# Copyright 2023 The GLIGEN Authors and 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
import warnings
from typing import Any, Callable, Dict, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPFeatureExtractor,
    CLIPProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionModelWithProjection,
)

from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention import GatedSelfAttentionDense
from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .clip_image_project_model import CLIPImageProjection
from .safety_checker import StableDiffusionSafetyChecker


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import StableDiffusionGLIGENTextImagePipeline
        >>> from diffusers.utils import load_image

        >>> # Insert objects described by image at the region defined by bounding boxes
        >>> pipe = StableDiffusionGLIGENTextImagePipeline.from_pretrained(
        ...     "anhnct/Gligen_Inpainting_Text_Image", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> input_image = load_image(
        ...     "https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/livingroom_modern.png"
        ... )
        >>> prompt = "a backpack"
        >>> boxes = [[0.2676, 0.4088, 0.4773, 0.7183]]
        >>> phrases = None
        >>> gligen_image = load_image(
        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/backpack.jpeg"
        ... )

        >>> images = pipe(
        ...     prompt=prompt,
        ...     gligen_phrases=phrases,
        ...     gligen_inpaint_image=input_image,
        ...     gligen_boxes=boxes,
        ...     gligen_images=[gligen_image],
        ...     gligen_scheduled_sampling_beta=1,
        ...     output_type="pil",
        ...     num_inference_steps=50,
        ... ).images

        >>> images[0].save("./gligen-inpainting-text-image-box.jpg")

        >>> # Generate an image described by the prompt and
        >>> # insert objects described by text and image at the region defined by bounding boxes
        >>> pipe = StableDiffusionGLIGENTextImagePipeline.from_pretrained(
        ...     "anhnct/Gligen_Text_Image", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> prompt = "a flower sitting on the beach"
        >>> boxes = [[0.0, 0.09, 0.53, 0.76]]
        >>> phrases = ["flower"]
        >>> gligen_image = load_image(
        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/gligen/pexels-pixabay-60597.jpg"
        ... )

        >>> images = pipe(
        ...     prompt=prompt,
        ...     gligen_phrases=phrases,
        ...     gligen_images=[gligen_image],
        ...     gligen_boxes=boxes,
        ...     gligen_scheduled_sampling_beta=1,
        ...     output_type="pil",
        ...     num_inference_steps=50,
        ... ).images

        >>> images[0].save("./gligen-generation-text-image-box.jpg")

        >>> # Generate an image described by the prompt and
        >>> # transfer style described by image at the region defined by bounding boxes
        >>> pipe = StableDiffusionGLIGENTextImagePipeline.from_pretrained(
        ...     "anhnct/Gligen_Text_Image", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> prompt = "a dragon flying on the sky"
        >>> boxes = [[0.4, 0.2, 1.0, 0.8], [0.0, 1.0, 0.0, 1.0]]  # Set `[0.0, 1.0, 0.0, 1.0]` for the style

        >>> gligen_image = load_image(
        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
        ... )

        >>> gligen_placeholder = load_image(
        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
        ... )

        >>> images = pipe(
        ...     prompt=prompt,
        ...     gligen_phrases=[
        ...         "dragon",
        ...         "placeholder",
        ...     ],  # Can use any text instead of `placeholder` token, because we will use mask here
        ...     gligen_images=[
        ...         gligen_placeholder,
        ...         gligen_image,
        ...     ],  # Can use any image in gligen_placeholder, because we will use mask here
        ...     input_phrases_mask=[1, 0],  # Set 0 for the placeholder token
        ...     input_images_mask=[0, 1],  # Set 0 for the placeholder image
        ...     gligen_boxes=boxes,
        ...     gligen_scheduled_sampling_beta=1,
        ...     output_type="pil",
        ...     num_inference_steps=50,
        ... ).images

        >>> images[0].save("./gligen-generation-text-image-box-style-transfer.jpg")
        ```
"""


class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion with Grounded-Language-to-Image Generation (GLIGEN).

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.).

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
        text_encoder ([`~transformers.CLIPTextModel`]):
            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer ([`~transformers.CLIPTokenizer`]):
            A `CLIPTokenizer` to tokenize text.
        processor ([`~transformers.CLIPProcessor`]):
            A `CLIPProcessor` to procces reference image.
        image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
            Frozen image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        image_project ([`CLIPImageProjection`]):
            A `CLIPImageProjection` to project image embedding into phrases embedding space.
        unet ([`UNet2DConditionModel`]):
            A `UNet2DConditionModel` to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
            about a model's potential harms.
        feature_extractor ([`~transformers.CLIPImageProcessor`]):
            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
    """
    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        processor: CLIPProcessor,
        image_encoder: CLIPVisionModelWithProjection,
        image_project: CLIPImageProjection,
        unet: UNet2DConditionModel,
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
        requires_safety_checker: bool = True,
    ):
        super().__init__()

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            image_encoder=image_encoder,
            processor=processor,
            image_project=image_project,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    def enable_vae_slicing(self):
        r"""
        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.vae.enable_slicing()

    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    def enable_vae_tiling(self):
        r"""
        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
        processing larger images.
        """
        self.vae.enable_tiling()

    def disable_vae_tiling(self):
        r"""
        Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_tiling()

    def enable_model_cpu_offload(self, gpu_id=0):
        r"""
        Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
        time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
        Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
        iterative execution of the `unet`.
        """
        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError("`enable_model_offload` requires `accelerate v0.17.0` or higher.")

        device = torch.device(f"cuda:{gpu_id}")

        if self.device.type != "cpu":
            self.to("cpu", silence_dtype_warnings=True)
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        hook = None
        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)

        if self.safety_checker is not None:
            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)

        # We'll offload the last model manually.
        self.final_offload_hook = hook

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
    def encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        lora_scale: Optional[float] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            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`).
            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.
            lora_scale (`float`, *optional*):
                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
        """
        # set lora scale so that monkey patched LoRA
        # function of text encoder can correctly access it
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale

            # dynamically adjust the LoRA scale
            adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)

        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]

        if prompt_embeds is None:
            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)

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

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device),
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]

        if self.text_encoder is not None:
            prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None:
            prompt_embeds_dtype = self.unet.dtype
        else:
            prompt_embeds_dtype = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif 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 isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            # textual inversion: procecss multi-vector tokens if necessary
            if isinstance(self, TextualInversionLoaderMixin):
                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)

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

        return prompt_embeds, negative_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
    def run_safety_checker(self, image, device, dtype):
        if self.safety_checker is None:
            has_nsfw_concept = None
        else:
            if torch.is_tensor(image):
                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
            else:
                feature_extractor_input = self.image_processor.numpy_to_pil(image)
            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
            image, has_nsfw_concept = self.safety_checker(
                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
            )
        return image, has_nsfw_concept

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
    def check_inputs(
        self,
        prompt,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=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_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        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 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}."
                )

    # 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, height // self.vae_scale_factor, 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)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    def enable_fuser(self, enabled=True):
        for module in self.unet.modules():
            if type(module) is GatedSelfAttentionDense:
                module.enabled = enabled

    def draw_inpaint_mask_from_boxes(self, boxes, size):
        """
        Create an inpainting mask based on given boxes. This function generates an inpainting mask using the provided
        boxes to mark regions that need to be inpainted.
        """
        inpaint_mask = torch.ones(size[0], size[1])
        for box in boxes:
            x0, x1 = box[0] * size[0], box[2] * size[0]
            y0, y1 = box[1] * size[1], box[3] * size[1]
            inpaint_mask[int(y0) : int(y1), int(x0) : int(x1)] = 0
        return inpaint_mask

    def crop(self, im, new_width, new_height):
        """
        Crop the input image to the specified dimensions.
        """
        width, height = im.size
        left = (width - new_width) / 2
        top = (height - new_height) / 2
        right = (width + new_width) / 2
        bottom = (height + new_height) / 2
        return im.crop((left, top, right, bottom))

    def target_size_center_crop(self, im, new_hw):
        """
        Crop and resize the image to the target size while keeping the center.
        """
        width, height = im.size
        if width != height:
            im = self.crop(im, min(height, width), min(height, width))
        return im.resize((new_hw, new_hw), PIL.Image.LANCZOS)

    def complete_mask(self, has_mask, max_objs, device):
        """
        Based on the input mask corresponding value `0 or 1` for each phrases and image, mask the features
        corresponding to phrases and images.
        """
        mask = torch.ones(1, max_objs).type(self.text_encoder.dtype).to(device)
        if has_mask is None:
            return mask

        if isinstance(has_mask, int):
            return mask * has_mask
        else:
            for idx, value in enumerate(has_mask):
                mask[0, idx] = value
            return mask

    def get_clip_feature(self, input, normalize_constant, device, is_image=False):
        """
        Get image and phrases embedding by using CLIP pretrain model. The image embedding is transformed into the
        phrases embedding space through a projection.
        """
        if is_image:
            if input is None:
                return None
            inputs = self.processor(images=[input], return_tensors="pt").to(device)
            outputs = self.image_encoder(**inputs)
            feature = outputs.image_embeds
            feature = self.image_project(feature).squeeze(0)
            feature = (feature / feature.norm()) * normalize_constant
            feature = feature.unsqueeze(0)
        else:
            if input is None:
                return None
            inputs = self.tokenizer(input, return_tensors="pt", padding=True).to(device)
            outputs = self.text_encoder(**inputs)
            feature = outputs.pooler_output
        return feature

    def get_cross_attention_kwargs_with_grounded(
        self,
        hidden_size,
        gligen_phrases,
        gligen_images,
        gligen_boxes,
        input_phrases_mask,
        input_images_mask,
        repeat_batch,
        normalize_constant,
        max_objs,
        device,
    ):
        """
        Prepare the cross-attention kwargs containing information about the grounded input (boxes, mask, image
        embedding, phrases embedding).
        """
        phrases, images = gligen_phrases, gligen_images
        images = [None] * len(phrases) if images is None else images
        phrases = [None] * len(images) if phrases is None else phrases

        boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)
        masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        phrases_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        image_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        phrases_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)
        image_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)

        text_features = []
        image_features = []
        for phrase, image in zip(phrases, images):
            text_features.append(self.get_clip_feature(phrase, normalize_constant, device, is_image=False))
            image_features.append(self.get_clip_feature(image, normalize_constant, device, is_image=True))

        for idx, (box, text_feature, image_feature) in enumerate(zip(gligen_boxes, text_features, image_features)):
            boxes[idx] = torch.tensor(box)
            masks[idx] = 1
            if text_feature is not None:
                phrases_embeddings[idx] = text_feature
                phrases_masks[idx] = 1
            if image_feature is not None:
                image_embeddings[idx] = image_feature
                image_masks[idx] = 1

        input_phrases_mask = self.complete_mask(input_phrases_mask, max_objs, device)
        phrases_masks = phrases_masks.unsqueeze(0).repeat(repeat_batch, 1) * input_phrases_mask
        input_images_mask = self.complete_mask(input_images_mask, max_objs, device)
        image_masks = image_masks.unsqueeze(0).repeat(repeat_batch, 1) * input_images_mask
        boxes = boxes.unsqueeze(0).repeat(repeat_batch, 1, 1)
        masks = masks.unsqueeze(0).repeat(repeat_batch, 1)
        phrases_embeddings = phrases_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1)
        image_embeddings = image_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1)

        out = {
            "boxes": boxes,
            "masks": masks,
            "phrases_masks": phrases_masks,
            "image_masks": image_masks,
            "phrases_embeddings": phrases_embeddings,
            "image_embeddings": image_embeddings,
        }

        return out

    def get_cross_attention_kwargs_without_grounded(self, hidden_size, repeat_batch, max_objs, device):
        """
        Prepare the cross-attention kwargs without information about the grounded input (boxes, mask, image embedding,
        phrases embedding) (All are zero tensor).
        """
        boxes = torch.zeros(max_objs, 4, device=device, dtype=self.text_encoder.dtype)
        masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        phrases_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        image_masks = torch.zeros(max_objs, device=device, dtype=self.text_encoder.dtype)
        phrases_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)
        image_embeddings = torch.zeros(max_objs, hidden_size, device=device, dtype=self.text_encoder.dtype)

        out = {
            "boxes": boxes.unsqueeze(0).repeat(repeat_batch, 1, 1),
            "masks": masks.unsqueeze(0).repeat(repeat_batch, 1),
            "phrases_masks": phrases_masks.unsqueeze(0).repeat(repeat_batch, 1),
            "image_masks": image_masks.unsqueeze(0).repeat(repeat_batch, 1),
            "phrases_embeddings": phrases_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1),
            "image_embeddings": image_embeddings.unsqueeze(0).repeat(repeat_batch, 1, 1),
        }

        return out

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        gligen_scheduled_sampling_beta: float = 0.3,
        gligen_phrases: List[str] = None,
        gligen_images: List[PIL.Image.Image] = None,
        input_phrases_mask: Union[int, List[int]] = None,
        input_images_mask: Union[int, List[int]] = None,
        gligen_boxes: List[List[float]] = None,
        gligen_inpaint_image: Optional[PIL.Image.Image] = None,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        gligen_normalize_constant: float = 28.7,
    ):
        r"""
        The call function to the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            gligen_phrases (`List[str]`):
                The phrases to guide what to include in each of the regions defined by the corresponding
                `gligen_boxes`. There should only be one phrase per bounding box.
            gligen_images (`List[PIL.Image.Image]`):
                The images to guide what to include in each of the regions defined by the corresponding `gligen_boxes`.
                There should only be one image per bounding box
            input_phrases_mask (`int` or `List[int]`):
                pre phrases mask input defined by the correspongding `input_phrases_mask`
            input_images_mask (`int` or `List[int]`):
                pre images mask input defined by the correspongding `input_images_mask`
            gligen_boxes (`List[List[float]]`):
                The bounding boxes that identify rectangular regions of the image that are going to be filled with the
                content described by the corresponding `gligen_phrases`. Each rectangular box is defined as a
                `List[float]` of 4 elements `[xmin, ymin, xmax, ymax]` where each value is between [0,1].
            gligen_inpaint_image (`PIL.Image.Image`, *optional*):
                The input image, if provided, is inpainted with objects described by the `gligen_boxes` and
                `gligen_phrases`. Otherwise, it is treated as a generation task on a blank input image.
            gligen_scheduled_sampling_beta (`float`, defaults to 0.3):
                Scheduled Sampling factor from [GLIGEN: Open-Set Grounded Text-to-Image
                Generation](https://arxiv.org/pdf/2301.07093.pdf). Scheduled Sampling factor is only varied for
                scheduled sampling during inference for improved quality and controllability.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                A [`torch.Generator`](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 is 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 (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that calls every `callback_steps` steps during inference. The function is called with the
                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function is called. If not specified, the callback is called at
                every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            gligen_normalize_constant (`float`, *optional*, defaults to 28.7):
                The normalize value of the image embedding.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
                otherwise a `tuple` is returned where the first element is a list with the generated images and the
                second element is a list of `bool`s indicating whether the corresponding generated image contains
                "not-safe-for-work" (nsfw) content.
        """
        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        # 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
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        if do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

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

        # 5.1 Prepare GLIGEN variables
        max_objs = 30
        if len(gligen_boxes) > max_objs:
            warnings.warn(
                f"More that {max_objs} objects found. Only first {max_objs} objects will be processed.",
                FutureWarning,
            )
            gligen_phrases = gligen_phrases[:max_objs]
            gligen_boxes = gligen_boxes[:max_objs]
            gligen_images = gligen_images[:max_objs]

        repeat_batch = batch_size * num_images_per_prompt

        if do_classifier_free_guidance:
            repeat_batch = repeat_batch * 2

        if cross_attention_kwargs is None:
            cross_attention_kwargs = {}

        hidden_size = prompt_embeds.shape[2]

        cross_attention_kwargs["gligen"] = self.get_cross_attention_kwargs_with_grounded(
            hidden_size=hidden_size,
            gligen_phrases=gligen_phrases,
            gligen_images=gligen_images,
            gligen_boxes=gligen_boxes,
            input_phrases_mask=input_phrases_mask,
            input_images_mask=input_images_mask,
            repeat_batch=repeat_batch,
            normalize_constant=gligen_normalize_constant,
            max_objs=max_objs,
            device=device,
        )

        cross_attention_kwargs_without_grounded = {}
        cross_attention_kwargs_without_grounded["gligen"] = self.get_cross_attention_kwargs_without_grounded(
            hidden_size=hidden_size, repeat_batch=repeat_batch, max_objs=max_objs, device=device
        )

        # Prepare latent variables for GLIGEN inpainting
        if gligen_inpaint_image is not None:
            # if the given input image is not of the same size as expected by VAE
            # center crop and resize the input image to expected shape
            if gligen_inpaint_image.size != (self.vae.sample_size, self.vae.sample_size):
                gligen_inpaint_image = self.target_size_center_crop(gligen_inpaint_image, self.vae.sample_size)
            # Convert a single image into a batch of images with a batch size of 1
            # The resulting shape becomes (1, C, H, W), where C is the number of channels,
            # and H and W are the height and width of the image.
            # scales the pixel values to a range [-1, 1]
            gligen_inpaint_image = self.image_processor.preprocess(gligen_inpaint_image)
            gligen_inpaint_image = gligen_inpaint_image.to(dtype=self.vae.dtype, device=self.vae.device)
            # Run AutoEncoder to get corresponding latents
            gligen_inpaint_latent = self.vae.encode(gligen_inpaint_image).latent_dist.sample()
            gligen_inpaint_latent = self.vae.config.scaling_factor * gligen_inpaint_latent
            # Generate an inpainting mask
            # pixel value = 0, where the object is present (defined by bounding boxes above)
            #               1, everywhere else
            gligen_inpaint_mask = self.draw_inpaint_mask_from_boxes(gligen_boxes, gligen_inpaint_latent.shape[2:])
            gligen_inpaint_mask = gligen_inpaint_mask.to(
                dtype=gligen_inpaint_latent.dtype, device=gligen_inpaint_latent.device
            )
            gligen_inpaint_mask = gligen_inpaint_mask[None, None]
            gligen_inpaint_mask_addition = torch.cat(
                (gligen_inpaint_latent * gligen_inpaint_mask, gligen_inpaint_mask), dim=1
            )
            # Convert a single mask into a batch of masks with a batch size of 1
            gligen_inpaint_mask_addition = gligen_inpaint_mask_addition.expand(repeat_batch, -1, -1, -1).clone()

        int(gligen_scheduled_sampling_beta * len(timesteps))
        self.enable_fuser(True)

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                if latents.shape[1] != 4:
                    latents = torch.randn_like(latents[:, :4])

                if gligen_inpaint_image is not None:
                    gligen_inpaint_latent_with_noise = (
                        self.scheduler.add_noise(gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), t)
                        .expand(latents.shape[0], -1, -1, -1)
                        .clone()
                    )
                    latents = gligen_inpaint_latent_with_noise * gligen_inpaint_mask + latents * (
                        1 - gligen_inpaint_mask
                    )

                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                if gligen_inpaint_image is not None:
                    latent_model_input = torch.cat((latent_model_input, gligen_inpaint_mask_addition), dim=1)

                # predict the noise residual with grounded information
                noise_pred_with_grounding = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                ).sample

                # predict the noise residual without grounded information
                noise_pred_without_grounding = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs_without_grounded,
                ).sample

                # perform guidance
                if do_classifier_free_guidance:
                    # Using noise_pred_text from noise residual with grounded information and noise_pred_uncond from noise residual without grounded information
                    _, noise_pred_text = noise_pred_with_grounding.chunk(2)
                    noise_pred_uncond, _ = noise_pred_without_grounding.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                else:
                    noise_pred = noise_pred_with_grounding

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

                # 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 callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
        else:
            image = latents
            has_nsfw_concept = None

        if has_nsfw_concept is None:
            do_denormalize = [True] * image.shape[0]
        else:
            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
