).images[0]
+        ```
+"""
+
+
+def _tile2pixel_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
+    """Given a tile row and column numbers returns the range of pixels affected by that tiles in the overall image
+
+    Returns a tuple with:
+        - Starting coordinates of rows in pixel space
+        - Ending coordinates of rows in pixel space
+        - Starting coordinates of columns in pixel space
+        - Ending coordinates of columns in pixel space
+    """
+    px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap)
+    px_row_end = px_row_init + tile_height
+    px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap)
+    px_col_end = px_col_init + tile_width
+    return px_row_init, px_row_end, px_col_init, px_col_end
+
+
+def _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end):
+    """Translates coordinates in pixel space to coordinates in latent space"""
+    return px_row_init // 8, px_row_end // 8, px_col_init // 8, px_col_end // 8
+
+
+def _tile2latent_indices(tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap):
+    """Given a tile row and column numbers returns the range of latents affected by that tiles in the overall image
+
+    Returns a tuple with:
+        - Starting coordinates of rows in latent space
+        - Ending coordinates of rows in latent space
+        - Starting coordinates of columns in latent space
+        - Ending coordinates of columns in latent space
+    """
+    px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
+        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+    )
+    return _pixel2latent_indices(px_row_init, px_row_end, px_col_init, px_col_end)
+
+
+def _tile2latent_exclusive_indices(
+    tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, rows, columns
+):
+    """Given a tile row and column numbers returns the range of latents affected only by that tile in the overall image
+
+    Returns a tuple with:
+        - Starting coordinates of rows in latent space
+        - Ending coordinates of rows in latent space
+        - Starting coordinates of columns in latent space
+        - Ending coordinates of columns in latent space
+    """
+    row_init, row_end, col_init, col_end = _tile2latent_indices(
+        tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+    )
+    row_segment = segment(row_init, row_end)
+    col_segment = segment(col_init, col_end)
+    # Iterate over the rest of tiles, clipping the region for the current tile
+    for row in range(rows):
+        for column in range(columns):
+            if row != tile_row and column != tile_col:
+                clip_row_init, clip_row_end, clip_col_init, clip_col_end = _tile2latent_indices(
+                    row, column, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                )
+                row_segment = row_segment - segment(clip_row_init, clip_row_end)
+                col_segment = col_segment - segment(clip_col_init, clip_col_end)
+    # return row_init, row_end, col_init, col_end
+    return row_segment[0], row_segment[1], col_segment[0], col_segment[1]
+
+
+def _get_crops_coords_list(num_rows, num_cols, output_width):
+    """
+    Generates a list of lists of `crops_coords_top_left` tuples for focusing on
+    different horizontal parts of an image, and repeats this list for the specified
+    number of rows in the output structure.
+
+    This function calculates `crops_coords_top_left` tuples to create horizontal
+    focus variations (like left, center, right focus) based on `output_width`
+    and `num_cols` (which represents the number of horizontal focus points/columns).
+    It then repeats the *list* of these horizontal focus tuples `num_rows` times to
+    create the final list of lists output structure.
+
+    Args:
+        num_rows (int): The desired number of rows in the output list of lists.
+                          This determines how many times the list of horizontal
+                          focus variations will be repeated.
+        num_cols (int): The number of horizontal focus points (columns) to generate.
+                          This determines how many horizontal focus variations are
+                          created based on dividing the `output_width`.
+        output_width (int): The desired width of the output image.
+
+    Returns:
+        list[list[tuple[int, int]]]: A list of lists of tuples. Each inner list
+                                     contains `num_cols` tuples of `(ctop, cleft)`,
+                                     representing horizontal focus points. The outer list
+                                     contains `num_rows` such inner lists.
+    """
+    crops_coords_list = []
+    if num_cols <= 0:
+        crops_coords_list = []
+    elif num_cols == 1:
+        crops_coords_list = [(0, 0)]
+    else:
+        section_width = output_width / num_cols
+        for i in range(num_cols):
+            cleft = int(round(i * section_width))
+            crops_coords_list.append((0, cleft))
+
+    result_list = []
+    for _ in range(num_rows):
+        result_list.append(list(crops_coords_list))
+
+    return result_list
+
+
+# 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):
+    r"""
+    Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on
+    Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are
+    Flawed](https://huggingface.co/papers/2305.08891).
+
+    Args:
+        noise_cfg (`torch.Tensor`):
+            The predicted noise tensor for the guided diffusion process.
+        noise_pred_text (`torch.Tensor`):
+            The predicted noise tensor for the text-guided diffusion process.
+        guidance_rescale (`float`, *optional*, defaults to 0.0):
+            A rescale factor applied to the noise predictions.
+
+    Returns:
+        noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
+    """
+    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.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` 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 and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    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)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, 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 StableDiffusionXLTilingPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    FromSingleFileMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+):
+    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
+
+    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.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        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) if getattr(self, "vae", None) else 8
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+        self.default_sample_size = (
+            self.unet.config.sample_size
+            if hasattr(self, "unet") and self.unet is not None and hasattr(self.unet.config, "sample_size")
+            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
+
+    class SeedTilesMode(Enum):
+        """Modes in which the latents of a particular tile can be re-seeded"""
+
+        FULL = "full"
+        EXCLUSIVE = "exclusive"
+
+    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.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = 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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor`, *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 = 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]
+        )
+
+        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}"
+                    )
+
+                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
+                if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
+                    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
+                if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
+                    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://huggingface.co/papers/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_inputs(self, prompt, height, width, grid_cols, seed_tiles_mode, tiles_mode):
+        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 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)}")
+
+        if not isinstance(prompt, list) or not all(isinstance(row, list) for row in prompt):
+            raise ValueError(f"`prompt` has to be a list of lists but is {type(prompt)}")
+
+        if not all(len(row) == grid_cols for row in prompt):
+            raise ValueError("All prompt rows must have the same number of prompt columns")
+
+        if not isinstance(seed_tiles_mode, str) and (
+            not isinstance(seed_tiles_mode, list) or not all(isinstance(row, list) for row in seed_tiles_mode)
+        ):
+            raise ValueError(f"`seed_tiles_mode` has to be a string or list of lists but is {type(prompt)}")
+
+        if any(mode not in tiles_mode for row in seed_tiles_mode for mode in row):
+            raise ValueError(f"Seed tiles mode must be one of {tiles_mode}")
+
+    def _get_add_time_ids(
+        self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
+    ):
+        add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        passed_add_embed_dim = (
+            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
+        )
+        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+        if expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        return add_time_ids
+
+    def _gaussian_weights(self, tile_width, tile_height, nbatches, device, dtype):
+        """Generates a gaussian mask of weights for tile contributions"""
+        import numpy as np
+        from numpy import exp, pi, sqrt
+
+        latent_width = tile_width // 8
+        latent_height = tile_height // 8
+
+        var = 0.01
+        midpoint = (latent_width - 1) / 2  # -1 because index goes from 0 to latent_width - 1
+        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 = np.outer(y_probs, x_probs)
+        weights_torch = torch.tensor(weights_np, device=device)
+        weights_torch = weights_torch.to(dtype)
+        return torch.tile(weights_torch, (nbatches, self.unet.config.in_channels, 1, 1))
+
+    def upcast_vae(self):
+        deprecate("`upcast_vae` is deprecated")
+
+    # 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.Tensor:
+        """
+        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.Tensor`: 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
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @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://huggingface.co/papers/2205.11487 . `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 num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        negative_prompt: 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,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        original_size: Optional[Tuple[int, int]] = None,
+        crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
+        target_size: Optional[Tuple[int, int]] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Optional[List[List[Tuple[int, int]]]] = None,
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        tile_height: Optional[int] = 1024,
+        tile_width: Optional[int] = 1024,
+        tile_row_overlap: Optional[int] = 128,
+        tile_col_overlap: Optional[int] = 128,
+        guidance_scale_tiles: Optional[List[List[float]]] = None,
+        seed_tiles: Optional[List[List[int]]] = None,
+        seed_tiles_mode: Optional[Union[str, List[List[str]]]] = "full",
+        seed_reroll_regions: Optional[List[Tuple[int, int, int, int, int]]] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            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.
+            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.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://huggingface.co/papers/2205.11487). 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`).
+            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://huggingface.co/papers/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.
+            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).
+            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).
+            crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). 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).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. 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). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`List[List[Tuple[int, int]]]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. 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). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. 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). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            tile_height (`int`, *optional*, defaults to 1024):
+                Height of each grid tile in pixels.
+            tile_width (`int`, *optional*, defaults to 1024):
+                Width of each grid tile in pixels.
+            tile_row_overlap (`int`, *optional*, defaults to 128):
+                Number of overlapping pixels between tiles in consecutive rows.
+            tile_col_overlap (`int`, *optional*, defaults to 128):
+                Number of overlapping pixels between tiles in consecutive columns.
+            guidance_scale_tiles (`List[List[float]]`, *optional*):
+                Specific weights for classifier-free guidance in each tile. If `None`, the value provided in `guidance_scale` will be used.
+            seed_tiles (`List[List[int]]`, *optional*):
+                Specific seeds for the initialization latents in each tile. These will override the latents generated for the whole canvas using the standard `generator` parameter.
+            seed_tiles_mode (`Union[str, List[List[str]]]`, *optional*, defaults to `"full"`):
+                Mode for seeding tiles, can be `"full"` or `"exclusive"`. If `"full"`, all the latents affected by the tile will be overridden. If `"exclusive"`, only the latents that are exclusively affected by this tile (and no other tiles) will be overridden.
+            seed_reroll_regions (`List[Tuple[int, int, int, int, int]]`, *optional*):
+                A list of tuples in the form of `(start_row, end_row, start_column, end_column, seed)` defining regions in pixel space for which the latents will be overridden using the given seed. Takes priority over `seed_tiles`.
+            **kwargs (`Dict[str, Any]`, *optional*):
+                 Additional optional keyword arguments to be passed to the `unet.__call__` and `scheduler.step` functions.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLTilingPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLTilingPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        # 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)
+        negative_original_size = negative_original_size or (height, width)
+        negative_target_size = negative_target_size or (height, width)
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
+
+        grid_rows = len(prompt)
+        grid_cols = len(prompt[0])
+
+        tiles_mode = [mode.value for mode in self.SeedTilesMode]
+
+        if isinstance(seed_tiles_mode, str):
+            seed_tiles_mode = [[seed_tiles_mode for _ in range(len(row))] for row in prompt]
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            grid_cols,
+            seed_tiles_mode,
+            tiles_mode,
+        )
+
+        if seed_reroll_regions is None:
+            seed_reroll_regions = []
+
+        batch_size = 1
+
+        device = self._execution_device
+
+        # update crops coords list
+        crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_crops_coords_top_left = _get_crops_coords_list(grid_rows, grid_cols, tile_width)
+
+        # update height and width tile size and tile overlap size
+        height = tile_height + (grid_rows - 1) * (tile_height - tile_row_overlap)
+        width = tile_width + (grid_cols - 1) * (tile_width - tile_col_overlap)
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+        text_embeddings = [
+            [
+                self.encode_prompt(
+                    prompt=col,
+                    device=device,
+                    num_images_per_prompt=num_images_per_prompt,
+                    do_classifier_free_guidance=self.do_classifier_free_guidance,
+                    negative_prompt=negative_prompt,
+                    prompt_embeds=None,
+                    negative_prompt_embeds=None,
+                    pooled_prompt_embeds=None,
+                    negative_pooled_prompt_embeds=None,
+                    lora_scale=lora_scale,
+                    clip_skip=self.clip_skip,
+                )
+                for col in row
+            ]
+            for row in prompt
+        ]
+
+        # 3. Prepare latents
+        latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
+        dtype = text_embeddings[0][0][0].dtype
+        latents = randn_tensor(latents_shape, generator=generator, device=device, dtype=dtype)
+
+        # 3.1 overwrite latents for specific tiles if provided
+        if seed_tiles is not None:
+            for row in range(grid_rows):
+                for col in range(grid_cols):
+                    if (seed_tile := seed_tiles[row][col]) is not None:
+                        mode = seed_tiles_mode[row][col]
+                        if mode == self.SeedTilesMode.FULL.value:
+                            row_init, row_end, col_init, col_end = _tile2latent_indices(
+                                row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                            )
+                        else:
+                            row_init, row_end, col_init, col_end = _tile2latent_exclusive_indices(
+                                row,
+                                col,
+                                tile_width,
+                                tile_height,
+                                tile_row_overlap,
+                                tile_col_overlap,
+                                grid_rows,
+                                grid_cols,
+                            )
+                        tile_generator = torch.Generator(device).manual_seed(seed_tile)
+                        tile_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
+                        latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
+                            tile_shape, generator=tile_generator, device=device
+                        )
+
+        # 3.2 overwrite again for seed reroll regions
+        for row_init, row_end, col_init, col_end, seed_reroll in seed_reroll_regions:
+            row_init, row_end, col_init, col_end = _pixel2latent_indices(
+                row_init, row_end, col_init, col_end
+            )  # to latent space coordinates
+            reroll_generator = torch.Generator(device).manual_seed(seed_reroll)
+            region_shape = (latents_shape[0], latents_shape[1], row_end - row_init, col_end - col_init)
+            latents[:, :, row_init:row_end, col_init:col_end] = torch.randn(
+                region_shape, generator=reroll_generator, device=device
+            )
+
+        # 4. Prepare timesteps
+        accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
+        extra_set_kwargs = {}
+        if accepts_offset:
+            extra_set_kwargs["offset"] = 1
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, None, None, **extra_set_kwargs
+        )
+
+        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents * self.scheduler.sigmas[0]
+
+        # 5. 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)
+
+        # 6. Prepare added time ids & embeddings
+        # text_embeddings order: prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+        embeddings_and_added_time = []
+        for row in range(grid_rows):
+            addition_embed_type_row = []
+            for col in range(grid_cols):
+                # extract generated values
+                prompt_embeds = text_embeddings[row][col][0]
+                negative_prompt_embeds = text_embeddings[row][col][1]
+                pooled_prompt_embeds = text_embeddings[row][col][2]
+                negative_pooled_prompt_embeds = text_embeddings[row][col][3]
+
+                add_text_embeds = pooled_prompt_embeds
+                if self.text_encoder_2 is None:
+                    text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+                else:
+                    text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+                add_time_ids = self._get_add_time_ids(
+                    original_size,
+                    crops_coords_top_left[row][col],
+                    target_size,
+                    dtype=prompt_embeds.dtype,
+                    text_encoder_projection_dim=text_encoder_projection_dim,
+                )
+                if negative_original_size is not None and negative_target_size is not None:
+                    negative_add_time_ids = self._get_add_time_ids(
+                        negative_original_size,
+                        negative_crops_coords_top_left[row][col],
+                        negative_target_size,
+                        dtype=prompt_embeds.dtype,
+                        text_encoder_projection_dim=text_encoder_projection_dim,
+                    )
+                else:
+                    negative_add_time_ids = add_time_ids
+
+                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)
+                    add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+                prompt_embeds = prompt_embeds.to(device)
+                add_text_embeds = add_text_embeds.to(device)
+                add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+                addition_embed_type_row.append((prompt_embeds, add_text_embeds, add_time_ids))
+            embeddings_and_added_time.append(addition_embed_type_row)
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 7. Mask for tile weights strength
+        tile_weights = self._gaussian_weights(tile_width, tile_height, batch_size, device, torch.float32)
+
+        # 8. Denoising loop
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # Diffuse each tile
+                noise_preds = []
+                for row in range(grid_rows):
+                    noise_preds_row = []
+                    for col in range(grid_cols):
+                        if self.interrupt:
+                            continue
+                        px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
+                            row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                        )
+                        tile_latents = latents[:, :, px_row_init:px_row_end, px_col_init:px_col_end]
+                        # expand the latents if we are doing classifier free guidance
+                        latent_model_input = (
+                            torch.cat([tile_latents] * 2) if self.do_classifier_free_guidance else tile_latents
+                        )
+                        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                        # predict the noise residual
+                        added_cond_kwargs = {
+                            "text_embeds": embeddings_and_added_time[row][col][1],
+                            "time_ids": embeddings_and_added_time[row][col][2],
+                        }
+                        with torch.amp.autocast(device.type, dtype=dtype, enabled=dtype != self.unet.dtype):
+                            noise_pred = self.unet(
+                                latent_model_input,
+                                t,
+                                encoder_hidden_states=embeddings_and_added_time[row][col][0],
+                                cross_attention_kwargs=self.cross_attention_kwargs,
+                                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)
+                            guidance = (
+                                guidance_scale
+                                if guidance_scale_tiles is None or guidance_scale_tiles[row][col] is None
+                                else guidance_scale_tiles[row][col]
+                            )
+                            noise_pred_tile = noise_pred_uncond + guidance * (noise_pred_text - noise_pred_uncond)
+                            noise_preds_row.append(noise_pred_tile)
+                    noise_preds.append(noise_preds_row)
+
+                # Stitch noise predictions for all tiles
+                noise_pred = torch.zeros(latents.shape, device=device)
+                contributors = torch.zeros(latents.shape, device=device)
+
+                # Add each tile contribution to overall latents
+                for row in range(grid_rows):
+                    for col in range(grid_cols):
+                        px_row_init, px_row_end, px_col_init, px_col_end = _tile2latent_indices(
+                            row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap
+                        )
+                        noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += (
+                            noise_preds[row][col] * tile_weights
+                        )
+                        contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights
+
+                # Average overlapping areas with more than 1 contributor
+                noise_pred /= contributors
+                noise_pred = noise_pred.to(dtype)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                if latents.dtype != latents_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
+                        latents = latents.to(latents_dtype)
+
+                # update progress bar
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        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.vae.to(torch.float32)
+                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)
diff --git a/examples/community/mod_controlnet_tile_sr_sdxl.py b/examples/community/mod_controlnet_tile_sr_sdxl.py
index 27249ce3fb5e..686ea7502f59 100644
--- a/examples/community/mod_controlnet_tile_sr_sdxl.py
+++ b/examples/community/mod_controlnet_tile_sr_sdxl.py
@@ -39,16 +39,13 @@
     MultiControlNetModel,
     UNet2DConditionModel,
 )
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
 from diffusers.schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler
 from diffusers.utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -1222,21 +1219,7 @@ def prepare_tiles(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1820,7 +1803,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/examples/community/pipeline_controlnet_xl_kolors.py b/examples/community/pipeline_controlnet_xl_kolors.py
index dc90aacdbc6b..71b26490bd60 100644
--- a/examples/community/pipeline_controlnet_xl_kolors.py
+++ b/examples/community/pipeline_controlnet_xl_kolors.py
@@ -40,10 +40,6 @@
     MultiControlNetModel,
     UNet2DConditionModel,
 )
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from diffusers.pipelines.kolors import ChatGLMModel, ChatGLMTokenizer
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -760,21 +756,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1331,7 +1313,7 @@ def _cn_patch_forward(*args, **kwargs):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             latents = latents / self.vae.config.scaling_factor
diff --git a/examples/community/pipeline_controlnet_xl_kolors_img2img.py b/examples/community/pipeline_controlnet_xl_kolors_img2img.py
index 189d0312143f..a9199dd2c5b1 100644
--- a/examples/community/pipeline_controlnet_xl_kolors_img2img.py
+++ b/examples/community/pipeline_controlnet_xl_kolors_img2img.py
@@ -40,10 +40,6 @@
     MultiControlNetModel,
     UNet2DConditionModel,
 )
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from diffusers.pipelines.kolors import ChatGLMModel, ChatGLMTokenizer
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -930,21 +926,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1533,7 +1515,7 @@ def _cn_patch_forward(*args, **kwargs):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             latents = latents / self.vae.config.scaling_factor
diff --git a/examples/community/pipeline_controlnet_xl_kolors_inpaint.py b/examples/community/pipeline_controlnet_xl_kolors_inpaint.py
index 3abd98482980..b54bef63e259 100644
--- a/examples/community/pipeline_controlnet_xl_kolors_inpaint.py
+++ b/examples/community/pipeline_controlnet_xl_kolors_inpaint.py
@@ -39,10 +39,6 @@
     MultiControlNetModel,
     UNet2DConditionModel,
 )
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from diffusers.pipelines.kolors import ChatGLMModel, ChatGLMTokenizer
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -1006,21 +1002,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def denoising_end(self):
@@ -1847,7 +1829,7 @@ def _cn_patch_forward(*args, **kwargs):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             latents = latents / self.vae.config.scaling_factor
diff --git a/examples/community/pipeline_demofusion_sdxl.py b/examples/community/pipeline_demofusion_sdxl.py
index 119b39cefe68..5bacbf15fe67 100644
--- a/examples/community/pipeline_demofusion_sdxl.py
+++ b/examples/community/pipeline_demofusion_sdxl.py
@@ -16,11 +16,11 @@
     TextualInversionLoaderMixin,
 )
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.utils import (
+    deprecate,
     is_accelerate_available,
     is_accelerate_version,
     is_invisible_watermark_available,
@@ -614,18 +614,7 @@ def tiled_decode(self, latents, current_height, current_width):
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
@@ -997,7 +986,7 @@ def __call__(
                 needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
                 if needs_upcasting:
-                    self.upcast_vae()
+                    self.vae.to(torch.float32)
                     latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
                 print("### Phase 1 Decoding ###")
                 image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
@@ -1257,7 +1246,7 @@ def __call__(
                     needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
                     if needs_upcasting:
-                        self.upcast_vae()
+                        self.vae.to(torch.float32)
                         latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
                     print("### Phase {} Decoding ###".format(current_scale_num))
diff --git a/examples/community/pipeline_faithdiff_stable_diffusion_xl.py b/examples/community/pipeline_faithdiff_stable_diffusion_xl.py
index a8fdc133d08b..40124f443f31 100644
--- a/examples/community/pipeline_faithdiff_stable_diffusion_xl.py
+++ b/examples/community/pipeline_faithdiff_stable_diffusion_xl.py
@@ -40,13 +40,6 @@
     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
@@ -1637,24 +1630,7 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype
         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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1782,7 +1758,7 @@ def prepare_image_latents(
             # needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
             # if needs_upcasting:
             #     image = image.float()
-            #     self.upcast_vae()
+            #     self.vae.to(torch.float32)
             self.unet.denoise_encoder.to(device=image.device, dtype=image.dtype)
             image_latents = self.unet.denoise_encoder(image)
             self.unet.denoise_encoder.to("cpu")
@@ -2235,7 +2211,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/examples/community/pipeline_kolors_differential_img2img.py b/examples/community/pipeline_kolors_differential_img2img.py
index d299c839815e..89cd95a6604f 100644
--- a/examples/community/pipeline_kolors_differential_img2img.py
+++ b/examples/community/pipeline_kolors_differential_img2img.py
@@ -22,13 +22,12 @@
 from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import IPAdapterMixin, StableDiffusionXLLoraLoaderMixin
 from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from diffusers.models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from diffusers.pipelines.kolors.pipeline_output import KolorsPipelineOutput
 from diffusers.pipelines.kolors.text_encoder import ChatGLMModel
 from diffusers.pipelines.kolors.tokenizer import ChatGLMTokenizer
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
+from diffusers.utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
 from diffusers.utils.torch_utils import randn_tensor
 
 
@@ -711,22 +710,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1252,7 +1236,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/examples/community/pipeline_kolors_inpainting.py b/examples/community/pipeline_kolors_inpainting.py
index 26517819ebd0..53f59d6c22a6 100644
--- a/examples/community/pipeline_kolors_inpainting.py
+++ b/examples/community/pipeline_kolors_inpainting.py
@@ -32,12 +32,6 @@
     TextualInversionLoaderMixin,
 )
 from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    XFormersAttnProcessor,
-)
 from diffusers.pipelines.kolors import ChatGLMModel, ChatGLMTokenizer
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -1008,23 +1002,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1680,7 +1658,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/examples/community/pipeline_sdxl_style_aligned.py b/examples/community/pipeline_sdxl_style_aligned.py
index 51547599f531..e8cdf7ef6171 100644
--- a/examples/community/pipeline_sdxl_style_aligned.py
+++ b/examples/community/pipeline_sdxl_style_aligned.py
@@ -45,8 +45,6 @@
 from diffusers.models.attention_processor import (
     Attention,
     AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
 )
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
@@ -1151,22 +1149,7 @@ def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, d
         return add_time_ids
 
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     def _enable_shared_attention_processors(
         self,
@@ -1892,7 +1875,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
diff --git a/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py b/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py
index e358f66b4acc..f5de8f7b2471 100644
--- a/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py
+++ b/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py
@@ -736,7 +736,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.scaling_factor, return_dict=False)[0]
diff --git a/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py b/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py
index 65df4c03eb2c..8e9eacc8154d 100644
--- a/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py
+++ b/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py
@@ -35,12 +35,6 @@
     TextualInversionLoaderMixin,
 )
 from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    LoRAAttnProcessor2_0,
-    LoRAXFormersAttnProcessor,
-    XFormersAttnProcessor,
-)
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -1282,23 +1276,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
@@ -2274,7 +2252,7 @@ def denoising_value_valid(dnv):
             latents = latents[-1:]
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py
index 6dade126f282..e63d23327a75 100644
--- a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py
+++ b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py
@@ -25,7 +25,6 @@
 from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
 from diffusers.models import AutoencoderKL, ControlNetModel, MultiAdapter, T2IAdapter, UNet2DConditionModel
-from diffusers.models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
@@ -34,6 +33,7 @@
 from diffusers.utils import (
     PIL_INTERPOLATION,
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -795,18 +795,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.t2i_adapter.pipeline_stable_diffusion_adapter.StableDiffusionAdapterPipeline._default_height_width
     def _default_height_width(self, height, width, image):
@@ -1385,7 +1374,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
diff --git a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py
index 9ec6a90b072d..098a93a684f3 100644
--- a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py
+++ b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py
@@ -43,7 +43,6 @@
     T2IAdapter,
     UNet2DConditionModel,
 )
-from diffusers.models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
 from diffusers.pipelines.pipeline_utils import StableDiffusionMixin
@@ -52,6 +51,7 @@
 from diffusers.utils import (
     PIL_INTERPOLATION,
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -1132,18 +1132,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.t2i_adapter.pipeline_stable_diffusion_adapter.StableDiffusionAdapterPipeline._default_height_width
     def _default_height_width(self, height, width, image):
@@ -1825,7 +1814,7 @@ def denoising_value_valid(dnv):
 
         # make sure the VAE is in float32 mode, as it overflows in float16
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
         if output_type != "latent":
diff --git a/examples/community/pipeline_stable_diffusion_xl_differential_img2img.py b/examples/community/pipeline_stable_diffusion_xl_differential_img2img.py
index 89388e10cb19..1f6e2fcf473c 100644
--- a/examples/community/pipeline_stable_diffusion_xl_differential_img2img.py
+++ b/examples/community/pipeline_stable_diffusion_xl_differential_img2img.py
@@ -35,10 +35,6 @@
     TextualInversionLoaderMixin,
 )
 from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -848,21 +844,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1421,7 +1403,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/examples/community/pipeline_stable_diffusion_xl_instandid_img2img.py b/examples/community/pipeline_stable_diffusion_xl_instandid_img2img.py
index 33bf5ad346f4..dc2e6006a4f3 100644
--- a/examples/community/pipeline_stable_diffusion_xl_instandid_img2img.py
+++ b/examples/community/pipeline_stable_diffusion_xl_instandid_img2img.py
@@ -1050,7 +1050,7 @@ def __call__(
             # 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()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
diff --git a/examples/community/pipeline_stable_diffusion_xl_instantid.py b/examples/community/pipeline_stable_diffusion_xl_instantid.py
index 45876b91f7d8..729c63ee7dd5 100644
--- a/examples/community/pipeline_stable_diffusion_xl_instantid.py
+++ b/examples/community/pipeline_stable_diffusion_xl_instantid.py
@@ -1039,7 +1039,7 @@ def __call__(
             # 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()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
diff --git a/examples/community/pipeline_stable_diffusion_xl_ipex.py b/examples/community/pipeline_stable_diffusion_xl_ipex.py
index aa2b24f3965a..9e259ad220ed 100644
--- a/examples/community/pipeline_stable_diffusion_xl_ipex.py
+++ b/examples/community/pipeline_stable_diffusion_xl_ipex.py
@@ -32,10 +32,6 @@
     TextualInversionLoaderMixin,
 )
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from diffusers.models.lora import adjust_lora_scale_text_encoder
 from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
 from diffusers.schedulers import KarrasDiffusionSchedulers
@@ -660,21 +656,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
@@ -1150,7 +1132,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
@@ -1363,7 +1345,7 @@ def prepare_for_ipex(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # cast back to fp16 if needed
diff --git a/examples/community/stable_diffusion_xl_controlnet_reference.py b/examples/community/stable_diffusion_xl_controlnet_reference.py
index 421e67f5bba6..575e3ac13bf2 100644
--- a/examples/community/stable_diffusion_xl_controlnet_reference.py
+++ b/examples/community/stable_diffusion_xl_controlnet_reference.py
@@ -195,7 +195,7 @@ def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do
         refimage = refimage.to(device=device)
         needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
         if needs_upcasting:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
         if refimage.dtype != self.vae.dtype:
             refimage = refimage.to(dtype=self.vae.dtype)
@@ -1326,7 +1326,7 @@ def hacked_UpBlock2D_forward(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/examples/community/stable_diffusion_xl_reference.py b/examples/community/stable_diffusion_xl_reference.py
index eb055574966d..acbe29d2c084 100644
--- a/examples/community/stable_diffusion_xl_reference.py
+++ b/examples/community/stable_diffusion_xl_reference.py
@@ -141,7 +141,7 @@ def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do
         refimage = refimage.to(device=device)
         needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
         if needs_upcasting:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
         if refimage.dtype != self.vae.dtype:
             refimage = refimage.to(dtype=self.vae.dtype)
@@ -1156,7 +1156,7 @@ def hacked_UpBlock2D_forward(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py b/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py
index 56d319027595..e1c254ee5302 100644
--- a/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py
+++ b/src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py
@@ -32,11 +32,6 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, MotionAdapter, UNet2DConditionModel, UNetMotionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import (
     DDIMScheduler,
@@ -48,6 +43,7 @@
 )
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     is_torch_xla_available,
     logging,
     replace_example_docstring,
@@ -783,22 +779,7 @@ def _get_add_time_ids(
         return add_time_ids
 
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1286,7 +1267,7 @@ def __call__(
         needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
         if needs_upcasting:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
         # 10. Post processing
diff --git a/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py b/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py
index 6251ca443533..1ea47d102644 100644
--- a/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py
+++ b/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py
@@ -21,10 +21,10 @@
 from ...image_processor import VaeImageProcessor
 from ...loaders import AuraFlowLoraLoaderMixin
 from ...models import AuraFlowTransformer2DModel, AutoencoderKL
-from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     is_torch_xla_available,
     logging,
     replace_example_docstring,
@@ -406,22 +406,7 @@ def prepare_latents(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -663,7 +648,7 @@ def __call__(
             # 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()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
             image = self.image_processor.postprocess(image, output_type=output_type)
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
index 397ab15715c2..1cede169d2fa 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
@@ -36,10 +36,6 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ControlNetModel, ImageProjection, MultiControlNetModel, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -1132,21 +1128,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1850,7 +1832,7 @@ def denoising_value_valid(dnv):
 
         # make sure the VAE is in float32 mode, as it overflows in float16
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
         # If we do sequential model offloading, let's offload unet and controlnet
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
index 8b37d38b9099..53b3f8a7f5a2 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
@@ -39,10 +39,6 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ControlNetModel, ImageProjection, MultiControlNetModel, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -929,21 +925,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1574,7 +1556,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py
index 4d4845c5a0a3..2c6a4d4455c2 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py
@@ -39,10 +39,6 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ControlNetModel, ImageProjection, MultiControlNetModel, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -1044,21 +1040,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1639,7 +1621,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_inpaint_sd_xl.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_inpaint_sd_xl.py
index fb58b222112a..459e398e6a9d 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_inpaint_sd_xl.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_inpaint_sd_xl.py
@@ -41,10 +41,6 @@
     MultiControlNetUnionModel,
     UNet2DConditionModel,
 )
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -1111,21 +1107,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1877,7 +1859,7 @@ def denoising_value_valid(dnv):
 
         # make sure the VAE is in float32 mode, as it overflows in float16
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
         # If we do sequential model offloading, let's offload unet and controlnet
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl.py
index 5961d389effb..8a4b952b599a 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl.py
@@ -44,14 +44,11 @@
     MultiControlNetUnionModel,
     UNet2DConditionModel,
 )
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -904,21 +901,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1578,7 +1561,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py
index 8fedb6d8609a..964bcd21dfb3 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py
@@ -44,10 +44,6 @@
     MultiControlNetUnionModel,
     UNet2DConditionModel,
 )
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -1032,21 +1028,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1683,7 +1665,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py
index c10931a0f44a..9e1005554443 100644
--- a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py
+++ b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs_sd_xl.py
@@ -32,14 +32,11 @@
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, ControlNetXSAdapter, UNet2DConditionModel, UNetControlNetXSModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -685,21 +682,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale
@@ -1095,7 +1078,7 @@ def __call__(
 
         # manually for max memory savings
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
         if not output_type == "latent":
@@ -1103,7 +1086,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
diff --git a/src/diffusers/pipelines/kolors/pipeline_kolors.py b/src/diffusers/pipelines/kolors/pipeline_kolors.py
index 948f73ed91eb..a406e52ccb61 100644
--- a/src/diffusers/pipelines/kolors/pipeline_kolors.py
+++ b/src/diffusers/pipelines/kolors/pipeline_kolors.py
@@ -21,9 +21,8 @@
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import IPAdapterMixin, StableDiffusionLoraLoaderMixin
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import KolorsPipelineOutput
@@ -580,22 +579,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1042,7 +1026,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py b/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py
index 67d49b9a8c5e..f9a528e93519 100644
--- a/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py
+++ b/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py
@@ -22,9 +22,8 @@
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import IPAdapterMixin, StableDiffusionXLLoraLoaderMixin
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import empty_device_cache, randn_tensor
 from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
 from .pipeline_output import KolorsPipelineOutput
@@ -708,22 +707,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1222,7 +1206,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
index c1f9a98f0632..5ad5c4811c00 100644
--- a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
+++ b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
@@ -37,8 +37,6 @@
 from ...models.attention_processor import (
     Attention,
     AttnProcessor,
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
 )
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler
@@ -688,21 +686,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1421,7 +1405,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
@@ -1472,7 +1456,7 @@ def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="
 
         if needs_upcasting:
             image = image.float()
-            self.upcast_vae()
+            self.vae.to(torch.float32)
 
         x0 = self.vae.encode(image).latent_dist.mode()
         x0 = x0.to(dtype)
@@ -1638,7 +1622,7 @@ def invert(
 
         # autoencoder reconstruction
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
+            self.vae.to(torch.float32)
             x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             image_rec = self.vae.decode(
                 x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl.py
index 24cbab43c966..3775df7993e0 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl.py
@@ -39,14 +39,11 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ControlNetModel, ImageProjection, MultiControlNetModel, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -936,21 +933,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1586,7 +1569,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py
index a6df1b22c8b9..b01aa87f118a 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py
@@ -39,14 +39,11 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ControlNetModel, ImageProjection, MultiControlNetModel, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -1049,21 +1046,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -1655,7 +1638,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             # unscale/denormalize the latents
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_kolors.py b/src/diffusers/pipelines/pag/pipeline_pag_kolors.py
index 1368358db6ba..4fa7a1c29d34 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_kolors.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_kolors.py
@@ -21,9 +21,8 @@
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import IPAdapterMixin, StableDiffusionXLLoraLoaderMixin
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils import deprecate, is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ..kolors.pipeline_output import KolorsPipelineOutput
 from ..kolors.text_encoder import ChatGLMModel
@@ -598,22 +597,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1105,7 +1089,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl.py
index 6b62ddcc7ca5..dba8079f956d 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl.py
@@ -32,15 +32,11 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     is_invisible_watermark_available,
     is_torch_xla_available,
     logging,
@@ -762,22 +758,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1305,7 +1286,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py
index b6422b23648c..0c0cbde56bff 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py
@@ -34,14 +34,11 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     is_invisible_watermark_available,
     is_torch_xla_available,
     logging,
@@ -910,21 +907,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1501,7 +1484,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
index 2a8f7a448d5e..fb117aba0d07 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
@@ -34,14 +34,11 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     is_invisible_watermark_available,
     is_torch_xla_available,
     logging,
@@ -1001,21 +998,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1729,7 +1712,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
index 02f7526b7102..24e2f13c0272 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
@@ -24,10 +24,6 @@
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import FromSingleFileMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers
 from ...utils import (
@@ -529,21 +525,7 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype
         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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     @torch.no_grad()
     def __call__(
@@ -791,7 +773,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
 
             # Ensure latents are always the same type as the VAE
             latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
diff --git a/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py b/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py
index 766ca37d8142..03b4341bbe79 100644
--- a/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py
+++ b/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py
@@ -33,15 +33,11 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler
 from ...utils import (
     USE_PEFT_BACKEND,
+    deprecate,
     logging,
     replace_example_docstring,
     scale_lora_layers,
@@ -545,22 +541,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     @property
     def guidance_scale(self):
@@ -880,7 +861,7 @@ def model_fn(x, t):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
index b97cf6f1f6f8..146027601049 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
@@ -33,11 +33,6 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -750,22 +745,7 @@ def _get_add_time_ids(
         return add_time_ids
 
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1267,7 +1247,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
index 44e8f4fe4b54..121026887ebb 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
@@ -34,10 +34,6 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -897,21 +893,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1458,7 +1440,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
index 88cc7515b051..a8d19eb73fcf 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
@@ -35,10 +35,6 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -1002,21 +998,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
     def get_guidance_scale_embedding(
@@ -1692,7 +1674,7 @@ def denoising_value_valid(dnv):
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py
index 58b008361782..8776bfbea236 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py
@@ -22,11 +22,6 @@
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -531,7 +526,7 @@ def prepare_image_latents(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
             if needs_upcasting:
                 image = image.float()
-                self.upcast_vae()
+                self.vae.to(torch.float32)
 
             image_latents = retrieve_latents(self.vae.encode(image), sample_mode="argmax")
 
@@ -590,22 +585,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
@@ -955,7 +935,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != self.vae.dtype:
                 if torch.backends.mps.is_available():
diff --git a/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py b/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py
index 2802d690f3cc..6cd215d366f7 100644
--- a/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py
+++ b/src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py
@@ -34,15 +34,12 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, MultiAdapter, T2IAdapter, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     PIL_INTERPOLATION,
     USE_PEFT_BACKEND,
+    deprecate,
     is_torch_xla_available,
     logging,
     replace_example_docstring,
@@ -779,21 +776,7 @@ def _get_add_time_ids(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
     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,
-            ),
-        )
-        # 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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.t2i_adapter.pipeline_stable_diffusion_adapter.StableDiffusionAdapterPipeline._default_height_width
     def _default_height_width(self, height, width, image):
@@ -1288,7 +1271,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
diff --git a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py
index 288aae6c0d44..e108e73dddb2 100644
--- a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py
+++ b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py
@@ -19,16 +19,12 @@
 from ...image_processor import VaeImageProcessor
 from ...loaders import StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    FusedAttnProcessor2_0,
-    XFormersAttnProcessor,
-)
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
     USE_PEFT_BACKEND,
     BaseOutput,
+    deprecate,
     is_invisible_watermark_available,
     logging,
     scale_lora_layers,
@@ -457,22 +453,7 @@ def prepare_extra_step_kwargs(self, generator, eta):
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
     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,
-                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)
+        deprecate("`upcast_vae` is deprecated")
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
     def _get_add_time_ids(
@@ -1326,7 +1307,7 @@ def __call__(
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
 
             if needs_upcasting:
-                self.upcast_vae()
+                self.vae.to(torch.float32)
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]

From 73906381ab76da96eb8f9b841177cd4f49861eb1 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Tue, 11 Nov 2025 11:36:22 +0530
Subject: [PATCH 2/2] update

---
 .../stable_diffusion_xl/decoders.py           | 22 +------------------
 1 file changed, 1 insertion(+), 21 deletions(-)

diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py
index feb78e1ef11b..bfc55e156a82 100644
--- a/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py
+++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py
@@ -21,7 +21,6 @@
 from ...configuration_utils import FrozenDict
 from ...image_processor import VaeImageProcessor
 from ...models import AutoencoderKL
-from ...models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 from ...utils import logging
 from ..modular_pipeline import (
     ModularPipelineBlocks,
@@ -74,25 +73,6 @@ def intermediate_outputs(self) -> List[str]:
             )
         ]
 
-    @staticmethod
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae with self->components
-    def upcast_vae(components):
-        dtype = components.vae.dtype
-        components.vae.to(dtype=torch.float32)
-        use_torch_2_0_or_xformers = isinstance(
-            components.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-            ),
-        )
-        # 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:
-            components.vae.post_quant_conv.to(dtype)
-            components.vae.decoder.conv_in.to(dtype)
-            components.vae.decoder.mid_block.to(dtype)
-
     @torch.no_grad()
     def __call__(self, components, state: PipelineState) -> PipelineState:
         block_state = self.get_block_state(state)
@@ -103,7 +83,7 @@ def __call__(self, components, state: PipelineState) -> PipelineState:
             block_state.needs_upcasting = components.vae.dtype == torch.float16 and components.vae.config.force_upcast
 
             if block_state.needs_upcasting:
-                self.upcast_vae(components)
+                self.components.vae.to(torch.float32)
                 latents = latents.to(next(iter(components.vae.post_quant_conv.parameters())).dtype)
             elif latents.dtype != components.vae.dtype:
                 if torch.backends.mps.is_available():