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@@ -53,6 +53,7 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 | Stable Diffusion Mixture Tiling Pipeline SD 1.5 | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending | [Stable Diffusion Mixture Tiling Pipeline SD 1.5](#stable-diffusion-mixture-tiling-pipeline-sd-15) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/albarji/mixture-of-diffusers) | [Álvaro B Jiménez](https://github.com/albarji/) |
 | Stable Diffusion Mixture Canvas Pipeline SD 1.5 | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending. Works by defining a list of Text2Image region objects that detail the region of influence of each diffuser. | [Stable Diffusion Mixture Canvas Pipeline SD 1.5](#stable-diffusion-mixture-canvas-pipeline-sd-15) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/albarji/mixture-of-diffusers) | [Álvaro B Jiménez](https://github.com/albarji/) |
 | Stable Diffusion Mixture Tiling Pipeline SDXL | A pipeline generates cohesive images by integrating multiple diffusion processes, each focused on a specific image region and considering boundary effects for smooth blending | [Stable Diffusion Mixture Tiling Pipeline SDXL](#stable-diffusion-mixture-tiling-pipeline-sdxl) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/elismasilva/mixture-of-diffusers-sdxl-tiling) | [Eliseu Silva](https://github.com/DEVAIEXP/) |
+| Stable Diffusion MoD ControlNet Tile SR Pipeline SDXL | This is an advanced pipeline that leverages ControlNet Tile and Mixture-of-Diffusers techniques, integrating tile diffusion directly into the latent space denoising process. Designed to overcome the limitations of conventional pixel-space tile processing, this pipeline delivers Super Resolution (SR) upscaling for higher-quality images, reduced processing time, and greater adaptability. | [Stable Diffusion MoD ControlNet Tile SR Pipeline SDXL](#stable-diffusion-mod-controlnet-tile-sr-pipeline-sdxl) | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/elismasilva/mod-control-tile-upscaler-sdxl) | [Eliseu Silva](https://github.com/DEVAIEXP/) |
 | FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_fabric.ipynb)| [Shauray Singh](https://shauray8.github.io/about_shauray/) |
 | sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
 | sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#stable-diffusion-xl-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
@@ -2630,6 +2631,103 @@ image = pipe(
 
 ![mixture_tiling_results](https://huggingface.co/datasets/elismasilva/results/resolve/main/mixture_of_diffusers_sdxl_1.png)
 
+### Stable Diffusion MoD ControlNet Tile SR Pipeline SDXL
+
+This pipeline implements the [MoD (Mixture-of-Diffusers)]("https://arxiv.org/pdf/2408.06072") tiled diffusion technique and combines it with SDXL's ControlNet Tile process to generate SR images.
+
+This works better with 4x scales, but you can try adjusts parameters to higher scales.
+
+````python
+import torch
+from diffusers import DiffusionPipeline, ControlNetUnionModel, AutoencoderKL, UniPCMultistepScheduler, UNet2DConditionModel
+from diffusers.utils import load_image
+from PIL import Image
+
+device = "cuda"
+
+# Initialize the models and pipeline
+controlnet = ControlNetUnionModel.from_pretrained(
+    "brad-twinkl/controlnet-union-sdxl-1.0-promax", torch_dtype=torch.float16
+).to(device=device)
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device=device)
+
+model_id = "SG161222/RealVisXL_V5.0"
+pipe = DiffusionPipeline.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    vae=vae,
+    controlnet=controlnet,
+    custom_pipeline="mod_controlnet_tile_sr_sdxl",    
+    use_safetensors=True,
+    variant="fp16",
+).to(device)
+
+unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet", variant="fp16", use_safetensors=True)
+
+#pipe.enable_model_cpu_offload()  # << Enable this if you have limited VRAM
+pipe.enable_vae_tiling() # << Enable this if you have limited VRAM
+pipe.enable_vae_slicing() # << Enable this if you have limited VRAM
+
+# Set selected scheduler
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+# Load image
+control_image = load_image("https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/1.jpg")
+original_height = control_image.height
+original_width = control_image.width
+print(f"Current resolution: H:{original_height} x W:{original_width}")
+
+# Pre-upscale image for tiling
+resolution = 4096
+tile_gaussian_sigma = 0.3
+max_tile_size = 1024 # or 1280
+
+current_size = max(control_image.size)
+scale_factor = max(2, resolution / current_size)
+new_size = (int(control_image.width * scale_factor), int(control_image.height * scale_factor))
+image = control_image.resize(new_size, Image.LANCZOS)
+
+# Update target height and width
+target_height = image.height
+target_width = image.width
+print(f"Target resolution: H:{target_height} x W:{target_width}")
+
+# Calculate overlap size
+normal_tile_overlap, border_tile_overlap = pipe.calculate_overlap(target_width, target_height)
+
+# Set other params
+tile_weighting_method = pipe.TileWeightingMethod.COSINE.value
+guidance_scale = 4
+num_inference_steps = 35
+denoising_strenght = 0.65
+controlnet_strength = 1.0
+prompt = "high-quality, noise-free edges, high quality, 4k, hd, 8k"
+negative_prompt = "blurry, pixelated, noisy, low resolution, artifacts, poor details"
+
+# Image generation
+generated_image = pipe(
+    image=image,
+    control_image=control_image,
+    control_mode=[6],
+    controlnet_conditioning_scale=float(controlnet_strength),
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    normal_tile_overlap=normal_tile_overlap,
+    border_tile_overlap=border_tile_overlap,
+    height=target_height,
+    width=target_width,
+    original_size=(original_width, original_height),
+    target_size=(target_width, target_height),
+    guidance_scale=guidance_scale,        
+    strength=float(denoising_strenght),
+    tile_weighting_method=tile_weighting_method,
+    max_tile_size=max_tile_size,
+    tile_gaussian_sigma=float(tile_gaussian_sigma),
+    num_inference_steps=num_inference_steps,
+)["images"][0]
+````
+![Upscaled](https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/1_input_4x.png)
+
 ### TensorRT Inpainting Stable Diffusion Pipeline
 
 The TensorRT Pipeline can be used to accelerate the Inpainting Stable Diffusion Inference run.

main/mod_controlnet_tile_sr_sdxl.py

@@ -0,0 +1,1862 @@
+# 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 numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from transformers import (
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+)
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import (
+    AutoencoderKL,
+    ControlNetModel,
+    ControlNetUnionModel,
+    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,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.import_utils import is_invisible_watermark_available
+from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
+
+
+if is_invisible_watermark_available():
+    from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
+
+from diffusers.utils import is_torch_xla_available
+
+
+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 DiffusionPipeline, ControlNetUnionModel, AutoencoderKL, UniPCMultistepScheduler
+        from diffusers.utils import load_image
+        from PIL import Image
+
+        device = "cuda"
+
+        # Initialize the models and pipeline
+        controlnet = ControlNetUnionModel.from_pretrained(
+            "brad-twinkl/controlnet-union-sdxl-1.0-promax", torch_dtype=torch.float16
+        ).to(device=device)
+        vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to(device=device)
+
+        model_id = "SG161222/RealVisXL_V5.0"
+        pipe = StableDiffusionXLControlNetTileSRPipeline.from_pretrained(
+            model_id, controlnet=controlnet, vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
+        ).to(device)
+
+        pipe.enable_model_cpu_offload()  # << Enable this if you have limited VRAM
+        pipe.enable_vae_tiling() # << Enable this if you have limited VRAM
+        pipe.enable_vae_slicing() # << Enable this if you have limited VRAM
+
+        # Set selected scheduler
+        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+        # Load image
+        control_image = load_image("https://huggingface.co/datasets/DEVAIEXP/assets/resolve/main/1.jpg")
+        original_height = control_image.height
+        original_width = control_image.width
+        print(f"Current resolution: H:{original_height} x W:{original_width}")
+
+        # Pre-upscale image for tiling
+        resolution = 4096
+        tile_gaussian_sigma = 0.3
+        max_tile_size = 1024 # or 1280
+
+        current_size = max(control_image.size)
+        scale_factor = max(2, resolution / current_size)
+        new_size = (int(control_image.width * scale_factor), int(control_image.height * scale_factor))
+        image = control_image.resize(new_size, Image.LANCZOS)
+
+        # Update target height and width
+        target_height = image.height
+        target_width = image.width
+        print(f"Target resolution: H:{target_height} x W:{target_width}")
+
+        # Calculate overlap size
+        normal_tile_overlap, border_tile_overlap = calculate_overlap(target_width, target_height)
+
+        # Set other params
+        tile_weighting_method = TileWeightingMethod.COSINE.value
+        guidance_scale = 4
+        num_inference_steps = 35
+        denoising_strenght = 0.65
+        controlnet_strength = 1.0
+        prompt = "high-quality, noise-free edges, high quality, 4k, hd, 8k"
+        negative_prompt = "blurry, pixelated, noisy, low resolution, artifacts, poor details"
+
+        # Image generation
+        control_image = pipe(
+            image=image,
+            control_image=control_image,
+            control_mode=[6],
+            controlnet_conditioning_scale=float(controlnet_strength),
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            normal_tile_overlap=normal_tile_overlap,
+            border_tile_overlap=border_tile_overlap,
+            height=target_height,
+            width=target_width,
+            original_size=(original_width, original_height),
+            target_size=(target_width, target_height),
+            guidance_scale=guidance_scale,
+            strength=float(denoising_strenght),
+            tile_weighting_method=tile_weighting_method,
+            max_tile_size=max_tile_size,
+            tile_gaussian_sigma=float(tile_gaussian_sigma),
+            num_inference_steps=num_inference_steps,
+        )["images"][0]
+        ```
+"""
+
+
+# This function was copied and adapted from https://huggingface.co/spaces/gokaygokay/TileUpscalerV2, licensed under Apache 2.0.
+def _adaptive_tile_size(image_size, base_tile_size=512, max_tile_size=1280):
+    """
+    Calculate the adaptive tile size based on the image dimensions, ensuring the tile
+    respects the aspect ratio and stays within the specified size limits.
+    """
+    width, height = image_size
+    aspect_ratio = width / height
+
+    if aspect_ratio > 1:
+        # Landscape orientation
+        tile_width = min(width, max_tile_size)
+        tile_height = min(int(tile_width / aspect_ratio), max_tile_size)
+    else:
+        # Portrait or square orientation
+        tile_height = min(height, max_tile_size)
+        tile_width = min(int(tile_height * aspect_ratio), max_tile_size)
+
+    # Ensure the tile size is not smaller than the base_tile_size
+    tile_width = max(tile_width, base_tile_size)
+    tile_height = max(tile_height, base_tile_size)
+
+    return tile_width, tile_height
+
+
+# Copied and adapted from https://github.com/huggingface/diffusers/blob/main/examples/community/mixture_tiling.py
+def _tile2pixel_indices(
+    tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, image_width, image_height
+):
+    """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
+    """
+    # Calculate initial indices
+    px_row_init = 0 if tile_row == 0 else tile_row * (tile_height - tile_row_overlap)
+    px_col_init = 0 if tile_col == 0 else tile_col * (tile_width - tile_col_overlap)
+
+    # Calculate end indices
+    px_row_end = px_row_init + tile_height
+    px_col_end = px_col_init + tile_width
+
+    # Ensure the last tile does not exceed the image dimensions
+    px_row_end = min(px_row_end, image_height)
+    px_col_end = min(px_col_end, image_width)
+
+    return px_row_init, px_row_end, px_col_init, px_col_end
+
+
+# Copied and adapted from https://github.com/huggingface/diffusers/blob/main/examples/community/mixture_tiling.py
+def _tile2latent_indices(
+    tile_row, tile_col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, image_width, image_height
+):
+    """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
+    """
+    # Get pixel indices
+    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, image_width, image_height
+    )
+
+    # Convert to latent space
+    latent_row_init = px_row_init // 8
+    latent_row_end = px_row_end // 8
+    latent_col_init = px_col_init // 8
+    latent_col_end = px_col_end // 8
+    latent_height = image_height // 8
+    latent_width = image_width // 8
+
+    # Ensure the last tile does not exceed the latent dimensions
+    latent_row_end = min(latent_row_end, latent_height)
+    latent_col_end = min(latent_col_end, latent_width)
+
+    return latent_row_init, latent_row_end, latent_col_init, latent_col_end
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class StableDiffusionXLControlNetTileSRPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    TextualInversionLoaderMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    FromSingleFileMixin,
+):
+    r"""
+    Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet guidance.
+
+    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.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 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.
+        controlnet ([`ControlNetUnionModel`]):
+            Provides additional conditioning to the unet during the denoising process.
+        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`].
+        requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
+            Whether the `unet` requires an `aesthetic_score` condition to be passed during inference. Also see the
+            config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
+        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->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,
+        controlnet: ControlNetUnionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        requires_aesthetics_score: bool = False,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+    ):
+        super().__init__()
+
+        if not isinstance(controlnet, ControlNetUnionModel):
+            raise ValueError("Expected `controlnet` to be of type `ControlNetUnionModel`.")
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+        )
+        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, do_convert_rgb=True)
+        self.control_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
+        )
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+        )
+        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
+
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+        self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
+
+    def calculate_overlap(self, width, height, base_overlap=128):
+        """
+        Calculates dynamic overlap based on the image's aspect ratio.
+
+        Args:
+            width (int): Width of the image in pixels.
+            height (int): Height of the image in pixels.
+            base_overlap (int, optional): Base overlap value in pixels. Defaults to 128.
+
+        Returns:
+            tuple: A tuple containing:
+                - row_overlap (int): Overlap between tiles in consecutive rows.
+                - col_overlap (int): Overlap between tiles in consecutive columns.
+        """
+        ratio = height / width
+        if ratio < 1:  # Image is wider than tall
+            return base_overlap // 2, base_overlap
+        else:  # Image is taller than wide
+            return base_overlap, base_overlap * 2
+
+    class TileWeightingMethod(Enum):
+        """Mode in which the tile weights will be generated"""
+
+        COSINE = "Cosine"
+        GAUSSIAN = "Gaussian"
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    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]
+        )
+        dtype = text_encoders[0].dtype
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+                text_encoder.to(dtype)
+                prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                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://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        image,
+        strength,
+        num_inference_steps,
+        normal_tile_overlap,
+        border_tile_overlap,
+        max_tile_size,
+        tile_gaussian_sigma,
+        tile_weighting_method,
+        controlnet_conditioning_scale=1.0,
+        control_guidance_start=0.0,
+        control_guidance_end=1.0,
+    ):
+        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 strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+        if num_inference_steps is None:
+            raise ValueError("`num_inference_steps` cannot be None.")
+        elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:
+            raise ValueError(
+                f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
+                f" {type(num_inference_steps)}."
+            )
+        if normal_tile_overlap is None:
+            raise ValueError("`normal_tile_overlap` cannot be None.")
+        elif not isinstance(normal_tile_overlap, int) or normal_tile_overlap < 64:
+            raise ValueError(
+                f"`normal_tile_overlap` has to be greater than 64 but is {normal_tile_overlap} of type"
+                f" {type(normal_tile_overlap)}."
+            )
+        if border_tile_overlap is None:
+            raise ValueError("`border_tile_overlap` cannot be None.")
+        elif not isinstance(border_tile_overlap, int) or border_tile_overlap < 128:
+            raise ValueError(
+                f"`border_tile_overlap` has to be greater than 128 but is {border_tile_overlap} of type"
+                f" {type(border_tile_overlap)}."
+            )
+        if max_tile_size is None:
+            raise ValueError("`max_tile_size` cannot be None.")
+        elif not isinstance(max_tile_size, int) or max_tile_size not in (1024, 1280):
+            raise ValueError(
+                f"`max_tile_size` has to be in 1024 or 1280 but is {max_tile_size} of type" f" {type(max_tile_size)}."
+            )
+        if tile_gaussian_sigma is None:
+            raise ValueError("`tile_gaussian_sigma` cannot be None.")
+        elif not isinstance(tile_gaussian_sigma, float) or tile_gaussian_sigma <= 0:
+            raise ValueError(
+                f"`tile_gaussian_sigma` has to be a positive float but is {tile_gaussian_sigma} of type"
+                f" {type(tile_gaussian_sigma)}."
+            )
+        if tile_weighting_method is None:
+            raise ValueError("`tile_weighting_method` cannot be None.")
+        elif not isinstance(tile_weighting_method, str) or tile_weighting_method not in [
+            t.value for t in self.TileWeightingMethod
+        ]:
+            raise ValueError(
+                f"`tile_weighting_method` has to be a string in ({[t.value for t in self.TileWeightingMethod]}) but is {tile_weighting_method} of type"
+                f" {type(tile_weighting_method)}."
+            )
+
+        # Check `image`
+        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
+            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
+        )
+        if (
+            isinstance(self.controlnet, ControlNetModel)
+            or is_compiled
+            and isinstance(self.controlnet._orig_mod, ControlNetModel)
+        ):
+            self.check_image(image, prompt)
+        elif (
+            isinstance(self.controlnet, ControlNetUnionModel)
+            or is_compiled
+            and isinstance(self.controlnet._orig_mod, ControlNetUnionModel)
+        ):
+            self.check_image(image, prompt)
+        else:
+            assert False
+
+        # Check `controlnet_conditioning_scale`
+        if (
+            isinstance(self.controlnet, ControlNetUnionModel)
+            or is_compiled
+            and isinstance(self.controlnet._orig_mod, ControlNetUnionModel)
+        ) or (
+            isinstance(self.controlnet, MultiControlNetModel)
+            or is_compiled
+            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
+        ):
+            if not isinstance(controlnet_conditioning_scale, float):
+                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
+        elif (
+            isinstance(self.controlnet, MultiControlNetModel)
+            or is_compiled
+            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
+        ):
+            if isinstance(controlnet_conditioning_scale, list):
+                if any(isinstance(i, list) for i in controlnet_conditioning_scale):
+                    raise ValueError("A single batch of multiple conditionings are supported at the moment.")
+            elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
+                self.controlnet.nets
+            ):
+                raise ValueError(
+                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
+                    " the same length as the number of controlnets"
+                )
+        else:
+            assert False
+
+        if not isinstance(control_guidance_start, (tuple, list)):
+            control_guidance_start = [control_guidance_start]
+
+        if not isinstance(control_guidance_end, (tuple, list)):
+            control_guidance_end = [control_guidance_end]
+
+        if len(control_guidance_start) != len(control_guidance_end):
+            raise ValueError(
+                f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
+            )
+
+        for start, end in zip(control_guidance_start, control_guidance_end):
+            if start >= end:
+                raise ValueError(
+                    f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
+                )
+            if start < 0.0:
+                raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
+            if end > 1.0:
+                raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
+
+    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.check_image
+    def check_image(self, image, prompt):
+        image_is_pil = isinstance(image, Image.Image)
+        image_is_tensor = isinstance(image, torch.Tensor)
+        image_is_np = isinstance(image, np.ndarray)
+        image_is_pil_list = isinstance(image, list) and isinstance(image[0], Image.Image)
+        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
+        image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
+
+        if (
+            not image_is_pil
+            and not image_is_tensor
+            and not image_is_np
+            and not image_is_pil_list
+            and not image_is_tensor_list
+            and not image_is_np_list
+        ):
+            raise TypeError(
+                f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
+            )
+
+        if image_is_pil:
+            image_batch_size = 1
+        else:
+            image_batch_size = len(image)
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+
+        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
+
+    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.prepare_image
+    def prepare_control_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance and not guess_mode:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
+    def get_timesteps(self, num_inference_steps, strength):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+        if hasattr(self.scheduler, "set_begin_index"):
+            self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.prepare_latents
+    def prepare_latents(
+        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
+    ):
+        if not isinstance(image, (torch.Tensor, Image.Image, list)):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+            )
+
+        latents_mean = latents_std = None
+        if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
+            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
+        if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
+            latents_std = torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1)
+
+        # Offload text encoder if `enable_model_cpu_offload` was enabled
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.text_encoder_2.to("cpu")
+            torch.cuda.empty_cache()
+
+        image = image.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+
+        if image.shape[1] == 4:
+            init_latents = image
+
+        else:
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            if self.vae.config.force_upcast:
+                image = image.float()
+                self.vae.to(dtype=torch.float32)
+
+            if isinstance(generator, list) and len(generator) != batch_size:
+                raise ValueError(
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+                )
+
+            elif isinstance(generator, list):
+                if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
+                    image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
+                elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image.shape[0]} to effective batch_size {batch_size} "
+                    )
+
+                init_latents = [
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
+                ]
+                init_latents = torch.cat(init_latents, dim=0)
+            else:
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+            if self.vae.config.force_upcast:
+                self.vae.to(dtype)
+
+            init_latents = init_latents.to(dtype)
+            if latents_mean is not None and latents_std is not None:
+                latents_mean = latents_mean.to(device=device, dtype=dtype)
+                latents_std = latents_std.to(device=device, dtype=dtype)
+                init_latents = (init_latents - latents_mean) * self.vae.config.scaling_factor / latents_std
+            else:
+                init_latents = self.vae.config.scaling_factor * init_latents
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        if add_noise:
+            shape = init_latents.shape
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # get latents
+            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+        latents = init_latents
+
+        return latents
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
+    def _get_add_time_ids(
+        self,
+        original_size,
+        crops_coords_top_left,
+        target_size,
+        aesthetic_score,
+        negative_aesthetic_score,
+        negative_original_size,
+        negative_crops_coords_top_left,
+        negative_target_size,
+        dtype,
+        text_encoder_projection_dim=None,
+    ):
+        if self.config.requires_aesthetics_score:
+            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
+            add_neg_time_ids = list(
+                negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,)
+            )
+        else:
+            add_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_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
+            and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
+            )
+        elif (
+            expected_add_embed_dim < passed_add_embed_dim
+            and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_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. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
+            )
+        elif 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)
+        add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
+
+        return add_time_ids, add_neg_time_ids
+
+    def _generate_cosine_weights(self, tile_width, tile_height, nbatches, device, dtype):
+        """
+        Generates cosine weights as a PyTorch tensor for blending tiles.
+
+        Args:
+            tile_width (int): Width of the tile in pixels.
+            tile_height (int): Height of the tile in pixels.
+            nbatches (int): Number of batches.
+            device (torch.device): Device where the tensor will be allocated (e.g., 'cuda' or 'cpu').
+            dtype (torch.dtype): Data type of the tensor (e.g., torch.float32).
+
+        Returns:
+            torch.Tensor: A tensor containing cosine weights for blending tiles, expanded to match batch and channel dimensions.
+        """
+        # Convert tile dimensions to latent space
+        latent_width = tile_width // 8
+        latent_height = tile_height // 8
+
+        # Generate x and y coordinates in latent space
+        x = np.arange(0, latent_width)
+        y = np.arange(0, latent_height)
+
+        # Calculate midpoints
+        midpoint_x = (latent_width - 1) / 2
+        midpoint_y = (latent_height - 1) / 2
+
+        # Compute cosine probabilities for x and y
+        x_probs = np.cos(np.pi * (x - midpoint_x) / latent_width)
+        y_probs = np.cos(np.pi * (y - midpoint_y) / latent_height)
+
+        # Create a 2D weight matrix using the outer product
+        weights_np = np.outer(y_probs, x_probs)
+
+        # Convert to a PyTorch tensor with the correct device and dtype
+        weights_torch = torch.tensor(weights_np, device=device, dtype=dtype)
+
+        # Expand for batch and channel dimensions
+        tile_weights_expanded = torch.tile(weights_torch, (nbatches, self.unet.config.in_channels, 1, 1))
+
+        return tile_weights_expanded
+
+    def _generate_gaussian_weights(self, tile_width, tile_height, nbatches, device, dtype, sigma=0.05):
+        """
+        Generates Gaussian weights as a PyTorch tensor for blending tiles in latent space.
+
+        Args:
+            tile_width (int): Width of the tile in pixels.
+            tile_height (int): Height of the tile in pixels.
+            nbatches (int): Number of batches.
+            device (torch.device): Device where the tensor will be allocated (e.g., 'cuda' or 'cpu').
+            dtype (torch.dtype): Data type of the tensor (e.g., torch.float32).
+            sigma (float, optional): Standard deviation of the Gaussian distribution. Controls the smoothness of the weights. Defaults to 0.05.
+
+        Returns:
+            torch.Tensor: A tensor containing Gaussian weights for blending tiles, expanded to match batch and channel dimensions.
+        """
+        # Convert tile dimensions to latent space
+        latent_width = tile_width // 8
+        latent_height = tile_height // 8
+
+        # Generate Gaussian weights in latent space
+        x = np.linspace(-1, 1, latent_width)
+        y = np.linspace(-1, 1, latent_height)
+        xx, yy = np.meshgrid(x, y)
+        gaussian_weight = np.exp(-(xx**2 + yy**2) / (2 * sigma**2))
+
+        # Convert to a PyTorch tensor with the correct device and dtype
+        weights_torch = torch.tensor(gaussian_weight, device=device, dtype=dtype)
+
+        # Expand for batch and channel dimensions
+        weights_expanded = weights_torch.unsqueeze(0).unsqueeze(0)  # Add batch and channel dimensions
+        weights_expanded = weights_expanded.expand(nbatches, -1, -1, -1)  # Expand to the number of batches
+
+        return weights_expanded
+
+    def _get_num_tiles(self, height, width, tile_height, tile_width, normal_tile_overlap, border_tile_overlap):
+        """
+        Calculates the number of tiles needed to cover an image, choosing the appropriate formula based on the
+        ratio between the image size and the tile size.
+
+        This function automatically selects between two formulas:
+        1. A universal formula for typical cases (image-to-tile ratio <= 6:1).
+        2. A specialized formula with border tile overlap for larger or atypical cases (image-to-tile ratio > 6:1).
+
+        Args:
+            height (int): Height of the image in pixels.
+            width (int): Width of the image in pixels.
+            tile_height (int): Height of each tile in pixels.
+            tile_width (int): Width of each tile in pixels.
+            normal_tile_overlap (int): Overlap between tiles in pixels for normal (non-border) tiles.
+            border_tile_overlap (int): Overlap between tiles in pixels for border tiles.
+
+        Returns:
+            tuple: A tuple containing:
+                - grid_rows (int): Number of rows in the tile grid.
+                - grid_cols (int): Number of columns in the tile grid.
+
+        Notes:
+            - The function uses the universal formula (without border_tile_overlap) for typical cases where the
+            image-to-tile ratio is 6:1 or smaller.
+            - For larger or atypical cases (image-to-tile ratio > 6:1), it uses a specialized formula that includes
+            border_tile_overlap to ensure complete coverage of the image, especially at the edges.
+        """
+        # Calculate the ratio between the image size and the tile size
+        height_ratio = height / tile_height
+        width_ratio = width / tile_width
+
+        # If the ratio is greater than 6:1, use the formula with border_tile_overlap
+        if height_ratio > 6 or width_ratio > 6:
+            grid_rows = int(np.ceil((height - border_tile_overlap) / (tile_height - normal_tile_overlap))) + 1
+            grid_cols = int(np.ceil((width - border_tile_overlap) / (tile_width - normal_tile_overlap))) + 1
+        else:
+            # Otherwise, use the universal formula
+            grid_rows = int(np.ceil((height - normal_tile_overlap) / (tile_height - normal_tile_overlap)))
+            grid_cols = int(np.ceil((width - normal_tile_overlap) / (tile_width - normal_tile_overlap)))
+
+        return grid_rows, grid_cols
+
+    def prepare_tiles(
+        self,
+        grid_rows,
+        grid_cols,
+        tile_weighting_method,
+        tile_width,
+        tile_height,
+        normal_tile_overlap,
+        border_tile_overlap,
+        width,
+        height,
+        tile_sigma,
+        batch_size,
+        device,
+        dtype,
+    ):
+        """
+        Processes image tiles by dynamically adjusting overlap and calculating Gaussian or cosine weights.
+
+        Args:
+            grid_rows (int): Number of rows in the tile grid.
+            grid_cols (int): Number of columns in the tile grid.
+            tile_weighting_method (str): Method for weighting tiles. Options: "Gaussian" or "Cosine".
+            tile_width (int): Width of each tile in pixels.
+            tile_height (int): Height of each tile in pixels.
+            normal_tile_overlap (int): Overlap between tiles in pixels for normal tiles.
+            border_tile_overlap (int): Overlap between tiles in pixels for border tiles.
+            width (int): Width of the image in pixels.
+            height (int): Height of the image in pixels.
+            tile_sigma (float): Sigma parameter for Gaussian weighting.
+            batch_size (int): Batch size for weight tiles.
+            device (torch.device): Device where tensors will be allocated (e.g., 'cuda' or 'cpu').
+            dtype (torch.dtype): Data type of the tensors (e.g., torch.float32).
+
+        Returns:
+            tuple: A tuple containing:
+                - tile_weights (np.ndarray): Array of weights for each tile.
+                - tile_row_overlaps (np.ndarray): Array of row overlaps for each tile.
+                - tile_col_overlaps (np.ndarray): Array of column overlaps for each tile.
+        """
+
+        # Create arrays to store dynamic overlaps and weights
+        tile_row_overlaps = np.full((grid_rows, grid_cols), normal_tile_overlap)
+        tile_col_overlaps = np.full((grid_rows, grid_cols), normal_tile_overlap)
+        tile_weights = np.empty((grid_rows, grid_cols), dtype=object)  # Stores Gaussian or cosine weights
+
+        # Iterate over tiles to adjust overlap and calculate weights
+        for row in range(grid_rows):
+            for col in range(grid_cols):
+                # Calculate the size of the current tile
+                px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
+                    row, col, tile_width, tile_height, normal_tile_overlap, normal_tile_overlap, width, height
+                )
+                current_tile_width = px_col_end - px_col_init
+                current_tile_height = px_row_end - px_row_init
+                sigma = tile_sigma
+
+                # Adjust overlap for smaller tiles
+                if current_tile_width < tile_width:
+                    px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
+                        row, col, tile_width, tile_height, border_tile_overlap, border_tile_overlap, width, height
+                    )
+                    current_tile_width = px_col_end - px_col_init
+                    tile_col_overlaps[row, col] = border_tile_overlap
+                    sigma = tile_sigma * 1.2
+                if current_tile_height < tile_height:
+                    px_row_init, px_row_end, px_col_init, px_col_end = _tile2pixel_indices(
+                        row, col, tile_width, tile_height, border_tile_overlap, border_tile_overlap, width, height
+                    )
+                    current_tile_height = px_row_end - px_row_init
+                    tile_row_overlaps[row, col] = border_tile_overlap
+                    sigma = tile_sigma * 1.2
+
+                # Calculate weights for the current tile
+                if tile_weighting_method == self.TileWeightingMethod.COSINE.value:
+                    tile_weights[row, col] = self._generate_cosine_weights(
+                        tile_width=current_tile_width,
+                        tile_height=current_tile_height,
+                        nbatches=batch_size,
+                        device=device,
+                        dtype=torch.float32,
+                    )
+                else:
+                    tile_weights[row, col] = self._generate_gaussian_weights(
+                        tile_width=current_tile_width,
+                        tile_height=current_tile_height,
+                        nbatches=batch_size,
+                        device=device,
+                        dtype=dtype,
+                        sigma=sigma,
+                    )
+
+        return tile_weights, tile_row_overlaps, tile_col_overlaps
+
+    # 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)
+
+    @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://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @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,
+        image: PipelineImageInput = None,
+        control_image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        strength: float = 0.9999,
+        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,
+        latents: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        control_mode: Optional[Union[int, List[int]]] = None,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        aesthetic_score: float = 6.0,
+        negative_aesthetic_score: float = 2.5,
+        clip_skip: Optional[int] = None,
+        normal_tile_overlap: int = 64,
+        border_tile_overlap: int = 128,
+        max_tile_size: int = 1024,
+        tile_gaussian_sigma: float = 0.05,
+        tile_weighting_method: str = "Cosine",
+        **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`.
+            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`, *optional*):
+                The initial image to be used as the starting point for the image generation process. Can also accept
+                image latents as `image`, if passing latents directly, they will not be encoded again.
+            control_image (`PipelineImageInput`, *optional*):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance for Unet.
+                If the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also
+                be accepted as an image. The dimensions of the output image default to `image`'s dimensions. If height
+                and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
+                init, images must be passed as a list such that each element of the list can be correctly batched for
+                input to a single ControlNet.
+            height (`int`, *optional*):
+                The height in pixels of the generated image. If not provided, defaults to the height of `control_image`.
+            width (`int`, *optional*):
+                The width in pixels of the generated image. If not provided, defaults to the width of `control_image`.
+            strength (`float`, *optional*, defaults to 0.9999):
+                Indicates the extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+                starting point, and more noise is added the higher the `strength`. The number of denoising steps depends
+                on the amount of noise initially added. When `strength` is 1, added noise is maximum, and the denoising
+                process runs for the full number of iterations specified in `num_inference_steps`.
+            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://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
+                Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages generating
+                images 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://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will be generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated 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.StableDiffusionPipelineOutput`] 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).
+            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original UNet. If multiple ControlNets are specified in init, you can set the
+                corresponding scale as a list.
+            guess_mode (`bool`, *optional*, defaults to `False`):
+                In this mode, the ControlNet encoder will try to recognize the content of the input image even if
+                you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
+            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
+            control_mode (`int` or `List[int]`, *optional*):
+                The mode of ControlNet guidance. Can be used to specify different behaviors for multiple ControlNets.
+            original_size (`Tuple[int, int]`, *optional*):
+                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.
+            crops_coords_top_left (`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.
+            target_size (`Tuple[int, int]`, *optional*):
+                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.
+            negative_original_size (`Tuple[int, int]`, *optional*):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning.
+            negative_crops_coords_top_left (`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.
+            negative_target_size (`Tuple[int, int]`, *optional*):
+                To negatively condition the generation process based on a target image resolution. It should be the same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning.
+            aesthetic_score (`float`, *optional*, defaults to 6.0):
+                Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
+                Part of SDXL's micro-conditioning.
+            negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
+                Used to simulate an aesthetic score of the generated image by influencing the negative text condition.
+                Part of SDXL's micro-conditioning.
+            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.
+            normal_tile_overlap (`int`, *optional*, defaults to 64):
+                Number of overlapping pixels between tiles in consecutive rows.
+            border_tile_overlap (`int`, *optional*, defaults to 128):
+                Number of overlapping pixels between tiles at the borders.
+            max_tile_size (`int`, *optional*, defaults to 1024):
+                Maximum size of a tile in pixels.
+            tile_gaussian_sigma (`float`, *optional*, defaults to 0.3):
+                Sigma parameter for Gaussian weighting of tiles.
+            tile_weighting_method (`str`, *optional*, defaults to "Cosine"):
+                Method for weighting tiles. Options: "Cosine" or "Gaussian".
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple`
+            containing the output images.
+        """
+
+        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+        # align format for control guidance
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+
+        if not isinstance(control_image, list):
+            control_image = [control_image]
+        else:
+            control_image = control_image.copy()
+
+        if control_mode is None or isinstance(control_mode, list) and len(control_mode) == 0:
+            raise ValueError("The value for `control_mode` is expected!")
+
+        if not isinstance(control_mode, list):
+            control_mode = [control_mode]
+
+        if len(control_image) != len(control_mode):
+            raise ValueError("Expected len(control_image) == len(control_mode)")
+
+        num_control_type = controlnet.config.num_control_type
+
+        # 0. Set internal use parameters
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.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 original_size
+        negative_target_size = negative_target_size or target_size
+        control_type = [0 for _ in range(num_control_type)]
+        control_type = torch.Tensor(control_type)
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
+        batch_size = 1
+        device = self._execution_device
+        global_pool_conditions = controlnet.config.global_pool_conditions
+        guess_mode = guess_mode or global_pool_conditions
+
+        # 1. Check inputs
+        for _image, control_idx in zip(control_image, control_mode):
+            control_type[control_idx] = 1
+            self.check_inputs(
+                prompt,
+                height,
+                width,
+                _image,
+                strength,
+                num_inference_steps,
+                normal_tile_overlap,
+                border_tile_overlap,
+                max_tile_size,
+                tile_gaussian_sigma,
+                tile_weighting_method,
+                controlnet_conditioning_scale,
+                control_guidance_start,
+                control_guidance_end,
+            )
+
+        # 2 Get tile width and tile height size
+        tile_width, tile_height = _adaptive_tile_size((width, height), max_tile_size=max_tile_size)
+
+        # 2.1 Calculate the number of tiles needed
+        grid_rows, grid_cols = self._get_num_tiles(
+            height, width, tile_height, tile_width, normal_tile_overlap, border_tile_overlap
+        )
+
+        # 2.2 Expand prompt to number of tiles
+        if not isinstance(prompt, list):
+            prompt = [[prompt] * grid_cols] * grid_rows
+
+        # 2.3 Update height and width tile size by tile size and tile overlap size
+        width = (grid_cols - 1) * (tile_width - normal_tile_overlap) + min(
+            tile_width, width - (grid_cols - 1) * (tile_width - normal_tile_overlap)
+        )
+        height = (grid_rows - 1) * (tile_height - normal_tile_overlap) + min(
+            tile_height, height - (grid_rows - 1) * (tile_height - normal_tile_overlap)
+        )
+
+        # 3. Encode input prompt
+        text_encoder_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=text_encoder_lora_scale,
+                    clip_skip=self.clip_skip,
+                )
+                for col in row
+            ]
+            for row in prompt
+        ]
+
+        # 4. Prepare latent image
+        image_tensor = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
+
+        # 4.1 Prepare controlnet_conditioning_image
+        control_image = self.prepare_control_image(
+            image=image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=controlnet.dtype,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            guess_mode=guess_mode,
+        )
+        control_type = (
+            control_type.reshape(1, -1)
+            .to(device, dtype=controlnet.dtype)
+            .repeat(batch_size * num_images_per_prompt * 2, 1)
+        )
+
+        # 5. 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
+        self.scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Prepare latent variables
+        dtype = text_embeddings[0][0][0].dtype
+        if latents is None:
+            latents = self.prepare_latents(
+                image_tensor,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                dtype,
+                device,
+                generator,
+                True,
+            )
+
+        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents * self.scheduler.sigmas[0]
+
+        # 7. 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)
+
+        # 8. Create tensor stating which controlnets to keep
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            controlnet_keep.append(
+                1.0
+                - float(i / len(timesteps) < control_guidance_start or (i + 1) / len(timesteps) > control_guidance_end)
+            )
+
+        # 8.1 Prepare added time ids & embeddings
+        # text_embeddings order: prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+        embeddings_and_added_time = []
+        crops_coords_top_left = negative_crops_coords_top_left = (tile_width, tile_height)
+        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]
+
+                if negative_original_size is None:
+                    negative_original_size = original_size
+                if negative_target_size is None:
+                    negative_target_size = target_size
+                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, add_neg_time_ids = self._get_add_time_ids(
+                    original_size,
+                    crops_coords_top_left,
+                    target_size,
+                    aesthetic_score,
+                    negative_aesthetic_score,
+                    negative_original_size,
+                    negative_crops_coords_top_left,
+                    negative_target_size,
+                    dtype=prompt_embeds.dtype,
+                    text_encoder_projection_dim=text_encoder_projection_dim,
+                )
+                add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+
+                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_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+                    add_time_ids = torch.cat([add_neg_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)
+                addition_embed_type_row.append((prompt_embeds, add_text_embeds, add_time_ids))
+
+            embeddings_and_added_time.append(addition_embed_type_row)
+
+        # 9. Prepare tiles weights and latent overlaps size to denoising process
+        tile_weights, tile_row_overlaps, tile_col_overlaps = self.prepare_tiles(
+            grid_rows,
+            grid_cols,
+            tile_weighting_method,
+            tile_width,
+            tile_height,
+            normal_tile_overlap,
+            border_tile_overlap,
+            width,
+            height,
+            tile_gaussian_sigma,
+            batch_size,
+            device,
+            dtype,
+        )
+
+        # 10. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        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
+                        tile_row_overlap = tile_row_overlaps[row, col]
+                        tile_col_overlap = tile_col_overlaps[row, col]
+
+                        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, width, height
+                        )
+
+                        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  # 1, 4, ...
+                        )
+                        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],
+                        }
+
+                        # controlnet(s) inference
+                        if guess_mode and self.do_classifier_free_guidance:
+                            # Infer ControlNet only for the conditional batch.
+                            control_model_input = tile_latents
+                            control_model_input = self.scheduler.scale_model_input(control_model_input, t)
+                            controlnet_prompt_embeds = embeddings_and_added_time[row][col][0].chunk(2)[1]
+                            controlnet_added_cond_kwargs = {
+                                "text_embeds": embeddings_and_added_time[row][col][1].chunk(2)[1],
+                                "time_ids": embeddings_and_added_time[row][col][2].chunk(2)[1],
+                            }
+                        else:
+                            control_model_input = latent_model_input
+                            controlnet_prompt_embeds = embeddings_and_added_time[row][col][0]
+                            controlnet_added_cond_kwargs = added_cond_kwargs
+
+                        if isinstance(controlnet_keep[i], list):
+                            cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+                        else:
+                            controlnet_cond_scale = controlnet_conditioning_scale
+                            if isinstance(controlnet_cond_scale, list):
+                                controlnet_cond_scale = controlnet_cond_scale[0]
+                            cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
+                        px_row_init_pixel, px_row_end_pixel, px_col_init_pixel, px_col_end_pixel = _tile2pixel_indices(
+                            row, col, tile_width, tile_height, tile_row_overlap, tile_col_overlap, width, height
+                        )
+
+                        tile_control_image = control_image[
+                            :, :, px_row_init_pixel:px_row_end_pixel, px_col_init_pixel:px_col_end_pixel
+                        ]
+
+                        down_block_res_samples, mid_block_res_sample = self.controlnet(
+                            control_model_input,
+                            t,
+                            encoder_hidden_states=controlnet_prompt_embeds,
+                            controlnet_cond=[tile_control_image],
+                            control_type=control_type,
+                            control_type_idx=control_mode,
+                            conditioning_scale=cond_scale,
+                            guess_mode=guess_mode,
+                            added_cond_kwargs=controlnet_added_cond_kwargs,
+                            return_dict=False,
+                        )
+
+                        if guess_mode and self.do_classifier_free_guidance:
+                            # Inferred ControlNet only for the conditional batch.
+                            # To apply the output of ControlNet to both the unconditional and conditional batches,
+                            # add 0 to the unconditional batch to keep it unchanged.
+                            down_block_res_samples = [
+                                torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples
+                            ]
+                            mid_block_res_sample = torch.cat(
+                                [torch.zeros_like(mid_block_res_sample), mid_block_res_sample]
+                            )
+
+                        # predict the noise residual
+                        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,
+                                down_block_additional_residuals=down_block_res_samples,
+                                mid_block_additional_residual=mid_block_res_sample,
+                                added_cond_kwargs=added_cond_kwargs,
+                                return_dict=False,
+                            )[0]
+
+                        # perform guidance
+                        if self.do_classifier_free_guidance:
+                            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                            noise_pred_tile = noise_pred_uncond + guidance_scale * (
+                                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):
+                        tile_row_overlap = tile_row_overlaps[row, col]
+                        tile_col_overlap = tile_col_overlaps[row, col]
+                        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, width, height
+                        )
+                        tile_weights_resized = tile_weights[row, col]
+
+                        noise_pred[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += (
+                            noise_preds[row][col] * tile_weights_resized
+                        )
+                        contributors[:, :, px_row_init:px_row_end, px_col_init:px_col_end] += tile_weights_resized
+
+                # 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 we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        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)
+
+            # 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)
+
+            # 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)
+        else:
+            image = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        result = StableDiffusionXLPipelineOutput(images=image)
+        if not return_dict:
+            return (image,)
+
+        return result