# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import AutoTokenizer, PreTrainedModel

from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, ZImageLoraLoaderMixin
from ...models.autoencoders import AutoencoderKL
from ...models.transformers import ZImageTransformer2DModel
from ...pipelines.pipeline_utils import DiffusionPipeline
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import is_torch_xla_available, logging, replace_example_docstring
from ...utils.torch_utils import randn_tensor
from .pipeline_output import ZImagePipelineOutput


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 ZImageInpaintPipeline
        >>> from diffusers.utils import load_image

        >>> pipe = ZImageInpaintPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16)
        >>> pipe.to("cuda")

        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
        >>> init_image = load_image(url).resize((1024, 1024))

        >>> # Create a mask (white = inpaint, black = preserve)
        >>> import numpy as np
        >>> from PIL import Image

        >>> mask = np.zeros((1024, 1024), dtype=np.uint8)
        >>> mask[256:768, 256:768] = 255  # Inpaint center region
        >>> mask_image = Image.fromarray(mask)

        >>> prompt = "A beautiful lake with mountains in the background"
        >>> image = pipe(
        ...     prompt,
        ...     image=init_image,
        ...     mask_image=mask_image,
        ...     strength=1.0,
        ...     num_inference_steps=9,
        ...     guidance_scale=0.0,
        ...     generator=torch.Generator("cuda").manual_seed(42),
        ... ).images[0]
        >>> image.save("zimage_inpaint.png")
        ```
"""


# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
def calculate_shift(
    image_seq_len,
    base_seq_len: int = 256,
    max_seq_len: int = 4096,
    base_shift: float = 0.5,
    max_shift: float = 1.15,
):
    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
    b = base_shift - m * base_seq_len
    mu = image_seq_len * m + b
    return mu


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
    encoder_output: torch.Tensor, generator: torch.Generator | None = None, sample_mode: str = "sample"
):
    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
        return encoder_output.latent_dist.sample(generator)
    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
        return encoder_output.latent_dist.mode()
    elif hasattr(encoder_output, "latents"):
        return encoder_output.latents
    else:
        raise AttributeError("Could not access latents of provided encoder_output")


# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
    scheduler,
    num_inference_steps: int | None = None,
    device: str | torch.device | None = None,
    timesteps: list[int] | None = None,
    sigmas: list[float] | None = None,
    **kwargs,
):
    r"""
    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.

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

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


class ZImageInpaintPipeline(DiffusionPipeline, ZImageLoraLoaderMixin, FromSingleFileMixin):
    r"""
    The ZImage pipeline for inpainting.

    Args:
        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 ([`PreTrainedModel`]):
            A text encoder model to encode text prompts.
        tokenizer ([`AutoTokenizer`]):
            A tokenizer to tokenize text prompts.
        transformer ([`ZImageTransformer2DModel`]):
            A ZImage transformer model to denoise the encoded image latents.
    """

    model_cpu_offload_seq = "text_encoder->transformer->vae"
    _optional_components = []
    _callback_tensor_inputs = ["latents", "prompt_embeds", "mask", "masked_image_latents"]

    def __init__(
        self,
        scheduler: FlowMatchEulerDiscreteScheduler,
        vae: AutoencoderKL,
        text_encoder: PreTrainedModel,
        tokenizer: AutoTokenizer,
        transformer: ZImageTransformer2DModel,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            scheduler=scheduler,
            transformer=transformer,
        )
        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 * 2)
        self.mask_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor * 2,
            do_normalize=False,
            do_binarize=True,
            do_convert_grayscale=True,
        )

    # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline.encode_prompt
    def encode_prompt(
        self,
        prompt: str | list[str],
        device: torch.device | None = None,
        do_classifier_free_guidance: bool = True,
        negative_prompt: str | list[str] | None = None,
        prompt_embeds: list[torch.FloatTensor] | None = None,
        negative_prompt_embeds: torch.FloatTensor | None = None,
        max_sequence_length: int = 512,
    ):
        prompt = [prompt] if isinstance(prompt, str) else prompt
        prompt_embeds = self._encode_prompt(
            prompt=prompt,
            device=device,
            prompt_embeds=prompt_embeds,
            max_sequence_length=max_sequence_length,
        )

        if do_classifier_free_guidance:
            if negative_prompt is None:
                negative_prompt = ["" for _ in prompt]
            else:
                negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
            assert len(prompt) == len(negative_prompt)
            negative_prompt_embeds = self._encode_prompt(
                prompt=negative_prompt,
                device=device,
                prompt_embeds=negative_prompt_embeds,
                max_sequence_length=max_sequence_length,
            )
        else:
            negative_prompt_embeds = []
        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.z_image.pipeline_z_image.ZImagePipeline._encode_prompt
    def _encode_prompt(
        self,
        prompt: str | list[str],
        device: torch.device | None = None,
        prompt_embeds: list[torch.FloatTensor] | None = None,
        max_sequence_length: int = 512,
    ) -> list[torch.FloatTensor]:
        device = device or self._execution_device

        if prompt_embeds is not None:
            return prompt_embeds

        if isinstance(prompt, str):
            prompt = [prompt]

        for i, prompt_item in enumerate(prompt):
            messages = [
                {"role": "user", "content": prompt_item},
            ]
            prompt_item = self.tokenizer.apply_chat_template(
                messages,
                tokenize=False,
                add_generation_prompt=True,
                enable_thinking=True,
            )
            prompt[i] = prompt_item

        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            return_tensors="pt",
        )

        text_input_ids = text_inputs.input_ids.to(device)
        prompt_masks = text_inputs.attention_mask.to(device).bool()

        prompt_embeds = self.text_encoder(
            input_ids=text_input_ids,
            attention_mask=prompt_masks,
            output_hidden_states=True,
        ).hidden_states[-2]

        embeddings_list = []

        for i in range(len(prompt_embeds)):
            embeddings_list.append(prompt_embeds[i][prompt_masks[i]])

        return embeddings_list

    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.get_timesteps
    def get_timesteps(self, num_inference_steps, strength, device):
        # get the original timestep using init_timestep
        init_timestep = min(num_inference_steps * strength, num_inference_steps)

        t_start = int(max(num_inference_steps - init_timestep, 0))
        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
        if hasattr(self.scheduler, "set_begin_index"):
            self.scheduler.set_begin_index(t_start * self.scheduler.order)

        return timesteps, num_inference_steps - t_start

    def prepare_mask_latents(
        self,
        mask,
        masked_image,
        batch_size,
        height,
        width,
        dtype,
        device,
        generator,
    ):
        """Prepare mask and masked image latents for inpainting.

        Args:
            mask: Binary mask tensor where 1 = inpaint region, 0 = preserve region.
            masked_image: Original image with masked regions zeroed out.
            batch_size: Number of images to generate.
            height: Output image height.
            width: Output image width.
            dtype: Data type for the tensors.
            device: Device to place tensors on.
            generator: Random generator for reproducibility.

        Returns:
            Tuple of (mask, masked_image_latents) prepared for the denoising loop.
        """
        # Calculate latent dimensions
        latent_height = 2 * (int(height) // (self.vae_scale_factor * 2))
        latent_width = 2 * (int(width) // (self.vae_scale_factor * 2))

        # Resize mask to latent dimensions
        mask = torch.nn.functional.interpolate(mask, size=(latent_height, latent_width), mode="nearest")
        mask = mask.to(device=device, dtype=dtype)

        # Encode masked image to latents
        masked_image = masked_image.to(device=device, dtype=dtype)
        if isinstance(generator, list):
            masked_image_latents = [
                retrieve_latents(self.vae.encode(masked_image[i : i + 1]), generator=generator[i])
                for i in range(masked_image.shape[0])
            ]
            masked_image_latents = torch.cat(masked_image_latents, dim=0)
        else:
            masked_image_latents = retrieve_latents(self.vae.encode(masked_image), generator=generator)

        # Apply VAE scaling
        masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor

        # Expand for batch size
        if mask.shape[0] < batch_size:
            if not batch_size % mask.shape[0] == 0:
                raise ValueError(
                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
                    " of masks that you pass is divisible by the total requested batch size."
                )
            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
        if masked_image_latents.shape[0] < batch_size:
            if not batch_size % masked_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 {masked_image_latents.shape[0]} images were passed."
                    " Make sure the number of images that you pass is divisible by the total requested batch size."
                )
            masked_image_latents = masked_image_latents.repeat(batch_size // masked_image_latents.shape[0], 1, 1, 1)

        return mask, masked_image_latents

    def prepare_latents(
        self,
        image,
        timestep,
        batch_size,
        num_channels_latents,
        height,
        width,
        dtype,
        device,
        generator,
        latents=None,
    ):
        """Prepare latents for inpainting, returning noise and image_latents for blending.

        Returns:
            Tuple of (latents, noise, image_latents) where:
            - latents: Noised image latents for denoising
            - noise: The noise tensor used for blending
            - image_latents: Clean image latents for blending
        """
        height = 2 * (int(height) // (self.vae_scale_factor * 2))
        width = 2 * (int(width) // (self.vae_scale_factor * 2))

        shape = (batch_size, num_channels_latents, height, width)

        if latents is not None:
            # Generate noise for blending even if latents are provided
            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
            # Encode image for blending
            image = image.to(device=device, dtype=dtype)
            if isinstance(generator, list):
                image_latents = [
                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                    for i in range(image.shape[0])
                ]
                image_latents = torch.cat(image_latents, dim=0)
            else:
                image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
            image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
            if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
                image_latents = torch.cat([image_latents] * (batch_size // image_latents.shape[0]), dim=0)
            return latents.to(device=device, dtype=dtype), noise, image_latents

        # Encode the input image
        image = image.to(device=device, dtype=dtype)
        if image.shape[1] != num_channels_latents:
            if isinstance(generator, list):
                image_latents = [
                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
                    for i in range(image.shape[0])
                ]
                image_latents = torch.cat(image_latents, dim=0)
            else:
                image_latents = retrieve_latents(self.vae.encode(image), generator=generator)

            # Apply scaling (inverse of decoding: decode does latents/scaling_factor + shift_factor)
            image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
        else:
            image_latents = image

        # Handle batch size expansion
        if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
            additional_image_per_prompt = batch_size // image_latents.shape[0]
            image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
        elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
            )

        # Generate noise for both initial noising and later blending
        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

        # Add noise using flow matching scale_noise
        latents = self.scheduler.scale_noise(image_latents, timestep, noise)

        return latents, noise, image_latents

    @property
    def guidance_scale(self):
        return self._guidance_scale

    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 0

    @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

    def check_inputs(
        self,
        prompt,
        image,
        mask_image,
        strength,
        height,
        width,
        output_type,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        callback_on_step_end_tensor_inputs=None,
    ):
        if strength < 0 or strength > 1:
            raise ValueError(f"The value of strength should be in [0.0, 1.0] but is {strength}")

        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 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)}")

        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."
            )

        if image is None:
            raise ValueError("`image` input cannot be undefined for inpainting.")

        if mask_image is None:
            raise ValueError("`mask_image` input cannot be undefined for inpainting.")

        if output_type not in ["latent", "pil", "np", "pt"]:
            raise ValueError(f"`output_type` must be one of 'latent', 'pil', 'np', or 'pt', but got {output_type}")

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        image: PipelineImageInput = None,
        mask_image: PipelineImageInput = None,
        masked_image_latents: Optional[torch.FloatTensor] = None,
        strength: float = 1.0,
        height: int | None = None,
        width: int | None = None,
        num_inference_steps: int = 50,
        sigmas: list[float] | None = None,
        guidance_scale: float = 5.0,
        cfg_normalization: bool = False,
        cfg_truncation: float = 1.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        generator: torch.Generator | list[torch.Generator] | None = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[List[torch.FloatTensor]] = None,
        negative_prompt_embeds: Optional[List[torch.FloatTensor]] = None,
        output_type: str = "pil",
        return_dict: bool = True,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        max_sequence_length: int = 512,
    ):
        r"""
        Function invoked when calling the pipeline for inpainting.

        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.
            image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
                numpy array and pytorch tensor, the expected value range is between `[0, 1]`. If it's a tensor or a
                list of tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or
                a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)`.
            mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
                `Image`, numpy array or tensor representing a mask image for inpainting. White pixels (value 1) in the
                mask will be inpainted, black pixels (value 0) will be preserved from the original image.
            masked_image_latents (`torch.FloatTensor`, *optional*):
                Pre-encoded masked image latents. If provided, the masked image encoding step will be skipped.
            strength (`float`, *optional*, defaults to 1.0):
                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
                essentially ignores `image` in the masked region.
            height (`int`, *optional*, defaults to 1024):
                The height in pixels of the generated image. If not provided, uses the input image height.
            width (`int`, *optional*, defaults to 1024):
                The width in pixels of the generated image. If not provided, uses the input image width.
            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.
            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.
            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.
            cfg_normalization (`bool`, *optional*, defaults to False):
                Whether to apply configuration normalization.
            cfg_truncation (`float`, *optional*, defaults to 1.0):
                The truncation value for configuration.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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 be generated by sampling using the supplied random `generator`.
            prompt_embeds (`List[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 (`List[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.
            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.ZImagePipelineOutput`] 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.
            max_sequence_length (`int`, *optional*, defaults to 512):
                Maximum sequence length to use with the `prompt`.

        Examples:

        Returns:
            [`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if
            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
            generated images.
        """
        # 1. Check inputs
        self.check_inputs(
            prompt=prompt,
            image=image,
            mask_image=mask_image,
            strength=strength,
            height=height,
            width=width,
            output_type=output_type,
            negative_prompt=negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
        )

        # 2. Preprocess image and mask
        init_image = self.image_processor.preprocess(image)
        init_image = init_image.to(dtype=torch.float32)

        # Get dimensions from the preprocessed image if not specified
        if height is None:
            height = init_image.shape[-2]
        if width is None:
            width = init_image.shape[-1]

        vae_scale = self.vae_scale_factor * 2
        if height % vae_scale != 0:
            raise ValueError(
                f"Height must be divisible by {vae_scale} (got {height}). "
                f"Please adjust the height to a multiple of {vae_scale}."
            )
        if width % vae_scale != 0:
            raise ValueError(
                f"Width must be divisible by {vae_scale} (got {width}). "
                f"Please adjust the width to a multiple of {vae_scale}."
            )

        # Preprocess mask
        mask = self.mask_processor.preprocess(mask_image, height=height, width=width)

        device = self._execution_device

        self._guidance_scale = guidance_scale
        self._joint_attention_kwargs = joint_attention_kwargs
        self._interrupt = False
        self._cfg_normalization = cfg_normalization
        self._cfg_truncation = cfg_truncation

        # 3. 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 = len(prompt_embeds)

        # If prompt_embeds is provided and prompt is None, skip encoding
        if prompt_embeds is not None and prompt is None:
            if self.do_classifier_free_guidance and negative_prompt_embeds is None:
                raise ValueError(
                    "When `prompt_embeds` is provided without `prompt`, "
                    "`negative_prompt_embeds` must also be provided for classifier-free guidance."
                )
        else:
            (
                prompt_embeds,
                negative_prompt_embeds,
            ) = self.encode_prompt(
                prompt=prompt,
                negative_prompt=negative_prompt,
                do_classifier_free_guidance=self.do_classifier_free_guidance,
                prompt_embeds=prompt_embeds,
                negative_prompt_embeds=negative_prompt_embeds,
                device=device,
                max_sequence_length=max_sequence_length,
            )

        # 4. Prepare latent variables
        num_channels_latents = self.transformer.in_channels

        # Repeat prompt_embeds for num_images_per_prompt
        if num_images_per_prompt > 1:
            prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
            if self.do_classifier_free_guidance and negative_prompt_embeds:
                negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]

        actual_batch_size = batch_size * num_images_per_prompt

        # Calculate latent dimensions for image_seq_len
        latent_height = 2 * (int(height) // (self.vae_scale_factor * 2))
        latent_width = 2 * (int(width) // (self.vae_scale_factor * 2))
        image_seq_len = (latent_height // 2) * (latent_width // 2)

        # 5. Prepare timesteps
        mu = calculate_shift(
            image_seq_len,
            self.scheduler.config.get("base_image_seq_len", 256),
            self.scheduler.config.get("max_image_seq_len", 4096),
            self.scheduler.config.get("base_shift", 0.5),
            self.scheduler.config.get("max_shift", 1.15),
        )
        self.scheduler.sigma_min = 0.0
        scheduler_kwargs = {"mu": mu}
        timesteps, num_inference_steps = retrieve_timesteps(
            self.scheduler,
            num_inference_steps,
            device,
            sigmas=sigmas,
            **scheduler_kwargs,
        )

        # 6. Adjust timesteps based on strength
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
        if num_inference_steps < 1:
            raise ValueError(
                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline "
                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
            )
        latent_timestep = timesteps[:1].repeat(actual_batch_size)

        # 7. Prepare latents from image (returns noise and image_latents for blending)
        latents, noise, image_latents = self.prepare_latents(
            init_image,
            latent_timestep,
            actual_batch_size,
            num_channels_latents,
            height,
            width,
            prompt_embeds[0].dtype,
            device,
            generator,
            latents,
        )

        # 8. Prepare mask and masked image latents
        # Create masked image: preserve only unmasked regions (mask=0)
        if masked_image_latents is None:
            masked_image = init_image * (mask < 0.5)
        else:
            masked_image = None  # Will use provided masked_image_latents

        mask, masked_image_latents = self.prepare_mask_latents(
            mask,
            masked_image if masked_image is not None else init_image,
            actual_batch_size,
            height,
            width,
            prompt_embeds[0].dtype,
            device,
            generator,
        )

        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        self._num_timesteps = len(timesteps)

        # 9. Denoising loop
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if self.interrupt:
                    continue

                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                timestep = t.expand(latents.shape[0])
                timestep = (1000 - timestep) / 1000
                # Normalized time for time-aware config (0 at start, 1 at end)
                t_norm = timestep[0].item()

                # Handle cfg truncation
                current_guidance_scale = self.guidance_scale
                if (
                    self.do_classifier_free_guidance
                    and self._cfg_truncation is not None
                    and float(self._cfg_truncation) <= 1
                ):
                    if t_norm > self._cfg_truncation:
                        current_guidance_scale = 0.0

                # Run CFG only if configured AND scale is non-zero
                apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0

                if apply_cfg:
                    latents_typed = latents.to(self.transformer.dtype)
                    latent_model_input = latents_typed.repeat(2, 1, 1, 1)
                    prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
                    timestep_model_input = timestep.repeat(2)
                else:
                    latent_model_input = latents.to(self.transformer.dtype)
                    prompt_embeds_model_input = prompt_embeds
                    timestep_model_input = timestep

                latent_model_input = latent_model_input.unsqueeze(2)
                latent_model_input_list = list(latent_model_input.unbind(dim=0))

                model_out_list = self.transformer(
                    latent_model_input_list,
                    timestep_model_input,
                    prompt_embeds_model_input,
                )[0]

                if apply_cfg:
                    # Perform CFG
                    pos_out = model_out_list[:actual_batch_size]
                    neg_out = model_out_list[actual_batch_size:]

                    noise_pred = []
                    for j in range(actual_batch_size):
                        pos = pos_out[j].float()
                        neg = neg_out[j].float()

                        pred = pos + current_guidance_scale * (pos - neg)

                        # Renormalization
                        if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
                            ori_pos_norm = torch.linalg.vector_norm(pos)
                            new_pos_norm = torch.linalg.vector_norm(pred)
                            max_new_norm = ori_pos_norm * float(self._cfg_normalization)
                            if new_pos_norm > max_new_norm:
                                pred = pred * (max_new_norm / new_pos_norm)

                        noise_pred.append(pred)

                    noise_pred = torch.stack(noise_pred, dim=0)
                else:
                    noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)

                noise_pred = noise_pred.squeeze(2)
                noise_pred = -noise_pred

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
                assert latents.dtype == torch.float32

                # Inpainting blend: combine denoised latents with original image latents
                init_latents_proper = image_latents

                # Re-scale original latents to current noise level for proper blending
                if i < len(timesteps) - 1:
                    noise_timestep = timesteps[i + 1]
                    init_latents_proper = self.scheduler.scale_noise(
                        init_latents_proper, torch.tensor([noise_timestep]), noise
                    )

                # Blend: mask=1 for inpaint region (use denoised), mask=0 for preserve region (use original)
                latents = (1 - mask) * init_latents_proper + mask * latents

                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)
                    mask = callback_outputs.pop("mask", mask)
                    masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents)

                # 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.to(self.vae.dtype)
            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor

            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 ZImagePipelineOutput(images=image)
