pipeline_sd3_controlnet_inpainting.py

# Copyright 2024 Stability AI, The HuggingFace Team and The InstantX 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 typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from transformers import (
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    T5EncoderModel,
    T5TokenizerFast,
)

from PIL import Image, ImageOps
import numpy as np
import os
from torchvision.transforms import v2

from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, SD3LoraLoaderMixin
from diffusers.models.autoencoders import AutoencoderKL
from diffusers.models.transformers import SD3Transformer2DModel
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils import (
    is_torch_xla_available,
    logging,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion_3.pipeline_output import (
    StableDiffusion3PipelineOutput,
)
from torchvision.transforms.functional import resize, InterpolationMode

from controlnet_sd3 import SD3ControlNetModel, SD3MultiControlNetModel

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 StableDiffusion3ControlNetPipeline
        >>> from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
        >>> from diffusers.utils import load_image

        >>> controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)

        >>> pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
        ...     "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")
        >>> control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
        >>> prompt = "A girl holding a sign that says InstantX"
        >>> image = pipe(prompt, control_image=control_image, controlnet_conditioning_scale=0.7).images[0]
        >>> image.save("sd3.png")
        ```
"""

def one_image_and_mask(image, mask, size = None, latent_scale = 8 , invert_mask = False):
    '''
    Image : PIL Image, Torch Tensor [-1, 1], Path, B,C,H,W
    Mask : PIL Image , Torch Tensor [0, 1], Path, B,1,H,W
    '''
    # size = (W, H)
    if size is not None:
        if not ( type(size) == list or type(size) == tuple):
            size = (size, size)
        
    # Get image @ torch tensor
    if type(image) == str and os.path.exists(image):
        image = Image.open(image)
    
    if isinstance(image, Image.Image): 
        image = image.convert("RGB")
        if size is not None:
            image = image.resize(size, Image.Resampling.LANCZOS)
        pil_image = image
        image_arr = np.array(image)
        assert image_arr.ndim == 3
        assert image_arr.shape[2] == 3
        th_image = torch.from_numpy(image_arr).float() / 127. - 1
        th_image = th_image.permute(2, 0, 1)
    else:
        th_image = image
        pil_image = None
    
    # Get BCHW
    assert isinstance(th_image, torch.Tensor)
    if len(th_image.shape) == 3:
        th_image = th_image.unsqueeze(0)
    H, W = th_image.shape[-2:]
    assert H % 8 == 0 and W % 8 == 0
    
    # Get mask @ torch tensor
    if type(mask) == str and os.path.exists(mask):
        mask = Image.open(mask)
    
    if isinstance(mask, Image.Image):
        mask = mask.convert("L")
        if invert_mask:
            mask = ImageOps.invert(mask)
        mask = mask.resize((W, H), Image.Resampling.LANCZOS)
        pil_mask = mask
        mask_arr = np.array(mask)
        if mask_arr.ndim == 3 and mask_arr.shape[2] == 3:
            mask_arr = mask_arr[:, :, 0] # H, W
        th_mask = torch.from_numpy(mask_arr).float() / 255.
        th_mask = th_mask.unsqueeze(0)
    else:
        th_mask = mask
        pil_mask = None
        
    assert isinstance(th_mask, torch.Tensor)
    if len(th_mask.shape) == 3:
        th_mask = th_mask.unsqueeze(0)
    
    # Get mask at latent space
    th_mask_latent = torch.nn.functional.interpolate(
            th_mask, size=(H // latent_scale, W // latent_scale), mode="bilinear", antialias=True
        )
    
    # Get masked image for vae-cond
    masked_image = th_image.clone()
    masked_image[(th_mask < 0.5).repeat(1,3,1,1)] = - 1. # set 0. like power paint @ https://github.com/open-mmlab/PowerPaint/blob/main/powerpaint/pipelines/pipeline_PowerPaint.py
    
    # Get pil masked image
    pil_masked_image = v2.ToPILImage()((masked_image/2 + 1/2).clip(0, 1).squeeze(0))
    
    # Get masked image
    return {
        'image': th_image,
        'mask': th_mask,
        'mask_latent': th_mask_latent,
        'masked_image': masked_image,
        'pil_image': pil_image,
        'pil_mask': pil_mask,
        'pil_masked_image': pil_masked_image
    }

# 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,
):
    """
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

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

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


class StableDiffusion3ControlNetInpaintingPipeline(
    DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin
):
    r"""
    Args:
        transformer ([`SD3Transformer2DModel`]):
            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModelWithProjection`]):
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
            with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
            as its dimension.
        text_encoder_2 ([`CLIPTextModelWithProjection`]):
            [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.
        text_encoder_3 ([`T5EncoderModel`]):
            Frozen text-encoder. Stable Diffusion 3 uses
            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) 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).
        tokenizer_3 (`T5TokenizerFast`):
            Tokenizer of class
            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
        controlnet ([`SD3ControlNetModel`] or `List[SD3ControlNetModel]` or [`SD3MultiControlNetModel`]):
            Provides additional conditioning to the `unet` during the denoising process. If you set multiple
            ControlNets as a list, the outputs from each ControlNet are added together to create one combined
            additional conditioning.
    """

    model_cpu_offload_seq = (
        "text_encoder->text_encoder_2->text_encoder_3->transformer->vae"
    )
    _optional_components = []
    _callback_tensor_inputs = [
        "latents",
        "prompt_embeds",
        "negative_prompt_embeds",
        "negative_pooled_prompt_embeds",
    ]

    def __init__(
        self,
        transformer: SD3Transformer2DModel,
        scheduler: FlowMatchEulerDiscreteScheduler,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModelWithProjection,
        tokenizer: CLIPTokenizer,
        text_encoder_2: CLIPTextModelWithProjection,
        tokenizer_2: CLIPTokenizer,
        text_encoder_3: T5EncoderModel,
        tokenizer_3: T5TokenizerFast,
        controlnet: Union[
            SD3ControlNetModel,
            List[SD3ControlNetModel],
            Tuple[SD3ControlNetModel],
            SD3MultiControlNetModel,
        ],
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            text_encoder_2=text_encoder_2,
            text_encoder_3=text_encoder_3,
            tokenizer=tokenizer,
            tokenizer_2=tokenizer_2,
            tokenizer_3=tokenizer_3,
            transformer=transformer,
            scheduler=scheduler,
            controlnet=controlnet,
        )
        self.vae_scale_factor = (
            2 ** (len(self.vae.config.block_out_channels) - 1)
            if hasattr(self, "vae") and self.vae is not None
            else 8
        )
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
        self.control_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor,
            do_convert_rgb=True,
            do_normalize=False,
        )
        self.tokenizer_max_length = (
            self.tokenizer.model_max_length
            if hasattr(self, "tokenizer") and self.tokenizer is not None
            else 77
        )
        self.default_sample_size = (
            self.transformer.config.sample_size
            if hasattr(self, "transformer") and self.transformer is not None
            else 128
        )

    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
    def _get_t5_prompt_embeds(
        self,
        prompt: Union[str, List[str]] = None,
        num_images_per_prompt: int = 1,
        device: Optional[torch.device] = None,
        dtype: Optional[torch.dtype] = None,
    ):
        device = device or self._execution_device
        dtype = dtype or self.text_encoder.dtype

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

        if self.text_encoder_3 is None:
            return torch.zeros(
                (
                    batch_size,
                    self.tokenizer_max_length,
                    self.transformer.config.joint_attention_dim,
                ),
                device=device,
                dtype=dtype,
            )

        text_inputs = self.tokenizer_3(
            prompt,
            padding="max_length",
            max_length=self.tokenizer_max_length,
            truncation=True,
            add_special_tokens=True,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids
        untruncated_ids = self.tokenizer_3(
            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 = self.tokenizer_3.batch_decode(
                untruncated_ids[:, self.tokenizer_max_length - 1 : -1]
            )
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                f" {self.tokenizer_max_length} tokens: {removed_text}"
            )

        prompt_embeds = self.text_encoder_3(text_input_ids.to(device))[0]

        dtype = self.text_encoder_3.dtype
        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)

        _, seq_len, _ = prompt_embeds.shape

        # duplicate text embeddings and attention mask 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(
            batch_size * num_images_per_prompt, seq_len, -1
        )

        return prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_clip_prompt_embeds
    def _get_clip_prompt_embeds(
        self,
        prompt: Union[str, List[str]],
        num_images_per_prompt: int = 1,
        device: Optional[torch.device] = None,
        clip_skip: Optional[int] = None,
        clip_model_index: int = 0,
    ):
        device = device or self._execution_device

        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
        clip_text_encoders = [self.text_encoder, self.text_encoder_2]

        tokenizer = clip_tokenizers[clip_model_index]
        text_encoder = clip_text_encoders[clip_model_index]

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

        text_inputs = tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer_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[:, self.tokenizer_max_length - 1 : -1]
            )
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                f" {self.tokenizer_max_length} tokens: {removed_text}"
            )
        prompt_embeds = text_encoder(
            text_input_ids.to(device), output_hidden_states=True
        )
        pooled_prompt_embeds = prompt_embeds[0]

        if clip_skip is None:
            prompt_embeds = prompt_embeds.hidden_states[-2]
        else:
            prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

        _, 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(
            batch_size * num_images_per_prompt, seq_len, -1
        )

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

        return prompt_embeds, pooled_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_prompt
    def encode_prompt(
        self,
        prompt: Union[str, List[str]],
        prompt_2: Union[str, List[str]],
        prompt_3: Union[str, List[str]],
        device: Optional[torch.device] = None,
        num_images_per_prompt: int = 1,
        do_classifier_free_guidance: bool = True,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        negative_prompt_3: Optional[Union[str, List[str]]] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        clip_skip: Optional[int] = None,
    ):
        r"""

        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 all text-encoders
            prompt_3 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
                used in all 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 all the text-encoders.
            negative_prompt_2 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
                `text_encoder_3`. If not defined, `negative_prompt` is used in both text-encoders
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                input argument.
            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

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

        if prompt_embeds is None:
            prompt_2 = prompt_2 or prompt
            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2

            prompt_3 = prompt_3 or prompt
            prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3

            prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
                prompt=prompt,
                device=device,
                num_images_per_prompt=num_images_per_prompt,
                clip_skip=clip_skip,
                clip_model_index=0,
            )
            prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
                prompt=prompt_2,
                device=device,
                num_images_per_prompt=num_images_per_prompt,
                clip_skip=clip_skip,
                clip_model_index=1,
            )
            clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)

            t5_prompt_embed = self._get_t5_prompt_embeds(
                prompt=prompt_3,
                num_images_per_prompt=num_images_per_prompt,
                device=device,
            )

            clip_prompt_embeds = torch.nn.functional.pad(
                clip_prompt_embeds,
                (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1]),
            )

            prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
            pooled_prompt_embeds = torch.cat(
                [pooled_prompt_embed, pooled_prompt_2_embed], dim=-1
            )

        if do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            negative_prompt_2 = negative_prompt_2 or negative_prompt
            negative_prompt_3 = negative_prompt_3 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
            )
            negative_prompt_3 = (
                batch_size * [negative_prompt_3]
                if isinstance(negative_prompt_3, str)
                else negative_prompt_3
            )

            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`."
                )

            negative_prompt_embed, negative_pooled_prompt_embed = (
                self._get_clip_prompt_embeds(
                    negative_prompt,
                    device=device,
                    num_images_per_prompt=num_images_per_prompt,
                    clip_skip=None,
                    clip_model_index=0,
                )
            )
            negative_prompt_2_embed, negative_pooled_prompt_2_embed = (
                self._get_clip_prompt_embeds(
                    negative_prompt_2,
                    device=device,
                    num_images_per_prompt=num_images_per_prompt,
                    clip_skip=None,
                    clip_model_index=1,
                )
            )
            negative_clip_prompt_embeds = torch.cat(
                [negative_prompt_embed, negative_prompt_2_embed], dim=-1
            )

            t5_negative_prompt_embed = self._get_t5_prompt_embeds(
                prompt=negative_prompt_3,
                num_images_per_prompt=num_images_per_prompt,
                device=device,
            )

            negative_clip_prompt_embeds = torch.nn.functional.pad(
                negative_clip_prompt_embeds,
                (
                    0,
                    t5_negative_prompt_embed.shape[-1]
                    - negative_clip_prompt_embeds.shape[-1],
                ),
            )

            negative_prompt_embeds = torch.cat(
                [negative_clip_prompt_embeds, t5_negative_prompt_embed], dim=-2
            )
            negative_pooled_prompt_embeds = torch.cat(
                [negative_pooled_prompt_embed, negative_pooled_prompt_2_embed], dim=-1
            )

        return (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        )

    def check_inputs(
        self,
        prompt,
        prompt_2,
        prompt_3,
        height,
        width,
        negative_prompt=None,
        negative_prompt_2=None,
        negative_prompt_3=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        pooled_prompt_embeds=None,
        negative_pooled_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
    ):
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
            )

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

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt_2 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt_3 is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt_3`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (
            not isinstance(prompt, str) and not isinstance(prompt, list)
        ):
            raise ValueError(
                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
            )
        elif prompt_2 is not None and (
            not isinstance(prompt_2, str) and not isinstance(prompt_2, list)
        ):
            raise ValueError(
                f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}"
            )
        elif prompt_3 is not None and (
            not isinstance(prompt_3, str) and not isinstance(prompt_3, list)
        ):
            raise ValueError(
                f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}"
            )

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )
        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )
        elif negative_prompt_3 is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

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

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

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

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(
        self,
        batch_size,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        shape = (
            batch_size,
            num_channels_latents,
            int(height) // self.vae_scale_factor,
            int(width) // self.vae_scale_factor,
        )

        if isinstance(generator, list) and len(generator) != batch_size:
            raise ValueError(
                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
            )

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

        return latents

    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
    def prepare_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

    def prepare_image_with_mask(
        self,
        image,
        mask,
        width,
        height,
        batch_size,
        num_images_per_prompt,
        device,
        dtype,
        do_classifier_free_guidance=False,
        guess_mode=False,
    ):

        if isinstance(image, torch.Tensor):
            pass
        else:
            image = self.image_processor.preprocess(
                image, height=height, width=width
            )  # C,H,W

        if isinstance(mask, torch.Tensor):
            pass
        else:
            raise "Control Mask must be tensor"

        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)
        mask = mask.repeat_interleave(repeat_by, dim=0)

        image = image.to(device=device, dtype=self.vae.dtype)
        mask = mask.to(device=device, dtype=dtype)

        image_latents = self.vae.encode(image).latent_dist.sample()
        image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
        image_latents = image_latents.to(dtype)

        # cat image and mask
        mask = torch.nn.functional.interpolate(
            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
        )
        
        control_image = torch.cat([image_latents, mask], dim=1)

        if do_classifier_free_guidance and not guess_mode:
            control_image = torch.cat([control_image] * 2)
        return control_image

    @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 joint_attention_kwargs(self):
        return self._joint_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,
        prompt_2: Optional[Union[str, List[str]]] = None,
        prompt_3: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 28,
        timesteps: List[int] = None,
        guidance_scale: float = 7.0,
        control_guidance_start: Union[float, List[float]] = 0.0,
        control_guidance_end: Union[float, List[float]] = 1.0,
        control_image: Union[
            PipelineImageInput,
            List[PipelineImageInput],
        ] = None,
        control_mask=None,
        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
        controlnet_pooled_projections: Optional[torch.FloatTensor] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        negative_prompt_3: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        clip_skip: Optional[int] = None,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
    ):
        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 `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
                will be used instead
            prompt_3 (`str` or `List[str]`, *optional*):
                The prompt or prompts to be sent to `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
                will be used instead
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image. This is set to 1024 by default for the best results.
            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.
            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.
            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.
            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_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
                    `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
                The ControlNet input condition to provide guidance to the `unet` for generation. 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 defaults 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.
            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.
            controlnet_pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`):
                Embeddings projected from the embeddings of controlnet input conditions.
            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 instead
            negative_prompt_3 (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation to be sent to `tokenizer_3` and
                `text_encoder_3`. If not defined, `negative_prompt` is used instead
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
                of a plain tuple.
            joint_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).
            callback_on_step_end (`Callable`, *optional*):
                A function that calls at the end of each denoising steps during the inference. The function is called
                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
                `callback_on_step_end_tensor_inputs`.
            callback_on_step_end_tensor_inputs (`List`, *optional*):
                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
                `._callback_tensor_inputs` attribute of your pipeline class.

        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.
        """

        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor

        # 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]
        elif not isinstance(control_guidance_start, list) and not isinstance(
            control_guidance_end, list
        ):
            mult = (
                len(self.controlnet.nets)
                if isinstance(self.controlnet, SD3MultiControlNetModel)
                else 1
            )
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            prompt_3,
            height,
            width,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            negative_prompt_3=negative_prompt_3,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
        )

        self._guidance_scale = guidance_scale
        self._clip_skip = clip_skip
        self._joint_attention_kwargs = joint_attention_kwargs
        self._interrupt = False

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        dtype = self.transformer.dtype

        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            prompt_3=prompt_3,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            negative_prompt_3=negative_prompt_3,
            do_classifier_free_guidance=self.do_classifier_free_guidance,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            device=device,
            clip_skip=self.clip_skip,
            num_images_per_prompt=num_images_per_prompt,
        )

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

        # 3. Prepare control image
        if isinstance(self.controlnet, SD3ControlNetModel):
            control_image = self.prepare_image_with_mask(
                image=control_image,
                mask=control_mask,
                width=width,
                height=height,
                batch_size=batch_size * num_images_per_prompt,
                num_images_per_prompt=num_images_per_prompt,
                device=device,
                dtype=self.controlnet.dtype,
                do_classifier_free_guidance=self.do_classifier_free_guidance,
            )
            height, width = control_image.shape[-2:]
            height = height * self.vae_scale_factor
            width = width * self.vae_scale_factor
        elif isinstance(self.controlnet, SD3MultiControlNetModel):
            images = []
            for image_ in control_image:
                image_ = self.prepare_image_with_mask(
                    image=image_,
                    mask=control_mask,
                    width=width,
                    height=height,
                    batch_size=batch_size * num_images_per_prompt,
                    num_images_per_prompt=num_images_per_prompt,
                    device=device,
                    dtype=self.controlnet.dtype,
                    do_classifier_free_guidance=self.do_classifier_free_guidance,
                )
                images.append(image_)

            control_image = images
            height, width = control_image[0].shape[-2:]
            height = height * self.vae_scale_factor
            width = width * self.vae_scale_factor
        else:
            raise ValueError("ControlNet must be of type SD3ControlNetModel")

        if controlnet_pooled_projections is None:
            controlnet_pooled_projections = torch.zeros_like(pooled_prompt_embeds)
        else:
            controlnet_pooled_projections = (
                controlnet_pooled_projections or pooled_prompt_embeds
            )

        # 4. Prepare timesteps
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler, num_inference_steps, device, timesteps
        )
        num_warmup_steps = max(
            len(timesteps) - num_inference_steps * self.scheduler.order, 0
        )
        self._num_timesteps = len(timesteps)

        # 5. Prepare latent variables
        num_channels_latents = self.transformer.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Create tensor stating which controlnets to keep
        controlnet_keep = []
        for i in range(len(timesteps)):
            keeps = [
                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
                for s, e in zip(control_guidance_start, control_guidance_end)
            ]
            controlnet_keep.append(
                keeps[0] if isinstance(self.controlnet, SD3ControlNetModel) else keeps
            )

        # 7. Denoising loop
        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 self.do_classifier_free_guidance
                    else latents
                )
                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                timestep = t.expand(latent_model_input.shape[0])

                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]

                # controlnet(s) inference
                control_block_samples = self.controlnet(
                    hidden_states=latent_model_input,
                    timestep=timestep,
                    encoder_hidden_states=prompt_embeds,
                    pooled_projections=controlnet_pooled_projections,
                    joint_attention_kwargs=self.joint_attention_kwargs,
                    controlnet_cond=control_image,
                    conditioning_scale=cond_scale,
                    return_dict=False,
                )[0]

                noise_pred = self.transformer(
                    hidden_states=latent_model_input,
                    timestep=timestep,
                    encoder_hidden_states=prompt_embeds,
                    pooled_projections=pooled_prompt_embeds,
                    block_controlnet_hidden_states=control_block_samples,
                    joint_attention_kwargs=self.joint_attention_kwargs,
                    return_dict=False,
                )[0]

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

                # compute the previous noisy sample x_t -> x_t-1
                latents_dtype = latents.dtype
                latents = self.scheduler.step(
                    noise_pred, t, latents, 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
                    )
                    negative_pooled_prompt_embeds = callback_outputs.pop(
                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
                    )

                # call the callback, if provided
                if i == len(timesteps) - 1 or (
                    (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
                ):
                    progress_bar.update()

                if XLA_AVAILABLE:
                    xm.mark_step()

        if output_type == "latent":
            image = latents

        else:
            latents = (
                latents / self.vae.config.scaling_factor
            ) + self.vae.config.shift_factor
            latents = latents.to(dtype=self.vae.dtype)
            image = self.vae.decode(latents, return_dict=False)[0]
            image = self.image_processor.postprocess(image, output_type=output_type)

        # Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (image,)

        return StableDiffusion3PipelineOutput(images=image)