Update sdxl reference pipeline to latest sdxl pipeline (#9938)

* Update sdxl reference community pipeline

* Update README.md

Add example images.

* Style & quality

* Use example images from huggingface documentation-images repository

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

examples/community/README.md

@@ -2619,16 +2619,17 @@ for obj in range(bs):
 
 ### Stable Diffusion XL Reference
 
-This pipeline uses the Reference. Refer to the [stable_diffusion_reference](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#stable-diffusion-reference).
+This pipeline uses the Reference. Refer to the [Stable Diffusion Reference](https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#stable-diffusion-reference) section for more information.
 
 ```py
 import torch
-from PIL import Image
+# from diffusers import DiffusionPipeline
 from diffusers.utils import load_image
-from diffusers import DiffusionPipeline
 from diffusers.schedulers import UniPCMultistepScheduler
 
-input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+from .stable_diffusion_xl_reference import StableDiffusionXLReferencePipeline
+
+input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg")
 
 # pipe = DiffusionPipeline.from_pretrained(
 #     "stabilityai/stable-diffusion-xl-base-1.0",
@@ -2646,7 +2647,7 @@ pipe = StableDiffusionXLReferencePipeline.from_pretrained(
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 
 result_img = pipe(ref_image=input_image,
-      prompt="1girl",
+      prompt="a dog",
       num_inference_steps=20,
       reference_attn=True,
       reference_adain=True).images[0]
@@ -2654,14 +2655,14 @@ result_img = pipe(ref_image=input_image,
 
 Reference Image
 
-![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)
+![reference_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg)
 
 Output Image
 
-`prompt: 1 girl`
+`prompt: a dog`
 
-`reference_attn=True, reference_adain=True, num_inference_steps=20`
-![Output_image](https://github.com/zideliu/diffusers/assets/34944964/743848da-a215-48f9-ae39-b5e2ae49fb13)
+`reference_attn=False, reference_adain=True, num_inference_steps=20`
+![Output_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_adain_dog.png)
 
 Reference Image
 ![reference_image](https://github.com/huggingface/diffusers/assets/34944964/449bdab6-e744-4fb2-9620-d4068d9a741b)
@@ -4696,4 +4697,4 @@ with torch.no_grad():
 ```
 
 In the folder examples/pixart there is also a script that can be used to train new models.
-Please check the script `train_controlnet_hf_diffusers.sh` on how to start the training.
+Please check the script `train_controlnet_hf_diffusers.sh` on how to start the training.
\ No newline at end of file

examples/community/stable_diffusion_xl_reference.py

 # Based on stable_diffusion_reference.py
 
+import inspect
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -7,28 +8,33 @@ import PIL.Image
 import torch
 
 from diffusers import StableDiffusionXLPipeline
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput
 from diffusers.models.attention import BasicTransformerBlock
-from diffusers.models.unets.unet_2d_blocks import (
-    CrossAttnDownBlock2D,
-    CrossAttnUpBlock2D,
-    DownBlock2D,
-    UpBlock2D,
-)
-from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
-from diffusers.utils import PIL_INTERPOLATION, logging
+from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.utils import PIL_INTERPOLATION, deprecate, is_torch_xla_available, logging, replace_example_docstring
 from diffusers.utils.torch_utils import randn_tensor
 
 
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm  # type: ignore
+
+    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 UniPCMultistepScheduler
+        >>> from diffusers.schedulers import UniPCMultistepScheduler
         >>> from diffusers.utils import load_image
 
-        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+        >>> input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg")
 
         >>> pipe = StableDiffusionXLReferencePipeline.from_pretrained(
             "stabilityai/stable-diffusion-xl-base-1.0",
@@ -38,7 +44,7 @@ EXAMPLE_DOC_STRING = """
 
         >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
         >>> result_img = pipe(ref_image=input_image,
-                        prompt="1girl",
+                        prompt="a dog",
                         num_inference_steps=20,
                         reference_attn=True,
                         reference_adain=True).images[0]
@@ -56,8 +62,6 @@ def torch_dfs(model: torch.nn.Module):
 
 
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
-
-
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     """
     Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
@@ -72,33 +76,102 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
-class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
-    def _default_height_width(self, height, width, image):
-        # NOTE: It is possible that a list of images have different
-        # dimensions for each image, so just checking the first image
-        # is not _exactly_ correct, but it is simple.
-        while isinstance(image, list):
-            image = image[0]
-
-        if height is None:
-            if isinstance(image, PIL.Image.Image):
-                height = image.height
-            elif isinstance(image, torch.Tensor):
-                height = image.shape[2]
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
 
-            height = (height // 8) * 8  # round down to nearest multiple of 8
 
-        if width is None:
-            if isinstance(image, PIL.Image.Image):
-                width = image.width
-            elif isinstance(image, torch.Tensor):
-                width = image.shape[3]
+class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
+    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
+        refimage = refimage.to(device=device)
+        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
+            self.upcast_vae()
+            refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+        if refimage.dtype != self.vae.dtype:
+            refimage = refimage.to(dtype=self.vae.dtype)
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            ref_image_latents = [
+                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            ref_image_latents = torch.cat(ref_image_latents, dim=0)
+        else:
+            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
+        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
+
+        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
+        if ref_image_latents.shape[0] < batch_size:
+            if not batch_size % ref_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
 
-            width = (width // 8) * 8
+        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
 
-        return height, width
+        # aligning device to prevent device errors when concating it with the latent model input
+        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
+        return ref_image_latents
 
-    def prepare_image(
+    def prepare_ref_image(
         self,
         image,
         width,
@@ -151,41 +224,42 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
 
         return image
 
-    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
-        refimage = refimage.to(device=device)
-        if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
-            self.upcast_vae()
-            refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-        if refimage.dtype != self.vae.dtype:
-            refimage = refimage.to(dtype=self.vae.dtype)
-        # encode the mask image into latents space so we can concatenate it to the latents
-        if isinstance(generator, list):
-            ref_image_latents = [
-                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
-                for i in range(batch_size)
-            ]
-            ref_image_latents = torch.cat(ref_image_latents, dim=0)
-        else:
-            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
-        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
+    def check_ref_inputs(
+        self,
+        ref_image,
+        reference_guidance_start,
+        reference_guidance_end,
+        style_fidelity,
+        reference_attn,
+        reference_adain,
+    ):
+        ref_image_is_pil = isinstance(ref_image, PIL.Image.Image)
+        ref_image_is_tensor = isinstance(ref_image, torch.Tensor)
 
-        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
-        if ref_image_latents.shape[0] < batch_size:
-            if not batch_size % ref_image_latents.shape[0] == 0:
-                raise ValueError(
-                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
-                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
-                    " Make sure the number of images that you pass is divisible by the total requested batch size."
-                )
-            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
+        if not ref_image_is_pil and not ref_image_is_tensor:
+            raise TypeError(
+                f"ref image must be passed and be one of PIL image or torch tensor, but is {type(ref_image)}"
+            )
 
-        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
+        if not reference_attn and not reference_adain:
+            raise ValueError("`reference_attn` or `reference_adain` must be True.")
 
-        # aligning device to prevent device errors when concating it with the latent model input
-        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
-        return ref_image_latents
+        if style_fidelity < 0.0:
+            raise ValueError(f"style fidelity: {style_fidelity} can't be smaller than 0.")
+        if style_fidelity > 1.0:
+            raise ValueError(f"style fidelity: {style_fidelity} can't be larger than 1.0.")
+
+        if reference_guidance_start >= reference_guidance_end:
+            raise ValueError(
+                f"reference guidance start: {reference_guidance_start} cannot be larger or equal to reference guidance end: {reference_guidance_end}."
+            )
+        if reference_guidance_start < 0.0:
+            raise ValueError(f"reference guidance start: {reference_guidance_start} can't be smaller than 0.")
+        if reference_guidance_end > 1.0:
+            raise ValueError(f"reference guidance end: {reference_guidance_end} can't be larger than 1.0.")
 
     @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
         self,
         prompt: Union[str, List[str]] = None,
@@ -194,6 +268,8 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
         height: Optional[int] = None,
         width: Optional[int] = None,
         num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
         denoising_end: Optional[float] = None,
         guidance_scale: float = 5.0,
         negative_prompt: Optional[Union[str, List[str]]] = None,
@@ -206,28 +282,220 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
         negative_prompt_embeds: Optional[torch.Tensor] = None,
         pooled_prompt_embeds: Optional[torch.Tensor] = None,
         negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
-        callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         guidance_rescale: float = 0.0,
         original_size: Optional[Tuple[int, int]] = None,
         crops_coords_top_left: Tuple[int, int] = (0, 0),
         target_size: Optional[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,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         attention_auto_machine_weight: float = 1.0,
         gn_auto_machine_weight: float = 1.0,
+        reference_guidance_start: float = 0.0,
+        reference_guidance_end: float = 1.0,
         style_fidelity: float = 0.5,
         reference_attn: bool = True,
         reference_adain: bool = True,
+        **kwargs,
     ):
-        assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            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
+            ref_image (`torch.Tensor`, `PIL.Image.Image`):
+                The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.Tensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
+                also be accepted as an image.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.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 ge generated by sampling using the supplied random `generator`.
+            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.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+                Guidance rescale factor should fix overexposure when using zero terminal SNR.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            attention_auto_machine_weight (`float`):
+                Weight of using reference query for self attention's context.
+                If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
+            gn_auto_machine_weight (`float`):
+                Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
+            reference_guidance_start (`float`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the reference ControlNet starts applying.
+            reference_guidance_end (`float`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the reference ControlNet stops applying.
+            style_fidelity (`float`):
+                style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
+                elif style_fidelity=0.0, prompt more important, else balanced.
+            reference_attn (`bool`):
+                Whether to use reference query for self attention's context.
+            reference_adain (`bool`):
+                Whether to use reference adain.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
 
-        # 0. Default height and width to unet
-        # height, width = self._default_height_width(height, width, ref_image)
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
+        # 0. Default height and width to unet
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
+
         original_size = original_size or (height, width)
         target_size = target_size or (height, width)
 
@@ -244,8 +512,27 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             negative_prompt_embeds,
             pooled_prompt_embeds,
             negative_pooled_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self.check_ref_inputs(
+            ref_image,
+            reference_guidance_start,
+            reference_guidance_end,
+            style_fidelity,
+            reference_attn,
+            reference_adain,
         )
 
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._interrupt = False
+
         # 2. Define call parameters
         if prompt is not None and isinstance(prompt, str):
             batch_size = 1
@@ -256,15 +543,11 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
 
         device = self._execution_device
 
-        # 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.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
         # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
         )
+
         (
             prompt_embeds,
             negative_prompt_embeds,
@@ -275,17 +558,19 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             prompt_2=prompt_2,
             device=device,
             num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
             negative_prompt=negative_prompt,
             negative_prompt_2=negative_prompt_2,
             prompt_embeds=prompt_embeds,
             negative_prompt_embeds=negative_prompt_embeds,
             pooled_prompt_embeds=pooled_prompt_embeds,
             negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
         )
+
         # 4. Preprocess reference image
-        ref_image = self.prepare_image(
+        ref_image = self.prepare_ref_image(
             image=ref_image,
             width=width,
             height=height,
@@ -296,9 +581,9 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
         )
 
         # 5. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-
-        timesteps = self.scheduler.timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
 
         # 6. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
@@ -312,6 +597,7 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             generator,
             latents,
         )
+
         # 7. Prepare reference latent variables
         ref_image_latents = self.prepare_ref_latents(
             ref_image,
@@ -319,13 +605,21 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             prompt_embeds.dtype,
             device,
             generator,
-            do_classifier_free_guidance,
+            self.do_classifier_free_guidance,
         )
 
         # 8. 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)
 
-        # 9. Modify self attebtion and group norm
+        # 8.1 Create tensor stating which reference controlnets to keep
+        reference_keeps = []
+        for i in range(len(timesteps)):
+            reference_keep = 1.0 - float(
+                i / len(timesteps) < reference_guidance_start or (i + 1) / len(timesteps) > reference_guidance_end
+            )
+            reference_keeps.append(reference_keep)
+
+        # 8.2 Modify self attention and group norm
         MODE = "write"
         uc_mask = (
             torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
@@ -333,6 +627,8 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             .bool()
         )
 
+        do_classifier_free_guidance = self.do_classifier_free_guidance
+
         def hacked_basic_transformer_inner_forward(
             self,
             hidden_states: torch.Tensor,
@@ -604,7 +900,7 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             return hidden_states
 
         def hacked_UpBlock2D_forward(
-            self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, **kwargs
+            self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, *args, **kwargs
         ):
             eps = 1e-6
             for i, resnet in enumerate(self.resnets):
@@ -684,7 +980,7 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
                 module.var_bank = []
                 module.gn_weight *= 2
 
-        # 10. Prepare added time ids & embeddings
+        # 9. Prepare added time ids & embeddings
         add_text_embeds = pooled_prompt_embeds
         if self.text_encoder_2 is None:
             text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
@@ -698,62 +994,101 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             dtype=prompt_embeds.dtype,
             text_encoder_projection_dim=text_encoder_projection_dim,
         )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
 
-        if do_classifier_free_guidance:
+        if self.do_classifier_free_guidance:
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
             add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
 
         prompt_embeds = prompt_embeds.to(device)
         add_text_embeds = add_text_embeds.to(device)
         add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
 
-        # 11. Denoising loop
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 10. Denoising loop
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
 
         # 10.1 Apply denoising_end
-        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
+        if (
+            self.denoising_end is not None
+            and isinstance(self.denoising_end, float)
+            and self.denoising_end > 0
+            and self.denoising_end < 1
+        ):
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
-                    - (denoising_end * self.scheduler.config.num_train_timesteps)
+                    - (self.denoising_end * self.scheduler.config.num_train_timesteps)
                 )
             )
             num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
             timesteps = timesteps[:num_inference_steps]
 
+        # 11. Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
 
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
+                # predict the noise residual
                 added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
 
                 # ref only part
-                noise = randn_tensor(
-                    ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
-                )
-                ref_xt = self.scheduler.add_noise(
-                    ref_image_latents,
-                    noise,
-                    t.reshape(
-                        1,
-                    ),
-                )
-                ref_xt = self.scheduler.scale_model_input(ref_xt, t)
-
-                MODE = "write"
-
-                self.unet(
-                    ref_xt,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
-                    return_dict=False,
-                )
+                if reference_keeps[i] > 0:
+                    noise = randn_tensor(
+                        ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
+                    )
+                    ref_xt = self.scheduler.add_noise(
+                        ref_image_latents,
+                        noise,
+                        t.reshape(
+                            1,
+                        ),
+                    )
+                    ref_xt = self.scheduler.scale_model_input(ref_xt, t)
+
+                    MODE = "write"
+                    self.unet(
+                        ref_xt,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        cross_attention_kwargs=cross_attention_kwargs,
+                        added_cond_kwargs=added_cond_kwargs,
+                        return_dict=False,
+                    )
 
                 # predict the noise residual
                 MODE = "read"
@@ -761,22 +1096,44 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
                     latent_model_input,
                     t,
                     encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
                     added_cond_kwargs=added_cond_kwargs,
                     return_dict=False,
                 )[0]
 
                 # perform guidance
-                if do_classifier_free_guidance:
+                if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
 
-                if do_classifier_free_guidance and guidance_rescale > 0.0:
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
                     # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
 
                 # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = 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)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
@@ -785,6 +1142,9 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
                         step_idx = i // getattr(self.scheduler, "order", 1)
                         callback(step_idx, t, latents)
 
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16
             needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
@@ -792,25 +1152,43 @@ class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
             if needs_upcasting:
                 self.upcast_vae()
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
 
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            image = self.vae.decode(latents, return_dict=False)[0]
 
             # cast back to fp16 if needed
             if needs_upcasting:
                 self.vae.to(dtype=torch.float16)
         else:
             image = latents
-            return StableDiffusionXLPipelineOutput(images=image)
 
-        # apply watermark if available
-        if self.watermark is not None:
-            image = self.watermark.apply_watermark(image)
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
 
-        image = self.image_processor.postprocess(image, output_type=output_type)
+            image = self.image_processor.postprocess(image, output_type=output_type)
 
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
+        # Offload all models
+        self.maybe_free_model_hooks()
 
         if not return_dict:
             return (image,)